Historical depletion and future drought-driven risks to Gulf of Mexico fisheries production

Domain: neuroscience
Published: 2026-02-09
Editorial outcome: major_revision

Abstract
A major depletion of ~42% in total fisheries production occurred in the Gulf of Mexico in the early 1990’s but received limited scientific attention. Here, we show that terrestrial drought in the US during the late 1980s, combined with high Gulf menhaden (Brevoortia patronus) fishing pressure, led to a depleted forage base, with cascading effects on other species and fisheries in the Gulf, acting primarily via a reduction in Mississippi River flow. Ecosystem model simulations support these findings, and more importantly, indicate that anticipated frequent and intense drought conditions under the RCP 8.5 scenario in the US are projected to cause periodic depletions of biomass and catches of ~61% by 2050, and ~72% by 2100, substantially more acute than previous estimates for the Gulf. There is an urgent need to explore possible mitigation strategies and the mechanisms by which drought conditions affect the Gulf and other marine ecosystems.

Introduction
The Gulf of Mexico (GoM) harbors a great wealth of natural resources including diverse ecosystems and productive fisheries1. The GoM ecosystem is nutrient-limited2, with more than 90% of GoM nutrient input supplied by the Mississippi River3. This nutrient input results in extensive primary productivity in the estuarine, coastal, and shelf regions of the GoM, supporting the growth of phytoplankton, seagrass, and algae that, along with detrital sources, serve as the trophic foundation for the GoM ecosystem4–6.

Within this system, Gulf menhaden (Brevoortia patronus) is the main forage fish, consisting of the largest harvested fish stock in the GoM by weight1. The Gulf menhaden represents an important trophic link between primary and secondary producers, i.e., phytoplankton and zooplankton, and multiple groups of higher trophic predators such as marine mammals, sharks, and mackerels7,8.

Numerous studies have highlighted the significant relationship between Gulf menhaden population dynamics and environmental conditions such as temperature, wind, and river flow9–11. While some research shows that wet conditions and high river flow positively affect Gulf menhaden recruitment, other findings indicate that excessive river flows can hinder recruitment due to increased larval offshore transport and predation12,13. This contradiction emphasizes the complex, scale-dependent interactions between these abiotic factors. Decadal analyses reveal a positive correlation between higher average river discharge and enhanced recruitment, likely driven by increased nutrient input and primary productivity13. Additionally, interannual climate oscillations, such as the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and El Niño Southern Oscillation (ENSO), further complicate these dynamics. Beyond recruitment, research indicates that spring river discharge and winter ENSO conditions significantly influence menhaden oil content, a critical indicator of individual and population health, while fishing pressure and environmental conditions also exert contrasting effects on menhaden growth14.

River flow has long been recognized as a master variable regulating marine and estuarine ecosystems, influencing habitat availability, trophic dynamics, and fishers’ harvesting behavior15–19. Variability in river discharge controls nutrient delivery, sediment transport, and salinity gradients, all of which shape the productivity and distribution of marine organisms. In the GoM, a system otherwise strongly oligotrophic with limited nutrient input from oceanic currents or upwelling, riverine nutrient inputs are especially critical for sustaining fisheries yields20. This highlights a trophic-driven mechanism by which drought-induced reductions in river flow limit nutrient availability, cascading through the food web to affect forage fish, predators, fish migration and composition, and ultimately fisheries production.

The GoM fisheries are characterized by fluctuations in biomass and catches1. A notable decline occurred during the late 1980s and early 1990s1. During this period, cumulative biomass declined by 42% and cumulative catch by 34% relative to the 1982–1986 reference period, highlighting the severity of this system-wide depletion. The decline was evident in catches and biomass of a wide range of taxa including benthic primary producers, shrimp, oysters and crab, Gulf menhaden, cephalopods, as well as predators such as amberjack, cobia, king mackerel and Spanish mackerel1,21–23. Despite these wide systematic declines, limited attention has been given to their full scope of possible drivers and underlying mechanisms.

Prior to these declines, a severe and prolonged drought took place in the US, with increasingly dry conditions beginning in 1986 peaking in 1988, and ending in 1989. This drought caused damage of nearly 60 billion US$, resulting in massive agricultural losses, extensive wildfires, and severe interferences of barge transportation on the Mississippi River24. This severe drought resulted in a reduction of more than 50% in the Mississippi River flow and nutrient input to the GoM during summer months between 1985 and 198825.

Previous evidence shows that terrestrial drought significantly impacts fisheries collapse through hydrological, ecological, and socioeconomic mechanisms. Drought-related freshwater reductions elevate salinity, stressing taxa like oysters (Crassostrea virginica) and shrimp in GoM systems26–30. Sustained elevated salinity levels (>25 ppt) are linked to oyster mortality and recruitment failures27,28,30. In this study, we focus on the trophic pathway for three main reasons. First, nutrient-driven productivity is widely recognized as the dominant mechanism linking river flow to fisheries in this oligotrophic system, where oceanic nutrient supplies from upwelling or currents are limited. Second, our analytical framework is designed to represent trophic interactions at the ecosystem scale, rather than species-specific habitat–salinity responses. Third, the available long-term data are most consistent for nutrient inputs and productivity signals, making them more suitable for quantitative analysis.

Drought conditions and reduced river flow may influence the GoM ecosystem through the reduction of food web productivity via the reduction of nutrient input into the GoM through the Mississippi delta, around which the bulk of GoM fishery landings are harvested31. Based on this, we examine the connection between the drought-related nutrient input conditions and the subsequent declines in catch and biomass during the late 1980s; next, given the high probability of multi-decadal severe drought (megadrought) during the second half of the century32,33, we examine how multi-year extreme drought conditions anticipated in the Mississippi River basin during the 21st century may affect the GoM ecosystem using a trophic-dynamic ecosystem model. Specifically, we examine how biomass and catch in the GoM may respond to anticipated drought-related changes in the Mississippi River nutrient input to the GoM and compare drought-related impacts to those associated with increased fishing pressure on Gulf menhaden-the largest harvested fish stock in the GoM34.

Results
Analyses of historical time series of environmental data indicate a drying trend in the Mississippi River basin, starting in 1985 and peaking in 1988, and hitting record low Palmer’s Drought Severity Index (PDSI; a standard measure of drought intensity35) annual and summer levels of −3.3 and −4.1, respectively (Fig. 1a). As a result, the Mississippi River level at Vicksburg, Mississippi (MS) declined to record low annual and summer levels of 12.2 and 3.2 ft, respectively (Fig. 1b). Similarly, the nutrient load from the Mississippi River to the GoM reached a record low annual and summer value of 107,717 and 41,560 t, respectively (Fig. 1c). For these three variables, the year 1988 is characterized by exceptionally low summer values compared to the rest of the years (Fig. 1e–g). Sea surface temperature (SST) anomaly and hypoxic areas were relatively low during the years 1985-1988 compared to the rest of the years (Fig. 1d, h, Fig. S2). The number of named hurricanes, which did not show correspondence to the other environmental factors, are provided in Fig. S2.

Analysis of historical fisheries data indicates a loss of 42% in cumulative biomass (i.e., aggregate biomass summed across all functional groups) and 34% in cumulative catch during the periods of 1988–1992 compared to 1982–1986. This trend is dominated by Gulf menhaden, which represents the bulk of the biomass and catch, and shows declines of 43% and 38%, respectively (Fig. 1i, j). Importantly, aside from the sharp drought-related reductions in PDSI, river discharge, and nutrient inputs in 1988, other environmental indicators (e.g., SST anomalies) and overall fishing effort levels were broadly comparable between the two periods (Fig. 1, Fig. S2), supporting the interpretation that the observed declines primarily reflect the drought effect. Within this broader stability, the menhaden purse-seine fleet did show a 16% reduction in relative effort (Fig. 1k), though this has been attributed to decreases in fleet size and processing capacity, driven by corporate acquisitions, downsizing, and industrial consolidation, rather than improvements in efficiency or changes in fishing pressure36. Additional menhaden related time series, including number of recruits, and an abundance index for juveniles based on fisheries-independent data collected routinely in Louisiana estuaries corroborate these results by showing low juvenile abundances from 1985 to 1991 (Fig. S3).

The declining trend during and following the drought period is not limited to Gulf menhaden and is evident in biomass depletion in 40 out of 45 functional groups (89%; Fig. 1l), and for catches, depletion in 30 out of 47 functional groups (63%; Fig. 1m). Groups exhibiting depletion include coastal invertebrate feeders (e.g., croakers (Sciaenidae)) and crabs, whereas groups that indicated a slight increase in biomass include reef piscivores (e.g., moray eels (Muraenidae)) and cobia (Fig. 1l). Depletion in catch was strongest in large coastal sharks. The largest increase in catches over that period occurred in sandbar shark and gray triggerfish (Fig. 1m).

PDSI projections based on the Coupled Model Intercomparison Project 5 (CMIP5) model ensembles for the Mississippi River basin33 indicate that prolonged (5 yrs) drought conditions anticipated by 2050 are expected to reach a PDSI value of -3.88 (Fig. 2a), and prolonged drought conditions anticipated for 2051–2100 are expected to be more severe, reaching a low PDSI value of -5.66. The extreme drought events documented during our historical period, correspond in values to the lower 5 percentile range of the CMIP5 model ensembles (Fig. 2a). The gradual decrease in PDSI across time indicates an increasing probability and severity of prolonged extreme drought periods, in particular in the western and central regions of the Mississippi River basin (Fig. 2a). The drought conditions anticipated by 2050 and during 2051–2100 will be manifested as reductions in nutrient inputs to the GoM by factors of 0.57 and 0.45, respectively, based on the linear relationship between the two variables (Linear regression, p < 0.0001, F = 53.5, R2 = 0.6, Nut = 1.09 + 0.09 × PDSI, Fig. 2b).

Results indicate that the biomass and catch of Gulf menhaden are strongly affected by variability in nutrient input to the GoM, and by the fishing mortality of Gulf menhaden (Fig. 2c). Assuming no change in Gulf menhaden fishing mortality from the status quo, the model projection indicates that extreme drought conditions anticipated by 2050 will result in an approximate reduction of 61% of total biomass and catch compared to the status quo projection (Fig. 2c); whereas for drought conditions anticipated at the end of the 21st century, biomass and catch reduction could be reduced by as much as 72% (Fig. 2c). The effect of Gulf menhaden fishing mortality on the cumulative biomass and catch dynamics is smaller compared to the effect of anticipated droughts (Fig. 2c), suggesting that the ability of the ecosystem to compensate for menhaden harvest is compromised under drought conditions. This reduced influence of fishing mortality reflects the strong bottom-up limitation imposed by nutrient input, whereby menhaden biomass remains low even when fishing mortality (F) is set to 0, and compensatory mechanisms prevent collapse at low stock sizes. As a result, absolute removals and the ecosystem-level footprint of fishing diminish relative to the dominant drought effect. Notably, the model diagnostics indicated good skill overall, with MEF values of 0.65–0.68 across Gulf menhaden age classes, and 0.54 and 0.49 for the closely related groups Sardine–herring–scad and Mullet, respectively (see supplementary note S4, Table S5).

The model demonstrated that Gulf menhaden (Fig. 2d) and its predators, such as king (Fig. 2e) and Spanish (Fig. 2f) mackerels, respond to Gulf menhaden biomass such that when Gulf menhaden biomass increases, the biomass of its predators increases respectively, and vice-versa (Fig. 2d–f). Droughts during the 21st century, through the reduction of nutrient input to marine environment, are expected to result in drastic declines in biomass and catches of the Gulf menhaden and its associated predators, with a decreasing effect of the Gulf menhaden fishing mortality with increasing drought conditions (Fig. 2d–f).

For status quo nutrient input and increased (x2) Gulf menhaden fishing mortality conditions, corresponding to the maximum value observed historically (1980–2016 relative to 2016), there is a decrease in the biomass of several groups: Gulf menhaden, their associated predators, species that are bycatch in the menhaden purse seine fleet (e.g., seatrout), and other species that decline due to indirect trophic interactions. In contrast, the biomass of the competitors of these groups (e.g., cephalopods), increases (Fig. 3a). In conditions of reduced nutrient input anticipated for 2100 drought conditions, the biomasses of all groups are depleted, with a more marked effect on lower trophic species such as Gulf menhaden and shrimp (Fig. 3b). At the functional-group level, projections show that drought impacts are not uniform (Fig. 3b). Lower trophic levels, including benthic invertebrates, reef-associated species, and juvenile menhaden and Spanish mackerel, experience the strongest biomass reductions, consistent with bottom-up nutrient limitation. In contrast, some higher trophic-level piscivores (e.g., billfish, cobia, other tunas) show milder responses. These results highlight an overall contraction at the base and mid-levels of the food web, with heterogeneous responses among top predators. These dynamics explicitly account for technical interactions, as fleets in the model are linked to multiple functional groups through both targeted catch and bycatch based on observed composition and selectivity.

Methods
Historical analysis
The time period addressed in our historical analysis is 1980–2016. The start year was chosen due to data availability, as many biological sampling programs in the GoM were initiated at that time and most fisheries stock assessments characterize population dynamics from the 1980s. Time series with fewer than four years of missing data were used for the cumulative biomass and catch analyses. Biomass time series used for the cumulative time series plot (Fig. 1) include only SouthEast Data Assessment and Review (SEDAR) assessed groups (N=12), as these derived quantities were estimated by population dynamics models and accepted for use by fisheries experts. Only time series with two or less missing data points between 1980-1992 (the focal historical period) were used for this analysis. Missing data in biomass and catch time series were replaced with the geometric mean of the four adjacent values in the time series, i.e., two before and two after the missing value. If missing values were at the edges of the time series, i.e., t1, t2, tend-1, tend, available adjacent values were used. For example, if t1 was missing, it was replaced with the geometric mean of t2, t3, and t4. For computing the relative change between pre-drought and drought affected years we compared 1982–1986 and 1988–1992 at the group level. Only time series without missing data for these periods were used, which included data derived from SEDAR assessments, Gulf Data Assessment and Review (GDAR) assessments, SouthEast Area Monitoring and Assessment Program (SEAMAP) trawl surveys, and the pelagic observer program. Following data filtering, computations of cumulative biomass were conducted for the following species: blacktip, dusky, and sandbar sharks; large and small coastal sharks; greater amberjack; cobia; king mackerel (ages 0–1 and 1–2 years); Spanish mackerel (ages 0–1 and 1–2 years); gag and yellowedge groupers (0–3 and >3 years); goliath grouper; red snapper (0–1, 1–2, and >3 years); vermilion and mutton snappers; tilefish; gray triggerfish; and menhaden (1–2, 2–3, 3–4, and >4 years). Two missing data points (blacktip shark and mutton snapper in 1980) were addressed using geometric mean interpolation.

For assessed species, time series of commercial and recreational catches were obtained directly from the stock assessment output files (Table S2). For the remaining species, time series of catches were obtained by combining commercial fisheries landings with recreational landings from the Marine Recreational Information Program (MRIP), Southeast Region Headboat Survey (SRHS), and the Texas Parks and Wildlife Department (TPWD). Retained bycatch in the menhaden purse seine fishery was also included within the catch time series to capture removals of non-target species from the ecosystem (for more details see Table 9 in Berenshtein et al.6. Discards for other fisheries were not incorporated into catch time series.

We used catch rather than catch-per-unit-effort (CPUE) because our goal was to evaluate total fisheries production across the ecosystem, not standardized catch rates. Catch reflects the total biomass removed, making it directly comparable across functional groups, whereas CPUE data were inconsistent in availability, standardization, and coverage. Where available (e.g., Gulf menhaden), CPUE indices were used qualitatively to validate biomass patterns but not as dependent variables. Time series of commercial fishing effort by fleet and recreational fishing effort by mode (private boat, charter boat, headboat, and shore) were obtained from stock assessment documents when possible (Table S3). For the remaining commercial fishing fleets, time series of commercial effort were obtained from the National Marine Fisheries Service Vessel Operating Units (NMFS VOU) database59, which provides general trends in fishing effort dynamics (in terms of number of vessels or gear fished). Recreational fishing-effort time series (number of angler trips) were obtained from MRIP, SRHS, and TPWD (Table S3).

Environmental variables were downloaded from national and federal databases on a monthly resolution for the years 1980–2016. For measuring the degree of drought severity, we used the Palmer’s Drought Severity Index (PDSI)1, which is a drought index calculating soil moisture balance from inputs of precipitation and evapotranspiration. Mississippi basin PDSI was obtained from the National Centers for Environmental Information60 for the divisional region of the Mississippi River Basin & Tributaries (division code #220). Mississippi River level (ft) was obtained from the US army corps of engineers database61 for the Mississippi River gauge at Vicksburg, MS (32.312500N 90.906944W). Mississippi monthly nutrient loads (total nitrogen plus total phosphorus) data were obtained from the US Geological Survey25. Sea surface temperature was downloaded from the database of the GoM ecosystem status report1, which utilized data from daily high-resolution blended analysis for sea surface temperature62. Additional time series of named hurricanes, hypoxic area, and additional Gulf menhaden-related variables are provided in the extended time series section in supplementary note S1.

PDSI and Nutrient input projections until 2100 based on future scenario (RCP 8.5)
PDSI projections are based on the Coupled Model Intercomparison Project 5 (CMIP5) model output computed in Cook et al.,33, which were part of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). We focus on RCP 8.5-high emissions scenario (i.e., Representative Concentration Pathways resulting in a radiative forcing of 8.5 W/m2 at the end of the century)63. The calibration period for the PDSI calculations was 1931–1990. Potential evapotranspiration (PET) for the PDSI projections28 was computed based on the Penman-Monteith method64. The monthly PDSI values of each of the CMIP5 models (Table S4) were averaged spatially across all grid cells that are located within the Mississippi River basin65, based on horizontal resolution of 1.25o (Longitude) x 0.94o (Latitude). Specific PDSI values that are expected to represent prolonged droughts in the first and the second halves of the 21st century, were computed as the minimum mean PDSI value in a moving average window of five years for the periods of 2017-2050 and 2051–2100, respectively. MATLAB 2020b was used for all spatial and temporal analyses of the PDSI projections data. To project future nutrient input under drought conditions, we used linear least squares regression to model the relationship between historical nutrient input and the Palmer Drought Severity Index. This regression assumes the historical relationship is primarily climate-driven and does not explicitly account for potential long-term changes in agricultural practices or land use, which we note as a source of uncertainty. To ensure the assumptions of this linear regression were met, we assessed the normality of the residuals using a Q-Q plot and the Shapiro-Wilk test (p = 0.8), confirming normality and validating the statistical approach. The resulting regression equation was then used to estimate nutrient inputs for the first and second halves of the 21st century under projected drought conditions.

This study utilized CMIP5 PDSI projections for future drought scenarios due to data availability. However, recent CMIP6 projections indicate even more severe drought conditions for the Mississippi River basin65. While both CMIP5 and CMIP6 project drying trends, CMIP6 models show more extensive and severe drying in the Mississippi River basin region. Therefore, our CMIP5-based results likely represent a conservative estimate of future drought impacts (supplementary note S3).

Ecosystem modeling: Ecopath and Ecosim (EwE) analysis and data sources
We used the US Gulf-wide Ecopath with Ecosim (EwE) model4,6 to examine the possible effects of variability in nutrient input and Gulf menhaden fishing mortality on the GoM ecosystem. EwE66 is an ecosystem modeling framework that is widely used for exploring ecosystem trophic dynamics, and the effect of environmental and anthropogenic stressors on the ecosystem, namely fishing pressure. The modeling framework is based on two main components: (1) Ecopath, a static mass-balanced snapshot representing the ecosystem’s food web and static trophic interactions66 and (2) Ecosim, a temporal-dynamic model expressed through a series of differential equations simulating ecosystem dynamics over time67. Our analysis was conducted in Ecosim rather than Ecospace because our focus was on temporal dynamics at the scale of the entire GoM ecosystem, for which spatially explicit modelling was not required. We also did not include species-specific environmental response functions; instead, drought forcing was implemented through changes in nutrient input and primary productivity. Future work may extend this approach with Ecospace to explore spatial heterogeneity.

The model area represents all state and federal waters of the US GoM continental shelf and coastline (down to a bottom depth of ~400 m). The GoM food web model includes 78 trophic groups, including three marine mammal groups, separate aggregate groups for seabirds and sea turtles, eight elasmobranch groups, 52 fish groups, 18 of which are partitioned into multiple life stages, nine invertebrate groups, three primary producers, and one detritus group (supplementary Table S1). Twelve commercial fishing fleets are modeled based on gear and/or targeted species (bottom trawl (shrimp), bottom trawl (other), purse seine (menhaden), purse seine (other), pots and traps, handline, dredge, nets, pelagic longline, reef fish longline, shark longline, and other), and four recreational fleets include private angling, charter, headboat, and shore (supplementary Table S3).

In Berenshtein et al.6 and in Berenshtein et al.5 there is further description of data inputs, assumptions required for model development, model outputs, results and key system characteristics related to fisheries dynamics, biomass flow, ecological indices, and network analysis. Note that the difference between the model version used in the current study and that described in Berenshtein et al.6 is the estimation of the last year of the hindcast modeled period (2016) of the primary productivity anomaly time series. The primary productivity anomaly is computed as part of the automatic Ecosim fitting routine and is based on an iterative search algorithm which minimizes the errors’ sum of squares with respect to the reference time series5. The default estimation of the search algorithm for 2016 was unrealistically high because there was no subsequent year information to constrain this estimate. This would have generated biased estimations of the other groups biomasses for the last year of the hindcast modeled period (2016). Hence in the current version, the last year was not estimated and was set to the default value of one (i.e. no primary productivity anomaly).

The Ecosim model’s calibration employed 109 time series of biomass and landings (1980–2016) from stock assessments and monitoring surveys. While the model effectively represented economically important species with robust data (e.g., amberjack, Spanish mackerel), its accuracy diminished for data-poor and highly migratory species, reflecting data limitations and uncertainties. Model performance, as part of the fitting procedure described in Berenshtein et al.6, was assessed based on the sum-of-squared errors (SSE). Model diagnostics indicate good agreement between model predicted biomass with many reference time series and between model predicted estimates of the fishing mortality rate corresponding to the maximum sustainable yield (FMSY) and F estimates from stock assessments. Most cases of discrepancies between model and referenced data were for highly migratory species or data-poor species and likely result from data limitations or drivers not representative solely of the GoM6. In this study, we also evaluated performance across time series using modeling efficiency (MEF; supplementary note S4, supplementary Table S5), calculated as shown in Eq. 1:

MEF = 1 - Σ(Y^i - Yi)2 / Σ(Yi - Y_mean)2 (1)

This analysis indicated high MEF values for commercially important groups, including groupers, snappers, and mackerels. However, the model exhibited limitations in capturing high-frequency fluctuations in highly migratory species and younger age classes biomass. Despite these challenges, over 74% of the time series produced positive MEF values (see supplementary note S4, supplementary Table S5), demonstrating the overall efficacy of the model.

Nutrient input and primary production in EwE
Ecosim accounts for bottom-up drivers on primary production using a Michaelis-Menten nutrient uptake model. Specifically, the EwE modeling framework assumes that the total nutrient concentration can be divided between that which is available (free) and unavailable for uptake (e.g., bound in biomass). The total nutrient content is computed as the ratio between the total nutrient inflow rate from all nutrient loading sources, and the total loss rate considering all loss pathways (e.g., advection, sedimentation, and harvest). The Ecopath base estimate of total nutrient concentration is calculated by the EwE software based on the biomass pool value, its fixed nutrient content per unit of biomass pool, and the free nutrient concentration68.

The base free nutrient proportion ensures that as producer biomass increases, free nutrient availability declines and constrains further growth. The base free nutrient proportion was set in our simulations to the default value of 0.5, provides the basis for the time-dependent nutrient forcing, which is simulated using monthly multipliers of the Ecopath based nutrient content estimate that represents the relative nutrient loading. Here, nutrient loading, inputted as a forcing function time series, is represented by total monthly nitrogen and phosphorus input from the Mississippi River (t), scaled by the mean of the entire time period. such that:

Ni = Pi * (Nitrogenloadi + Phosphorosloadi) / (Nitrogenload_mean + Phosphorosload_mean) (2)

Where i is a given month and n is the total number of months in the hindcast modeled period (N=444). In each simulation, the free and bound nutrient fractions must sum to the total nutrient content, which results in a depletion of free nutrients under conditions of increased growth, limiting primary productivity. The primary productivity and the free nutrient concentrations are linked through a Michaelis-Menten uptake relationship, in which nutrient uptake increases asymptotically up to a maximum in response to increasing free nutrient concentrations68.

Ecosim simulations
Four types of simulations were conducted (Table S6). First, Biomass and catches comparison hindcast + projections: main runs (33 runs) examined functional group biomass and catch under varying nutrient input and fishing pressure levels for historical (1980s drought) and projected (2050, 2100) drought scenarios. Second, Biomass and catches comparison hindcast + projections: error bars (258 runs) calculated the standard deviation in biomass and catch based on the uncertainty of the CMIP5 climate projections used, derived from the 5th confidence interval percentile of the CMIP5 PDSI projections. Third, Biomass and catches comparison: tradeoff analysis (231 runs) assessed biomass and catch across a range of nutrient input and menhaden fishing pressure multipliers to investigate trade-offs between these factors. Finally, Sensitivity analysis of number of drought years (66 runs) evaluated the influence of drought duration (1-year vs. 3-year droughts) on the ecosystem under projected future climate conditions (supplementary note S5, Fig. S5). All projected drought and fishing scenarios were implemented as isolated 5-year simulations layered onto the 2014–2016 baseline conditions, with forcing applied throughout the run and the terminal (fifth) year used for analyses. This design allowed transitional dynamics under directed forcing to stabilize while remaining ecologically realistic. Both Gulf menhaden fishing mortality (F) and purse-seine fleet effort were implemented as forcing functions, with their values scaled by multipliers in the scenarios to reflect changes in fishing pressure and associated technical interactions (e.g., bycatch). Background climate variability beyond the applied drought and fishing scenarios was not included.

Quantifying changes in biomass and catches
We assessed changes in biomass and catches in response to historical and future drought conditions through a series of analyses. We chose to focus on Gulf menhaden due to its significant economic and ecological role in the GoM. This forage fish supports the largest fishery in the region by weight, serving as a crucial trophic link between phytoplankton, zooplankton, and various higher trophic level predators, including king mackerel, Spanish mackerel, and seatrout.

Historical perspective. We examined the differences between pre-drought conditions (1982–1986) and drought-affected conditions (1988–1992) using both historical time series data (Fig. 1l, m) and EwE time-dynamic Ecosim Model simulations (Fig. 2c). This involved analyzing input time series data (for sources, see Tables S1-3). Specifically, we compared the averages of the biomass and catches of the historical (H) time series data for these two distinct periods using the following (Eq. 3)

relative biomass change H = (B 1988-1992 - B 1982-1986) / (B 1988-1992 + B 1982-1986) (3)

This formulation bounds the relative change between -1 and 1, with a value of 0 indicating no change.

Future perspective. For the future analysis, we quantify changes in biomass and catches in response to projected drought conditions while accounting for varying levels of Gulf menhaden fishing mortality, conducting a trade-off analysis similar to the methodologies employed in Chagaris et al.69, and in Berenshtein et al.5. This approach is conceptually similar to methodologies for assessing potential climate change impacts in other ecosystems, such as temperature or zooplankton effects on the productivity of Ecosim models70.

We simulated a drought length of five years, a duration considered realistic given the anticipated increase in both the severity and frequency of droughts in the 21st century32. To model nutrient input under prolonged drought conditions, we implemented two specific nutrient values anticipated for the first and second halves of the century. Projected nutrient inputs were calculated using a least squares linear regression between the EwE Mississippi nutrient forcing and the Palmer Drought Severity Index (PDSI) in the Mississippi River basin. The resulting future nutrient input values were derived from this regression equation (Fig. 2a, b).

For each projection scenario, the terminal (fifth) year biomass was expressed relative to the terminal biomass of the status quo scenario, where nutrient and fishing conditions were set based on the geometric mean of the last three years of the US Gulf-wide EwE model (2014–2016). This relationship is articulated as:

B ratio = B T,sim / B T,statusquo (4)

where B T represents the terminal (fifth) year biomass from the climate scenario simulation and status quo projections.

To calculate the relative change in the projected biomass and catch for each group under future (F) scenarios, we used the following formula:

relative biomass change = (B F T,sim - B T,statusquo) / (B F T,sim + B T,statusquo) (5)

This formulation bounds the relative change between -1 and 1, with a value of 0 indicating no change.

Finally, to explore the trade-offs between nutrient input and Gulf menhaden fishing mortality (Fig. 2d–f), we conducted a series of five-year projection scenarios. Nutrient load and fishing mortality were modeled based on the geometric mean of the last three terminal years (2014–2016), with multipliers for nutrient forcing ranging from 0 to 2 in increments of 0.1 (N=21), and for fishing mortality from 0 to 5 in increments of 0.5 (N=11), resulting in a total of 231 scenarios (11 x 21). This approach enabled the simultaneous manipulation of menhaden fishing mortality, purse seine effort, and nutrient input, thus incorporating the trophic effects of nutrient inputs and menhaden harvest on the GoM ecosystem.

Model limitations
While the historical and projected fisheries declines are robust and systematic across the entire trophic range, it is important to note the limitations and uncertainties of the referenced data and the modeling framework. For example, most groups’ biomass reference data are a product of stock assessment models, which consider processes such as age-specific natural mortality, growth, recruitment, and fishing mortality37. These models, like others, encompass inherent uncertainty and are based on assumptions or best available data concerning the species biology and life history. However, they do represent the only source of absolute biomass for the entire GoM. To partially address this limitation, we cross-checked temporal trends against independent observational datasets, including standardized CPUE indices from the Gulf menhaden purse-seine fishery and fisheries-independent trawl-based juvenile abundance indices from Louisiana estuaries (supplementary note S1). While differences in design and coverage precluded full quantitative validation, these comparisons provided qualitative consistency checks. In addition, assessment output data are smoothed relative to observed data, which may result in the loss of high frequency variability. On the other hand, the thorough revision of data and model outputs enhances quality control and mitigates unrealistic fluctuations that may arise from data derived from methods such as bottom trawl surveys71. Catch data also harbors uncertainties related to unreported or misreported catches, as well as uncertain estimations of recreational landings. Furthermore, factors unrelated to environmental conditions and fishing pressure, such as management regulations, can significantly influence population and ecosystem dynamics38.

Limitations of the US Gulf-wide EwE model include lack of consideration of spatially explicit processes such as migration or spatially explicit mortality, for example of harmful algal blooms and hypoxic zones. In our model, hypoxia was not explicitly implemented, partially because our domain is Gulf-wide, and the hypoxic zone is localized near the mouth of the Mississippi River. However, in a previous study57, a spatially explicit EwE model for the northern GoM implemented hypoxia as well as nutrient input72, and concluded that the positive contribution of nutrient input on fisheries production outweighs the negative effect from hypoxic conditions. Consistent with this, empirical data (Fig. S2e) show that hypoxic area declines sharply during extreme drought years (e.g., 1988, 2000, 2012) and recovers once drought conditions diminish, suggesting that lagged effects are weak or absent. Our simulations used primary productivity anomalies as a proxy for drought forcing and did not explicitly capture match–mismatch dynamics with spawning or recruitment. Ecosim can incorporate seasonal egg production forcing, and future work should test such interactions to better represent drought effects on early life stages. In addition, we did not explicitly incorporate salinity–habitat interactions in our modeling framework. While salinity increases during droughts can stress estuarine-dependent invertebrates (e.g., oysters, shrimp)19, the ecosystem-wide declines observed here were reproduced based solely on nutrient forcing, supporting a parsimonious focus on trophic mechanisms. Part of the uncertainty in our model is attributed to knowledge gaps with respect to the life history and traits of functional groups in the GoM ecosystem, limited diet (stomach content) data, as well as limited capacity in correct characterization of highly migratory species. In addition, limitations exist in the taxonomic resolution for marine mammals, seabirds, and turtles as well as in the limited time series available for model calibration for these groups. Nevertheless, the US Gulf-wide EwE model characterizes the diverse GoM ecosystem and incorporates extensive reference data (161 reference and forcing time series), many of which correspond well with model predictions6.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper is a well written and timely evaluation of the impacts of climate change on future GoM ecosystem and fishery productivity. It is unique in the aspect that it connects climate impacts on land (drought) to those in marine systems via changes in freshwater nutrient inputs to GoM. This is an often postulated interaction (i.e., climate impacts on land and water will impact marine systems) but there is a paucity of published papers that demonstrate or evaluate the phenomenon. The authors use a combination of a nutrient-forced mass-balance ecosystem model (EwE) and mean future climate projections of a drought index (PDSI) from an ensemble of 20 General Circulation Models (GCMs) representing a subset of CMIP5 ensemble members under the high baseline Representative Carbon Pathway (RCP 8.5). The authors derived a linear relationship between historical nutrients and historical PDSI values and used the nutrient relationship to drive bottom up productivity changes in the EwE model. The authors then force the EwE model with profiles across ranges in nutrient changes (0 -2 fold 2014-2016 average nutrient levels) and ranges in fishing pressure (0-5 x average F from 2014-2016). This represents an innovative application of an approach that has been applied successfully to evaluate potential climate change impacts in other systems, albeit with different climate couplings (e.g., where temperature or zooplankton drive productivity of EwE models; sensu https://doi.org/10.1111/gcb.13471 ). In general this is the type of analysis that can be vital for risk assessments of climate change impacts on marine fisheries as well as cumulative impacts of changes on land on marine ecosystems. In general, I strongly support this type of approach, and applaud the authors for using an ecosystem model (which captures indirect and cascading impacts) to evaluate climate change impacts on marine species. This can be a more robust approach than a direct climate-linked single species modeling effort.\nHowever, there are a number of fundamental issues with the paper that I believe preclude it from being published until it is revised. I hope the authors are able to correct and address these issues so it can be used to advance understanding of climate change risk to marine ecosystems and fisheries.\nThe largest concern I have is that the paper is predicated on a linkage between droughts and historical declines in GoM catches that the authors do not demonstrate and which is not supported by the cited peer-reviewed papers (nor by the large number of non-peer reviewed technical reports that are cited). The authors postulate that a long (~3 year) drought in the mid- to late 1980’s induced a decline in biomass and catch that is reflected in the timeseries for both. These are plotted in Figure 1 (a) (e) and (f). However, one quickly notes that there are similar large drops in the PDSI index in ~2000 and ~2012 but no similar subsequent drop in catch or biomass timeseries as was seen in the late 1980’s. In the discussion section (lines 160-172) the authors try to address this and explain it as a difference in drought length (3 yrs for the earlier drought versus ~1 year for the others) and timing. This section is very speculative, without any citations save for one at the end of the paragraph stating that the severity of the drought can be related to the contrast between preceding wet years. In fact the largest concern I had in reading this section was that there was no mention of possible changes in fishery management or markets that could also explain the decline in catch. Additionally, following the sharp drop in in both catches and biomass in the late 1980’s catches remain low while biomass increases, pointing towards a non-biomass driver of low catches (e.g., shift in management towards lower harvest mortality rates). Finally there are multiple disparate patterns in the magnitude and sometimes the direction of biomass change and catch (this can be seen in Fig 1 (h) and (i); e.g., crab have the second largest decline in biomass but increases in catch, similarly, younger menhaden had the larger declines in biomass than older conspecifics but declines in older menhaden catches were larger than younger ones). This is of course a not unexpected and well cited issue in fisheries, especially as biomass declines where fishers can increase effort to offset declining biomass, or where catch declines but is stabilized over time and biomass increases due to implementation of policies that reduce harvest rates to sustainable levels. I believe the authors are aware of this issue (see plots of relative effort) and offer what is maybe the most parsimonious explanation in the “model limitation” section of the Methods (lines 506 -509):\n506 “...For example, fluctuations in Gulf menhaden catches may\n507 be related to economic processes, which occurred during the mid and late 1980s, when the Gulf\n508 menhaden fishery underwent corporate consolidation resulting in declines in the number of\n509 fishing vessels and processing plants. This may have contributed to the decline in catches 45. For\n510 species such as Goliath grouper, declines in biomass and catch are likely a result of long periods\n511 of overfishing or management regulations designed to reduce harvest (e.g., harvest moratorium).”\nSimilarly, the ESR (reference #1) states “The Gulf of Mexico Fisheries Management Council implemented a number of Fishery Management Plans in the 1980s, and major Amendments restricting fishing effort were passed in the 1990s.“ (p22).\nOf course these management measures could have been implemented to address the decline that was driven initially by drought. The way to address how much climate drove the decline is to run a hindcast or counterfactual simulation, although these are challenging with ecosystem models because of model drift. So another approach would be to use a sensitivity simulation to evaluate the hypothesis of prolonged drought versus short 1 year drought in driving declines. E.g., fixing the F rate at a few different levels (e.g., those observed in the 1985-1990 period vs those observed in 2013), simulate a) 3 year drought (sensu the 1985-1988) and b) a five year period with a 1 year drought. Something like this should be conducted to demonstrate and support the postulation that underlies the projection simulations. In the absence of the sensitivity simulation above, there is not sufficient evidence provided presently to rule out changes in harvest pressure, fisheries operations, management or markets in driving declines in catch in the late 1980’s relative to early 1980’s. Given that the paper is predicated on the postulated relationship between drought, nutrients, and declines in catches, establishing and supporting this is extremely important. Additionally, I strongly suggest providing a historical time-series of harvest mortality by age for menhaden (e.g., as a supplemental figure). It appears to my eye that the F rate for 1 year old fish is much higher during the 1985-1990 period than 2000 - 2016. Having the timeseries would help facilitate comparisons in relative fishing pressure over time..\nThe second issue with the paper is that it is very difficult to discern precisely what methods were used in the simulations. This needs to be clarified in the Methods simulation section. I have detailed a number of line by line suggestions below to help clarify. There were two separate simulations conducted. For the first, I believe the correct methods were used for the status quo versus future time series projections, but can’t confirm this based on the current methods section. For the first simulation, the authors ran the EwE model for a 5 year simulation period across Nutrient and F profiles based on average 2014-2016 values for each (Navg2014-2016 and Favg2014-2016). Within the intervals for each (Nutrients = (0,2) X Navg2014-2016 and F = (0,5) x Favg2014-2016) is the value of 1.0, i.e., a simulation where N = N_avg and F = F_avg. For the second the authors used linear regression to extrapolate the nutrient levels from the PDSI projections for the first and second halves of the century.\nFor both analyses it is not clear what the baseline status quo scenario includes. Can the authors confirm that in lines 471-475, comparisons of climate change simulations are relative to the terminal year of a 5 year control simulation where N =Navg2014-2016 and F = Favg2014-2016 rather than comparisons directly to 2016 values? This is important because ecosystem models can propagate error when adjusting to new forcings and including a control run helps establish that differences are due to forcings rather than model behavior. If this was done the authors need to clarify the start and end dates of each simulation and in figure and the results consistently use a term like “persistence simulation” or “control” rather than “2016” which is confusing. A table or figure of the simulation methodology would greatly help clarify the two simulations.\nFor the second simulation, it is not clear from the Methods which years are included in this extrapolation; values are reported in the Results section and Figure 2.b but again it is difficult to understand which years are included in the values as they are reported as “anticipated by 2050 and during 2051-2100). It appears they are averages of 2016-2050 and 2051-2100. Are they the mean of the 20 member ensemble for each year or the mean of all values from all simulations within those time frames? There is considerable range in the PDSI of the 20 member ensemble by end of the century and the upper range of the index ensemble mean includes values (PDSI>0) seen in non-drought years. As such I strongly suggest that the authors run the simulation for random draws from the mean and standard deviation of the ensemble for each time-period (e.g., 100 draws) in order to provide error bars for the figures in Fig. 2c; confidence intervals around projected declines may be large.\nA growing number of papers include comparisons of projections of high and low carbon emission scenarios, e.g., RCP8.5 vs RCP2.6 or the more recent CMIP6 SSP126 vs SSP 585. These types of comparisons are extremely useful in IPCC and other climate assessments because they reveal the relative risk of different levels of greenhouse gas emissions. To repeat this analysis for RCP2.6 would be a major undertaking and so I am not suggesting that has to be done here, but including multiple carbon emission scenarios is something the authors might consider in future work as it would help address an important need for relative climate risk information for fisheries and marine ecosystems.\nIn summary, I find the paper topic to be compelling, the methodology is established but novelly applied, and the figures and text are clear and well executed. I appreciate the inclusion of the code and data. If the authors are able to address the aforementioned concerns (and following line by line suggestions) I believe that this paper can be an important contribution to the field of climate change and fisheries. While these suggestions are not trivial I also believe that they are achievable revisions that will immensely strengthen the paper.\nData access note. I was not able to access the data as it appears to be stored on a microsoft sharepoint or similar site. I suggest a public permanent repository like Figshare or Zenodo via github if the files are not too large.\nReferences: Generally there is a lack of references for key statements. Of the references cited many are unpublished technical memos or reports; one non-data citation is for a defunct NOAA website. Some are quite old and newer more comprehensive papers are available. Some of the citations appear incomplete or incorrect.\nIn addition to the suggestions above the following issues should be addressed:\nLines 50-51 : this citation is for a NOAA Ecosystem Status report which compiles data and indices from multiple sources. Citation of the original stock assessments and/or a peer reviewed papers are additionally needed.\nLine 73 - this citation is a website that is no longer available. Citation of a stock assessment report or published paper is needed.\nLines 115 - “as compared to 2016” does not appear to match the methods of the status quo simulation using persistence values (average of 2014-2016). See note above regarding methods, and change to, as compared to a persistence [or control] scenario, or something similar.\nLines 116 - “will be around 72%” is too prescriptive. This is one of many future carbon emissions scenario. Change to “could be reduced by as much as” or something similar.\nLines 139 change to “ fishing mortality in projection simulations is smaller compared to”.\nLines 158-159 need a citation.\nLines 160-170, especially lines 169-171 are speculative as written and need references. The changes in fisheries management during the late 1980’s are not mentioned here so the corresponding text in the methods section (lines 506-509) should be moved here.\nLine 191 This citation seems out of place as it iis for Arctic ecosystems, there are many fisheries projected impacts papers, two of which are already cited (Lotze et al. 2019 and Titensor et al. 2021). If keeping the citation I suggest moving it to after “synergistic impacts” and adding the Tittensor and Lotze refs at the end of the sentence.\nLine 193 - there are more recent papers regarding hypoxia (see refs cited within https://www.science.org/doi/10.1126/science.aam7240)\nLine 205: It seems a missed opportunity here to not point out that during subsequent droughts there was reduced fishing pressure/mortality, which can be seen in Fig 1 and supports this statement.\nLine 222 - citation 5: no journal/publication source\nLine 250 citation 16 incomplete reference\nLines 252 Please check and correct the citation for the year for this publication; it appears the book was originally published in 1991.\nLine 263 citation 22 Page 404 and this is not a report it is a website.\nLines 338-339 - how many times were missing data interpolated? Specifically which years and data (species) were replaced. Provide a supplemental table or plot of the data indicating interpolated values.\nLine 372: Cite the original source of the data, not the ESR which compiles the indices, including the website address for the data access and date via instructions to authors.\nLine 378: RCP 8.5 is not “buisness as usual”, it is often incorrectly referred to as such. It is high baseline. Cite the RCP : Van Vuuren DP, Edmonds J, Thomson A, Riahi K, Kainuma M, Matsui T, Hurtt GC, Lamarque J-F, Meinshausen M, Smith S, Granier C, Rose SK, Hibbard KA (2011) Representative concentration pathways: an overview. Climatic Change 109:5–31\nLine 388: Information regarding the error distribution is needed here. What was the error distribution used for the linear regression? Because nutrients are not normally distributed (values can not be less than 0), a logistic regression, i.e., lognormal distribution should be used where Ln(Nutrients) ~f(PDSI,e) where e ~N(0,sigma) rather than linear model Nutrients~f(PDSI,e) where e ~N(0,sigma).\n416 “good agreement” how so? Provide a metric of skill or correlation.\n429 specify the years that were used.\n445 what is meant by “scaled by the mean” Z-scored - include an equation.\nLine 454 “approach in “ use the first author name convention for citation.\n475 this equation describes a ratio not a delta. For delta (proportional change) use B1=(Btsim-Bsq)/Bsq\nLine 481 : according to this equation if biomass in the simulations was 20% greater than the status quo simulation the delta B would be 9% this equation seems incorrect. I think that authors mean to express the proportional change in biomass, i.e., delta B = (Btsim-Btsq)/Btsq.\nLine 485 see above same issue.\nLines 488-489 are not needed- can be included with data supplemental material.\nLines 506-509 “For example….decline in catches 45.” should be moved to the discussion section lines 160 (see comment above)\nFigure 1 three panels in a-d on the right need letters.\nIt would be nice to have the colors for each species in h and i\nFigure 2 (d) - each subplot needs a letter, color ramp needs a label . X axis for (c ) is not fishing mortality it is times X average 2014-2016 F. Please fix the label. Same comment for (d).", "reviewer_2": "In the ms by Berenshtein et al., the authors present data that indicates a severe drought in the southeast U.S., combined with fishing pressure on ecologically important menhaden in the Gulf of Mexico, likely caused a decline in marine food webs and fisheries catch in the 1980s. Using these results the authors modeled future fisheries in the GoM under simulated drought conditions. First, I would like to thank the authors for expanding the knowledge on the ecosystem dynamics of this region, and further exploring the role of freshwater/nutrient inputs in ocean ecosystems. I believe this could add to the growing body of knowledge to an important socio-ecological system. With that said, I believe there are too many confounding factors that could contribute to the lack of nutrients being the main driver to population dynamics in the region, and this would be important for informing a more robust predictive model, and thus should be considered before publication.\nFor the “historic” analysis on trends, I was a bit underwhelmed by the focus on 1982-86, then 1986-1992. The authors use these two time series to compare trends in fisheries and environmental factors with the sole focus on drought and freshwater inputs. Why not include the entire time series in an analysis that includes nutrient load relationships with fisheries, but also the other known factors that influence marine food webs, such as temperature, wind/hurricanes, and the elephant in the room…hypoxia? I think if this ms is to be published then it will be important to apply quantitative analytical approaches on a beautiful historic time series that should be able to tease apart the influence of these various environmental factors from 1980-2016. I think without this analysis then the model is not fully informed. For example, in the time series in Fig. 1a, there appears to be two other significant drought events in the region 1997-2000 and 2011-2014 (eyeballing), but fish biomass (Fig. 1e) is stable or going up during those periods. What can explain that?\nOther Comments:\nTitle: why phrase the title as a question?\n“This severe drought resulted in a reduction of more than 50% in the Mississippi River flow and nutrient input to the GoM.”\nHaving a difficult time fully understanding the point of this. I do understand that freshwater connectivity is an important function of the Mississippi River/GOF system, but that same system in its current form brings an unnatural amount of nutrients from ag runoff to the GOF, which causes an annual Dead Zone for fish. Given the well known relationship\nIntro: “Numerous studies indicated a significant correspondence between Gulf menhaden population dynamics and environmental conditions such as temperature, wind, and river flow” What are those relationships between all of these abiotic factors and fish? Will help the reader to better understand the system.\n“River to the GoM reached a record low annual and summer value of 107,717 and 41,560 t, respectively (Fig. 1c).” Would be good to know what the historic pre-ag nutrient load was for the GoF. Are those data available or known?\nFigures: overall I thought they were impressive, but had to zoom in to 250% to read some of them. Also, the color schemes to identify various species and groups are too challenging to interpret.", "reviewer_3": "Igal Berenshtein\nDepartment of Marine Biology\nCharney School of Marine Sciences\nUniversity of Haifa\nAbba Khoushy Ave 199, Haifa, 3498838\niberensht@univ.haifa.ac.il\nMobile phone +972-54-533-8837\nPoint-by-point response to the reviewer’s comments\nReviewer #1 (Remarks to the Author):\nThis paper is a well written and timely evaluation of the impacts of climate change on future\nGoM ecosystem and fishery productivity. It is unique in the aspect that it connects climate\nimpacts on land (drought) to those in marine systems via changes in freshwater nutrient inputs to\nGoM. This is an often postulated interaction (i.e., climate impacts on land and water will impact\nmarine systems) but there is a paucity of published papers that demonstrate or evaluate the\nphenomenon. The authors use a combination of a nutrient-forced mass-balance ecosystem model\n(EwE) and mean future climate projections of a drought index (PDSI) from an ensemble of 20\nGeneral Circulation Models (GCMs) representing a subset of CMIP5 ensemble members under\nthe high baseline Representative Carbon Pathway (RCP 8.5). The authors derived a linear\nrelationship between historical nutrients and historical PDSI values and used the nutrient\nrelationship to drive bottom up productivity changes in the EwE model.\nThe authors then force the EwE model with profiles across ranges in nutrient changes (0 -2 fold\n2014-2016 average nutrient levels) and ranges in fishing pressure (0-5 x average F from 2014-\n2016). This represents an innovative application of an approach that has been applied\nsuccessfully to evaluate potential climate change impacts in other systems, albeit with different\nclimate couplings (e.g., where temperature or zooplankton drive productivity of EwE models;\nsensu https://doi.org/10.1111/gcb.13471 ). In general this is the type of analysis that can be vital\nfor risk assessments of climate change impacts on marine fisheries as well as cumulative impacts\nof changes on land on marine ecosystems. In general, I strongly support this type of approach,\nand applaud the authors for using an ecosystem model (which captures indirect and cascading\nimpacts) to evaluate climate change impacts on marine species. This can be a more robust\napproach than a direct climate-linked single species\nmodeling effort.\nHowever, there are a number of fundamental issues with the paper that I believe preclude it from\nbeing published until it is revised. I hope the authors are able to correct and address these issues\nso it can be used to advance understanding of climate change risk to marine ecosystems and\nfisheries.\nWe thank the reviewer for the positive comments. In the revised manuscript, we have\nthoroughly addressed the concerns raised by incorporating additional analyses, integrating\nnew data, and expanding the relevant sections as needed. We have also included the\ncitation of Woodworth-Jefcoats et al. (2017) in the text to enhance context and support our\nfindings.\nThe largest concern I have is that the paper is predicated on a linkage between droughts and\nhistorical declines in GoM catches that the authors do not demonstrate and which is not\nsupported by the cited peer-reviewed papers (nor by the large number of non-peer reviewed\ntechnical reports that are cited). The authors postulate that a long (~3 year) drought in the mid- to\nlate 1980’s induced a decline in biomass and catch that is reflected in the timeseries for both.\nThese are plotted in Figure 1 (a) (e) and (f). However, one quickly notes that there are similar\nlarge drops in the PDSI index in ~2000 and ~2012 but no similar subsequent drop in catch or\nbiomass timeseries as was seen in the late 1980’s. In the discussion section (lines 160-172) the\nauthors try to address this and explain it as a difference in drought length (3 yrs for the earlier\ndrought versus ~1 year for the others). This section is very speculative, without any\ncitations save for one at the end of the paragraph stating that the severity of the drought can be\nrelated to the contrast between preceding wet years. In fact the largest concern I had in reading\nthis section was that there was no mention of possible changes in fishery management or markets\nthat could also explain the decline in catch.\nWe appreciate the reviewer's insightful comments regarding the linkage between droughts\nand historical declines in Gulf of Mexico (GoM) catches. As noted in our manuscript, we\nhypothesize that significant ecosystem impacts, reflected in declines in biomass and catches,\nwould primarily occur following prolonged severe droughts lasting three years or more.\nThis hypothesis is based on the understanding that short-lived species, such as Gulf\nmenhaden, can typically rebound through density-dependent compensatory processes after\nbrief periods of low productivity. However, extended periods of low productivity can\nseverely affect multiple year-classes, leading to long-term detrimental impacts on these\npopulations. We further clarify that the apparent effects of the 1980s drought can be partly\nattributed to (1) elevated biomass and catches during the mid-1980s, following\nexceptionally wet conditions in early 1982-1983, and (2) the high fishing effort prevalent\nthroughout the 1980s. (3) our extended time series (Supplementary section S2)\ndemonstrates that episodic fluctuations in menhaden populations correlate with highly dry\nconditions (e.g., 2006, 2012) and wet periods (e.g., 2010).\nTo further address the reviewer’s concerns, we acknowledge that variations in fishery\nmanagement practices and market dynamics over time might also contribute to\nfluctuations in catch. In the revised manuscript, we have included additional discussion on\nthese factors to provide a more comprehensive context for the observed changes in catch\nand biomass.\nAdditionally, in response to the reviewer’s suggestion regarding the speculative nature of\nthe discussion, we have incorporated a new Supplementary section S6, which includes a\nsensitivity analysis that evaluates the effects of prolonged versus shorter drought periods\n(1- and 3- years duration). This analysis quantifies the potential impacts of different\ndrought durations and strengthens the robustness of our findings.\nAdditionally, following the sharp drop in in both catches and biomass in the late 1980’s catches\nremain low while biomass increases, pointing towards a non-biomass driver of low catches (e.g.,\nshift in management towards lower harvest mortality rates).\nWe appreciate the reviewer’s observations regarding the dynamics of catches and biomass\nin the late 1980s. Indeed, from 1992 onwards, after the drought of the late 1980s, we\nobserved a gradual decline in catches, averaging approximately 3 t/km² over 24 years. This\ntrend aligns with the expected relationship described by the equation (C = F×B), where\nfishing mortality (F) was reduced due to regulatory measures, consequently leading to\ndeclining annual catches. This regulatory action facilitated an increase in the remaining\nbiomass within the marine ecosystem, fostering a more sustainable fish population.\nTo clarify this we added supplementary section S3, which illustrates the contrasting\nrelationship between menhaden catches and fishing effort in the 1980s (a non-intuitive,\ndrought-dominated relationship) and the shift towards a more intuitive relationship from\nthe 1990s onward.\nFinally there are multiple disparate patterns in the magnitude and sometimes the direction of\nbiomass change and catch (this can be seen in Fig 1 (h) and (i); e.g., crab have the second largest\ndecline in biomass but increases in catch, similarly, younger menhaden had the larger declines in\nbiomass than older conspecifics but declines in older menhaden catches were larger than younger\nones). This is of course a not unexpected and well cited issue in fisheries, especially as biomass\ndeclines where fishers can increase effort to offset declining biomass, or where catch declines\nbut is stabilized over time and biomass increases due to implementation of policies that reduce\nharvest rates to sustainable levels. I believe the authors are aware of this issue (see plots of\nrelative effort) and offer what is maybe the most parsimonious explanation in the “model\nlimitation” section of the Methods (lines 506 -509):\n506 “...For example, fluctuations in Gulf menhaden catches may be related to economic\nprocesses, which occurred during the mid and late 1980s, when the Gulf menhaden fishery\nunderwent corporate consolidation resulting in declines in the number of fishing vessels and\nprocessing plants. This may have contributed to the decline in catches 45. For species such as\nGoliath grouper, declines in biomass and catch are likely a result of long periods of overfishing\nor management regulations designed to reduce harvest (e.g., harvest moratorium).”\nSimilarly, the ESR (reference #1) states “The Gulf of Mexico Fisheries Management Council\nimplemented a number of Fishery Management Plans in the 1980s, and major Amendments\nrestricting fishing effort were passed in the 1990s.“ (p22).\nWe appreciate the reviewer’s valuable comment regarding the complex patterns observed\nin biomass changes and catch dynamics. In addition, we indeed recognize that the\nfluctuations in biomass and catch, such as the decline in crab biomass coinciding with an\nincrease in catch, are well-documented phenomena in fisheries science. To address these\ncomplexities, we have enriched the \"model limitation\" section with further explanations\nabout the economic and regulatory factors at play, including the historical context of the\nGulf menhaden fishery and relevant management measures implemented over the years.\nAdditionally, we have included a non-fisheries-related index of juvenile Gulf menhaden\nabundance in Louisiana estuaries. This data reveals a decline in the juvenile Gulf\nmenhaden population during the late 1980s that is unrelated to fisheries management\nmeasures, further highlighting the influence of external environmental factors.\nOf course these management measures could have been implemented to address the decline that\nwas driven initially by drought. The way to address how much climate drove the decline is to run\na hindcast or counterfactual simulation, although these are challenging with ecosystem models\nbecause of model drift. So another approach would be to use a sensitivity simulation to evaluate\nthe hypothesis of prolonged drought versus short 1 year drought in driving declines. E.g., fixing\nthe F rate at a few different levels (e.g., those observed in the 1985-1990 period vs those\nobserved in 2013), simulate a) 3 year drought (sensu the 1985-1988) and b) a five year period\nwith a 1 year drought. Something like this should be conducted to demonstrate and support the\npostulation that underlies the projection simulations. In the absence of the sensitivity simulation\nabove, there is not sufficient evidence provided presently to rule out changes in harvest pressure,\nfisheries operations, management or markets in driving declines in catch in the late 1980’s\nrelative to early 1980’s. Given that the paper is predicated on the postulated relationship between\ndrought, nutrients, and declines in catches, establishing and supporting this is extremely\nimportant. Additionally, I strongly suggest providing a historical time-series of harvest mortality\nby age for menhaden (e.g., as a supplemental figure). It appears to my eye that the F rate for 1\nyear old fish is much higher during the 1985-1990 period than 2000 - 2016. Having the\ntimeseries would help facilitate comparisons in relative fishing pressure over time.\nWe thank the reviewer for the valuable insights. In response to the comments, we have\nincluded a sensitivity analysis in supplementary section S6 that evaluates the effects of\ndifferent drought durations, specifically simulating both 1-year and 3-year droughts under\nvariable F rates, in addition to the previously discussed 5-year drought scenario. This\nanalysis illustrates a clear correlation between the duration of drought and the cumulative\ndecline in biomass.\nAdditionally, we have incorporated the requested historical time series of harvest mortality\nby age for Gulf menhaden, which can be found in Figure S2c. As noted by the reviewer,\nthis time series confirms that fishing mortality was indeed higher during the late 1980s\ncompared to the subsequent period from 2000 to 2016. This rise in fishing mortality during\nthe late 1980s can be attributed to higher fishing efforts and catches, while the later period\nexperienced lower fishing effort, which ultimately supported a gradual recovery of the\nmenhaden population.\nImportantly, our findings suggest that changes in harvest pressure and fisheries operations\ncannot fully account for the observed trends. As detailed in supplementary section S2,\nmenhaden fishing effort was actually declining during the 1980s, coinciding with a decline\nin biomass, rather than the expected increase that would typically drive such reductions.\nThe second issue with the paper is that it is very difficult to discern precisely what methods were\nused in the simulations. This needs to be clarified in the Methods simulation section. I have\ndetailed a number of line by line suggestions below to help clarify. There were two separate\nsimulations conducted. For the first, I believe the correct methods were used for the status quo\nversus future time series projections, but can’t confirm this based on the current methods section.\nFor the first simulation, the authors ran the EwE model for a 5 year simulation period across\nNutrient and F profiles based on average 2014-2016 values for each (Navg2014-2016 and\nFavg2014-2016). Within the intervals for each (Nutrients = (0,2) X Navg2014-2016 and F =\n(0,5) x Favg2014-2016) is the value of 1.0, i.e., a simulation where N = N_avg and F = F_avg.\nFor the second the authors used linear regression to extrapolate the nutrient levels from the PDSI\nprojections for the first and second halves of the century. For both analyses it is not clear what\nthe baseline status quo scenario includes. Can the authors confirm that in lines 471-475,\ncomparisons of climate change simulations are relative to the terminal year of a 5 year control\nsimulation where N =Navg2014-2016 and F = Favg2014-2016 rather than comparisons directly\nto 2016 values? This is important because ecosystem models can propagate error when adjusting\nto new forcings and including a control run helps establish that differences are due to forcings\nrather than model behavior. If this was done the authors need to clarify the start and end dates of\neach simulation and in figure and the results consistently use a term like “persistence simulation”\nor “control” rather than “2016” which is confusing. A table or figure of the simulation\nmethodology would greatly help clarify the two simulations.\nWe sincerely thank the reviewer for the valuable feedback regarding the clarity of our\nmethods in the simulations. In response, we have made several revisions to enhance the\nclarity of our methodology. We have changed the text to include “Ecosim simulations” and\n“Quantifying changes biomass and catches” sections and added detailed explanations to\nenhance clarity. In the “Quantifying changes biomass and catches” section, we specifically\ndivided the methodologies into distinct sections: \"Historical Perspective\" and \"Future\nPerspective,\" clearly outlining the methods used in both analyses.\nIn the \"Historical Perspective,\" we have elaborated on how we examined the differences\nbetween pre-drought conditions (1982-1986) and drought-affected conditions (1988-1992),\nutilizing historical time series data and EwE time-dynamic Ecosim Model simulations. We\nincluded a detailed description of the equation used to analyze biomass and catches, as well\nas explaining how the fishing mortality and nutrient input forcing for Gulf menhaden were\nderived and implemented.\nIn the \"Future Perspective,\" we specified our approach to quantifying changes in biomass\nand catches in response to projected drought conditions, accounting for varying levels of\nGulf menhaden fishing mortality in a trade-off analysis. We highlighted the\nimplementation of a 5-year simulated drought and the use of linear regression to project\nnutrient inputs based on PDSI data. We also clarified how terminal biomass was expressed\nrelative to the status quo and how relative changes in biomass and catch were calculated.\nFurthermore, we used our sensitivity analysis simulations, which examine trade-offs\nbetween nutrient input and Gulf menhaden fishing mortality, detailing how we modeled\nnutrient loading and fishing mortality using various multipliers.\nRegarding the baseline status quo scenario. To clarify, we confirm that the comparisons of\nclimate change simulations presented in lines 471-475 are indeed relative to the terminal\nyear of a 5-year control simulation, in which nutrient inputs N =geom_mean(N ) and\n2014-2016\nF = geom_mean(F ), rather than being directly compared to 2016 values.\n2014-2016\nIn response to the reviewer’s suggestions, we have provided additional clarification\nregarding the start and end dates of each simulation in the revised manuscript. We have\nalso standardized terminology, using the term “status quo scenario” consistently\nthroughout the text to avoid any confusion with the term “2016.”. Furthermore, we have\nadded a table (supplementary Table S6) that outlines all simulations along with detailed\nexplanations of each simulation.\nFor the second simulation, it is not clear from the Methods which years are included in this\nextrapolation; values are reported in the Results section and Figure 2.b but again it is difficult to\nunderstand which years are included in the values as they are reported as “anticipated by 2050\nand during 2051-2100). It appears they are averages of 2016-2050 and 2051-2100. Are they the\nmean of the 20 member ensemble for each year or the mean of all values from all simulations\nwithin those time frames? There is considerable range in the PDSI of the 20 member ensemble\nby end of the century and the upper range of the index ensemble mean includes values (PDSI>0)\nseen in non-drought years. As such I strongly suggest that the authors run the simulation for\nrandom draws from the mean and standard deviation of the ensemble for each time-period (e.g.,\n100 draws) in order to provide error bars for the figures in Fig. 2c; confidence intervals around\nprojected declines may be large.\nWe appreciate the reviewer’s insightful comments regarding the second simulation. The\ndrought conditions for the specified years were computed based on the minimal moving\naverage of the 5th percentile PDSI value over a 5-year period, utilizing the variation from\nthe 20-member CMIP ensemble. This approach allows us to focus on the driest consistent\nspells, which are of particular interest in our study. The correspondence between the driest\nobserved PDSI (indicated by magenta markers in Fig. 2a) and the modeled PDSI\n(represented by the yellow shaded area) in Fig. 2a further justifies our emphasis on the 5th\npercentile of the modeled PDSI.\nWhile we acknowledge that wet periods corresponding to positive PDSI values will occur\nwithin the projected time frames (through the end of the century), our focus on the impacts\nof droughts necessitates prioritizing drought periods over wet ones. It is important to note\nthat the PDSI index is a \"long-memory\" index that accounts for soil moisture; thus, the\neffects of very wet periods can diminish quickly following extreme hot and dry conditions.\nWe have accepted the reviewer’s suggestion to utilize random draws (N=43 for each\nscenario) from the mean and standard deviation of the ensemble for each time period in\norder to provide error bars for the figures in Fig. 2c. We used the 5th percentile of the\nmodeled PDSI as the basis for our analysis based on the explanation above.\nA growing number of papers include comparisons of projections of high and low carbon\nemission scenarios, e.g., RCP8.5 vs RCP2.6 or the more recent CMIP6 SSP126 vs SSP 585.\nThese types of comparisons are extremely useful in IPCC and other climate assessments because\nthey reveal the relative risk of different levels of greenhouse gas emissions. To repeat this\nanalysis for RCP2.6 would be a major undertaking and so I am not suggesting that has to be done\nhere, but including multiple carbon emission scenarios is something the authors might consider\nin future work as it would help address an important need for relative climate risk information\nfor fisheries and marine ecosystems.\nWe appreciate the reviewer’s comments regarding the inclusion of comparisons between\nhigh and low carbon emission scenarios. In light of this, we have added text in the\ndiscussion section to address the potential for including various RCP scenarios in future\nresearch, with a specific focus on the implications of greenhouse gas emission mitigation\nand management for fisheries and marine ecosystems.\nIn summary, I find the paper topic to be compelling, the methodology is established but novelly\napplied, and the figures and text are clear and well executed. I appreciate the inclusion of the\ncode and data. If the authors are able to address the aforementioned concerns (and following line\nby line suggestions) I believe that this paper can be an important contribution to the field of\nclimate change and fisheries. While these suggestions are not trivial I also believe that they are\nachievable revisions that will immensely strengthen the paper.\nWe sincerely thank the reviewer for the positive feedback and encouraging remarks about\nour paper. We are pleased to hear that the reviewer found the topic compelling and the\nmethodology both established yet novel. We have carefully addressed all the concerns\nraised, along with the line-by-line suggestions provided. We believe that these revisions will\nsignificantly enhance the clarity and robustness of our work, and we appreciate the\nreviewer’s thoughtful insight, which has guided these improvements.\nData access note. I was not able to access the data as it appears to be stored on a microsoft\nsharepoint or similar site. I suggest a public permanent repository like Figshare or Zenodo via\ngithub if the files are not too large.\nThank you for your feedback regarding data access. We have uploaded all the code+data\nrequired to create the figures to Figshare at the following link:\nhttps://figshare.com/s/a4c759477e41c891421f.\nReferences: Generally there is a lack of references for key statements. Of the references cited\nmany are unpublished technical memos or reports; one non-data citation is for a defunct NOAA\nwebsite. Some are quite old and newer more comprehensive papers are available. Some of the\ncitations appear incomplete or incorrect.\nCitations were corrected and updated.\nIn addition to the suggestions above the following issues should be addressed:\nLines 50-51: this citation is for a NOAA Ecosystem Status report which compiles data and\nindices from multiple sources. Citation of the original stock assessments and/or a peer reviewed\npapers are additionally needed.\nThese citations were added.\nLine 73 - this citation is a website that is no longer available. Citation of a stock assessment\nreport or published paper is needed.\nThese citation was added.\nLines 115 - “as compared to 2016” does not appear to match the methods of the status quo\nsimulation using persistence values (average of 2014-2016). See note above regarding methods,\nand change to, as compared to a persistence [or control] scenario, or something similar.\nWe have clarified the methods used in the manuscript. We compare future conditions to a\nstatus quo scenario rather than directly to the year 2016. This comparison is made by\nprojecting drought-nutrient conditions derived from our analyses (Fig. 2), and using the 5th\nprojection year as reference for comparison.\nLines 116 - “will be around 72%” is too prescriptive. This is one of many future carbon\nemissions scenario. Change to “could be reduced by as much as” or something similar.\nWe thank the reviewer for the suggestion. We indeed adopted it.\nLines 139 change to “ fishing mortality in projection simulations is smaller compared to”.\nWe thank the reviewer for this suggestion. We have revised the text accordingly.\nLines 158-159 need a citation.\nCitation were added: Wiman, C., Hamilton, B., Dee, S. G. & Muñoz, S. E. Reduced lower\nMississippi River discharge during the Medieval era. Geophys Res Lett 48, e2020GL091182\n(2021).\nLines 160-170, especially lines 169-171 are speculative as written and need references. The\nchanges in fisheries management during the late 1980’s are not mentioned here so the\ncorresponding text in the methods section (lines 506-509) should be moved here.\nWe have relocated the text regarding changes in fisheries management during the late\n1980s from the methods section to this section, as suggested. Additionally, we have added\nreferences where relevant and rephrased the text to clearly distinguish between\nexplanations based on possible mechanisms and those supported by past studies.\nThe following references were added:\n- SEDAR 63; Southeast Data Assessment and Review. SEDAR 63 Gulf Menhaden\nAssessment Report. http://sedarweb.org/sedar-63. (2018).\n- Govoni, J. J. The association of the population recruitment of gulf menhaden,\nBrevoortia patronus, with Mississippi River discharge. J Mar Syst 12, 101–108\n(1997).\nLine 191 This citation seems out of place as it iis for Arctic ecosystems, there are many fisheries\nprojected impacts papers, two of which are already cited (Lotze et al. 2019 and Titensor et al.\n2021). If keeping the citation I suggest moving it to after “synergistic impacts” and adding the\nTittensor and Lotze refs at the end of the sentence.\nWe thank the reviewer for the suggestion. We have replaced this reference with a more\nsuitable reference: Pethybridge, Heidi R., et al. \"Contrasting futures for Australia’s\nfisheries stocks under IPCC RCP8. 5 emissions–a multi-ecosystem model\napproach.\" Frontiers in Marine Science 7 (2020): 577964.\nLine 193 - there are more recent papers regarding hypoxia (see refs cited within\nhttps://www.science.org/doi/10.1126/science.aam7240)\nWe appreciate the reviewer’s suggestion regarding recent literature on hypoxia. In\nresponse, we have updated the reference to include a more recent study cited in the paper\nyou recommended: Reed, Daniel C., and John A. Harrison. \"Linking nutrient loading and\noxygen in the coastal ocean: A new global scale model.\" Global Biogeochemical Cycles 30.3\n(2016): 447-459. This reference provides a more current perspective on the topic.\nLine 205: It seems a missed opportunity here to not point out that during subsequent droughts\nthere was reduced fishing pressure/mortality, which can be seen in Fig 1 and supports this\nstatement.\nWe thank the reviewer for this insightful suggestion. We have added the following\nsentence to line 205 to highlight the reduced fishing pressure during subsequent droughts\n(as shown in Fig. 1): \" The observed milder declines and faster recovery of fisheries\nfollowing the 2006 and 2012 droughts, for example, suggest that reduced fishing effort\nduring drought periods may play a significant mitigating role.”\nLine 222 - citation 5: no journal/publication source\nWe have added the source to this publication: “NOAA technical memorandum”\nLine 250 citation 16 incomplete reference\nThank you for pointing out the incomplete citation 16. The corrected reference is: Ward,\nC. H. Habitats and Biota of the Gulf of Mexico: Before the Deepwater Horizon Oil Spill:\nVolume 2: Fish Resources, Fisheries, Sea Turtles, Avian Resources, Marine Mammals,\nDiseases and Mortalities. Vol. 2. Springer, 2017.\nLines 252 Please check and correct the citation for the year for this publication; it appears the\nbook was originally published in 1991.\nThank you for pointing out the discrepancy in the publication date for this citation. While\nthe original work was published in 1991, the version we cited (Riebsame, 2019) is a widely\naccessed and relevant republication (cited 433 times according to Google Scholar). We\nbelieve this citation remains appropriate.\nLine 263 citation 22 Page 404 and this is not a report it is a website.\nThank you for pointing out the error in citation 22. We have corrected the reference, which\nis now properly listed as: National Marine Fisheries Service. Fisheries of the United States,\n2019. (2021)\nLines 338-339 - how many times were missing data interpolated? Specifically which years and\ndata (species) were replaced. Provide a supplemental table or plot of the data indicating\ninterpolated values.\nWe thank you for your thorough review. To address your comment regarding data\ninterpolation on lines 338–339, we have added the following to the Methods section:\n“Following data filtering, time series analyses were conducted on biomass data for the\nfollowing species: blacktip, dusky, and sandbar sharks; large and small coastal sharks;\ngreater amberjack; cobia; king mackerel (0–1 and 1–2 years); Spanish mackerel (0–1 and\n1–2 years); gag and yellowedge groupers (0–3 and >3 years); goliath grouper; red snapper\n(0–1, 1–3, and >3 years); vermilion and mutton snappers; tilefish; gray triggerfish; and\nmenhaden (1–2, 2–3, 3–4, and >4 years). Two missing data points (blacktip shark and\nmutton snapper in 1980) were addressed using geometric mean interpolation.”\nLines 372: Cite the original source of the data, not the ESR which compiles the indices,\nincluding the website address for the data access and date via instructions to authors.\nWe have added the source of the data:” Sea surface temperature was downloaded from the\ndatabase of the GoM ecosystem status report (Karnauskas et al., 2017), which utilized data\nfrom daily high-resolution blended analysis for sea surface temperature (Reynolds et al.,\n2007).”\nLine 378: RCP 8.5 is not “business as usual”, it is often incorrectly referred to as such. It is high\nbaseline. Cite the RCP : Van Vuuren DP, Edmonds J, Thomson A, Riahi K, Kainuma M, Matsui\nT, Hurtt GC, Lamarque J-F, Meinshausen M, Smith S, Granier C, Rose SK, Hibbard KA (2011)\nRepresentative concentration pathways: an overview. Climatic Change 109:5–31\nWe appreciate the reviewer’s comment. We have corrected the phrase to clarify that RCP\n8.5 is considered a \"high baseline emission scenario.\" Additionally, we have incorporated\nthe suggested citation: Van Vuuren et al. (2011) for reference.\nLine 388: Information regarding the error distribution is needed here. What was the error\ndistribution used for the linear regression? Because nutrients are not normally distributed (values\ncan not be less than 0), a logistic regression, i.e., lognormal distribution should be used where\nLn(Nutrients) ~f(PDSI,e) where e ~N(0,sigma) rather than linear model Nutrients~f(PDSI,e)\nwhere e ~N(0,sigma).\nWe appreciate the reviewer’s insightful comment. In response, we conducted an analysis of\nthe normality of the residuals from our linear regression model, where Nut_N_P_EwE\nserved as the response variable and PDSI_Miss_Basin as the predictor. The Q-Q plot\nindicated that the residuals closely followed a straight line, and the Shapiro-Wilk test\nyielded a p-value of 0.8, allowing us to accept the null hypothesis of normal distribution.\nThese results confirm that the assumptions of our linear regression analysis are met, thus\nvalidating our statistical approach. We have included this information in the methods\nsection of the manuscript.\nLine 416 “good agreement” how so? Provide a metric of skill or correlation.\nWe appreciate the reviewer’s inquiry regarding the phrase “good agreement” and the\nrequest for metrics of skill or correlation. By \"good agreement,\" we refer primarily to the\nvisual inspection of the time series fits. Additionally, we observed notable consistency\nbetween the model-predicted estimates of the fishing mortality rate corresponding to the\nmaximum sustainable yield (FMSY) and the FMSY estimates obtained from stock\nassessments (Berenshtein et al., 2023).\nIn addition, in response to the reviewer’s concern, we calculated the Modeling Efficiency\n(MEF), a statistical metric that assesses how effectively our model predicts observed data\ncompared to using the mean of the observed values. While Ecosim models do not typically\nyield a single measure of overall skill, modeling efficacy is often evaluated relative to the\nmodel itself through Relative Model Efficiency (RME) and the Akaike Information\nCriterion (AIC), as noted by Olsen et al. (2016).\nOur analysis indicates that 74% of the fitted time series yielded positive MEF values,\nsuggesting that the model’s predictions are more accurate than those based on the mean of\nthe observed data for roughly three-quarters of the assessed time series. We have added\nthis information to the Methods section and included a supplementary section\n(supplementary Section S5) that explains the assessment of Ecosim model fits, accompanied\nby a table of MEF values (supplementary Table S5).\nNotably, as explained in (Berenshtein et al., 2021, 2023) the time series that did not fit well\nprimarily involved highly migratory species and the early life stages of various groups,\nwhich pose challenges for the EwE framework. This finding highlights the predictive\ncapacity of our model, although we acknowledge that the application of MEF is not\nstandard practice in Ecopath with Ecosim (EwE) modeling, complicating direct\ncomparisons with other EwE models.\nLine 429 specify the years that were used.\nWe appreciate the reviewer’s request for clarification regarding the referenced years.\nWhile we are uncertain about the specific years the reviewer is referring to, we would like\nto highlight that, based on the mentioned line number, this section pertains to the last year\nof the hindcast modeling period (2016). We want to clarify that 2016 was not included in\nthe anomaly search algorithm and was therefore assigned a default value of one. This\ninformation has been explicitly stated in the revised manuscript for clarity.\nLine 445 what is meant by “scaled by the mean” Z-scored - include an equation.\nIn response to the reviewer’s question about the phrase “scaled by the mean,” we would\nlike to clarify that we are not using Z-scores. Instead, the nutrient forcing is represented by\nthe total monthly nitrogen and phosphorus input (in Mt per year) from the Mississippi\nRiver, divided by the mean of the entire time period. This is expressed mathematically as:\n𝑁𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑓𝑜𝑟𝑐𝑖𝑛𝑔 =\n(𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑎𝑑𝑖+ 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑜𝑠 𝑙𝑜𝑎𝑑𝑖 )\n𝑖 ∑𝑛\n𝑖\n(𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑎𝑑𝑖+ 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑜𝑠 𝑙𝑜𝑎𝑑𝑖)\n𝑛\nIn this equation, (i) represents a given month, and (n) is the total number of months in the\nhindcast modeling period, which is 444 months (37 years). We have added this information,\nalong with the equations, to the text for clarity.\nLine 454 “approach in “ use the first author name convention for citation.\nWe appreciate the reviewer’s comment regarding Line 454. We have updated the citation\nto follow the first author name convention. Additionally, we have included another relevant\nrecent citation for this methodology: “…similar to the approach in Chagaris et al. (2020)ref#\nand Berenshtein et al. (2023) ref#.”\nLine 475 this equation describes a ratio not a delta. For delta (proportional change) use\nB1=(Btsim-Bsq)/Bsq\nWe thank the reviewer for the valuable correction. We have updated the notation to reflect\nthe distinction between a ratio and a delta. The equation is now presented as a ratio:\n𝑩 = B /B\nratio T,sim T,status quo\nLine 481 : according to this equation if biomass in the simulations was 20% greater than the\nstatus quo simulation the delta B would be 9% this equation seems incorrect. I think that authors\nmean to express the proportional change in biomass, i.e., delta B = (Btsim-Btsq)/Btsq.\nWe appreciate the reviewer’s comment. This equation refers to the “piano plots” in figure\n1l,m and in Fig. 3, for which this was indeed the correct equation:\n𝜟𝑩= (B -B )/(B +B )\nT,sim T,status quo T,sim T,status quo\nThis equation represents relative change scaled between -1 and 1 because it calculates the\ndifference between the simulated biomass and the status quo biomass, normalizing this\ndifference by the total biomass (the sum of both values). This normalization ensures that\nthe result reflects the proportionate change in relation to the combined biomass, thereby\nconstraining the output within the range of -1 to 1. An output of -1 indicates complete\ndecline relative to the total biomass, 0 indicates no change, and 1 indicates complete\nincrease, effectively capturing the relative change in a symmetric manner. Following the\nreviewer’s comment above, we change the notation from 𝜟𝑩 to relative biomass change.\nLine 485 see above same issue.\nSee reply above.\nLines 488-489 are not needed- can be included with data supplemental material.\nThis text was transferred to the supplementary material as suggested by the reviewer.\nLines 506-509 “For example….decline in catches 45.” should be moved to the discussion section\nlines 160 (see comment above)\nThis text was transferred to the discussion as suggested by the reviewer.\nFigure 1 three panels in a-d on the right need letters.\nLetters were added to the panels.\nIt would be nice to have the colors for each species in h and i\nColors were added to the species according to the group-specific colors in (j) and (k), and\nconsistent throughout Figs 1-3..\nFigure 2 (d) - each subplot needs a letter, color ramp needs a label . X axis for (c ) is not fishing\nmortality it is times X average 2014-2016 F. Please fix the label. Same comment for (d).\nThank you for your feedback on Figure 2. We have added letters to each subplot and\nincluded a title for the color ramp. The X-axis labels for both subplots (c) and (d) have been\ncorrected to indicate \"Times X average 2014-2016 F\" instead of fishing mortality.\nReviewer #2 (Remarks to the Author):\nIn the ms by Berenshtein et al., the authors present data that indicates a severe drought in the\nsoutheast U.S., combined with fishing pressure on ecologically important menhaden in the Gulf\nof Mexico, likely caused a decline in marine food webs and fisheries catch in the 1980s. Using\nthese results the authors modeled future fisheries in the GoM under simulated drought\nconditions. First, I would like to thank the authors for expanding the knowledge on the\necosystem dynamics of this region, and further exploring the role of freshwater/nutrient inputs in\nocean ecosystems. I believe this could add to the growing body of knowledge to an important\nsocio-ecological system. With that said, I believe there are too many confounding factors that\ncould contribute to the lack of nutrients being the main driver to population dynamics in the\nregion, and this would be important for informing a more robust predictive model, and thus\nshould be considered before publication.\nWe thank the reviewer for their thoughtful comments on our manuscript and for\nrecognizing the contribution of our work. We appreciate the insights regarding the\nconfounding factors that may influence nutrient dynamics and population dynamics in the\nregion. We address the reviewer’s concerns in detail in the following replies to ensure the\nrobustness of our findings.\nFor the “historic” analysis on trends, I was a bit underwhelmed by the focus on 1982-86, then\n1986-1992. The authors use these two time series to compare trends in fisheries and\nenvironmental factors with the sole focus on drought and freshwater inputs. Why not include the\nentire time series in an analysis that includes nutrient load relationships with fisheries, but also\nthe other known factors that influence marine food webs, such as temperature, wind/hurricanes,\nand the elephant in the room…hypoxia? I think if this ms is to be published then it will be\nimportant to apply quantitative analytical approaches on a beautiful historic time series that\nshould be able to tease apart the influence of these various environmental factors from 1980-\n2016. I think without this analysis then the model is not fully informed. For example, in the time\nseries in Fig. 1a, there appears to be two other significant drought events in the region 1997-2000\nand 2011-2014 (eyeballing), but fish biomass (Fig. 1e) is stable or going up during those periods.\nWhat can explain that?\nWe thank the reviewer for the insightful comment. In this revised version, we have\nincluded several sections that successfully address the reviewer’s comments. First, we\nexplain in more detail why the effect of the 1980s drought is much more pronounced\ncompared to other drought events. First, the duration of the severe drought conditions\nwere for more than three years, inflicts a tremendous impact on short-lived fisheries such\nas the Gulf menhaden, which life span is approximately four years. In these conditions the\nweak year-classes three years in a row severely diminish the population, especially as other\n“non-estuary-dependent” species may utilize the diminishing population niche (e.g.,\nCephalopods), taking advantage of menhaden’s reduced density. In contrast, in an event of\nan extreme drought of a single year, the other menhaden’s year classes, that were less\naffected by the drought, compensate and “enjoy” more resources due to the new-year\ndiminished population, hence maintaining the overall health of the population. Second,\nfishing pressure on Gulf menhaden was much higher in the 1980s, leading to fewer\nspawners and reduced food for predators. Third, during this time, the fishery industry\nunderwent changes that decreased the number of fishing vessels, further affecting catch\nrates. Lastly, the wet years before the drought boosted fish populations, making the\ndrought's negative effects more noticeable. In accordance with the above explanation, we\nhave added supplementary section S5, which examines the predicted change in biomass\nand catches under conditions of one year, three years, and five years (as presented in the\nmain text) of drought conditions. In addition, we have added Supplementary section S2,\nwhich includes numerous additional time series related to Gulf menhaden, Hypoxia index,\nand named Hurricanes, demonstrating the correspondence between drought-related\nconditions with menhaden population dynamics, and the lack of such correspondence with\nother environmental factors such as SST, hypoxic area, and hurricanes.\nOther Comments:\nTitle: why phrase the title as a question?\nThe interrogative title serves to both engage the reader and highlight the nuanced nature of\nthe relationship between US mega-droughts and GoM fisheries. While our study provides\ncompelling evidence, framing the title as a question reflects the ongoing investigation into\nthe complex interactions within this ecosystem.\n“This severe drought resulted in a reduction of more than 50% in the Mississippi River flow and\nnutrient input to the GoM.” Having a difficult time fully understanding the point of this. I do\nunderstand that freshwater connectivity is an important function of the Mississippi River/GOF\nsystem, but that same system in its current form brings an unnatural amount of nutrients from ag\nrunoff to the GOF, which causes an annual Dead Zone for fish. Given the well known\nrelationship\nWhile acknowledging the significant issue of anthropogenic nutrient runoff and its\ncontribution to the GoM dead zone, our study emphasizes the impact of a reduction in\nnatural nutrient input caused by drought. Although both factors affect GoM ecosystem\nhealth, we posit that the decrease in natural nutrient supply via reduced Mississippi River\nflow, as seen during the 1980s drought, is the more significant driver of the observed\nbiomass decline, as evidenced by the data in Supplementary Section S2, showing that the\nhistorically lowest dead zone area (1988) coincided with the late-1980s biomass decline, and\nthe historically high dead area zone area (2002) was not associated with marked declines in\nbiomass or catches (supplementary Figs S1,2).\nIntro: “Numerous studies indicated a significant correspondence between Gulf menhaden\npopulation dynamics and environmental conditions such as temperature, wind, and river flow”\nWhat are those relationships between all of these abiotic factors and fish? Will help the reader to\nbetter understand the system.\nWe thank you for this suggestion. The Introduction now includes a more detailed\ndiscussion of the complex relationships between Gulf menhaden population dynamics and\nabiotic factors such as temperature, wind, and river flow. We describe the scale-dependent\neffects of river discharge on recruitment, highlighting both positive (enhanced primary\nproductivity at decadal scales) and negative (increased larval transport and predation risk\nat annual scales) relationships. The influence of climate oscillations (AMO, NAO, ENSO)\nand the impacts of these factors on menhaden oil content (Leaf, 2017) and growth (Midway\net al., 2020) are also discussed.\n“River to the GoM reached a record low annual and summer value of 107", "reviewer_4": "A well written paper on a topic of great interest to scientists and managers. This work advances our understanding of climatic impacts on marine ecosystems and fisheries due to changes in freshwater nutrient inputs. The authors evaluate the impacts of severe drought, combined with fishing pressure for menhaden, on fisheries production in the Gulf of Mexico (GoM). Firstly, the authors show that a prolonged and severe drought during the 1980s, combined with high fishing pressure for menhaden, led to a depleted forage base, with cascading effects on other species and fisheries in the GoM. Secondly, the authors use an ecosystem model to project changes in future fisheries production in the GoM under simulated drought conditions.\nOverall, this is a laudable paper that covers an important topic on the impacts of drought on fisheries production in the GoM. I applaud the authors for using an ecosystem model to evaluate climatic impacts on marine ecosystems and fisheries. The methodology is well described and sound, and the findings support the conclusions in the paper.\nWith that said, I found the paper to be lacking in its description of the mechanisms underlying the impacts of drought on marine fisheries production published in the scientific literature. Furthermore, the paper would benefit from providing more detailed information on the projected impacts of future droughts on the functional groups in the GoM. Nevertheless, the paper is worth publishing through a more focused route with consideration of the comments and suggestions offered below.\nMajor points\nIn the introduction, the mechanisms underlying the impacts of drought on marine fisheries production should be better described. Previous studies have shown that river flow is a ‘master variable’, which has a controlling influence on marine fisheries production by regulating habitat availability, trophic dynamics, and fishers’ harvesting behaviour. Several key references are missing and should be included in the paper (see the suggested additional citations at the end of this review).\nIt has been demonstrated that a trophic-driven mechanism for fisheries production is particularly important in the GoM, which is an oligotrophic coastal region that receives limited nutrient supplies from oceanic currents and upwelling events (Wissel and Fry, 2005). The inclusion of this information would help the reader to understand why the study focused on flow-induced changes in nutrient inputs rather than habitat availability.\nMore information on the projected impacts of future droughts on the functional groups in the GoM would be beneficial. For example, how did the model projections suggest that more severe droughts will affect species composition – should we expect less invertebrates and more piscivorous fish in the future?\nIt is unclear why the study used catches rather than catch-per-unit-effort (CPUE) as a dependent variable. Therefore, it should be explained why catch rates, which account for changes in fishing effort, were not considered in the methods section.\nMinor points\nLine 19: A ‘major depletion of fisheries’ should be better quantified. What constitutes a ‘major’ depletion? I suggest the authors include the percentage reduction in fisheries production relative to the reference period.\nLine 40: The authors should include a sentence here to explain that previous studies have shown that variation in river flow impacts fisheries production in the GoM mainly via changes in trophic dynamics rather than habitat availability. A trophic-driven mechanism for fisheries production is particularly important in the GoM, because this oligotrophic coastal region receives limited nutrient supplies from oceanic currents and upwelling events (Wissel and Fry, 2005). This would help the reader to understand why the study focused on changes in trophic dynamics as the mechanism underling the relationships between river flow and fisheries production in the GoM.\nLines 60-65: The authors should better quantity the declines in biomass and catches. What was the decline in biomass and catches in percentage terms relative to the reference period?\nLine 72: Previous studies describing the impacts of drought on marine fisheries production should be cited here (e.g., Gillanders and Kingsford 2002; Gillson, 2011; Broadley et al., 2022).\nLines 73-76: If drought-induced increases in salinity can stress invertebrates in the GoM, then this leads to the question why were these mechanisms not evaluated in the current study? It should be explained why this study focused on changes in trophic dynamics and not habitat availability. Kimmerer (2002) provides an interesting evaluation of the effects of river flow on estuarine fish via physical and trophic mechanisms.\nLines 77-79: Once again, it should be made clear why this study focused on the impacts of river flow on fisheries production in the GoM via trophic dynamics rather than habitat availability. The previous paragraph states that the physical effects of river flow on invertebrates can be important in the study area, but the next paragraph ignores this point.\nLine 93: Please either define the abbreviation ‘MS’ or spell it out in full.\nLine 105 (and elsewhere): All percentages should be presented to one decimal place to ensure consistency throughout the paper.\nLine 140: Add a comma after ‘(Fig. 2e,f)’.\nLine 144: This needs more explanation. Why would the effect of menhaden fishing mortality decrease? If drought reduces the biomass of menhaden and its associated predators, then the proportion of these stocks being exploited by fisheries would increase assuming status quo fishing effort (i.e., the F value would increase at smaller stock sizes under constant fishing effort).\nLine 161: Clarify what is being affected here. ‘The effect of variation in Gulf menhaden fishing mortality on XYZ…’\nLine 177: It is worth noting here that droughts can impact fishers’ harvesting behaviour. Gillson (2011) describes that river flow can indirectly impact fisheries production by altering the harvesting behaviour of commercial fishers.\nLines 212-213: It would be worth citing previous studies that have documented drought-induced changes in species composition in marine ecosystems (e.g., Gillson et al., 2012).\nLine 214: Amend this sentence as follows: ‘The declines in biomass and catches projected in our study…’.\nLine 221: Are there also projections of how onshore and offshore wind might change in the future? Wind conditions influence nutrient inputs by regulating upwelling and also affect the distribution of pelagic fish larvae.\nLine 235: Replace ‘Gulf of Mexico’ with ‘GoM’, because this abbreviation has previously been used earlier in the paper.\nSuggested additional citations\nBroadley, A., Stewart-Koster, B., Burford, M.A. and Brown, C.J. 2022. A global review of the critical link between river flows and productivity in marine fisheries. Reviews in Fish Biology and Fisheries 32(3): 805-825.\nGillanders, B. M., and M. J. Kingsford. 2002. Impact of changes in flow of freshwater on estuarine and open coastal habitats and the associated organisms. Oceanography and Marine Biology 40: 233–309.\nGillson, J. 2011. Freshwater flow and fisheries production in estuarine and coastal systems: Where a drop of rain is not lost. Reviews in Fisheries Science 19(3): 168–186.\nGillson, J., Suthers, I. and Scandol, J. 2012. Effects of flood and drought events on multi‐species, multi‐method estuarine and coastal fisheries in eastern Australia. Fisheries Management and Ecology 19(1): 54-68.\nKimmerer, W. J. 2002. Effects of freshwater flow on abundance of estuarine organisms: Physical effects or trophic linkages? Marine Ecology Progress Series 243: 39–55.\nWissel, B. and B. Fry. 2005. Tracing Mississippi River influences in estuarine food webs of coastal Louisiana. Oecologia 144: 659–672."}, "author_response": "Igal Berenshtein\nDepartment of Marine Biology\nCharney School of Marine Sciences\nUniversity of Haifa\nAbba Khoushy Ave 199, Haifa, 3498838\niberensht@univ.haifa.ac.il\nMobile phone +972-54-533-8837\nPoint-by-point response to the reviewer’s comments\nReviewer #1 (Remarks to the Author):\nThis paper is a well written and timely evaluation of the impacts of climate change on future\nGoM ecosystem and fishery productivity. It is unique in the aspect that it connects climate\nimpacts on land (drought) to those in marine systems via changes in freshwater nutrient inputs to\nGoM. This is an often postulated interaction (i.e., climate impacts on land and water will impact\nmarine systems) but there is a paucity of published papers that demonstrate or evaluate the\nphenomenon. The authors use a combination of a nutrient-forced mass-balance ecosystem model\n(EwE) and mean future climate projections of a drought index (PDSI) from an ensemble of 20\nGeneral Circulation Models (GCMs) representing a subset of CMIP5 ensemble members under\nthe high baseline Representative Carbon Pathway (RCP 8.5). The authors derived a linear\nrelationship between historical nutrients and historical PDSI values and used the nutrient\nrelationship to drive bottom up productivity changes in the EwE model.\nThe authors then force the EwE model with profiles across ranges in nutrient changes (0 -2 fold\n2014-2016 average nutrient levels) and ranges in fishing pressure (0-5 x average F from 2014-\n2016). This represents an innovative application of an approach that has been applied\nsuccessfully to evaluate potential climate change impacts in other systems, albeit with different\nclimate couplings (e.g., where temperature or zooplankton drive productivity of EwE models;\nsensu https://doi.org/10.1111/gcb.13471 ). In general this is the type of analysis that can be vital\nfor risk assessments of climate change impacts on marine fisheries as well as cumulative impacts\nof changes on land on marine ecosystems. In general, I strongly support this type of approach,\nand applaud the authors for using an ecosystem model (which captures indirect and cascading\nimpacts) to evaluate climate change impacts on marine species. This can be a more robust\napproach than a direct climate-linked single species\nmodeling effort.\nHowever, there are a number of fundamental issues with the paper that I believe preclude it from\nbeing published until it is revised. I hope the authors are able to correct and address these issues\nso it can be used to advance understanding of climate change risk to marine ecosystems and\nfisheries.\nWe thank the reviewer for the positive comments. In the revised manuscript, we have\nthoroughly addressed the concerns raised by incorporating additional analyses, integrating\nnew data, and expanding the relevant sections as needed. We have also included the\ncitation of Woodworth-Jefcoats et al. (2017) in the text to enhance context and support our\nfindings.\nThe largest concern I have is that the paper is predicated on a linkage between droughts and\nhistorical declines in GoM catches that the authors do not demonstrate and which is not\nsupported by the cited peer-reviewed papers (nor by the large number of non-peer reviewed\ntechnical reports that are cited). The authors postulate that a long (~3 year) drought in the mid- to\nlate 1980’s induced a decline in biomass and catch that is reflected in the timeseries for both.\nThese are plotted in Figure 1 (a) (e) and (f). However, one quickly notes that there are similar\nlarge drops in the PDSI index in ~2000 and ~2012 but no similar subsequent drop in catch or\nbiomass timeseries as was seen in the late 1980’s. In the discussion section (lines 160-172) the\nauthors try to address this and explain it as a difference in drought length (3 yrs for the earlier\ndrought versus ~1 year for the others) and timing. This section is very speculative, without any\ncitations save for one at the end of the paragraph stating that the severity of the drought can be\nrelated to the contrast between preceding wet years. In fact the largest concern I had in reading\nthis section was that there was no mention of possible changes in fishery management or markets\nthat could also explain the decline in catch.\nWe appreciate the reviewer's insightful comments regarding the linkage between droughts\nand historical declines in Gulf of Mexico (GoM) catches. As noted in our manuscript, we\nhypothesize that significant ecosystem impacts, reflected in declines in biomass and catches,\nwould primarily occur following prolonged severe droughts lasting three years or more.\nThis hypothesis is based on the understanding that short-lived species, such as Gulf\nmenhaden, can typically rebound through density-dependent compensatory processes after\nbrief periods of low productivity. However, extended periods of low productivity can\nseverely affect multiple year-classes, leading to long-term detrimental impacts on these\npopulations. We further clarify that the apparent effects of the 1980s drought can be partly\nattributed to (1) elevated biomass and catches during the mid-1980s, following\nexceptionally wet conditions in early 1982-1983, and (2) the high fishing effort prevalent\nthroughout the 1980s. (3) our extended time series (Supplementary section S2)\ndemonstrates that episodic fluctuations in menhaden populations correlate with highly dry\nconditions (e.g., 2006, 2012) and wet periods (e.g., 2010).\nTo further address the reviewer’s concerns, we acknowledge that variations in fishery\nmanagement practices and market dynamics over time might also contribute to\nfluctuations in catch. In the revised manuscript, we have included additional discussion on\nthese factors to provide a more comprehensive context for the observed changes in catch\nand biomass.\nAdditionally, in response to the reviewer’s suggestion regarding the speculative nature of\nthe discussion, we have incorporated a new Supplementary section S6, which includes a\nsensitivity analysis that evaluates the effects of prolonged versus shorter drought periods\n(1- and 3- years duration). This analysis quantifies the potential impacts of different\ndrought durations and strengthens the robustness of our findings.\nAdditionally, following the sharp drop in in both catches and biomass in the late 1980’s catches\nremain low while biomass increases, pointing towards a non-biomass driver of low catches (e.g.,\nshift in management towards lower harvest mortality rates).\nWe appreciate the reviewer’s observations regarding the dynamics of catches and biomass\nin the late 1980s. Indeed, from 1992 onwards, after the drought of the late 1980s, we\nobserved a gradual decline in catches, averaging approximately 3 t/km² over 24 years. This\ntrend aligns with the expected relationship described by the equation (C = F×B), where\nfishing mortality (F) was reduced due to regulatory measures, consequently leading to\ndeclining annual catches. This regulatory action facilitated an increase in the remaining\nbiomass within the marine ecosystem, fostering a more sustainable fish population.\nTo clarify this we added supplementary section S3, which illustrates the contrasting\nrelationship between menhaden catches and fishing effort in the 1980s (a non-intuitive,\ndrought-dominated relationship) and the shift towards a more intuitive relationship from\nthe 1990s onward.\nFinally there are multiple disparate patterns in the magnitude and sometimes the direction of\nbiomass change and catch (this can be seen in Fig 1 (h) and (i); e.g., crab have the second largest\ndecline in biomass but increases in catch, similarly, younger menhaden had the larger declines in\nbiomass than older conspecifics but declines in older menhaden catches were larger than younger\nones). This is of course a not unexpected and well cited issue in fisheries, especially as biomass\ndeclines where fishers can increase effort to offset declining biomass, or where catch declines\nbut is stabilized over time and biomass increases due to implementation of policies that reduce\nharvest rates to sustainable levels. I believe the authors are aware of this issue (see plots of\nrelative effort) and offer what is maybe the most parsimonious explanation in the “model\nlimitation” section of the Methods (lines 506 -509):\n506 “...For example, fluctuations in Gulf menhaden catches may be related to economic\nprocesses, which occurred during the mid and late 1980s, when the Gulf menhaden fishery\nunderwent corporate consolidation resulting in declines in the number of fishing vessels and\nprocessing plants. This may have contributed to the decline in catches 45. For species such as\nGoliath grouper, declines in biomass and catch are likely a result of long periods of overfishing\nor management regulations designed to reduce harvest (e.g., harvest moratorium).”\nSimilarly, the ESR (reference #1) states “The Gulf of Mexico Fisheries Management Council\nimplemented a number of Fishery Management Plans in the 1980s, and major Amendments\nrestricting fishing effort were passed in the 1990s.“ (p22).\nWe appreciate the reviewer’s valuable comment regarding the complex patterns observed\nin biomass changes and catch dynamics. In addition, we indeed recognize that the\nfluctuations in biomass and catch, such as the decline in crab biomass coinciding with an\nincrease in catch, are well-documented phenomena in fisheries science. To address these\ncomplexities, we have enriched the \"model limitation\" section with further explanations\nabout the economic and regulatory factors at play, including the historical context of the\nGulf menhaden fishery and relevant management measures implemented over the years.\nAdditionally, we have included a non-fisheries-related index of juvenile Gulf menhaden\nabundance in Louisiana estuaries. This data reveals a decline in the juvenile Gulf\nmenhaden population during the late 1980s that is unrelated to fisheries management\nmeasures, further highlighting the influence of external environmental factors.\nOf course these management measures could have been implemented to address the decline that\nwas driven initially by drought. The way to address how much climate drove the decline is to run\na hindcast or counterfactual simulation, although these are challenging with ecosystem models\nbecause of model drift. So another approach would be to use a sensitivity simulation to evaluate\nthe hypothesis of prolonged drought versus short 1 year drought in driving declines. E.g., fixing\nthe F rate at a few different levels (e.g., those observed in the 1985-1990 period vs those\nobserved in 2013), simulate a) 3 year drought (sensu the 1985-1988) and b) a five year period\nwith a 1 year drought. Something like this should be conducted to demonstrate and support the\npostulation that underlies the projection simulations. In the absence of the sensitivity simulation\nabove, there is not sufficient evidence provided presently to rule out changes in harvest pressure,\nfisheries operations, management or markets in driving declines in catch in the late 1980’s\nrelative to early 1980’s. Given that the paper is predicated on the postulated relationship between\ndrought, nutrients, and declines in catches, establishing and supporting this is extremely\nimportant. Additionally, I strongly suggest providing a historical time-series of harvest mortality\nby age for menhaden (e.g., as a supplemental figure). It appears to my eye that the F rate for 1\nyear old fish is much higher during the 1985-1990 period than 2000 - 2016. Having the\ntimeseries would help facilitate comparisons in relative fishing pressure over time.\nWe thank the reviewer for the valuable insights. In response to the comments, we have\nincluded a sensitivity analysis in supplementary section S6 that evaluates the effects of\ndifferent drought durations, specifically simulating both 1-year and 3-year droughts under\nvariable F rates, in addition to the previously discussed 5-year drought scenario. This\nanalysis illustrates a clear correlation between the duration of drought and the cumulative\ndecline in biomass.\nAdditionally, we have incorporated the requested historical time series of harvest mortality\nby age for Gulf menhaden, which can be found in Figure S2c. As noted by the reviewer,\nthis time series confirms that fishing mortality was indeed higher during the late 1980s\ncompared to the subsequent period from 2000 to 2016. This rise in fishing mortality during\nthe late 1980s can be attributed to higher fishing efforts and catches, while the later period\nexperienced lower fishing effort, which ultimately supported a gradual recovery of the\nmenhaden population.\nImportantly, our findings suggest that changes in harvest pressure and fisheries operations\ncannot fully account for the observed trends. As detailed in supplementary section S2,\nmenhaden fishing effort was actually declining during the 1980s, coinciding with a decline\nin biomass, rather than the expected increase that would typically drive such reductions.\nThe second issue with the paper is that it is very difficult to discern precisely what methods were\nused in the simulations. This needs to be clarified in the Methods simulation section. I have\ndetailed a number of line by line suggestions below to help clarify. There were two separate\nsimulations conducted. For the first, I believe the correct methods were used for the status quo\nversus future time series projections, but can’t confirm this based on the current methods section.\nFor the first simulation, the authors ran the EwE model for a 5 year simulation period across\nNutrient and F profiles based on average 2014-2016 values for each (Navg2014-2016 and\nFavg2014-2016). Within the intervals for each (Nutrients = (0,2) X Navg2014-2016 and F =\n(0,5) x Favg2014-2016) is the value of 1.0, i.e., a simulation where N = N_avg and F = F_avg.\nFor the second the authors used linear regression to extrapolate the nutrient levels from the PDSI\nprojections for the first and second halves of the century. For both analyses it is not clear what\nthe baseline status quo scenario includes. Can the authors confirm that in lines 471-475,\ncomparisons of climate change simulations are relative to the terminal year of a 5 year control\nsimulation where N =Navg2014-2016 and F = Favg2014-2016 rather than comparisons directly\nto 2016 values? This is important because ecosystem models can propagate error when adjusting\nto new forcings and including a control run helps establish that differences are due to forcings\nrather than model behavior. If this was done the authors need to clarify the start and end dates of\neach simulation and in figure and the results consistently use a term like “persistence simulation”\nor “control” rather than “2016” which is confusing. A table or figure of the simulation\nmethodology would greatly help clarify the two simulations.\nWe sincerely thank the reviewer for the valuable feedback regarding the clarity of our\nmethods in the simulations. In response, we have made several revisions to enhance the\nclarity of our methodology. We have changed the text to include “Ecosim simulations” and\n“Quantifying changes biomass and catches” sections and added detailed explanations to\nenhance clarity. In the “Quantifying changes biomass and catches” section, we specifically\ndivided the methodologies into distinct sections: \"Historical Perspective\" and \"Future\nPerspective,\" clearly outlining the methods used in both analyses.\nIn the \"Historical Perspective,\" we have elaborated on how we examined the differences\nbetween pre-drought conditions (1982-1986) and drought-affected conditions (1988-1992),\nutilizing historical time series data and EwE time-dynamic Ecosim Model simulations. We\nincluded a detailed description of the equation used to analyze biomass and catches, as well\nas explaining how the fishing mortality and nutrient input forcing for Gulf menhaden were\nderived and implemented.\nIn the \"Future Perspective,\" we specified our approach to quantifying changes in biomass\nand catches in response to projected drought conditions, accounting for varying levels of\nGulf menhaden fishing mortality in a trade-off analysis. We highlighted the\nimplementation of a 5-year simulated drought and the use of linear regression to project\nnutrient inputs based on PDSI data. We also clarified how terminal biomass was expressed\nrelative to the status quo and how relative changes in biomass and catch were calculated.\nFurthermore, we used our sensitivity analysis simulations, which examine trade-offs\nbetween nutrient input and Gulf menhaden fishing mortality, detailing how we modeled\nnutrient loading and fishing mortality using various multipliers.\nRegarding the baseline status quo scenario. To clarify, we confirm that the comparisons of\nclimate change simulations presented in lines 471-475 are indeed relative to the terminal\nyear of a 5-year control simulation, in which nutrient inputs N =geom_mean(N ) and\n2014-2016\nF = geom_mean(F ), rather than being directly compared to 2016 values.\n2014-2016\nIn response to the reviewer’s suggestions, we have provided additional clarification\nregarding the start and end dates of each simulation in the revised manuscript. We have\nalso standardized terminology, using the term “status quo scenario” consistently\nthroughout the text to avoid any confusion with the term “2016.”. Furthermore, we have\nadded a table (supplementary Table S6) that outlines all simulations along with detailed\nexplanations of each simulation.\nFor the second simulation, it is not clear from the Methods which years are included in this\nextrapolation; values are reported in the Results section and Figure 2.b but again it is difficult to\nunderstand which years are included in the values as they are reported as “anticipated by 2050\nand during 2051-2100). It appears they are averages of 2016-2050 and 2051-2100. Are they the\nmean of the 20 member ensemble for each year or the mean of all values from all simulations\nwithin those time frames? There is considerable range in the PDSI of the 20 member ensemble\nby end of the century and the upper range of the index ensemble mean includes values (PDSI>0)\nseen in non-drought years. As such I strongly suggest that the authors run the simulation for\nrandom draws from the mean and standard deviation of the ensemble for each time-period (e.g.,\n100 draws) in order to provide error bars for the figures in Fig. 2c; confidence intervals around\nprojected declines may be large.\nWe appreciate the reviewer’s insightful comments regarding the second simulation. The\ndrought conditions for the specified years were computed based on the minimal moving\naverage of the 5th percentile PDSI value over a 5-year period, utilizing the variation from\nthe 20-member CMIP ensemble. This approach allows us to focus on the driest consistent\nspells, which are of particular interest in our study. The correspondence between the driest\nobserved PDSI (indicated by magenta markers in Fig. 2a) and the modeled PDSI\n(represented by the yellow shaded area) in Fig. 2a further justifies our emphasis on the 5th\npercentile of the modeled PDSI.\nWhile we acknowledge that wet periods corresponding to positive PDSI values will occur\nwithin the projected time frames (through the end of the century), our focus on the impacts\nof droughts necessitates prioritizing drought periods over wet ones. It is important to note\nthat the PDSI index is a \"long-memory\" index that accounts for soil moisture; thus, the\neffects of very wet periods can diminish quickly following extreme hot and dry conditions.\nWe have accepted the reviewer’s suggestion to utilize random draws (N=43 for each\nscenario) from the mean and standard deviation of the ensemble for each time period in\norder to provide error bars for the figures in Fig. 2c. We used the 5th percentile of the\nmodeled PDSI as the basis for our analysis based on the explanation above.\nA growing number of papers include comparisons of projections of high and low carbon\nemission scenarios, e.g., RCP8.5 vs RCP2.6 or the more recent CMIP6 SSP126 vs SSP 585.\nThese types of comparisons are extremely useful in IPCC and other climate assessments because\nthey reveal the relative risk of different levels of greenhouse gas emissions. To repeat this\nanalysis for RCP2.6 would be a major undertaking and so I am not suggesting that has to be done\nhere, but including multiple carbon emission scenarios is something the authors might consider\nin future work as it would help address an important need for relative climate risk information\nfor fisheries and marine ecosystems.\nWe appreciate the reviewer’s comments regarding the inclusion of comparisons between\nhigh and low carbon emission scenarios. In light of this, we have added text in the\ndiscussion section to address the potential for including various RCP scenarios in future\nresearch, with a specific focus on the implications of greenhouse gas emission mitigation\nand management for fisheries and marine ecosystems.\nIn summary, I find the paper topic to be compelling, the methodology is established but novelly\napplied, and the figures and text are clear and well executed. I appreciate the inclusion of the\ncode and data. If the authors are able to address the aforementioned concerns (and following line\nby line suggestions) I believe that this paper can be an important contribution to the field of\nclimate change and fisheries. While these suggestions are not trivial I also believe that they are\nachievable revisions that will immensely strengthen the paper.\nWe sincerely thank the reviewer for the positive feedback and encouraging remarks about\nour paper. We are pleased to hear that the reviewer found the topic compelling and the\nmethodology both established yet novel. We have carefully addressed all the concerns\nraised, along with the line-by-line suggestions provided. We believe that these revisions will\nsignificantly enhance the clarity and robustness of our work, and we appreciate the\nreviewer’s thoughtful insight, which has guided these improvements.\nData access note. I was not able to access the data as it appears to be stored on a microsoft\nsharepoint or similar site. I suggest a public permanent repository like Figshare or Zenodo via\ngithub if the files are not too large.\nThank you for your feedback regarding data access. We have uploaded all the code+data\nrequired to create the figures to Figshare at the following link:\nhttps://figshare.com/s/a4c759477e41c891421f.\nReferences: Generally there is a lack of references for key statements. Of the references cited\nmany are unpublished technical memos or reports; one non-data citation is for a defunct NOAA\nwebsite. Some are quite old and newer more comprehensive papers are available. Some of the\ncitations appear incomplete or incorrect.\nCitations were corrected and updated.\nIn addition to the suggestions above the following issues should be addressed:\nLines 50-51: this citation is for a NOAA Ecosystem Status report which compiles data and\nindices from multiple sources. Citation of the original stock assessments and/or a peer reviewed\npapers are additionally needed.\nThese citations were added.\nLine 73 - this citation is a website that is no longer available. Citation of a stock assessment\nreport or published paper is needed.\nThese citation was added.\nLines 115 - “as compared to 2016” does not appear to match the methods of the status quo\nsimulation using persistence values (average of 2014-2016). See note above regarding methods,\nand change to, as compared to a persistence [or control] scenario, or something similar.\nWe have clarified the methods used in the manuscript. We compare future conditions to a\nstatus quo scenario rather than directly to the year 2016. This comparison is made by\nprojecting drought-nutrient conditions derived from our analyses (Fig. 2), and using the 5th\nprojection year as reference for comparison.\nLines 116 - “will be around 72%” is too prescriptive. This is one of many future carbon\nemissions scenario. Change to “could be reduced by as much as” or something similar.\nWe thank the reviewer for the suggestion. We indeed adopted it.\nLines 139 change to “ fishing mortality in projection simulations is smaller compared to”.\nWe thank the reviewer for this suggestion. We have revised the text accordingly.\nLines 158-159 need a citation.\nCitation were added: Wiman, C., Hamilton, B., Dee, S. G. & Muñoz, S. E. Reduced lower\nMississippi River discharge during the Medieval era. Geophys Res Lett 48, e2020GL091182\n(2021).\nLines 160-170, especially lines 169-171 are speculative as written and need references. The\nchanges in fisheries management during the late 1980’s are not mentioned here so the\ncorresponding text in the methods section (lines 506-509) should be moved here.\nWe have relocated the text regarding changes in fisheries management during the late\n1980s from the methods section to this section, as suggested. Additionally, we have added\nreferences where relevant and rephrased the text to clearly distinguish between\nexplanations based on possible mechanisms and those supported by past studies.\nThe following references were added:\n- SEDAR 63; Southeast Data Assessment and Review. SEDAR 63 Gulf Menhaden\nAssessment Report. http://sedarweb.org/sedar-63. (2018).\n- Govoni, J. J. The association of the population recruitment of gulf menhaden,\nBrevoortia patronus, with Mississippi River discharge. J Mar Syst 12, 101–108\n(1997).\nLine 191 This citation seems out of place as it iis for Arctic ecosystems, there are many fisheries\nprojected impacts papers, two of which are already cited (Lotze et al. 2019 and Titensor et al.\n2021). If keeping the citation I suggest moving it to after “synergistic impacts” and adding the\nTittensor and Lotze refs at the end of the sentence.\nWe thank the reviewer for the suggestion. We have replaced this reference with a more\nsuitable reference: Pethybridge, Heidi R., et al. \"Contrasting futures for Australia’s\nfisheries stocks under IPCC RCP8. 5 emissions–a multi-ecosystem model\napproach.\" Frontiers in Marine Science 7 (2020): 577964.\nLine 193 - there are more recent papers regarding hypoxia (see refs cited within\nhttps://www.science.org/doi/10.1126/science.aam7240)\nWe appreciate the reviewer’s suggestion regarding recent literature on hypoxia. In\nresponse, we have updated the reference to include a more recent study cited in the paper\nyou recommended: Reed, Daniel C., and John A. Harrison. \"Linking nutrient loading and\noxygen in the coastal ocean: A new global scale model.\" Global Biogeochemical Cycles 30.3\n(2016): 447-459. This reference provides a more current perspective on the topic.\nLine 205: It seems a missed opportunity here to not point out that during subsequent droughts\nthere was reduced fishing pressure/mortality, which can be seen in Fig 1 and supports this\nstatement.\nWe thank the reviewer for this insightful suggestion. We have added the following\nsentence to line 205 to highlight the reduced fishing pressure during subsequent droughts\n(as shown in Fig. 1): \" The observed milder declines and faster recovery of fisheries\nfollowing the 2006 and 2012 droughts, for example, suggest that reduced fishing effort\nduring drought periods may play a significant mitigating role.”\nLine 222 - citation 5: no journal/publication source\nWe have added the source to this publication: “NOAA technical memorandum”\nLine 250 citation 16 incomplete reference\nThank you for pointing out the incomplete citation 16. The corrected reference is: Ward,\nC. H. Habitats and Biota of the Gulf of Mexico: Before the Deepwater Horizon Oil Spill:\nVolume 2: Fish Resources, Fisheries, Sea Turtles, Avian Resources, Marine Mammals,\nDiseases and Mortalities. Vol. 2. Springer, 2017.\nLines 252 Please check and correct the citation for the year for this publication; it appears the\nbook was originally published in 1991.\nThank you for pointing out the discrepancy in the publication date for this citation. While\nthe original work was published in 1991, the version we cited (Riebsame, 2019) is a widely\naccessed and relevant republication (cited 433 times according to Google Scholar). We\nbelieve this citation remains appropriate.\nLine 263 citation 22 Page 404 and this is not a report it is a website.\nThank you for pointing out the error in citation 22. We have corrected the reference, which\nis now properly listed as: National Marine Fisheries Service. Fisheries of the United States,\n2019. (2021)\nLines 338-339 - how many times were missing data interpolated? Specifically which years and\ndata (species) were replaced. Provide a supplemental table or plot of the data indicating\ninterpolated values.\nWe thank you for your thorough review. To address your comment regarding data\ninterpolation on lines 338–339, we have added the following to the Methods section:\n“Following data filtering, time series analyses were conducted on biomass data for the\nfollowing species: blacktip, dusky, and sandbar sharks; large and small coastal sharks;\ngreater amberjack; cobia; king mackerel (0–1 and 1–2 years); Spanish mackerel (0–1 and\n1–2 years); gag and yellowedge groupers (0–3 and >3 years); goliath grouper; red snapper\n(0–1, 1–3, and >3 years); vermilion and mutton snappers; tilefish; gray triggerfish; and\nmenhaden (1–2, 2–3, 3–4, and >4 years). Two missing data points (blacktip shark and\nmutton snapper in 1980) were addressed using geometric mean interpolation.”\nLines 372: Cite the original source of the data, not the ESR which compiles the indices,\nincluding the website address for the data access and date via instructions to authors.\nWe have added the source of the data:” Sea surface temperature was downloaded from the\ndatabase of the GoM ecosystem status report (Karnauskas et al., 2017), which utilized data\nfrom daily high-resolution blended analysis for sea surface temperature (Reynolds et al.,\n2007).”\nLine 378: RCP 8.5 is not “business as usual”, it is often incorrectly referred to as such. It is high\nbaseline. Cite the RCP : Van Vuuren DP, Edmonds J, Thomson A, Riahi K, Kainuma M, Matsui\nT, Hurtt GC, Lamarque J-F, Meinshausen M, Smith S, Granier C, Rose SK, Hibbard KA (2011)\nRepresentative concentration pathways: an overview. Climatic Change 109:5–31\nWe appreciate the reviewer’s comment. We have corrected the phrase to clarify that RCP\n8.5 is considered a \"high baseline emission scenario.\" Additionally, we have incorporated\nthe suggested citation: Van Vuuren et al. (2011) for reference.\nLine 388: Information regarding the error distribution is needed here. What was the error\ndistribution used for the linear regression? Because nutrients are not normally distributed (values\ncan not be less than 0), a logistic regression, i.e., lognormal distribution should be used where\nLn(Nutrients) ~f(PDSI,e) where e ~N(0,sigma) rather than linear model Nutrients~f(PDSI,e)\nwhere e ~N(0,sigma).\nWe appreciate the reviewer’s insightful comment. In response, we conducted an analysis of\nthe normality of the residuals from our linear regression model, where Nut_N_P_EwE\nserved as the response variable and PDSI_Miss_Basin as the predictor. The Q-Q plot\nindicated that the residuals closely followed a straight line, and the Shapiro-Wilk test\nyielded a p-value of 0.8, allowing us to accept the null hypothesis of normal distribution.\nThese results confirm that the assumptions of our linear regression analysis are met, thus\nvalidating our statistical approach. We have included this information in the methods\nsection of the manuscript.\nLine 416 “good agreement” how so? Provide a metric of skill or correlation.\nWe appreciate the reviewer’s inquiry regarding the phrase “good agreement” and the\nrequest for metrics of skill or correlation. By \"good agreement,\" we refer primarily to the\nvisual inspection of the time series fits. Additionally, we observed notable consistency\nbetween the model-predicted estimates of the fishing mortality rate corresponding to the\nmaximum sustainable yield (FMSY) and the FMSY estimates obtained from stock\nassessments (Berenshtein et al., 2023).\nIn addition, in response to the reviewer’s concern, we calculated the Modeling Efficiency\n(MEF), a statistical metric that assesses how effectively our model predicts observed data\ncompared to using the mean of the observed values. While Ecosim models do not typically\nyield a single measure of overall skill, modeling efficacy is often evaluated relative to the\nmodel itself through Relative Model Efficiency (RME) and the Akaike Information\nCriterion (AIC), as noted by Olsen et al. (2016).\nOur analysis indicates that 74% of the fitted time series yielded positive MEF values,\nsuggesting that the model’s predictions are more accurate than those based on the mean of\nthe observed data for roughly three-quarters of the assessed time series. We have added\nthis information to the Methods section and included a supplementary section\n(supplementary Section S5) that explains the assessment of Ecosim model fits, accompanied\nby a table of MEF values (supplementary Table S5).\nNotably, as explained in (Berenshtein et al., 2021, 2023) the time series that did not fit well\nprimarily involved highly migratory species and the early life stages of various groups,\nwhich pose challenges for the EwE framework. This finding highlights the predictive\ncapacity of our model, although we acknowledge that the application of MEF is not\nstandard practice in Ecopath with Ecosim (EwE) modeling, complicating direct\ncomparisons with other EwE models.\nLine 429 specify the years that were used.\nWe appreciate the reviewer’s request for clarification regarding the referenced years.\nWhile we are uncertain about the specific years the reviewer is referring to, we would like\nto highlight that, based on the mentioned line number, this section pertains to the last year\nof the hindcast modeling period (2016). We want to clarify that 2016 was not included in\nthe anomaly search algorithm and was therefore assigned a default value of one. This\ninformation has been explicitly stated in the revised manuscript for clarity.\nLine 445 what is meant by “scaled by the mean” Z-scored - include an equation.\nIn response to the reviewer’s question about the phrase “scaled by the mean,” we would\nlike to clarify that we are not using Z-scores. Instead, the nutrient forcing is represented by\nthe total monthly nitrogen and phosphorus input (in Mt per year) from the Mississippi\nRiver, divided by the mean of the entire time period. This is expressed mathematically as:\n𝑁𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑓𝑜𝑟𝑐𝑖𝑛𝑔 =\n(𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑎𝑑𝑖+ 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑜𝑠 𝑙𝑜𝑎𝑑𝑖 )\n𝑖 ∑𝑛\n𝑖\n(𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑎𝑑𝑖+ 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑜𝑠 𝑙𝑜𝑎𝑑𝑖)\n𝑛\nIn this equation, (i) represents a given month, and (n) is the total number of months in the\nhindcast modeling period, which is 444 months (37 years). We have added this information,\nalong with the equations, to the text for clarity.\nLine 454 “approach in “ use the first author name convention for citation.\nWe appreciate the reviewer’s comment regarding Line 454. We have updated the citation\nto follow the first author name convention. Additionally, we have included another relevant\nrecent citation for this methodology: “…similar to the approach in Chagaris et al. (2020)ref#\nand Berenshtein et al. (2023) ref#.”\nLine 475 this equation describes a ratio not a delta. For delta (proportional change) use\nB1=(Btsim-Bsq)/Bsq\nWe thank the reviewer for the valuable correction. We have updated the notation to reflect\nthe distinction between a ratio and a delta. The equation is now presented as a ratio:\n𝑩 = B /B\nratio T,sim T,status quo\nLine 481 : according to this equation if biomass in the simulations was 20% greater than the\nstatus quo simulation the delta B would be 9% this equation seems incorrect. I think that authors\nmean to express the proportional change in biomass, i.e., delta B = (Btsim-Btsq)/Btsq.\nWe appreciate the reviewer’s comment. This equation refers to the “piano plots” in figure\n1l,m and in Fig. 3, for which this was indeed the correct equation:\n𝜟𝑩= (B -B )/(B +B )\nT,sim T,status quo T,sim T,status quo\nThis equation represents relative change scaled between -1 and 1 because it calculates the\ndifference between the simulated biomass and the status quo biomass, normalizing this\ndifference by the total biomass (the sum of both values). This normalization ensures that\nthe result reflects the proportionate change in relation to the combined biomass, thereby\nconstraining the output within the range of -1 to 1. An output of -1 indicates complete\ndecline relative to the total biomass, 0 indicates no change, and 1 indicates complete\nincrease, effectively capturing the relative change in a symmetric manner. Following the\nreviewer’s comment above, we change the notation from 𝜟𝑩 to relative biomass change.\nLine 485 see above same issue.\nSee reply above.\nLines 488-489 are not needed- can be included with data supplemental material.\nThis text was transferred to the supplementary material as suggested by the reviewer.\nLines 506-509 “For example….decline in catches 45.” should be moved to the discussion section\nlines 160 (see comment above)\nThis text was transferred to the discussion as suggested by the reviewer.\nFigure 1 three panels in a-d on the right need letters.\nLetters were added to the panels.\nIt would be nice to have the colors for each species in h and i\nColors were added to the species according to the group-specific colors in (j) and (k), and\nconsistent throughout Figs 1-3..\nFigure 2 (d) - each subplot needs a letter, color ramp needs a label . X axis for (c ) is not fishing\nmortality it is times X average 2014-2016 F. Please fix the label. Same comment for (d).\nThank you for your feedback on Figure 2. We have added letters to each subplot and\nincluded a title for the color ramp. The X-axis labels for both subplots (c) and (d) have been\ncorrected to indicate \"Times X average 2014-2016 F\" instead of fishing mortality.\nReviewer #2 (Remarks to the Author):\nIn the ms by Berenshtein et al., the authors present data that indicates a severe drought in the\nsoutheast U.S., combined with fishing pressure on ecologically important menhaden in the Gulf\nof Mexico, likely caused a decline in marine food webs and fisheries catch in the 1980s. Using\nthese results the authors modeled future fisheries in the GoM under simulated drought\nconditions. First, I would like to thank the authors for expanding the knowledge on the\necosystem dynamics of this region, and further exploring the role of freshwater/nutrient inputs in\nocean ecosystems. I believe this could add to the growing body of knowledge to an important\nsocio-ecological system. With that said, I believe there are too many confounding factors that\ncould contribute to the lack of nutrients being the main driver to population dynamics in the\nregion, and this would be important for informing a more robust predictive model, and thus\nshould be considered before publication.\nWe thank the reviewer for their thoughtful comments on our manuscript and for\nrecognizing the contribution of our work. We appreciate the insights regarding the\nconfounding factors that may influence nutrient dynamics and population dynamics in the\nregion. We address the reviewer’s concerns in detail in the following replies to ensure the\nrobustness of our findings.\nFor the “historic” analysis on trends, I was a bit underwhelmed by the focus on 1982-86, then\n1986-1992. The authors use these two time series to compare trends in fisheries and\nenvironmental factors with the sole focus on drought and freshwater inputs. Why not include the\nentire time series in an analysis that includes nutrient load relationships with fisheries, but also\nthe other known factors that influence marine food webs, such as temperature, wind/hurricanes,\nand the elephant in the room…hypoxia? I think if this ms is to be published then it will be\nimportant to apply quantitative analytical approaches on a beautiful historic time series that\nshould be able to tease apart the influence of these various environmental factors from 1980-\n2016. I think without this analysis then the model is not fully informed. For example, in the time\nseries in Fig. 1a, there appears to be two other significant drought events in the region 1997-2000\nand 2011-2014 (eyeballing), but fish biomass (Fig. 1e) is stable or going up during those periods.\nWhat can explain that?\nWe thank the reviewer for the insightful comment. In this revised version, we have\nincluded several sections that successfully address the reviewer’s comments. First, we\nexplain in more detail why the effect of the 1980s drought is much more pronounced\ncompared to other drought events. First, the duration of the severe drought conditions\nwere for more than three years, inflicts a tremendous impact on short-lived fisheries such\nas the Gulf menhaden, which life span is approximately four years. In these conditions the\nweak year-classes three years in a row severely diminish the population, especially as other\n“non-estuary-dependent” species may utilize the diminishing population niche (e.g.,\nCephalopods), taking advantage of menhaden’s reduced density. In contrast, in an event of\nan extreme drought of a single year, the other menhaden’s year classes, that were less\naffected by the drought, compensate and “enjoy” more resources due to the new-year\ndiminished population, hence maintaining the overall health of the population. Second,\nfishing pressure on Gulf menhaden was much higher in the 1980s, leading to fewer\nspawners and reduced food for predators. Third, during this time, the fishery industry\nunderwent changes that decreased the number of fishing vessels, further affecting catch\nrates. Lastly, the wet years before the drought boosted fish populations, making the\ndrought's negative effects more noticeable. In accordance with the above explanation, we\nhave added supplementary section S5, which examines the predicted change in biomass\nand catches under conditions of one year, three years, and five years (as presented in the\nmain text) of drought conditions. In addition, we have added Supplementary section S2,\nwhich includes numerous additional time series related to Gulf menhaden, Hypoxia index,\nand named Hurricanes, demonstrating the correspondence between drought-related\nconditions with menhaden population dynamics, and the lack of such correspondence with\nother environmental factors such as SST, hypoxic area, and hurricanes.\nOther Comments:\nTitle: why phrase the title as a question?\nThe interrogative title serves to both engage the reader and highlight the nuanced nature of\nthe relationship between US mega-droughts and GoM fisheries. While our study provides\ncompelling evidence, framing the title as a question reflects the ongoing investigation into\nthe complex interactions within this ecosystem.\n“This severe drought resulted in a reduction of more than 50% in the Mississippi River flow and\nnutrient input to the GoM.” Having a difficult time fully understanding the point of this. I do\nunderstand that freshwater connectivity is an important function of the Mississippi River/GOF\nsystem, but that same system in its current form brings an unnatural amount of nutrients from ag\nrunoff to the GOF, which causes an annual Dead Zone for fish. Given the well known\nrelationship\nWhile acknowledging the significant issue of anthropogenic nutrient runoff and its\ncontribution to the GoM dead zone, our study emphasizes the impact of a reduction in\nnatural nutrient input caused by drought. Although both factors affect GoM ecosystem\nhealth, we posit that the decrease in natural nutrient supply via reduced Mississippi River\nflow, as seen during the 1980s drought, is the more significant driver of the observed\nbiomass decline, as evidenced by the data in Supplementary Section S2, showing that the\nhistorically lowest dead zone area (1988) coincided with the late-1980s biomass decline, and\nthe historically high dead area zone area (2002) was not associated with marked declines in\nbiomass or catches (supplementary Figs S1,2).\nIntro: “Numerous studies indicated a significant correspondence between Gulf menhaden\npopulation dynamics and environmental conditions such as temperature, wind, and river flow”\nWhat are those relationships between all of these abiotic factors and fish? Will help the reader to\nbetter understand the system.\nWe thank you for this suggestion. The Introduction now includes a more detailed\ndiscussion of the complex relationships between Gulf menhaden population dynamics and\nabiotic factors such as temperature, wind, and river flow. We describe the scale-dependent\neffects of river discharge on recruitment, highlighting both positive (enhanced primary\nproductivity at decadal scales) and negative (increased larval transport and predation risk\nat annual scales) relationships. The influence of climate oscillations (AMO, NAO, ENSO)\nand the impacts of these factors on menhaden oil content (Leaf, 2017) and growth (Midway\net al., 2020) are also discussed.\n“River to the GoM reached a record low annual and summer value of 107,717 and 41,560 t,\nrespectively (Fig. 1c).” Would be good to know what the historic pre-ag nutrient load was for the\nGoF. Are those data available or known?\nAlthough precise pre-agricultural nutrient load data for the GoM are lacking, substantial\nincreases in nutrient input have occurred since the mid-20th century due to agricultural\nintensification (Rabalais et al., 1996; Turner & Rabalais, 2003). Our study centers on\ndrought-induced reductions in natural nutrient flow, relative to this existing, higher\nbaseline.\nFigures: overall I thought they were impressive, but had to zoom in to 250% to read some of\nthem. Also, the color schemes to identify various species and groups are too challenging to\ninterpret.\nThank you for your feedback. Revised figures feature larger font sizes and improved color\nschemes for enhanced clarity and better differentiation of species and groups.\nReferences\nBerenshtein, I., Sagarese, S. R., Lauretta, M. V, Nuttall, M. A., & Chagaris, D. D. (2021). Technical\ndocumentation of a US Gulf of Mexico Ecopath with Ecosim model. NOAA Technical\nMemorandum. https://doi.org/10.25923/zj8t-e656\nBerenshtein, I., Sagarese, S. R., Lauretta, M. V, Schueller, A. M., & Chagaris, D. D. (2023).\nIdentifying trade-offs and reference points in support of ecosystem approaches to managing Gulf\nof Mexico menhaden. Frontiers in Marine Science, 9, 935324.\nKarnauskas, M., Kelble, C. R., Regan, S., Quenée, C., Allee, R., Jepson, M., Freitag, A., Craig, J. K.,\nCarollo, C., & Barbero, L. (2017). Ecosystem status report update for the Gulf of Mexico. NOAA\nTechnical Memorandum NMFS-SEFSC-706, 51.\nLeaf, R. T. (2017). Environmental determinants of Gulf menhaden (Brevoortia patronus) oil content\nin the northern Gulf of Mexico. Ecological Indicators, 82, 551–557.\nMidway, S. R., Schueller, A. M., Leaf, R. T., Nesslage, G. M., & Mroch III, R. M. (2020).\nMacroscale drivers of Atlantic and Gulf Menhaden growth. Fisheries Oceanography, 29(3),\n252–264.\nOlsen, E., Fay, G., Gaichas, S., Gamble, R., Lucey, S., & Link, J. S. (2016). Ecosystem model skill\nassessment. Yes we can! PLoS One, 11(1), e0146467.\nReynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily\nhigh-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22), 5473–\n5496.\nWoodworth‐Jefcoats, P. A., Polovina, J. J., & Drazen, J. C. (2017). Climate change is projected to\nreduce carrying capacity and redistribute species richness in North Pacific pelagic marine\necosystems. Global Change Biology, 23(3), 1000–1008.\nIgal Berenshtein\nDepartment of Marine Biology\nCharney School of Marine Sciences\nUniversity of Haifa\nAbba Khoushy Ave 199, Haifa, 3498838\niberensht@univ.haifa.ac.il\nMobile phone +972-54-533-8837\nPoint-by-point response to the reviewer’s comments\nReviewer #2 (Remarks to the Author):\nIn the revision of the MS by Berenshtein et al., the authors responded to the comments made by\nmyself and another reviewer. Both reviews, offered important critiques to the study, especially\nabout model inference and interpretation, and the confounding factors that go along with it, e.g.,\nhypoxia. R1 made comments about hindcasting and using a sensitivity approach to test for the\nreliability of the model, I agree with R1, and I commend the author’s for taking that approach.\nAlong with addressing that big suggestion, I though the author’s did a great job addressing the\nother comments. It is clear that they carefully took the time to fully address my issues, and given\nthe novelty of the approach and the big message for climate change I believe that this paper is\nsuitable for publication in Nature Communications. Good luck!\nWe sincerely thank the reviewer for the positive feedback, as well as for the helpful\nsuggestions and comments provided during the previous revision round.\nReviewer #3 (Remarks to the Author):\nReview Berenshtein et al - Will mega-droughts in the US drive the collapse of fisheries in the\nGulf of Mexico?\nThis paper looks at the relationship between drought conditions and fisheries production in the\nGulf of Mexico. These relationships are reproduced in an ecosystem model that is then used at\nthe implications of climate change driven droughts into the future.\nThe authors have obviously put a lot of effort into revising the paper based on previous feedback.\nThe paper is well written and there are no typos or minor changes like that required. However, I\nfeel there is some further clarification required in a few spots as described below.\nWe thank the reviewer for their positive assessment of our revisions and writing, and we\nappreciate their careful review of the updated manuscript.\n• Line 105 “decline of 16% in the relative fishing effort by the purse menhaden fleet”: Is there\nany tech creep to consider?\nWe thank the reviewer for this valuable comment. There is no explicit evidence for\ntechnological creep (i.e., gradual improvements in fishing efficiency due to advances in\nvessel or gear technology) during the 1980s that could cause nominal effort to decrease\nwhile actual effective effort remained stable or increased. Rather, studies (e.g., Vaughan et\nal., 2007) attribute the observed decline in nominal purse-seine effort in that period to\nexplicit reductions in the number of vessels and processing plants, driven by corporate\nacquisitions, company downsizing, and industrial consolidation. We have clarified this\npoint in the revised manuscript (lines 127-130) as follows: “Within this broader stability, the\nmenhaden purse-seine fleet did show a 16% reduction in relative effort (Fig. 1k), though this\nhas been attributed to decreases in fleet size and processing capacity, driven by corporate\nacquisitions, downsizing, and industrial consolidation, rather than improvements in efficiency\nor changes in fishing pressure”.\n• Line 116: Because the methods are at the end, please explain what it is on first use as s not all\nreaders will know.\nWe thank the reviewer for pointing this out. The term in question is the Palmer Drought\nSeverity Index (PDSI). In the revised manuscript, we now spell out the full name and\nprovide a concise definition at its first mention in the Results section (lines 108--109) as\nfollows:” Palmer’s Drought Severity Index (PDSI; a standard measure of drought intensity)”.\n• Line 135-138 “The effect of Gulf menhaden fishing mortality on the cumulative biomass and\ncatch dynamics is smaller compared to the effect of anticipated droughts (Fig. 2c), suggesting\nthat the ability of the ecosystem to compensate for menhaden harvest is compromised under\ndrought conditions.”: Have you tried to run it with the broken stick harvest control rule in closed\nloop mode to see if that helps recovery time? What about effort drops as seen in the historical\nperiod?\nWe thank the reviewer for this valuable suggestion. While we have not simulated the\nbroken stick harvest control rule in closed-loop mode, we did explore a wide range of effort\nreductions and increases when generating the contour plots in Figures 2d–f. These\nsimulations are not identical to replicating the precise historical effort trajectories, but they\ndo capture the consequences of varying fishing pressure on biomass and recovery dynamics\nunder different drought scenarios. Together, they provide partial insight into the\nreviewer’s question regarding effort drops. Exploring fully dynamic harvest control rules\nand explicitly testing historical effort patterns remain promising avenues for future\nresearch, particularly for assessing adaptive management strategies that could mitigate\ndrought impacts. We now highlight this point in the Discussion (lines 224--228) as follows:\n“Notably, while we did not simulate harvest control rules (e.g., broken-stick) in our projection,\nour contour-plot analyses (Figs. 2d–f) spanned a wide range of fishing mortality levels under\ndrought. These results provide partial insight into effort reductions, though explicit tests of\ndynamic harvest rules and historical effort trajectories remain an important direction for\nfuture work.”.\n• Line 146: Rationale for the specific x2 increase?\nWe thank the reviewer for requesting clarification. The two-fold increase corresponds to\nthe maximum observed value in the historical range of variation for this parameter,\nensuring that the scenario represents an extreme yet empirically grounded case. We have\nclarified this rationale in the Results section (lines 177-181) as follows: ” For status quo\nnutrient input and increased (x2) Gulf menhaden fishing mortality conditions, corresponding\nto the maximum value observed historically (1980–2016 relative to 2016), there is a decrease\nin the biomass of several groups: Gulf menhaden, their associated predators, species that are\nbycatch in the menhaden purse seine fleet (e.g., sea trout), and other species that decline due\nto indirect trophic interactions.”.\n• Line 149-150 “In contrast, the biomass of the competitors of these groups (e.g., cephalopods),\nincreases (Fig. 3a).”: Are technical interactions accounted for?\nWe thank the reviewer for this question. Yes, technical interactions are accounted for in\nour EwE model through the explicit linkage of each fleet to all functional groups it exploits\nthrough harvest and discards, based on observed catch composition and gear selectivity.\nWe have clarified this point in the Results section (lines 190-192) as follows: “These\ndynamics explicitly account for technical interactions, as fleets in the model are linked to\nmultiple functional groups through both targeted catch and bycatch based on observed\ncomposition and selectivity.”.\n• Line 213 “jellyfish, become more dominant”: Possible to comment on cascading implications?\nWe thank the reviewer for this suggestion. In the revised manuscript, we now briefly\nelaborate on the potential cascading implications of increased jellyfish dominance.\nSpecifically, jellyfish blooms can reduce prey availability for planktivorous fish and\ncompete with larval stages of many fish species for zooplankton resources, potentially\nimpairing recruitment success and altering trophic energy flow. These cascading effects are\nnow mentioned in the discussion. We have clarified this point in the Discussion section\n(lines 289-292) as follows: “Such jellyfish dominance could have cascading effects, as blooms\nreduce prey availability for planktivorous fish and compete with the larval stages of many fish\nspecies for zooplankton resources, potentially impairing recruitment success and altering\ntrophic energy flow.”.\n• Line 231-233 “This is supported, for example, by the low cumulative biomass and catches\nduring 1988 (Fig. 1), which was characterized by the lowest nutrient input, and consequently\nlowest hypoxic area during the considered period25 (supplementary section S2, Figs. S1-2).”:\nThis is an argument by absence rather than unpacking of attribution when both present, not as\nconvincing.\nWe thank the reviewer for this observation and agree with the concern raised. In the\nrevised text, we have modified our response to emphasize that our interpretation is\nsupported by: (1) the generally negative relationship observed between biomass, nutrient\ninput, and catches versus hypoxic area across multiple years; and (2) the well-established\nmechanistic understanding that reduced nutrient input can limit primary productivity,\nthereby lowering biomass and catches, even when hypoxic area is minimal. We have\nclarified this point in the Discussion section (lines 310-314) as follows: “This interpretation\nis further supported by the generally negative relationship observed across years between\nbiomass, nutrient input, and catches versus hypoxic area, and by the well-established\nmechanism through which reduced nutrient input lowers primary productivity and,\nconsequently, biomass and catches, for example, in years such as 1988, 2000, and 2012, when\nhypoxic area was minimal (Fig. 1, supplementary section S3, Figs. S2–3).”.\n• General comment on the end of the discussion: Would it be fair to characterise the results as an\nexample of the pattern of the forcing function on production being amplified up the model vs less\nF effects? This pattern of differential sensitivity has been seen before in EwE comparative work\n(e.g. Fulton and Smith 2004 Afr. J. Mar. Sci. 26: 219–243). If it is this might be a point for\nflagging caution and uncertainty around the results of the work.\nWe thank the reviewer for this insightful comment. We agree that our results may reflect,\nat least in part, the EwE model’s tendency to show stronger ecosystem-wide responses to\nexternal forcing functions (e.g., changes in primary productivity) compared to changes in\nfishing mortality, as reported in previous comparative studies such as Fulton and Smith\n(2004). In the revised Discussion, we now highlight this as a potential source of uncertainty\nand caution when interpreting the relative magnitudes of drought versus fishing effects in\nour simulations. At the same time, we do not interpret this pattern as a systematic bias of\nEwE models. Rather, it reflects the fundamental difference in the scope of these drivers:\nchanges in primary productivity inherently cascade through many trophic levels and\nfunctional groups, whereas changes in fishing mortality tend to be more targeted in scope.\nThis distinction is especially important in the Gulf of Mexico, where nutrient inputs from\nthe Mississippi River are massive and represent a primary driver of ecosystem\nproductivity. Thus, while EwE may emphasize the broad reach of bottom-up forcing\nrelative to fishing effects, the strong drought effect observed in our simulations is consistent\nwith empirical evidence for bottom-up control in the Gulf, lending confidence to the\nqualitative direction of our findings. We have added a paragraph in the Discussion (lines\n236-247) to reflect this point as follows: “Our results may also reflect a broader pattern noted\nin EwE comparative studies (e.g., Fulton and Smith, 2004), where external forcing functions\nsuch as changes in primary productivity tend to propagate more strongly through food webs\nthan changes in fishing mortality. This differential sensitivity should not be interpreted as a\nsystematic bias in the EwE framework. Instead, it reflects the fundamental difference that\nproductivity changes cascade across many trophic levels and functional groups, while fishing\nmortality is more targeted in scope. In the GoM, where nutrient inputs from the Mississippi\nRiver are massive and act as a primary driver of ecosystem productivity, such bottom-up\nforcing is expected to have particularly broad consequences. While we acknowledge this\ndistinction as a source of uncertainty when comparing the relative magnitudes of drought and\nfishing effects, the strong drought effects observed in our simulations remain consistent with\nempirical evidence for bottom-up control in the region, lending confidence to the qualitative\ndirection of our findings.”.\n• Line 411 “1982-1986 and 1988-1992”: What is the comparability of environmental conditions\nand human use in general (so can isolate the drought effect)?\nWe thank the reviewer for this question. The two periods, 1982–1986 and 1988–1992,\nwere selected to bracket the 1988 drought, enabling a comparison of pre- and post-drought\nconditions. Examination of environmental indicators (Fig. 1, Fig. S2, previously Fig. S1)\nshows that, apart from the sharp drought-related drop in PDSI, river discharge, and\nnutrient load in 1988, the two periods are broadly comparable: SST anomalies are similar\nin magnitude and variability, and river and nutrient inputs partially recover in the later\nperiod. Human-use indicators, such as relative fishing effort per fleet, are also comparable\nbetween the periods, with no evidence for major technological or regulatory changes, and\ninterannual variability remains within historical ranges. The lower catches and biomass\nobserved in 1988–1992 coincide with the nutrient decline rather than changes in fishing\npressure. While we acknowledge the potential influence of other environmental and\nanthropogenic factors, these similarities suggest that the observed differences primarily\nreflect the drought effect. To address this point, we clarified in the Results section (lines\n123-126) that environmental and human-use indicators were broadly comparable across\nthe two periods, supporting the interpretation that the observed differences are primarily\nattributable to the drought as follows: “Importantly, aside from the sharp drought-related\nreductions in PDSI, river discharge, and nutrient inputs in 1988, other environmental\nindicators (e.g., SST anomalies) and overall fishing effort levels were broadly comparable\nbetween the two periods (Fig. 1, Fig. S2), supporting the interpretation that the observed\ndeclines primarily reflect the drought effect.”.\n• Line 415 “computations of cumulative biomass”: This is aggregate biomass correct? It is likely\nfine as it is but aggregate is probably the more commonly used term.\nWe thank the reviewer for this clarification. Yes, “cumulative biomass” in this context\nrefers to the aggregate biomass summed across all functional groups in the model. In the\nrevised text, we have added a note indicating that this term is equivalent to aggregate\nbiomass. A clarification note was added in the Results section (lines 119-121), as follows:\n“Analysis of historical fisheries data indicates a loss of 41.742% in cumulative biomass (i.e.,\naggregate biomass summed across all functional groups) and 34.1% in cumulative catch\nduring the periods of 1988-1992 compared to 1982-1986”.\n• Line 429 “Discards for other fisheries were not incorporated into catch time series.”:\nImplications? Estimate of how big a biomass take this was? Some groups effected more than\nothers?\nWe thank the reviewer for raising this point. In general, discards from other fisheries were\nnot systematically incorporated into the Ecosim catch time series because consistent\nhistorical data were not available across the full study period. For most functional groups,\ncatch time series were constructed from NOAA landings data, meaning that discards were\nlargely excluded. An exception is assessed species (e.g., red snapper, groupers, king\nmackerel, sharks, tunas) for which SEDAR assessments report total removals (landings +\ndead discards); in these cases, the Ecosim inputs generally included both components.\nThus, while discard mortality is partially represented for assessed species, it is likely\nunderrepresented for other groups, especially non-target bycatch species and those\nprimarily interacting with shrimp trawls and reef fisheries.\nAvailable records indicate that discards in the Gulf of Mexico outside the menhaden\nfishery represent a relatively small share of total removals at the ecosystem scale. While\nincluding discard biomass would slightly increase total removals and could influence\nbiomass trends for some groups, we expect the ecosystem-level patterns and the relative\ncontrast between drought- and fishing-driven impacts to remain robust. We have added a\nnote in the Discussion (lines 315-322) to acknowledge this limitation, along with its\nimplications for certain functional groups that are more discard-prone, as follows:\n“Another limitation relates to discards. While discard mortality was partially represented for\nsome assessed species where total removals (landings + dead discards) were available, it is\nlikely underrepresented for other commercial and especially recreational fisheries. Available\nrecords suggest that discards constitute a relatively small proportion of total removals at the\necosystem scale, but they can be substantial for certain groups, particularly small pelagics,\njuvenile demersals, and non-target invertebrates. Incorporating more complete discard\ninformation in future models could refine biomass estimates for these groups, though we do\nnot expect it to alter the overall contrast between drought- and fishing-driven effects reported\nhere.”.\n• Line 464-465 “linear least squares regression to model the relationship between historical\nnutrient input and the Palmer Drought Severity Index”: What about potential for changed\nagricultural practices due to hypoxia concern? Implications of land use from ISIMIP\n(https://www.isimip.org/) runs?\nWe thank the reviewer for this important point. Our regression assumes the historical\nrelationship between nutrient input and PDSI is primarily driven by hydrological\nvariability and does not account for long-term changes in agricultural practices. In the\nMississippi–Atchafalaya River Basin, conservation measures since the 1980s have reduced\nnitrogen loads by 5–34% in high-intensity areas (García et al., 2016), though phosphorus\nreductions have been weaker. These gains are often offset by hydroclimate variability, with\n~72% of monthly nitrogen load variability since 1980 attributed to climate and extreme-\nflow years delivering ≈30% higher loads (Lu et al., 2020). Thus, while agricultural\nmanagement has some mitigating influence, climate remains the dominant driver of\ninterannual nutrient export variability. Our projections did not incorporate dynamic land-\nuse or management-change scenarios, such as those from ISIMIP, which could alter long-\nterm nutrient inputs through conservation, cropping choices, or policy interventions. We\nnow note this as a source of uncertainty and recommend future work explicitly integrate\nland-use and management scenarios alongside hydroclimate drivers to improve\nprojections. The revised text in the Methods section (lines 607-609) now reads: “This\nregression assumes the historical relationship is primarily climate-driven and does not\nexplicitly account for potential long-term changes in agricultural practices or land use, which\nwe note as a source of uncertainty.”.\n• Line 486: Why no Ecospace? And in Ecosim were environmental relationships used? [I can see\nfrom the supplied model the answer is no, but please state this in the paper]\nWe thank the reviewer for this question. Our analysis was conducted in Ecosim rather than\nEcospace because our focus was on temporal dynamics at the scale of the entire Gulf of\nMexico ecosystem, for which spatially explicit modelling was not required. In Ecosim, we\ndid not include environmental response functions for individual functional groups, as our\nobjective was to assess the system’s response to drought forcing through changes in\nnutrient input and primary productivity rather than to parameterize species-specific\nenvironmental tolerances. We have now explicitly stated these points in the Methods\nsection for clarity. Future work may extend this analysis using Ecospace to examine the\nspatiotemporal dynamics of drought impacts on the Gulf of Mexico ecosystem. We added a\nnote clarifying this point in the Methods section (lines 631-636) as follows: “Our analysis\nwas conducted in Ecosim rather than Ecospace because our focus was on temporal dynamics\nat the scale of the entire GoM ecosystem, for which spatially explicit modelling was not\nrequired. We also did not include species-specific environmental response functions; instead,\ndrought forcing was implemented through changes in nutrient input and primary productivity.\nFuture work may extend this approach with Ecospace to explore spatial heterogeneity.”.\n• Line 505 “primary productivity anomaly”: Interaction with drought year reproduction?\nWe thank the reviewer for this observation. In our simulations, primary productivity\nanomalies were applied as a proxy for the bottom-up effects of drought on the ecosystem,\nbut we did not explicitly model potential interactions with the timing of reproduction for\nindividual functional groups. We acknowledge that drought-induced changes in primary\nproductivity could overlap with spawning seasons or early life-history stages, thereby\namplifying recruitment impacts. Although Ecosim has the capability to incorporate\nseasonal egg production forcing and can be used to test match–mismatch hypotheses,\nthese features were not implemented in the present study. We have now added a clarification\nnoting that phenological interactions were not explicitly represented here but could\ninfluence recruitment dynamics, and should be incorporated in future work.\nThe revised section in the Discussion (lines 322-326) is as follows: “In addition, our\nsimulations used primary productivity anomalies as a proxy for drought forcing and did not\nexplicitly capture match–mismatch dynamics with spawning or recruitment; Ecosim can\nincorporate seasonal egg production forcing, and future work should test such interactions to\nbetter represent drought effects on early life stages.”.\n• Line 533-534 “MEF values (see supplementary section S5, supplementary Table S5),\ndemonstrating the overall efficacy of the model”: Given the topic discussed here what was it for\nmenhaden and closely related species?\nWe thank the reviewer for this question. In the supplementary material (Table S5), the\nmodel efficiency (MEF) values for Gulf menhaden were 0.65 (Age 1), 0.68 (Age 2), and 0.66\n(Age 3), indicating good agreement between modelled and observed biomass trends across\nage classes. For the most closely related functional groups, MEF values were 0.54 for\n“Sardine–herring–scad” and 0.49 for “Mullet,” both reflecting reasonable skill in\nreproducing historical dynamics. We have now added these specific values to the Results\nsection (lines 167-169) for clarity: “Notably, the model diagnostics indicated good skill\noverall, with MEF values of 0.65–0.68 across Gulf menhaden age classes, and 0.54 and 0.49\nfor the closely related groups “Sardine–herring–scad” and “Mullet,” respectively (see\nsupplementary section S6, Table S5).”.\n• Line 556-558 “The primary productivity and the free nutrient concentrations are linked through\na Michaelis-Menten uptake relationship, in which nutrient uptake increases asymptotically up to\na maximum in response to increasing free nutrient concentrations”: How exactly was it entered\ninto Ecosim? Straight primary productivity scalar or via an environmental response relationship\nor ?\nWe thank the reviewer for this question. The Michaelis–Menten uptake relationship is an\ninternal feature of Ecosim’s primary producer dynamics and is not entered manually by\nthe user. In our simulations, free nutrient concentrations influenced primary productivity\nthrough this built-in saturating uptake function, whereby nutrient-limited growth\nincreases asymptotically toward a maximum rate. This relationship operates automatically\nwithin Ecosim based on the specified nutrient time series, ensuring that primary\nproductivity dynamically responds to modeled nutrient concentration changes over time.\nWe have updated the Methods (lines 694-698) section to clarify this point as follows: “The\nbase free nutrient proportion ,ensures that as producer biomass increases, free nutrient\navailability declines and constrains further growth. The base free nutrient proportion was set\nin our simulations to the default value of 0.5, provides the basis for the time-dependent\nnutrient forcing, which is simulated using monthly multipliers of the Ecopath based nutrient\ncontent estimate that represents the relative nutrient loading”.\n• Line 559: Ecosim simulations: State the length of the simulation. Just the drought period or\nlonger? How long was the elevated F? Entire run just 5 years or are these periods layered into\nlonger runs? How is transitional change under directed forcing allowed for (background climate\ndriven shift)\nWe thank the reviewer for this question. For the historical simulations, we used actual\nenvironmental and fishing data at yearly or monthly resolution, depending on availability.\nFor the projected 2050 and 2100 drought scenarios, nutrient input and fishing pressure\nchanges were applied over five consecutive years, with the terminal (fifth) year used for\nanalyses; the same approach was used for fishing pressure forcing. Transitional change\nunder directed forcing was represented by this five-year adjustment period, which\npreliminary trials showed to be sufficient for the ecosystem to reach a new stable state\nwhile remaining ecologically realistic and representative of isolated events. Background\nclimate variability was not included; instead, all projections used the mean conditions of\nthe last three years of the time series (geometric mean of 2014–2016) as the reference\nbaseline. We have updated the Methods section (lines 723-727) to clarify this point, as\nfollows: “All projected drought and fishing scenarios were implemented as isolated 5-year\nsimulations layered onto the 2014–2016 baseline conditions, with forcing applied throughout\nthe run and the terminal (fifth) year used for analyses. This design allowed transitional\ndynamics under directed forcing to stabilize while remaining ecologically realistic.”\n• Line 628 “purse seine effort”: How was this done? Or are you saying that is what the changing\nF represents? Was it done via effort time series manipulation or F forcing?\nWe thank the reviewer for this question. In the simulations, both fishing mortality (F) and\npurse-seine effort were implemented as forcing functions. For the scenarios, their values\nwere multiplied by the specified multipliers to represent changes in fishing pressure. We\nadded a note in the Methods section (lines: 727-731) clarifying this point as follows: “Both\nGulf menhaden fishing mortality (F) and purse-seine fleet effort were implemented as forcing\nfunctions, with their values scaled by multipliers in the scenarios to reflect changes in fishing\npressure and associated technical interactions (e.g., bycatch). Background climate variability\nbeyond the applied drought and fishing scenarios was not included.”.\n• Line 636-639 “These models, like others, encompass inherent uncertainty and are based on\nassumptions or best available data concerning the species biology and life history. However, they\ndo represent the only source of absolute biomass for the entire GoM.”: We all do it, but not great\nto condition a model with another model. Was there any cross check with data, like standardised\ncpue or survey data etc?\nWe thank the reviewer for raising this important point. We acknowledge that conditioning\nan ecosystem model with outputs from other models introduces additional assumptions and\nuncertainties. In our case, not all biomass time series were model-based—many were\nderived directly from observational datasets, including bottom trawl surveys and video-\nbased indices (Table S2). For certain groups, such as Gulf menhaden, absolute biomass\nestimates were taken from available single-species assessment models due to the absence of\ndirect, fishery-independent absolute biomass measurements for the entire Gulf of Mexico.\nNevertheless, our analyses also incorporate fisheries-independent time series, including a\nmenhaden CPUE index based on abundance per trawl in Louisiana estuaries—the main\nnursery area for Gulf menhaden—collected routinely by the Louisiana Department of\nWildlife and Fisheries (LDWF) (Section S3). These data, together with standardized CPUE\nindices from the Gulf menhaden purse-seine fishery (adjusted for vessel, gear, and seasonal\neffects; Berenshtein et al., 2021), were used to qualitatively check the consistency of\ntemporal patterns. While differences in spatial coverage, sampling design, and target\nspecies composition precluded full quantitative validation, we now explicitly acknowledge\nthis limitation in the revised manuscript and recommend future integration of a broader\nrange of standardized observational datasets for cross-validation. We have updated the\nMethods (Model limitations) section (lines 799-804) accordingly, as follows: “To partially\naddress this limitation, we cross-checked temporal trends against independent observational\ndatasets, including standardized CPUE indices from the Gulf menhaden purse-seine fishery\nand fisheries-independent trawl-based juvenile abundance indices from Louisiana estuaries\n(Section S3). While differences in design and coverage precluded full quantitative validation,\nthese comparisons provided qualitative consistency checks”.\n• Line 652-653 “the positive contribution of nutrient input on fisheries production outweighs the\nnegative effect from hypoxic conditions.”: Yes but what happens in years with low inflow and\nlow nutrient input? Less hypoxia and related mortality along with the nutrient drop or is there a\nlag so get hypoxia as well as the nutrient drop at least in the first year/s of the drought?\nWe thank the reviewer for this question. In our study, we did not explicitly model hypoxia\nor its direct effects; instead, we referred to de Mutsert et al. (2016) to illustrate how\nnutrient input changes can influence both primary production and hypoxic conditions. In\nthat study, hypoxic area was not simulated through a multi-year lag mechanism; rather, it\nwas linked directly to annual nutrient loading in the same year using empirically derived\nrelationships within the coupled physical–biological hypoxia model of Fennel et al. (2011),\nwhich provided dissolved oxygen and chlorophyll-a values for each model cell at each time\nstep. As such, in years with low inflow and nutrient input, their model produced both\nreduced primary production and reduced hypoxic area within that year, without an\nexplicit carryover of hypoxia from prior years. In addition, the empirical data on hypoxic\narea presented in our study (Fig. S2e) show that, at the yearly scale, extreme declines in\nhypoxic area (e.g., 1988, 2000, and 2012) were immediately associated with extreme\ndrought conditions, and that hypoxic area was restored once drought conditions\ndiminished. These results suggest that lagged effects are weak or absent. We have added a\nclarifying comment on in the Methods (Model limitations) section (lines 819-828) as\nfollows: “Consistent with this, empirical data (Fig. S2e) show that hypoxic area declines\nsharply during extreme drought years (e.g., 1988, 2000, 2012) and recovers once drought\nconditions diminish, suggesting that lagged effects are weak or absent. Our simulations used\nprimary productivity anomalies as a proxy for drought forcing and did not explicitly capture\nmatch–mismatch dynamics with spawning or recruitment. Ecosim can incorporate seasonal\negg production forcing, and future work should test such interactions to better represent\ndrought effects on early life stages. In addition, we did not explicitly incorporate salinity–\nhabitat interactions in our modeling framework. While salinity increases during droughts can\nstress estuarine-dependent invertebrates (e.g., oysters, shrimp), the ecosystem-wide declines\nobserved here were reproduced based solely on nutrient forcing, supporting a parsimonious\nfocus on trophic mechanisms.”.\n• HMS. Perhaps just say Highly Migratory Species. It’s not used a lot and not sure it adds a lot to\nuse the acronym in terms of ease of reading the text.\nWe thank the reviewer for this suggestion. We agree and have replaced “HMS” with\n“Highly Migratory Species” throughout the text to improve clarity.\n• Figure 1: All useful information but so much of the story is hard to discern. The drought period\ndoesn’t look enormously exceptional beyond 1988 so it would be good to see the other years also\nrepresented in panels e-h perhaps red bold 1988 and other drought period years in unbolded red?\nThen pull the menhaden time series for the biomass, catch and effort out form the rest and\npresent them separately. Its ok to present the rest of the groups as a bulk presentation here but it\nwould also be good to have individual time series for each (e.g. using facet wrap style set of\npanels for the individual time series) in the supplementary materials. Area plot for effort perhaps\nmore useful?\nWe thank the reviewer for these constructive suggestions to improve Figure 1. In the\nrevised manuscript, we modified panels e–h to highlight 1988 in bold red and to indicate\nother drought-period years (2000 and 2012) in unbolded red, thereby providing clearer\ntemporal context for the 1988 drought relative to other events. We considered pulling the\nGulf menhaden time series (biomass, catch, and effort) into the main figure, but Figure 1 is\nalready visually dense, and adding additional plots made the layout overly crowded.\nInstead, to emphasize their central role, we now provide menhaden-specific time series in\nthe supplementary material (new Section S2), including a single consolidated panel (Fig.\nS4) alongside extended time series. In addition, individual time series for all functional\ngroups are now presented in a facet-wrap style format in the supplementary material (new\nFig. S1), allowing clearer comparison across groups. Regarding the suggestion of an area\nplot for effort, we note that because effort is expressed in relative units and actual\nmagnitudes differ substantially among fleets, aggregating them would be misleading; we\nhave therefore retained separate time-series plots for clarity.\n• Figure 2 line 694 “ with dashed lines indicating “: Say horizontal dashed lines.\nWe thank the reviewer for the suggestion. We have revised the caption for Figure 2 to\nspecify that the dashed lines are horizontal dashed lines.\n• Table S5: Let the reader know what is good value in general (e.g. close to 1) vs what is poor\nWe thank the reviewer for this helpful suggestion. In the revised manuscript, we have\nadded a note to the caption of Table S5 clarifying that MEF values closer to 1 indicate\nbetter model performance, whereas values approaching –1 indicate poor model\nperformance.\n• Supplementary materials a typo for “Mississippi reiver”. Should be river.\nWe thank the reviewer for catching this typo. It has been corrected.\n• Figure S2: Explain the period highlighted and why when highlighted drought period in the\nmain text figures. Also F (other than on 1 Yr olds) stayed high even as effort dropped. Why and\nwhat does that mean for signature assumed into bycatch species in the runs?\nWe thank the reviewer for these observations. In Figure S2 (now Figure S3), the\nhighlighted period corresponds to the time period which was affected by the late 1980s\nprolonged drought, which is the historical drought event analyzed in detail in the main\ntext. Regarding the observation that fishing mortality (F) for age classes other than 1-year-\nolds remained high even as effort declined, this reflects the age-structured nature of the\nsingle-species assessment outputs used to inform our model. Specifically, older age classes\nmay experience sustained fishing mortality due to their continued vulnerability to the\nfishery, even when overall effort is reduced, because catch composition and selectivity\npatterns disproportionately target these cohorts. It is also possible that F remained elevated\nbecause drought-driven biomass reductions (lower denominator in F = C/B) amplified the\nestimated mortality rates. In the context of our simulations, this means that for bycatch\nspecies linked to menhaden fishing, the modelled signature of fishing pressure is still\ninfluenced by these higher F values on older age classes, even when total effort is lower. We\nhave added clarifying text in the supplementary materials to explain the highlighted period\n(lines 101-102, 111-112) as follows: “Shaded area represents the time period which was\naffected by the late 1980s prolonged drought.”.\nWe have also added a note clarifying the implications of the F–effort relationship for\nbycatch species assumptions (lines 79-83) as follows: “The apparent persistence of high F\ndespite declining effort likely reflects two processes: (i) the age-structured nature of the\nassessment outputs, where older cohorts remain selectively targeted even under reduced effort;\nand (ii) drought-driven reductions in biomass, which amplify the ratio of catch to biomass (F\n= C/B), thereby inflating estimated mortality rates.”.\n• Supplementary materials “While the dynamics of the post-1990 years align with the\nexpectation that as biomass increases, fishing effort should decrease, and vice versa, the\ndynamics during the 1980s appear counterintuitive, as biomass decreased in parallel with a\ndecrease in fishing effort, supporting the dominance of an external environmental force such as\nnutrient input deficiency.”: Could this be a case of effort reduction in response to biomass drop, so\nstart high together and then effort reduced in response to drop in biomass, with effort\nreduction continuing on into later period where stocks then rebuilt?\nWe thank the reviewer for this insightful interpretation. The 1980s pattern may indeed\nreflect a lagged management or economic response, with high initial effort reduced\nfollowing biomass declines and continuing to drop as stocks rebuilt. This aligns with our\nproposed mechanism in which drought reduced biomass, leading to lower catches and\nmodest effort reductions. We have noted this explanation in the supplementary materials\n(lines 126-128) as follows: “This pattern may also reflect a lagged management or economic\nresponse, where effort initially remained high, then dropped in reaction to declining biomass,\nand continued to decrease into the subsequent period as stocks rebuilt.”.\n• Supplementary materials “Model quality was demonstrated in good agreement between model\npredicted biomass”: Plots possible so reader gets a sense of what this looks like?\nWe thank the reviewer for this suggestion. Plots comparing observed and model-predicted\nbiomass for key functional groups are already presented in detail in Berenshtein et al.,\n2021, 2023, where model calibration and validation are described extensively. To\nstrengthen the current manuscript, we have added clarifying text in the supplementary\nmaterials (Section S6, lines 169-172) explicitly directing readers to these sources, and we\nemphasize that these figures demonstrate good agreement between modeled and observed\nbiomass dynamics, as follows: “For detailed plots comparing observed vs. model-predicted\nbiomass for key functional groups, see Berenshtein et al. (2021, 2023), where calibration and\nvalidation are presented extensively. These figures visually illustrate the quality of model fit\nand support the MEF values reported here.”.\n• Supplementary materials “model predicted estimates of the fishing mortality rate corresponding\nto the maximum sustainable yield (FMSY) and FMSY estimates from stock assessments.”:\nEstimated in Ecosim/Ecopath how? Multiple ways this can be done.\nWe thank the reviewer for this question. In our analysis, F was estimated directly\nMSY\nwithin Ecosim using the compensatory MSY search routine. This procedure incrementally\nadjusts fishing mortality rates for each functional group while holding other parameters\nconstant, to identify the rate that maximizes long-term equilibrium yield. The resulting\nF values were then compared with independent estimates from single-species stock\nMSY\nassessments, as reported in the supplementary materials. We have added this explanation\nto Supplementary Section S6 (lines 166-168) to clarify our approach as follows: “Ecosim-\nderived Fₘₛᵧ values was estimated within Ecosim using the built-in equilibrium MSY\n(compensatory) search routine, which iteratively adjusts fishing mortality to identify the rate\nthat maximizes long-term equilibrium yield (see Berenshtein et al., 2021; 2023)”.\n• Supplementary materials “As noted in the main text, the relative impact of fishing mortality\ndiminishes as drought-related effects on biomass intensify.”: As F not effort so catch drops in\nstep with productivity? Any consideration of whether could become synergistic if lag in F\nresponse? Also bigger cumulative effect if rate of F in place longer before drought hits? Also\ndate of model base F vs 1988 F?\nWe thank the reviewer for these insightful points. In our model, fishing pressure was\napplied as F (fishing mortality rate) and purse menhaden fleet effort, so catches varied\nproportionally with changes in productivity and biomass. We did not explicitly model\nsynergistic effects from a lagged F response; however, we acknowledge that in reality,\ndelayed reductions in F could intensify biomass declines during drought. Similarly, higher\npre-drought F sustained over a longer period could produce larger cumulative impacts\nwhen a drought occurs. The model’s baseline F values were taken from the EwE last\nhidcast years (2014-2016), whereas the historical 1988 drought scenario used observed\nfishing mortality rates from that period. We have added a note on this in the\nsupplementary materials (lines 172-179) as follows: “Note that our simulations applied both\nfishing mortality (F) and purse-seine fleet effort, such that catches varied proportionally with\nbiomass and productivity. While this approach captures the relative impact of fishing under\ndrought forcing, it does not account for possible synergistic effects if reductions in F lag\nbehind biomass declines. In reality, such delays could intensify stock depletion, particularly\nunder extreme drought conditions. Likewise, sustained high F in the years preceding drought\nevents could produce stronger cumulative impacts once drought occurs. These potential\ninteractions were not explicitly represented here and should be explored in future model\nextensions.”.\nReviewer #3 (Remarks on code availability):\nI read over the code (though did not run it) and it all looked straightforward. I also opened the\nEwE model and it looked reasonable\nWe thank the reviewer for taking the time to review the code and examine the EwE model.\nWe appreciate the positive feedback and are glad the model structure and implementation\nappear reasonable.\nReferences\nBerenshtein, I., Sagarese, S. R., Lauretta, M. V, Nuttall, M. A., & Chagaris, D. D. (2021). Technical\ndocumentation of a US Gulf of Mexico Ecopath with Ecosim model. NOAA Technical\nMemorandum. https://doi.org/10.25923/zj8t-e656\nBerenshtein, I., Sagarese, S. R., Lauretta, M. V, Schueller, A. M., & Chagaris, D. D. (2023).\nIdentifying trade-offs and reference points in support of ecosystem approaches to managing Gulf\nof Mexico menhaden. Frontiers in Marine Science, 9, 935324.\nFennel, K., Hetland, R., Feng, Y., DiMarco, S., 2011. A coupled physical–biological model of the\nnorthern Gulf of Mexico shelf: model description, validation and analysis of phytoplankton\nvariability. Biogeosci. Dis. 8, 1881–1899, http://dx.doi.org/10.5194/bg-8-1881-2011.\nKarnauskas, M., Kelble, C. R., Regan, S., Quenée, C., Allee, R., Jepson, M., Freitag, A., Craig, J. K.,\nCarollo, C., & Barbero, L. (2017). Ecosystem status report update for the Gulf of Mexico. NOAA\nTechnical Memorandum NMFS-SEFSC-706, 51.\nLeaf, R. T. (2017). Environmental determinants of Gulf menhaden (Brevoortia patronus) oil content\nin the northern Gulf of Mexico. Ecological Indicators, 82, 551–557.\nMidway, S. R., Schueller, A. M., Leaf, R. T., Nesslage, G. M., & Mroch III, R. M. (2020).\nMacroscale drivers of Atlantic and Gulf Menhaden growth. Fisheries Oceanography, 29(3),\n252–264.\nOlsen, E., Fay, G., Gaichas, S., Gamble, R., Lucey, S., & Link, J. S. (2016). Ecosystem model skill\nassessment. Yes we can! PLoS One, 11(1), e0146467.\nReynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily\nhigh-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22), 5473–\n5496.\nWoodworth‐Jefcoats, P. A., Polovina, J. J., & Drazen, J. C. (2017). Climate change is projected to\nreduce carrying capacity and redistribute species richness in North Pacific pelagic marine\necosystems. Global Change Biology, 23(3), 1000–1008.\nReviewer #4 (Remarks to the Author):\nA well written paper on a topic of great interest to scientists and managers. This work advances\nour understanding of climatic impacts on marine ecosystems and fisheries due to changes in\nfreshwater nutrient inputs. The authors evaluate the impacts of severe drought, combined with\nfishing pressure for menhaden, on fisheries production in the Gulf of Mexico (GoM). Firstly, the\nauthors show that a prolonged and severe drought during the 1980s, combined with high fishing\npressure for menhaden, led to a depleted forage base, with cascading effects on other species and\nfisheries in the GoM. Secondly, the authors use an ecosystem model to project changes in future\nfisheries production in the GoM under simulated drought conditions.\nOverall, this is a laudable paper that covers an important topic on the impacts of drought on\nfisheries production in the GoM. I applaud the authors for using an ecosystem model to evaluate\nclimatic impacts on marine ecosystems and fisheries. The methodology is well described and\nsound, and the findings support the conclusions in the paper.\nWe thank the reviewer for their positive and encouraging assessment of our study and its\ncontribution to understanding climatic impacts on Gulf of Mexico fisheries.\nWith that said, I found the paper to be lacking in its description of the mechanisms underlying\nthe impacts of drought on marine fisheries production published in the scientific literature.\nFurthermore, the paper would benefit from providing more detailed information on the projected\nimpacts of future droughts on the functional groups in the GoM. Nevertheless, the paper is worth\npublishing through a more focused route with consideration of the comments and suggestions\noffered below.\nWe thank the reviewer for these constructive comments. We have expanded the\nIntroduction and Discussion to better outline drought–fisheries mechanisms from the\nliterature and added more detail on projected drought impacts for individual functional\ngroups in the Gulf of Mexico. The specific revisions, including line numbers and inserted\ntext, are provided in our responses to the detailed comments below.\nMajor points\nIn the introduction, the mechanisms underlying the impacts of drought on marine fisheries\nproduction should be better described. Previous studies have shown that river flow is a ‘master\nvariable’, which has a controlling influence on marine fisheries production by regulating habitat\navailability, trophic dynamics, and fishers’ harvesting behaviour. Several key references are\nmissing and should be included in the paper (see the suggested additional citations at the end of\nthis review). It has been demonstrated that a trophic-driven mechanism for fisheries production is\nparticularly important in the GoM, which is an oligotrophic coastal region that receives limited\nnutrient supplies from oceanic currents and upwelling events (Wissel and Fry, 2005). The\ninclusion of this information would help the reader to understand why the study focused on flow-\ninduced changes in nutrient inputs rather than habitat availability.\nWe thank the reviewer for these valuable suggestions. In the revised manuscript, we have\nexpanded the Introduction to better describe the mechanisms linking drought to marine\nfisheries production, emphasizing river flow as a “master variable” influencing habitat\navailability, trophic dynamics, and fishers’ harvesting behavior. We now highlight evidence\nfor a trophic-driven mechanism in the Gulf of Mexico, where nutrient inputs from river\ndischarge are especially critical in this otherwise oligotrophic system with limited oceanic\nnutrient supply (e.g., Wissel and Fry, 2005). We have also incorporated the suggested key\nreferences and clarified that our focus on flow-induced changes in nutrient inputs reflects\nthe central role of nutrient-driven productivity in this region. The added text in the\nIntroduction section (lines 60-68) reads as follows: “River flow has long been recognized as a\n“master variable” regulating marine and estuarine ecosystems, influencing habitat\navailability, trophic dynamics, and fishers’ harvesting behavior 15–19. Variability in river\ndischarge controls nutrient delivery, sediment transport, and salinity gradients, all of which\nshape the productivity and distribution of marine organisms. In the GoM, a system otherwise\nstrongly oligotrophic with limited nutrient input from oceanic currents or upwelling, riverine\nnutrient inputs are especially critical for sustaining fisheries yields 20. This highlights a\ntrophic-driven mechanism by which drought-induced reductions in river flow limit nutrient\navailability, cascading through the food web to affect forage fish, predators, and ultimately\nfisheries production.”.\nMore information on the projected impacts of future droughts on the functional groups in the\nGoM would be beneficial. For example, how did the model projections suggest that more severe\ndroughts will affect species composition – should we expect less invertebrates and more\npiscivorous fish in the future?\nWe thank the reviewer for this suggestion. In the revised manuscript, we have expanded\nthe Results and Discussion to describe functional-group–specific responses to future\ndroughts, as shown in Fig. 3b. Model projections indicate that groups at lower trophic\nlevels, particularly benthic invertebrates (e.g., benthic coastal invertebrate feeders, benthic\npiscivores, and cephalopods), reef-associated species, and juvenile stages of menhaden and\nSpanish mackerel, experience the strongest biomass reductions under severe drought\nconditions, reflecting the bottom-up effects of reduced nutrient input on primary and\nsecondary production. In contrast, some higher trophic-level groups, including certain\npiscivorous fishes (e.g., billfish, cobia, and other tunas), show milder declines, likely due to\ntemporary increases in prey availability. Other predators, such as sharks and groupers,\ndecline more strongly in line with prey losses. Overall, the projections suggest that severe\ndroughts contract biomass at the base and mid-levels of the food web, disproportionately\nimpacting invertebrates and planktivores while producing heterogeneous responses among\npiscivores. This compositional shift implies reduced energy transfer efficiency and\nheightened vulnerability of ecosystem resilience. We have added this interpretation to the\nResults and Discussion.\nResults (lines 184-190): “At the functional-group level, projections show that drought impacts\nare not uniform (Fig. 3b). Lower trophic levels, including benthic invertebrates, reef-\nassociated species, and juvenile menhaden and Spanish mackerel, experience the strongest\nbiomass reductions, consistent with bottom-up nutrient limitation. In contrast, some higher\ntrophic-level piscivores (e.g., billfish, cobia, other tunas) show milder responses. These results\nhighlight an overall contraction at the base and mid-levels of the food web, with\nheterogeneous responses among top predators.”.\nDiscussion (lines 219-224): “Our projections further suggest that drought impacts are not\nuniform across functional groups: lower trophic levels such as invertebrates, shrimp, and\nplanktivorous fish show the strongest biomass declines, while some higher trophic predators\nexhibit smaller or even modestly positive responses. This pattern highlights that drought-\ninduced nutrient limitation primarily contracts lower trophic levels, with cascading\nconsequences for energy transfer and ecosystem resilience.”.\nIt is unclear why the study used catches rather than catch-per-unit-effort (CPUE) as a dependent\nvariable. Therefore, it should be explained why catch rates, which account for changes in fishing\neffort, were not considered in the methods section.\nWe thank the reviewer for raising this point. In our analysis, we used catch rather than\nCPUE because our primary aim was to evaluate total fisheries production in the Gulf of\nMexico under varying environmental and fishing scenarios, rather than to assess fishery-\nspecific catch rates, which is a standardized measure. Catch integrates both stock\nabundance and fishing effort, providing a measure of the total biomass removed from the\necosystem, which is directly comparable across functional groups in the model. In contrast,\nCPUE data, while useful for tracking trends in stock abundance, were not consistently\navailable for all functional groups or over the full historical period considered, and existing\ntime series varied in spatial coverage, sampling methods, and standardization procedures.\nWhere standardized CPUE indices were available (e.g., Gulf menhaden), we used them\nqualitatively to validate biomass trends, but they were not suited for inclusion as a uniform\ndependent variable across all groups. We have clarified this rationale in the Methods\nsection (lines 566-571) of the revised manuscript as follows: “We used catch rather than\ncatch-per-unit-effort (CPUE) because our goal was to evaluate total fisheries production\nacross the ecosystem, not standardized catch rates. Catch reflects the total biomass removed,\nmaking it directly comparable across functional groups, whereas CPUE data were\ninconsistent in availability, standardization, and coverage. Where available (e.g., Gulf\nmenhaden), CPUE indices were used qualitatively to validate biomass patterns but not as\ndependent variables.”.\nMinor points\nLine 19: A ‘major depletion of fisheries’ should be better quantified. What constitutes a ‘major’\ndepletion? I suggest the authors include the percentage reduction in fisheries production relative\nto the reference period.\nWe thank the reviewer for this suggestion. In the revised manuscript, we now quantify the\n“major depletion” as a ~42% reduction in total fisheries production during the late-1980s\ndrought, relative to the reference period (average production for 1982–1986). This value is\nstated in the abstract to provide a clear, quantitative measure of depletion.\nLine 40: The authors should include a sentence here to explain that previous studies have shown\nthat variation in river flow impacts fisheries production in the GoM mainly via changes in\ntrophic dynamics rather than habitat availability. A trophic-driven mechanism for fisheries\nproduction is particularly important in the GoM, because this oligotrophic coastal region receives\nlimited nutrient supplies from oceanic currents and upwelling events (Wissel and Fry, 2005).\nThis would help the reader to understand why the study focused on changes in trophic dynamics\nas the mechanism underling the relationships between river flow and fisheries production in the\nGoM.\nWe thank the reviewer for this helpful suggestion. In the revised manuscript, we have\nadded a sentence in the Introduction noting that previous studies have shown variation in\nriver flow affects Gulf of Mexico fisheries production primarily through changes in trophic\ndynamics (Wissel and Fry, 2005 – reference #20). We also note that this trophic-driven\nmechanism is particularly important in the oligotrophic GoM, where nutrient supplies\nfrom oceanic currents and upwelling are limited. This point was added to the introduction\nsection (lines 60-68) as follows: “River flow has long been recognized as a “master variable”\nregulating marine and estuarine ecosystems, influencing habitat availability, trophic\ndynamics, and fishers’ harvesting behavior 15–19. Variability in river discharge controls\nnutrient delivery, sediment transport, and salinity gradients, all of which shape the\nproductivity and distribution of marine organisms. In the GoM, a system otherwise strongly\noligotrophic with limited nutrient input from oceanic currents or upwelling, riverine nutrient\ninputs are especially critical for sustaining fisheries yields 20. This highlights a trophic-driven\nmechanism by which drought-induced reductions in river flow limit nutrient availability,\ncascading through the food web to affect forage fish, predators, fish migration and\ncomposition, and ultimately fisheries production.”.\nLines 60-65: The authors should better quantity the declines in biomass and catches. What was\nthe decline in biomass and catches in percentage terms relative to the reference period?\nWe thank the reviewer for this helpful suggestion. In the revised manuscript, we now\nquantify these changes explicitly. In the Introduction (lines 70–72), we report the\npercentage declines in biomass and catches and direct readers to the Results section for\nfurther details. The added text reads: “During this period, cumulative biomass declined by\n42% and cumulative catch by 34% relative to the 1982–1986 reference period, highlighting the\nseverity of this system-wide depletion.”.\nLine 72: Previous studies describing the impacts of drought on marine fisheries production\nshould be cited here (e.g., Gillanders and Kingsford 2002; Gillson, 2011; Broadley et al., 2022).\nWe thank the reviewer for this suggestion. In the revised manuscript, we have added the\nrecommended citations (Gillanders and Kingsford, 2002; Gillson, 2011; Broadley et al.,\n2022 – references #15-19) to strengthen the background on drought impacts on marine\nfisheries production. The added Introduction text (lines 60-68) reads as follows: “River\nflow has long been recognized as a “master variable” regulating marine and estuarine ecosystems,\ninfluencing habitat availability, trophic dynamics, and fishers’ harvesting behavior 15–19.\nVariability in river discharge controls nutrient delivery, sediment transport, and salinity\ngradients, all of which shape the productivity and distribution of marine organisms. In the\nGoM, a system otherwise strongly oligotrophic with limited nutrient input from oceanic\ncurrents or upwelling, riverine nutrient inputs are especially critical for sustaining fisheries\nyields 20. This highlights a trophic-driven mechanism by which drought-induced reductions in\nriver flow limit nutrient availability, cascading through the food web to affect forage fish,\npredators, fish migration and composition, and ultimately fisheries production.”.\nLines 73-76: If drought-induced increases in salinity can stress invertebrates in the GoM,\nthen this leads to the question why were these mechanisms not evaluated in the current study?\nIt should be explained why this study focused on changes in trophic dynamics and not habitat\navailability. Kimmerer (2002) provides an interesting evaluation of the effects of river flow on\nestuarine fish via physical and trophic mechanisms.\nWe thank the reviewer for this comment and for highlighting Kimmerer (2002). In the\nrevised manuscript, we have clarified in the Introduction that our study focused on trophic\ndynamics rather than habitat availability because nutrient-driven changes in productivity\nare the primary mechanism linking river flow to fisheries production in the oligotrophic\nGoM. While drought-induced salinity increases can affect invertebrates, our approach did\nnot explicitly model habitat–salinity interactions. Importantly, our ability to reproduce the\nobserved dynamics based solely on nutrient flow supports the principle of parsimony in\nmodel design. We now note in the Methods (lines 825-828) as follows: “In addition, we did\nnot explicitly incorporate salinity–habitat interactions in our modeling framework. While\nsalinity increases during droughts can stress estuarine-dependent invertebrates (e.g., oysters,\nshrimp)19, the ecosystem-wide declines observed here were reproduced based solely on nutrient\nforcing, supporting a parsimonious focus on trophic mechanisms.”.\nLines 77-79: Once again, it should be made clear why this study focused on the impacts of river\nflow on fisheries production in the GoM via trophic dynamics rather than habitat availability.\nThe previous paragraph states that the physical effects of river flow on invertebrates can be\nimportant in the study area, but the next paragraph ignores this point.\nWe thank the reviewer for this helpful clarification request. In the revised text, we now\nexplicitly state that river flow can influence fisheries production in the Gulf of Mexico\nthrough both physical (habitat–salinity) and trophic pathways. However, we clarify that\nthis study focuses on trophic dynamics for three reasons: (i) nutrient-driven productivity is\nwidely recognized as the dominant mechanism in this oligotrophic system, where oceanic\nnutrient supplies are limited (e.g., Wissel and Fry, 2005), (ii) our ecosystem modeling\nframework (EwE) is specifically designed to represent trophic interactions rather than\nspecies-specific habitat–salinity responses, and (iii) the long-term datasets most consistently\navailable are those describing nutrient and productivity signals. We also acknowledge that\nsalinity-driven habitat effects represent an important complementary mechanism, and we\nnote this explicitly in the Introduction (lines 87–93) as follows: “In this study, we focus on\nthe trophic pathway for three main reasons. First, nutrient-driven productivity is widely\nrecognized as the dominant mechanism linking river flow to fisheries in this oligotrophic\nsystem, where oceanic nutrient supplies from upwelling or currents are limited. Second, our\nanalytical framework is designed to represent trophic interactions at the ecosystem scale,\nrather than species-specific habitat–salinity responses. Third, the available long-term data are\nmost consistent for nutrient inputs and productivity signals, making them more suitable for\nquantitative analysis.”\nLine 93: Please either define the abbreviation ‘MS’ or spell it out in full.\nWe thank the reviewer for noting this. In the revised manuscript, we have spelled out\n“Mississippi (MS)” in full at first use to ensure clarity for all readers, followed by the\nabbreviation in parentheses for subsequent mentions.\nLine 105 (and elsewhere): All percentages should be presented to one decimal place to ensure\nconsistency throughout the paper.\nWe thank the reviewer for this comment. In the revised manuscript, we have reformatted\nand rounded all percentage values to zero decimal places to ensure consistency throughout\nthe text.\nLine 140: Add a comma after ‘(Fig. 2e,f)’.\nWe thank the reviewer for this suggestion. In the revised manuscript, we have added the\ncomma after “(Fig. 2e,f)” as recommended.\nLine 144: This needs more explanation. Why would the effect of menhaden fishing mortality\ndecrease? If drought reduces the biomass of menhaden and its associated predators, then the\nproportion of these stocks being exploited by fisheries would increase assuming status quo\nfishing effort (i.e., the F value would increase at smaller stock sizes under constant fishing\neffort).\nWe thank the reviewer for raising this point. In our Ecosim simulations, the reduced effect\nof menhaden fishing mortality under drought arises from the foraging‐arena dynamics and\nthe strong bottom-up constraint imposed by nutrient limitation. Under severe drought\nconditions, menhaden biomass remained low even at F=0, indicating that stock recovery\nwas prevented by food limitation rather than fishing pressure. At low stock sizes,\ncompensatory mechanisms (enhanced survival and growth) further reduce the sensitivity of\nthe stock to fishing, preventing complete collapse. This resilience to fishing mortality is\nconsistent with Gulf menhaden stock assessments and reflects their short-lived life history\nand fishery selectivity patterns. Consequently, even under unchanged effort or F,\nabsolute\nremovals and the ecosystem-level footprint of fishing diminish relative to the dominant\nbottom-up constraint imposed by drought. We have added a brief explanation of this\nmechanism in the Results (lines 163-166) to clarify why the relative influence of F declines\nwhen productivity and biomass are depressed as follows: “This reduced influence of fishing\nmortality reflects the strong bottom-up limitation imposed by nutrient input, whereby\nmenhaden biomass remains low even when F is set to 0, and compensatory mechanisms\nprevent collapse at low stock sizes. As a result, absolute removals and the ecosystem-level\nfootprint of fishing diminish relative to the dominant drought effect.”.\nLine 161: Clarify what is being affected here. ‘The effect of variation in Gulf menhaden fishing\nmortality on XYZ…’\nWe thank the reviewer for noting this ambiguity. In the revised manuscript, we now clarify\nthat this refers specifically to “the effect of variation in Gulf menhaden fishing mortality on\nthe biomass and catch of the GoM ecosystem, based on the examined functional groups.\nLine 177: It is worth noting here that droughts can impact fishers’ harvesting behaviour.\nGillson (2011) describes that river flow can indirectly impact fisheries production by altering\nthe harvesting behaviour of commercial fishers.\nWe thank the reviewer for this observation. In the revised manuscript, we now note that\ndroughts can also influence fishers’ harvesting behavior. Freshwater flow can indirectly\naffect fisheries production by altering the behavior of commercial fishers, who respond to\nvariable flows by opportunistically exploiting changes in species catchability. For example,\ncatchability of estuarine migrant species can increase during floods, while marine\nestuarine-opportunist species may be more catchable during droughts. Recognizing this\nbehavioral component is important for understanding the full relationship between\nfreshwater flow and fisheries production, as it complements the ecological responses\nconsidered in our analysis. We have incorporated this perspective into the text and cited\nGillson (2011) accordingly. The added text (lines 212-219) reads as follows: “Beyond\necological processes, droughts can also influence fishers’ harvesting behavior, as variability in\nfreshwater flow alters species catchability and may drive commercial fishers to adjust their\ntargeting strategies17. Similar drought-induced shifts in species composition and fishing\npractices have been documented elsewhere; for example, in eastern Australia, drought\nconditions shifted landings from estuarine migrant species to marine estuarine-opportunist\nspecies, accompanied by adaptive changes in effort and methods18. This behavioral\ndimension, though not represented in our model, likely interacts with ecological responses and\nadds an additional pathway by which drought affects fisheries production.”\nLines 212-213: It would be worth citing previous studies that have documented drought-induced\nchanges in species composition in marine ecosystems (e.g., Gillson et al., 2012).\nWe thank the reviewer for this suggestion. In the revised manuscript, we now cite Gillson\net al. (2012), which showed that drought can shift fisheries landings from estuarine migrant\nspecies to marine estuarine-opportunist species. The added text in the Introduction (lines\n60-68) reads as follows: “River flow has long been recognized as a “master variable”\nregulating marine and estuarine ecosystems, influencing habitat availability, trophic\ndynamics, and fishers’ harvesting behavior 15–19. Variability in river discharge controls\nnutrient delivery, sediment transport, and salinity gradients, all of which shape the\nproductivity and distribution of marine organisms. In the GoM, a system otherwise strongly\noligotrophic with limited nutrient input from oceanic currents or upwelling, riverine nutrient\ninputs are especially critical for sustaining fisheries yields 20. This highlights a trophic-driven\nmechanism by which drought-induced reductions in river flow limit nutrient availability,\ncascading through the food web to affect forage fish, predators, fish migration and\ncomposition, and ultimately fisheries production.”.\nLine 214: Amend this sentence as follows: ‘The declines in biomass and catches projected in our\nstudy…’.\nWe have amended the sentence as requested.\nLine 221: Are there also projections of how onshore and offshore wind might change in the\nfuture? Wind conditions influence nutrient inputs by regulating upwelling and also affect the\ndistribution of pelagic fish larvae.\nWe thank the reviewer for raising this important point. Projections of future wind\nconditions, both onshore and offshore, are available from regional climate modeling\nstudies, and these can indeed have important implications for the Gulf of Mexico\necosystem, particularly in the west Florida shelf region, which is characterized by wind-\ndriven upwelling. While our study did not explicitly incorporate wind projections, we agree\nthat integrating this factor, especially under future climate scenarios, would provide a\nmore comprehensive understanding of potential changes in productivity and recruitment.\nWe note that this effect is expected to be more pronounced in major wind-driven upwelling\nregions, such as the Eastern Boundary Upwelling Systems (e.g., California, Peru–Chile,\nand Benguela/Namibia). We have added this point to the Discussion (lines 258-264) as\nfollows: “In addition to river flow, future changes in wind conditions could affect nutrient\ndelivery and recruitment in the GoM. Regional climate projections indicate shifts in onshore\nand offshore winds, which regulate upwelling and larval dispersal, particularly along the west\nFlorida shelf. While not included in our simulations, incorporating wind forcing under\nclimate change scenarios would improve ecosystem projections. Such effects are expected to\nbe especially pronounced in major upwelling systems worldwide (e.g., California, Peru–Chile,\nBenguela/Namibia)46.”.\nLine 235: Replace ‘Gulf of Mexico’ with ‘GoM’, because this abbreviation has previously been\nused earlier in the paper.\nWe thank the reviewer for pointing this out. We have replaced “Gulf of Mexico” with\n“GoM” at line 235 to maintain consistency with the abbreviation used earlier in the\nmanuscript.\nSuggested additional citations\nBroadley, A., Stewart-Koster, B., Burford, M.A. and Brown, C.J. 2022. A global review of the\ncritical link between river flows and productivity in marine fisheries. Reviews in Fish Biology\nand Fisheries 32(3): 805-825.\nGillanders, B. M., and M. J. Kingsford. 2002. Impact of changes in flow of freshwater on\nestuarine and open coastal habitats and the associated organisms. Oceanography and Marine\nBiology 40: 233–309.\nGillson, J. 2011. Freshwater flow and fisheries production in estuarine and coastal systems:\nWhere a drop of rain is not lost. Reviews in Fisheries Science 19(3): 168–186.\nGillson, J., Suthers, I. and Scandol, J. 2012. Effects of flood and drought events on multi‐species,\nmulti‐method estuarine and coastal fisheries in eastern Australia. Fisheries Management and\nEcology 19(1): 54-68.\nKimmerer, W. J. 2002. Effects of freshwater flow on abundance of estuarine organisms: Physical\neffects or trophic linkages? Marine Ecology Progress Series 243: 39–55.\nWissel, B. and B. Fry. 2005. Tracing Mississippi River influences in estuarine food webs of\ncoastal Louisiana. Oecologia 144: 659–672.\nWe thank the reviewer for these valuable suggestions. We have incorporated the\nrecommended citations into the revised manuscript.\nReferences\nBerenshtein, I., Sagarese, S. R., Lauretta, M. V, Nuttall, M. A., & Chagaris, D. D. (2021).\nTechnical documentation of a US Gulf of Mexico Ecopath with Ecosim model. NOAA\nTechnical Memorandum. https://doi.org/10.25923/zj8t-e656\nBerenshtein, I., Sagarese, S. R., Lauretta, M. V, Schueller, A. M., & Chagaris, D. D. (2023).\nIdentifying trade-offs and reference points in support of ecosystem approaches to managing\nGulf of Mexico menhaden. Frontiers in Marine Science, 9, 935324.\nGarcía, A. M., Alexander, R. B., Arnold, J. G., Norfleet, L., White, M. J., Robertson, D. M., &\nSchwarz, G. (2016). Regional Effects of Agricultural Conservation Practices on Nutrient\nTransport in the Upper Mississippi River Basin. Environmental Science & Technology,\n50(13), 6991–7000. https://doi.org/10.1021/acs.est.5b03543\nLu, C., Zhang, J., Tian, H., Crumpton, W. G., Helmers, M. J., Cai, W.-J., Hopkinson, C. S., &\nLohrenz, S. E. (2020). Increased extreme precipitation challenges nitrogen load\nmanagement to the Gulf of Mexico. Communications Earth & Environment, 1(1), 21.\nhttps://doi.org/10.1038/s43247-020-00020-7\nIgal Berenshtein\nDepartment of Marine Biology\nCharney School of Marine Sciences\nUniversity of Haifa\nAbba Khoushy Ave 199, Haifa, 3498838\niberensht@univ.haifa.ac.il\nMobile phone +972-54-533-8837\nPoint-by-point response to the reviewer’s comments\nREVIEWERS' COMMENTS\nReviewer #3 (Remarks to the Author):\nThis paper examines the relationship between drought conditions and fisheries production in the\nGulf of Mexico using an Ecopath with Ecosim modelling framework. They have put effort into\naddressing the comments of all reviewers in multiple rounds of review. They have addressed and\nclarified all the questions I had (clarifying methods, acknowledging limitations and pointing to\nopportunities for future work) and I think the paper is now ready for publication.\nWe thank the reviewer for the positive assessment and appreciation of the revisions. We\nare pleased that the manuscript is now considered ready for publication.\nReviewer #3 (Remarks on code availability):\nI have not tried to execute the code, but I have read over it and the logic and content makes sense\nto me with no immediately obvious issues.\nWe thank the reviewer for reviewing the code and for the positive feedback.\nReviewer #4 (Remarks to the Author):\nIn the revised paper, the authors have satisfactorily addressed the comments and suggestions\npreviously provided by myself. A more comprehensive description of the mechanisms\nunderlying the impacts of drought on marine fisheries production reported in the scientific\nliterature has been provided. In addition, the authors have also included further details on the\nprojected impacts of future droughts on the functional groups in the Gulf of Mexico.\nCongratulations to the authors for taking onboard the feedback in the revised paper and for\nproducing a laudable study that advances our understanding of climatic impacts on marine\nfisheries production due to changes in freshwater nutrient inputs.\nI have provided some additional, very minor comments that are typographical in nature below.\nOnce addressed, I believe that this paper is suitable for publication in Nature Communications.\nWe thank the reviewer for the encouraging remarks and for identifying the minor issues\nlisted below. All have now been corrected.\nMinor points\nLine 164: As ‘F’ has not been previously defined, please amend this sentence to include the word\n‘fishing mortality’ as follows: ‘…when fishing mortality (F) is set to 0…’.\nCorrected as requested. The sentence now reads:\n“…when fishing mortality (F) is set to 0…”\nLine 230: The latter part of sentence does not make sense because it is missing a linking word.\nPlease therefore insert the word ‘and’ before ‘…may have contributed…’.\nCorrected. The sentence now includes the linking word “and”.\nLines 236-237: Delete ‘Fulton and Smith, 2004’, so the citation is just the number ‘39’.\nDone. The citation now appears solely as numerical reference 39.\nLine 671: Ensure that the words ‘Highly Migratory Species’ are all in lowercase font.\nCorrected to lowercase (“highly migratory species”).\nReviewer #4 (Remarks on code availability):\nI reviewed the R code and it looked eloquently compiled, reproducible, and straightforward to\nrun.\nWe thank the reviewer for taking the time to evaluate the code and for the positive\ncomments."}, {"round": 2, "reviewer_comments": {"reviewer_2": "In the revision of the MS by Berenshtein et al., the authors responded to the comments made by myself and another reviewer. Both reviews, offered important critiques to the study, especially about model inference and interpretation, and the confounding factors that go along with it, e.g., hypoxia. R1 made comments about hindcasting and using a sensitivity approach to test for the reliability of the model, I agree with R1, and I commend the author’s for taking that approach. Along with addressing that big suggestion, I though the author’s did a great job addressing the other comments. It is clear that they carefully took the time to fully address my issues, and given the novelty of the approach and the big message for climate change I believe that this paper is suitable for publication in Nature Communications. Good luck!\n(Remarks on code availability)", "reviewer_3": "Review Berenshtein et al - Will mega-droughts in the US drive the collapse of fisheries in the Gulf of Mexico?\nThis paper looks at the relationship between drought conditions and fisheries production in the Gulf of Mexico. These relationships are reproduced in an ecosystem model that is then used at the implications of climate change driven droughts into the future.\nThe authors have obviously put a lot of effort into revising the paper based on previous feedback. The paper is well written and there are no typos or minor changes like that required. However, I feel there is some further clarification required in a few spots as described below.\n• Line 105 “decline of 16% in the relative fishing effort by the purse menhaden fleet”: Is there any tech creep to consider?\n• Line 116: Because the methods are at the end, please explain what it is on first use as s not all readers will know.\n• Line 135-138 “The effect of Gulf menhaden fishing mortality on the cumulative biomass and catch dynamics is smaller compared to the effect of anticipated droughts (Fig. 2c), suggesting that the ability of the ecosystem to compensate for menhaden harvest is compromised under drought conditions.”: Have you tried to run it with the broken stick harvest control rule in closed loop mode to see if that helps recovery time? What about effort drops as seen in the historical period?\n• Line 146: Rationale for the specific x2 increase?\n• Line 149-150 “In contrast, the biomass of the competitors of these groups (e.g., cephalopods), increases (Fig. 3a).”: Are technical interactions accounted for?\n• Line 213 “jellyfish, become more dominant”: Possible to comment on cascading implications?\n• Line 231-233 “This is supported, for example, by the low cumulative biomass and catches during 1988 (Fig. 1), which was characterized by the lowest nutrient input, and consequently lowest hypoxic area during the considered period25 (supplementary section S2, Figs. S1-2).”: This is an argument by absence rather than unpacking of attribution when both present, not as convincing.\n• General comment on the end of the discussion: Would it be fair to characterise the results as an example of the pattern of the forcing function on production being amplified up the model vs less F effects? This pattern of differential sensitivity has been seen before in EwE comparative work (e.g. Fulton and Smith 2004 Afr. J. Mar. Sci. 26: 219–243). If it is this might be a point for flagging caution and uncertainty around the results of the work.\n• Line 411 “1982-1986 and 1988-1992”: What is the comparability of environmental conditions and human use in general (so can isolate the drought effect)?\n• Line 415 “computations of cumulative biomass”: This is aggregate biomass correct? It is likely fine as it is but aggregate is probably the more commonly used term.\n• Line 429 “Discards for other fisheries were not incorporated into catch time series.”: Implications? Estimate of how big a biomass take this was? Some groups effected more than others?\n• Line 464-465 “linear least squares regression to model the relationship between historical nutrient input and the Palmer Drought Severity Index”: What about potential for changed agricultural practices due to hypoxia concern? Implications of land use from ISIMIP (https://www.isimip.org/) runs?\n• Line 486: Why no Ecospace? And in Ecosim were environmental relationships used? [I can see from the supplied model the answer is no, but please state this in the paper]\n• Line 505 “primary productivity anomaly”: Interaction with drought year reproduction?\n• Line 533-534 “MEF values (see supplementary section S5, supplementary Table S5), demonstrating the overall efficacy of the model”: Given the topic discussed here what was it for menhaden and closely related species?\n• Line 556-558 “The primary productivity and the free nutrient concentrations are linked through a Michaelis-Menten uptake relationship, in which nutrient uptake increases asymptotically up to a maximum in response to increasing free nutrient concentrations”: How exactly was it entered into Ecosim? Straight primary productivity scalar or via an environmental response relationship or ?\n• Line 559: Ecosim simulations: State the length of the simulation. Just the drought period or longer? How long was the elevated F? Entire run just 5 years or are these periods layered into longer runs? How is transitional change under directed forcing allowed for (background climate driven shift)\n• Line 628 “purse seine effort”: How was this done? Or are you saying that is what the changing F represents? Was it done via effort time series manipulation or F forcing?\n• Line 636-639 “These models, like others, encompass inherent uncertainty and are based on assumptions or best available data concerning the species biology and life history. However, they do represent the only source of absolute biomass for the entire GoM.”: We all do it, but not great to condition a model with another model. Was there any cross check with data, like standardised cpue or survey data etc?\n• Line 652-653 “the positive contribution of nutrient input on fisheries production outweighs the negative effect from hypoxic conditions.”: Yes but what happens in years with low inflow and low nutrient input? Less hypoxia and related mortality along with the nutrient drop or is there a lag so get hypoxia as well as the nutrient drop at least in the first year/s of the drought?\n• HMS. Perhaps just say Highly Migratory Species. It’s not used a lot and not sure it adds a lot to use the acronym in terms of ease of reading the text.\n• Figure 1: All useful information but so much of the story is hard to discern. The drought period doesn’t look enormously exceptional beyond 1988 so it would be good to see the other years also represented in panels e-h perhaps red bold 1988 and other drought period years in unbolded red? Then pull the menhaden time series for the biomass, catch and effort out form the rest and present them separately. Its ok to present the rest of the groups as a bulk presentation here but it would also be good to have individual time series for each (e.g. using facet wrap style set of panels for the individual time series) in the supplementary materials. Area plot for effort perhaps more useful?\n• Figure 2 line 694 “ with dashed lines indicating “: Say horizontal dashed lines.\n• Table S5: Let the reader know what is good value in general (e.g. close to 1) vs what is poor\n• Supplementary materials a typo for “Mississippi reiver”. Should be river.\n• Figure S2: Explain the period highlighted and why when highlighted drought period in the main text figures. Also F (other than on 1 Yr olds) stayed high even as effort dropped. Why and what does that mean for signature assumed into bycatch species in the runs?\n• Supplementary materials “While the dynamics of the post-1990 years align with the expectation that as biomass increases, fishing effort should decrease, and vice versa, the dynamics during the 1980s appear counterintuitive, as biomass decreased in parallel with a decrease in fishing effort, supporting the dominance of an external environmental force such as nutrient input deficiency.”: Could this be a case of effort reduction in response to biomass drop, so start high together and then effort reduced in response to drop in biomass, with effort reduction continuing on into later period where stocks then rebuilt?\n• Supplementary materials “Model quality was demonstrated in good agreement between model predicted biomass”: Plots possible so reader gets a sense of what this looks like?\n• Supplementary materials “model predicted estimates of the fishing mortality rate corresponding to the maximum sustainable yield (FMSY) and FMSY estimates from stock assessments.”: Estimated in Ecosim/Ecopath how? Multiple ways this can be done.\n• Supplementary materials “As noted in the main text, the relative impact of fishing mortality diminishes as drought-related effects on biomass intensify.”: As F not effort so catch drops in step with productivity? Any consideration of whether could become synergistic if lag in F response? Also bigger cumulative effect if rate of F in place longer before drought hits? Also date of model base F vs 1988 F?\n(Remarks on code availability)", "reviewer_4": "A well written paper on a topic of great interest to scientists and managers. This work advances our understanding of climatic impacts on marine ecosystems and fisheries due to changes in freshwater nutrient inputs. The authors evaluate the impacts of severe drought, combined with fishing pressure for menhaden, on fisheries production in the Gulf of Mexico (GoM). Firstly, the authors show that a prolonged and severe drought during the 1980s, combined with high fishing pressure for menhaden, led to a depleted forage base, with cascading effects on other species and fisheries in the GoM. Secondly, the authors use an ecosystem model to project changes in future fisheries production in the GoM under simulated drought conditions.\nOverall, this is a laudable paper that covers an important topic on the impacts of drought on fisheries production in the GoM. I applaud the authors for using an ecosystem model to evaluate climatic impacts on marine ecosystems and fisheries. The methodology is well described and sound, and the findings support the conclusions in the paper.\nWith that said, I found the paper to be lacking in its description of the mechanisms underlying the impacts of drought on marine fisheries production published in the scientific literature. Furthermore, the paper would benefit from providing more detailed information on the projected impacts of future droughts on the functional groups in the GoM. Nevertheless, the paper is worth publishing through a more focused route with consideration of the comments and suggestions offered below.\nMajor points\nIn the introduction, the mechanisms underlying the impacts of drought on marine fisheries production should be better described. Previous studies have shown that river flow is a ‘master variable’, which has a controlling influence on marine fisheries production by regulating habitat availability, trophic dynamics, and fishers’ harvesting behaviour. Several key references are missing and should be included in the paper (see the suggested additional citations at the end of this review).\nIt has been demonstrated that a trophic-driven mechanism for fisheries production is particularly important in the GoM, which is an oligotrophic coastal region that receives limited nutrient supplies from oceanic currents and upwelling events (Wissel and Fry, 2005). The inclusion of this information would help the reader to understand why the study focused on flow-induced changes in nutrient inputs rather than habitat availability.\nMore information on the projected impacts of future droughts on the functional groups in the GoM would be beneficial. For example, how did the model projections suggest that more severe droughts will affect species composition – should we expect less invertebrates and more piscivorous fish in the future?\nIt is unclear why the study used catches rather than catch-per-unit-effort (CPUE) as a dependent variable. Therefore, it should be explained why catch rates, which account for changes in fishing effort, were not considered in the methods section.\nMinor points\nLine 19: A ‘major depletion of fisheries’ should be better quantified. What constitutes a ‘major’ depletion? I suggest the authors include the percentage reduction in fisheries production relative to the reference period.\nLine 40: The authors should include a sentence here to explain that previous studies have shown that variation in river flow impacts fisheries production in the GoM mainly via changes in trophic dynamics rather than habitat availability. A trophic-driven mechanism for fisheries production is particularly important in the GoM, because this oligotrophic coastal region receives limited nutrient supplies from oceanic currents and upwelling events (Wissel and Fry, 2005). This would help the reader to understand why the study focused on changes in trophic dynamics as the mechanism underling the relationships between river flow and fisheries production in the GoM.\nLines 60-65: The authors should better quantity the declines in biomass and catches. What was the decline in biomass and catches in percentage terms relative to the reference period?\nLine 72: Previous studies describing the impacts of drought on marine fisheries production should be cited here (e.g., Gillanders and Kingsford 2002; Gillson, 2011; Broadley et al., 2022).\nLines 73-76: If drought-induced increases in salinity can stress invertebrates in the GoM, then this leads to the question why were these mechanisms not evaluated in the current study? It should be explained why this study focused on changes in trophic dynamics and not habitat availability. Kimmerer (2002) provides an interesting evaluation of the effects of river flow on estuarine fish via physical and trophic mechanisms.\nLines 77-79: Once again, it should be made clear why this study focused on the impacts of river flow on fisheries production in the GoM via trophic dynamics rather than habitat availability. The previous paragraph states that the physical effects of river flow on invertebrates can be important in the study area, but the next paragraph ignores this point.\nLine 93: Please either define the abbreviation ‘MS’ or spell it out in full.\nLine 105 (and elsewhere): All percentages should be presented to one decimal place to ensure consistency throughout the paper.\nLine 140: Add a comma after ‘(Fig. 2e,f)’.\nLine 144: This needs more explanation. Why would the effect of menhaden fishing mortality decrease? If drought reduces the biomass of menhaden and its associated predators, then the proportion of these stocks being exploited by fisheries would increase assuming status quo fishing effort (i.e., the F value would increase at smaller stock sizes under constant fishing effort).\nLine 161: Clarify what is being affected here. ‘The effect of variation in Gulf menhaden fishing mortality on XYZ…’\nLine 177: It is worth noting here that droughts can impact fishers’ harvesting behaviour. Gillson (2011) describes that river flow can indirectly impact fisheries production by altering the harvesting behaviour of commercial fishers.\nLines 212-213: It would be worth citing previous studies that have documented drought-induced changes in species composition in marine ecosystems (e.g., Gillson et al., 2012).\nLine 214: Amend this sentence as follows: ‘The declines in biomass and catches projected in our study…’.\nLine 221: Are there also projections of how onshore and offshore wind might change in the future? Wind conditions influence nutrient inputs by regulating upwelling and also affect the distribution of pelagic fish larvae.\nLine 235: Replace ‘Gulf of Mexico’ with ‘GoM’, because this abbreviation has previously been used earlier in the paper.\nSuggested additional citations\nBroadley, A., Stewart-Koster, B., Burford, M.A. and Brown, C.J. 2022. A global review of the critical link between river flows and productivity in marine fisheries. Reviews in Fish Biology and Fisheries 32(3): 805-825.\nGillanders, B. M., and M. J. Kingsford. 2002. Impact of changes in flow of freshwater on estuarine and open coastal habitats and the associated organisms. Oceanography and Marine Biology 40: 233–309.\nGillson, J. 2011. Freshwater flow and fisheries production in estuarine and coastal systems: Where a drop of rain is not lost. Reviews in Fisheries Science 19(3): 168–186.\nGillson, J., Suthers, I. and Scandol, J. 2012. Effects of flood and drought events on multi‐species, multi‐method estuarine and coastal fisheries in eastern Australia. Fisheries Management and Ecology 19(1): 54-68.\nKimmerer, W. J. 2002. Effects of freshwater flow on abundance of estuarine organisms: Physical effects or trophic linkages? Marine Ecology Progress Series 243: 39–55.\nWissel, B. and B. Fry. 2005. Tracing Mississippi River influences in estuarine food webs of coastal Louisiana. Oecologia 144: 659–672.\n(Remarks on code availability)"}, "author_response": "Igal Berenshtein\nDepartment of Marine Biology\nCharney School of Marine Sciences\nUniversity of Haifa\nAbba Khoushy Ave 199, Haifa, 3498838\niberensht@univ.haifa.ac.il\nMobile phone +972-54-533-8837\nPoint-by-point response to the reviewer’s comments\nReviewer #1 (Remarks to the Author):\nThis paper is a well written and timely evaluation of the impacts of climate change on future\nGoM ecosystem and fishery productivity. It is unique in the aspect that it connects climate\nimpacts on land (drought) to those in marine systems via changes in freshwater nutrient inputs to\nGoM. This is an often postulated interaction (i.e., climate impacts on land and water will impact\nmarine systems) but there is a paucity of published papers that demonstrate or evaluate the\nphenomenon. The authors use a combination of a nutrient-forced mass-balance ecosystem model\n(EwE) and mean future climate projections of a drought index (PDSI) from an ensemble of 20\nGeneral Circulation Models (GCMs) representing a subset of CMIP5 ensemble members under\nthe high baseline Representative Carbon Pathway (RCP 8.5). The authors derived a linear\nrelationship between historical nutrients and historical PDSI values and used the nutrient\nrelationship to drive bottom up productivity changes in the EwE model.\nThe authors then force the EwE model with profiles across ranges in nutrient changes (0 -2 fold\n2014-2016 average nutrient levels) and ranges in fishing pressure (0-5 x average F from 2014-\n2016). This represents an innovative application of an approach that has been applied\nsuccessfully to evaluate potential climate change impacts in other systems, albeit with different\nclimate couplings (e.g., where temperature or zooplankton drive productivity of EwE models;\nsensu https://doi.org/10.1111/gcb.13471 ). In general this is the type of analysis that can be vital\nfor risk assessments of climate change impacts on marine fisheries as well as cumulative impacts\nof changes on land on marine ecosystems. In general, I strongly support this type of approach,\nand applaud the authors for using an ecosystem model (which captures indirect and cascading\nimpacts) to evaluate climate change impacts on marine species. This can be a more robust\napproach than a direct climate-linked single species\nmodeling effort.\nHowever, there are a number of fundamental issues with the paper that I believe preclude it from\nbeing published until it is revised. I hope the authors are able to correct and address these issues\nso it can be used to advance understanding of climate change risk to marine ecosystems and\nfisheries.\nWe thank the reviewer for the positive comments. In the revised manuscript, we have\nthoroughly addressed the concerns raised by incorporating additional analyses, integrating\nnew data, and expanding the relevant sections as needed. We have also included the\ncitation of Woodworth-Jefcoats et al. (2017) in the text to enhance context and support our\nfindings.\nThe largest concern I have is that the paper is predicated on a linkage between droughts and\nhistorical declines in GoM catches that the authors do not demonstrate and which is not\nsupported by the cited peer-reviewed papers (nor by the large number of non-peer reviewed\ntechnical reports that are cited). The authors postulate that a long (~3 year) drought in the mid- to\nlate 1980’s induced a decline in biomass and catch that is reflected in the timeseries for both.\nThese are plotted in Figure 1 (a) (e) and (f). However, one quickly notes that there are similar\nlarge drops in the PDSI index in ~2000 and ~2012 but no similar subsequent drop in catch or\nbiomass timeseries as was seen in the late 1980’s. In the discussion section (lines 160-172) the\nauthors try to address this and explain it as a difference in drought length (3 yrs for the earlier\ndrought versus ~1 year for the others) and timing. This section is very speculative, without any\ncitations save for one at the end of the paragraph stating that the severity of the drought can be\nrelated to the contrast between preceding wet years. In fact the largest concern I had in reading\nthis section was that there was no mention of possible changes in fishery management or markets\nthat could also explain the decline in catch.\nWe appreciate the reviewer's insightful comments regarding the linkage between droughts\nand historical declines in Gulf of Mexico (GoM) catches. As noted in our manuscript, we\nhypothesize that significant ecosystem impacts, reflected in declines in biomass and catches,\nwould primarily occur following prolonged severe droughts lasting three years or more.\nThis hypothesis is based on the understanding that short-lived species, such as Gulf\nmenhaden, can typically rebound through density-dependent compensatory processes after\nbrief periods of low productivity. However, extended periods of low productivity can\nseverely affect multiple year-classes, leading to long-term detrimental impacts on these\npopulations. We further clarify that the apparent effects of the 1980s drought can be partly\nattributed to (1) elevated biomass and catches during the mid-1980s, following\nexceptionally wet conditions in early 1982-1983, and (2) the high fishing effort prevalent\nthroughout the 1980s. (3) our extended time series (Supplementary section S2)\ndemonstrates that episodic fluctuations in menhaden populations correlate with highly dry\nconditions (e.g., 2006, 2012) and wet periods (e.g., 2010).\nTo further address the reviewer’s concerns, we acknowledge that variations in fishery\nmanagement practices and market dynamics over time might also contribute to\nfluctuations in catch. In the revised manuscript, we have included additional discussion on\nthese factors to provide a more comprehensive context for the observed changes in catch\nand biomass.\nAdditionally, in response to the reviewer’s suggestion regarding the speculative nature of\nthe discussion, we have incorporated a new Supplementary section S6, which includes a\nsensitivity analysis that evaluates the effects of prolonged versus shorter drought periods\n(1- and 3- years duration). This analysis quantifies the potential impacts of different\ndrought durations and strengthens the robustness of our findings.\nAdditionally, following the sharp drop in in both catches and biomass in the late 1980’s catches\nremain low while biomass increases, pointing towards a non-biomass driver of low catches (e.g.,\nshift in management towards lower harvest mortality rates).\nWe appreciate the reviewer’s observations regarding the dynamics of catches and biomass\nin the late 1980s. Indeed, from 1992 onwards, after the drought of the late 1980s, we\nobserved a gradual decline in catches, averaging approximately 3 t/km² over 24 years. This\ntrend aligns with the expected relationship described by the equation (C = F×B), where\nfishing mortality (F) was reduced due to regulatory measures, consequently leading to\ndeclining annual catches. This regulatory action facilitated an increase in the remaining\nbiomass within the marine ecosystem, fostering a more sustainable fish population.\nTo clarify this we added supplementary section S3, which illustrates the contrasting\nrelationship between menhaden catches and fishing effort in the 1980s (a non-intuitive,\ndrought-dominated relationship) and the shift towards a more intuitive relationship from\nthe 1990s onward.\nFinally there are multiple disparate patterns in the magnitude and sometimes the direction of\nbiomass change and catch (this can be seen in Fig 1 (h) and (i); e.g., crab have the second largest\ndecline in biomass but increases in catch, similarly, younger menhaden had the larger declines in\nbiomass than older conspecifics but declines in older menhaden catches were larger than younger\nones). This is of course a not unexpected and well cited issue in fisheries, especially as biomass\ndeclines where fishers can increase effort to offset declining biomass, or where catch declines\nbut is stabilized over time and biomass increases due to implementation of policies that reduce\nharvest rates to sustainable levels. I believe the authors are aware of this issue (see plots of\nrelative effort) and offer what is maybe the most parsimonious explanation in the “model\nlimitation” section of the Methods (lines 506 -509):\n506 “...For example, fluctuations in Gulf menhaden catches may be related to economic\nprocesses, which occurred during the mid and late 1980s, when the Gulf menhaden fishery\nunderwent corporate consolidation resulting in declines in the number of fishing vessels and\nprocessing plants. This may have contributed to the decline in catches 45. For species such as\nGoliath grouper, declines in biomass and catch are likely a result of long periods of overfishing\nor management regulations designed to reduce harvest (e.g., harvest moratorium).”\nSimilarly, the ESR (reference #1) states “The Gulf of Mexico Fisheries Management Council\nimplemented a number of Fishery Management Plans in the 1980s, and major Amendments\nrestricting fishing effort were passed in the 1990s.“ (p22).\nWe appreciate the reviewer’s valuable comment regarding the complex patterns observed\nin biomass changes and catch dynamics. In addition, we indeed recognize that the\nfluctuations in biomass and catch, such as the decline in crab biomass coinciding with an\nincrease in catch, are well-documented phenomena in fisheries science. To address these\ncomplexities, we have enriched the \"model limitation\" section with further explanations\nabout the economic and regulatory factors at play, including the historical context of the\nGulf menhaden fishery and relevant management measures implemented over the years.\nAdditionally, we have included a non-fisheries-related index of juvenile Gulf menhaden\nabundance in Louisiana estuaries. This data reveals a decline in the juvenile Gulf\nmenhaden population during the late 1980s that is unrelated to fisheries management\nmeasures, further highlighting the influence of external environmental factors.\nOf course these management measures could have been implemented to address the decline that\nwas driven initially by drought. The way to address how much climate drove the decline is to run\na hindcast or counterfactual simulation, although these are challenging with ecosystem models\nbecause of model drift. So another approach would be to use a sensitivity simulation to evaluate\nthe hypothesis of prolonged drought versus short 1 year drought in driving declines. E.g., fixing\nthe F rate at a few different levels (e.g., those observed in the 1985-1990 period vs those\nobserved in 2013), simulate a) 3 year drought (sensu the 1985-1988) and b) a five year period\nwith a 1 year drought. Something like this should be conducted to demonstrate and support the\npostulation that underlies the projection simulations. In the absence of the sensitivity simulation\nabove, there is not sufficient evidence provided presently to rule out changes in harvest pressure,\nfisheries operations, management or markets in driving declines in catch in the late 1980’s\nrelative to early 1980’s. Given that the paper is predicated on the postulated relationship between\ndrought, nutrients, and declines in catches, establishing and supporting this is extremely\nimportant. Additionally, I strongly suggest providing a historical time-series of harvest mortality\nby age for menhaden (e.g., as a supplemental figure). It appears to my eye that the F rate for 1\nyear old fish is much higher during the 1985-1990 period than 2000 - 2016. Having the\ntimeseries would help facilitate comparisons in relative fishing pressure over time.\nWe thank the reviewer for the valuable insights. In response to the comments, we have\nincluded a sensitivity analysis in supplementary section S6 that evaluates the effects of\ndifferent drought durations, specifically simulating both 1-year and 3-year droughts under\nvariable F rates, in addition to the previously discussed 5-year drought scenario. This\nanalysis illustrates a clear correlation between the duration of drought and the cumulative\ndecline in biomass.\nAdditionally, we have incorporated the requested historical time series of harvest mortality\nby age for Gulf menhaden, which can be found in Figure S2c. As noted by the reviewer,\nthis time series confirms that fishing mortality was indeed higher during the late 1980s\ncompared to the subsequent period from 2000 to 2016. This rise in fishing mortality during\nthe late 1980s can be attributed to higher fishing efforts and catches, while the later period\nexperienced lower fishing effort, which ultimately supported a gradual recovery of the\nmenhaden population.\nImportantly, our findings suggest that changes in harvest pressure and fisheries operations\ncannot fully account for the observed trends. As detailed in supplementary section S2,\nmenhaden fishing effort was actually declining during the 1980s, coinciding with a decline\nin biomass, rather than the expected increase that would typically drive such reductions.\nThe second issue with the paper is that it is very difficult to discern precisely what methods were\nused in the simulations. This needs to be clarified in the Methods simulation section. I have\ndetailed a number of line by line suggestions below to help clarify. There were two separate\nsimulations conducted. For the first, I believe the correct methods were used for the status quo\nversus future time series projections, but can’t confirm this based on the current methods section.\nFor the first simulation, the authors ran the EwE model for a 5 year simulation period across\nNutrient and F profiles based on average 2014-2016 values for each (Navg2014-2016 and\nFavg2014-2016). Within the intervals for each (Nutrients = (0,2) X Navg2014-2016 and F =\n(0,5) x Favg2014-2016) is the value of 1.0, i.e., a simulation where N = N_avg and F = F_avg.\nFor the second the authors used linear regression to extrapolate the nutrient levels from the PDSI\nprojections for the first and second halves of the century. For both analyses it is not clear what\nthe baseline status quo scenario includes. Can the authors confirm that in lines 471-475,\ncomparisons of climate change simulations are relative to the terminal year of a 5 year control\nsimulation where N =Navg2014-2016 and F = Favg2014-2016 rather than comparisons directly\nto 2016 values? This is important because ecosystem models can propagate error when adjusting\nto new forcings and including a control run helps establish that differences are due to forcings\nrather than model behavior. If this was done the authors need to clarify the start and end dates of\neach simulation and in figure and the results consistently use a term like “persistence simulation”\nor “control” rather than “2016” which is confusing. A table or figure of the simulation\nmethodology would greatly help clarify the two simulations.\nWe sincerely thank the reviewer for the valuable feedback regarding the clarity of our\nmethods in the simulations. In response, we have made several revisions to enhance the\nclarity of our methodology. We have changed the text to include “Ecosim simulations” and\n“Quantifying changes biomass and catches” sections and added detailed explanations to\nenhance clarity. In the “Quantifying changes biomass and catches” section, we specifically\ndivided the methodologies into distinct sections: \"Historical Perspective\" and \"Future\nPerspective,\" clearly outlining the methods used in both analyses.\nIn the \"Historical Perspective,\" we have elaborated on how we examined the differences\nbetween pre-drought conditions (1982-1986) and drought-affected conditions (1988-1992),\nutilizing historical time series data and EwE time-dynamic Ecosim Model simulations. We\nincluded a detailed description of the equation used to analyze biomass and catches, as well\nas explaining how the fishing mortality and nutrient input forcing for Gulf menhaden were\nderived and implemented.\nIn the \"Future Perspective,\" we specified our approach to quantifying changes in biomass\nand catches in response to projected drought conditions, accounting for varying levels of\nGulf menhaden fishing mortality in a trade-off analysis. We highlighted the\nimplementation of a 5-year simulated drought and the use of linear regression to project\nnutrient inputs based on PDSI data. We also clarified how terminal biomass was expressed\nrelative to the status quo and how relative changes in biomass and catch were calculated.\nFurthermore, we used our sensitivity analysis simulations, which examine trade-offs\nbetween nutrient input and Gulf menhaden fishing mortality, detailing how we modeled\nnutrient loading and fishing mortality using various multipliers.\nRegarding the baseline status quo scenario. To clarify, we confirm that the comparisons of\nclimate change simulations presented in lines 471-475 are indeed relative to the terminal\nyear of a 5-year control simulation, in which nutrient inputs N =geom_mean(N ) and\n2014-2016\nF = geom_mean(F ), rather than being directly compared to 2016 values.\n2014-2016\nIn response to the reviewer’s suggestions, we have provided additional clarification\nregarding the start and end dates of each simulation in the revised manuscript. We have\nalso standardized terminology, using the term “status quo scenario” consistently\nthroughout the text to avoid any confusion with the term “2016.”. Furthermore, we have\nadded a table (supplementary Table S6) that outlines all simulations along with detailed\nexplanations of each simulation.\nFor the second simulation, it is not clear from the Methods which years are included in this\nextrapolation; values are reported in the Results section and Figure 2.b but again it is difficult to\nunderstand which years are included in the values as they are reported as “anticipated by 2050\nand during 2051-2100). It appears they are averages of 2016-2050 and 2051-2100. Are they the\nmean of the 20 member ensemble for each year or the mean of all values from all simulations\nwithin those time frames? There is considerable range in the PDSI of the 20 member ensemble\nby end of the century and the upper range of the index ensemble mean includes values (PDSI>0)\nseen in non-drought years. As such I strongly suggest that the authors run the simulation for\nrandom draws from the mean and standard deviation of the ensemble for each time-period (e.g.,\n100 draws) in order to provide error bars for the figures in Fig. 2c; confidence intervals around\nprojected declines may be large.\nWe appreciate the reviewer’s insightful comments regarding the second simulation. The\ndrought conditions for the specified years were computed based on the minimal moving\naverage of the 5th percentile PDSI value over a 5-year period, utilizing the variation from\nthe 20-member CMIP ensemble. This approach allows us to focus on the driest consistent\nspells, which are of particular interest in our study. The correspondence between the driest\nobserved PDSI (indicated by magenta markers in Fig. 2a) and the modeled PDSI\n(represented by the yellow shaded area) in Fig. 2a further justifies our emphasis on the 5th\npercentile of the modeled PDSI.\nWhile we acknowledge that wet periods corresponding to positive PDSI values will occur\nwithin the projected time frames (through the end of the century), our focus on the impacts\nof droughts necessitates prioritizing drought periods over wet ones. It is important to note\nthat the PDSI index is a \"long-memory\" index that accounts for soil moisture; thus, the\neffects of very wet periods can diminish quickly following extreme hot and dry conditions.\nWe have accepted the reviewer’s suggestion to utilize random draws (N=43 for each\nscenario) from the mean and standard deviation of the ensemble for each time period in\norder to provide error bars for the figures in Fig. 2c. We used the 5th percentile of the\nmodeled PDSI as the basis for our analysis based on the explanation above.\nA growing number of papers include comparisons of projections of high and low carbon\nemission scenarios, e.g., RCP8.5 vs RCP2.6 or the more recent CMIP6 SSP126 vs SSP 585.\nThese types of comparisons are extremely useful in IPCC and other climate assessments because\nthey reveal the relative risk of different levels of greenhouse gas emissions. To repeat this\nanalysis for RCP2.6 would be a major undertaking and so I am not suggesting that has to be done\nhere, but including multiple carbon emission scenarios is something the authors might consider\nin future work as it would help address an important need for relative climate risk information\nfor fisheries and marine ecosystems.\nWe appreciate the reviewer’s comments regarding the inclusion of comparisons between\nhigh and low carbon emission scenarios. In light of this, we have added text in the\ndiscussion section to address the potential for including various RCP scenarios in future\nresearch, with a specific focus on the implications of greenhouse gas emission mitigation\nand management for fisheries and marine ecosystems.\nIn summary, I find the paper topic to be compelling, the methodology is established but novelly\napplied, and the figures and text are clear and well executed. I appreciate the inclusion of the\ncode and data. If the authors are able to address the aforementioned concerns (and following line\nby line suggestions) I believe that this paper can be an important contribution to the field of\nclimate change and fisheries. While these suggestions are not trivial I also believe that they are\nachievable revisions that will immensely strengthen the paper.\nWe sincerely thank the reviewer for the positive feedback and encouraging remarks about\nour paper. We are pleased to hear that the reviewer found the topic compelling and the\nmethodology both established yet novel. We have carefully addressed all the concerns\nraised, along with the line-by-line suggestions provided. We believe that these revisions will\nsignificantly enhance the clarity and robustness of our work, and we appreciate the\nreviewer’s thoughtful insight, which has guided these improvements.\nData access note. I was not able to access the data as it appears to be stored on a microsoft\nsharepoint or similar site. I suggest a public permanent repository like Figshare or Zenodo via\ngithub if the files are not too large.\nThank you for your feedback regarding data access. We have uploaded all the code+data\nrequired to create the figures to Figshare at the following link:\nhttps://figshare.com/s/a4c759477e41c891421f.\nReferences: Generally there is a lack of references for key statements. Of the references cited\nmany are unpublished technical memos or reports; one non-data citation is for a defunct NOAA\nwebsite. Some are quite old and newer more comprehensive papers are available. Some of the\ncitations appear incomplete or incorrect.\nCitations were corrected and updated.\nIn addition to the suggestions above the following issues should be addressed:\nLines 50-51: this citation is for a NOAA Ecosystem Status report which compiles data and\nindices from multiple sources. Citation of the original stock assessments and/or a peer reviewed\npapers are additionally needed.\nThese citations were added.\nLine 73 - this citation is a website that is no longer available. Citation of a stock assessment\nreport or published paper is needed.\nThese citation was added.\nLines 115 - “as compared to 2016” does not appear to match the methods of the status quo\nsimulation using persistence values (average of 2014-2016). See note above regarding methods,\nand change to, as compared to a persistence [or control] scenario, or something similar.\nWe have clarified the methods used in the manuscript. We compare future conditions to a\nstatus quo scenario rather than directly to the year 2016. This comparison is made by\nprojecting drought-nutrient conditions derived from our analyses (Fig. 2), and using the 5th\nprojection year as reference for comparison.\nLines 116 - “will be around 72%” is too prescriptive. This is one of many future carbon\nemissions scenario. Change to “could be reduced by as much as” or something similar.\nWe thank the reviewer for the suggestion. We indeed adopted it.\nLines 139 change to “ fishing mortality in projection simulations is smaller compared to”.\nWe thank the reviewer for this suggestion. We have revised the text accordingly.\nLines 158-159 need a citation.\nCitation were added: Wiman, C., Hamilton, B., Dee, S. G. & Muñoz, S. E. Reduced lower\nMississippi River discharge during the Medieval era. Geophys Res Lett 48, e2020GL091182\n(2021).\nLines 160-170, especially lines 169-171 are speculative as written and need references. The\nchanges in fisheries management during the late 1980’s are not mentioned here so the\ncorresponding text in the methods section (lines 506-509) should be moved here.\nWe have relocated the text regarding changes in fisheries management during the late\n1980s from the methods section to this section, as suggested. Additionally, we have added\nreferences where relevant and rephrased the text to clearly distinguish between\nexplanations based on possible mechanisms and those supported by past studies.\nThe following references were added:\n- SEDAR 63; Southeast Data Assessment and Review. SEDAR 63 Gulf Menhaden\nAssessment Report. http://sedarweb.org/sedar-63. (2018).\n- Govoni, J. J. The association of the population recruitment of gulf menhaden,\nBrevoortia patronus, with Mississippi River discharge. J Mar Syst 12, 101–108\n(1997).\nLine 191 This citation seems out of place as it iis for Arctic ecosystems, there are many fisheries\nprojected impacts papers, two of which are already cited (Lotze et al. 2019 and Titensor et al.\n2021). If keeping the citation I suggest moving it to after “synergistic impacts” and adding the\nTittensor and Lotze refs at the end of the sentence.\nWe thank the reviewer for the suggestion. We have replaced this reference with a more\nsuitable reference: Pethybridge, Heidi R., et al. \"Contrasting futures for Australia’s\nfisheries stocks under IPCC RCP8. 5 emissions–a multi-ecosystem model\napproach.\" Frontiers in Marine Science 7 (2020): 577964.\nLine 193 - there are more recent papers regarding hypoxia (see refs cited within\nhttps://www.science.org/doi/10.1126/science.aam7240)\nWe appreciate the reviewer’s suggestion regarding recent literature on hypoxia. In\nresponse, we have updated the reference to include a more recent study cited in the paper\nyou recommended: Reed, Daniel C., and John A. Harrison. \"Linking nutrient loading and\noxygen in the coastal ocean: A new global scale model.\" Global Biogeochemical Cycles 30.3\n(2016): 447-459. This reference provides a more current perspective on the topic.\nLine 205: It seems a missed opportunity here to not point out that during subsequent droughts\nthere was reduced fishing pressure/mortality, which can be seen in Fig 1 and supports this\nstatement.\nWe thank the reviewer for this insightful suggestion. We have added the following\nsentence to line 205 to highlight the reduced fishing pressure during subsequent droughts\n(as shown in Fig. 1): \" The observed milder declines and faster recovery of fisheries\nfollowing the 2006 and 2012 droughts, for example, suggest that reduced fishing effort\nduring drought periods may play a significant mitigating role.”\nLine 222 - citation 5: no journal/publication source\nWe have added the source to this publication: “NOAA technical memorandum”\nLine 250 citation 16 incomplete reference\nThank you for pointing out the incomplete citation 16. The corrected reference is: Ward,\nC. H. Habitats and Biota of the Gulf of Mexico: Before the Deepwater Horizon Oil Spill:\nVolume 2: Fish Resources, Fisheries, Sea Turtles, Avian Resources, Marine Mammals,\nDiseases and Mortalities. Vol. 2. Springer, 2017.\nLines 252 Please check and correct the citation for the year for this publication; it appears the\nbook was originally published in 1991.\nThank you for pointing out the discrepancy in the publication date for this citation. While\nthe original work was published in 1991, the version we cited (Riebsame, 2019) is a widely\naccessed and relevant republication (cited 433 times according to Google Scholar). We\nbelieve this citation remains appropriate.\nLine 263 citation 22 Page 404 and this is not a report it is a website.\nThank you for pointing out the error in citation 22. We have corrected the reference, which\nis now properly listed as: National Marine Fisheries Service. Fisheries of the United States,\n2019. (2021)\nLines 338-339 - how many times were missing data interpolated? Specifically which years and\ndata (species) were replaced. Provide a supplemental table or plot of the data indicating\ninterpolated values.\nWe thank you for your thorough review. To address your comment regarding data\ninterpolation on lines 338–339, we have added the following to the Methods section:\n“Following data filtering, time series analyses were conducted on biomass data for the\nfollowing species: blacktip, dusky, and sandbar sharks; large and small coastal sharks;\ngreater amberjack; cobia; king mackerel (0–1 and 1–2 years); Spanish mackerel (0–1 and\n1–2 years); gag and yellowedge groupers (0–3 and >3 years); goliath grouper; red snapper\n(0–1, 1–3, and >3 years); vermilion and mutton snappers; tilefish; gray triggerfish; and\nmenhaden (1–2, 2–3, 3–4, and >4 years). Two missing data points (blacktip shark and\nmutton snapper in 1980) were addressed using geometric mean interpolation.”\nLines 372: Cite the original source of the data, not the ESR which compiles the indices,\nincluding the website address for the data access and date via instructions to authors.\nWe have added the source of the data:” Sea surface temperature was downloaded from the\ndatabase of the GoM ecosystem status report (Karnauskas et al., 2017), which utilized data\nfrom daily high-resolution blended analysis for sea surface temperature (Reynolds et al.,\n2007).”\nLine 378: RCP 8.5 is not “business as usual”, it is often incorrectly referred to as such. It is high\nbaseline. Cite the RCP : Van Vuuren DP, Edmonds J, Thomson A, Riahi K, Kainuma M, Matsui\nT, Hurtt GC, Lamarque J-F, Meinshausen M, Smith S, Granier C, Rose SK, Hibbard KA (2011)\nRepresentative concentration pathways: an overview. Climatic Change 109:5–31\nWe appreciate the reviewer’s comment. We have corrected the phrase to clarify that RCP\n8.5 is considered a \"high baseline emission scenario.\" Additionally, we have incorporated\nthe suggested citation: Van Vuuren et al. (2011) for reference.\nLine 388: Information regarding the error distribution is needed here. What was the error\ndistribution used for the linear regression? Because nutrients are not normally distributed (values\ncan not be less than 0), a logistic regression, i.e., lognormal distribution should be used where\nLn(Nutrients) ~f(PDSI,e) where e ~N(0,sigma) rather than linear model Nutrients~f(PDSI,e)\nwhere e ~N(0,sigma).\nWe appreciate the reviewer’s insightful comment. In response, we conducted an analysis of\nthe normality of the residuals from our linear regression model, where Nut_N_P_EwE\nserved as the response variable and PDSI_Miss_Basin as the predictor. The Q-Q plot\nindicated that the residuals closely followed a straight line, and the Shapiro-Wilk test\nyielded a p-value of 0.8, allowing us to accept the null hypothesis of normal distribution.\nThese results confirm that the assumptions of our linear regression analysis are met, thus\nvalidating our statistical approach. We have included this information in the methods\nsection of the manuscript.\nLine 416 “good agreement” how so? Provide a metric of skill or correlation.\nWe appreciate the reviewer’s inquiry regarding the phrase “good agreement” and the\nrequest for metrics of skill or correlation. By \"good agreement,\" we refer primarily to the\nvisual inspection of the time series fits. Additionally, we observed notable consistency\nbetween the model-predicted estimates of the fishing mortality rate corresponding to the\nmaximum sustainable yield (FMSY) and the FMSY estimates obtained from stock\nassessments (Berenshtein et al., 2023).\nIn addition, in response to the reviewer’s concern, we calculated the Modeling Efficiency\n(MEF), a statistical metric that assesses how effectively our model predicts observed data\ncompared to using the mean of the observed values. While Ecosim models do not typically\nyield a single measure of overall skill, modeling efficacy is often evaluated relative to the\nmodel itself through Relative Model Efficiency (RME) and the Akaike Information\nCriterion (AIC), as noted by Olsen et al. (2016).\nOur analysis indicates that 74% of the fitted time series yielded positive MEF values,\nsuggesting that the model’s predictions are more accurate than those based on the mean of\nthe observed data for roughly three-quarters of the assessed time series. We have added\nthis information to the Methods section and included a supplementary section\n(supplementary Section S5) that explains the assessment of Ecosim model fits, accompanied\nby a table of MEF values (supplementary Table S5).\nNotably, as explained in (Berenshtein et al., 2021, 2023) the time series that did not fit well\nprimarily involved highly migratory species and the early life stages of various groups,\nwhich pose challenges for the EwE framework. This finding highlights the predictive\ncapacity of our model, although we acknowledge that the application of MEF is not\nstandard practice in Ecopath with Ecosim (EwE) modeling, complicating direct\ncomparisons with other EwE models.\nLine 429 specify the years that were used.\nWe appreciate the reviewer’s request for clarification regarding the referenced years.\nWhile we are uncertain about the specific years the reviewer is referring to, we would like\nto highlight that, based on the mentioned line number, this section pertains to the last year\nof the hindcast modeling period (2016). We want to clarify that 2016 was not included in\nthe anomaly search algorithm and was therefore assigned a default value of one. This\ninformation has been explicitly stated in the revised manuscript for clarity.\nLine 445 what is meant by “scaled by the mean” Z-scored - include an equation.\nIn response to the reviewer’s question about the phrase “scaled by the mean,” we would\nlike to clarify that we are not using Z-scores. Instead, the nutrient forcing is represented by\nthe total monthly nitrogen and phosphorus input (in Mt per year) from the Mississippi\nRiver, divided by the mean of the entire time period. This is expressed mathematically as:\n𝑁𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑓𝑜𝑟𝑐𝑖𝑛𝑔 =\n(𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑎𝑑𝑖+ 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑜𝑠 𝑙𝑜𝑎𝑑𝑖 )\n𝑖 ∑𝑛\n𝑖\n(𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 𝑙𝑜𝑎𝑑𝑖+ 𝑃ℎ𝑜𝑠𝑝ℎ𝑜𝑟𝑜𝑠 𝑙𝑜𝑎𝑑𝑖)\n𝑛\nIn this equation, (i) represents a given month, and (n) is the total number of months in the\nhindcast modeling period, which is 444 months (37 years). We have added this information,\nalong with the equations, to the text for clarity.\nLine 454 “approach in “ use the first author name convention for citation.\nWe appreciate the reviewer’s comment regarding Line 454. We have updated the citation\nto follow the first author name convention. Additionally, we have included another relevant\nrecent citation for this methodology: “…similar to the approach in Chagaris et al. (2020)ref#\nand Berenshtein et al. (2023) ref#.”\nLine 475 this equation describes a ratio not a delta. For delta (proportional change) use\nB1=(Btsim-Bsq)/Bsq\nWe thank the reviewer for the valuable correction. We have updated the notation to reflect\nthe distinction between a ratio and a delta. The equation is now presented as a ratio:\n𝑩 = B /B\nratio T,sim T,status quo\nLine 481 : according to this equation if biomass in the simulations was 20% greater than the\nstatus quo simulation the delta B would be 9% this equation seems incorrect. I think that authors\nmean to express the proportional change in biomass, i.e., delta B = (Btsim-Btsq)/Btsq.\nWe appreciate the reviewer’s comment. This equation refers to the “piano plots” in figure\n1l,m and in Fig. 3, for which this was indeed the correct equation:\n𝜟𝑩= (B -B )/(B +B )\nT,sim T,status quo T,sim T,status quo\nThis equation represents relative change scaled between -1 and 1 because it calculates the\ndifference between the simulated biomass and the status quo biomass, normalizing this\ndifference by the total biomass (the sum of both values). This normalization ensures that\nthe result reflects the proportionate change in relation to the combined biomass, thereby\nconstraining the output within the range of -1 to 1. An output of -1 indicates complete\ndecline relative to the total biomass, 0 indicates no change, and 1 indicates complete\nincrease, effectively capturing the relative change in a symmetric manner. Following the\nreviewer’s comment above, we change the notation from 𝜟𝑩 to relative biomass change.\nLine 485 see above same issue.\nSee reply above.\nLines 488-489 are not needed- can be included with data supplemental material.\nThis text was transferred to the supplementary material as suggested by the reviewer.\nLines 506-509 “For example….decline in catches 45.” should be moved to the discussion section\nlines 160 (see comment above)\nThis text was transferred to the discussion as suggested by the reviewer.\nFigure 1 three panels in a-d on the right need letters.\nLetters were added to the panels.\nIt would be nice to have the colors for each species in h and i\nColors were added to the species according to the group-specific colors in (j) and (k), and\nconsistent throughout Figs 1-3..\nFigure 2 (d) - each subplot needs a letter, color ramp needs a label . X axis for (c ) is not fishing\nmortality it is times X average 2014-2016 F. Please fix the label. Same comment for (d).\nThank you for your feedback on Figure 2. We have added letters to each subplot and\nincluded a title for the color ramp. The X-axis labels for both subplots (c) and (d) have been\ncorrected to indicate \"Times X average 2014-2016 F\" instead of fishing mortality."}, {"round": 3, "reviewer_comments": {"reviewer_3": "This paper examines the relationship between drought conditions and fisheries production in the Gulf of Mexico using an Ecopath with Ecosim modelling framework. They have put effort into addressing the comments of all reviewers in multiple rounds of review. They have addressed and clarified all the questions I had (clarifying methods, acknowledging limitations and pointing to opportunities for future work) and I think the paper is now ready for publication.\n(Remarks on code availability)\nI have not tried to execute the code, but I have read over it and the logic and content makes sense to me with no immediately obvious issues.", "reviewer_4": "In the revised paper, the authors have satisfactorily addressed the comments and suggestions previously provided by myself. A more comprehensive description of the mechanisms underlying the impacts of drought on marine fisheries production reported in the scientific literature has been provided. In addition, the authors have also included further details on the projected impacts of future droughts on the functional groups in the Gulf of Mexico.\nCongratulations to the authors for taking onboard the feedback in the revised paper and for producing a laudable study that advances our understanding of climatic impacts on marine fisheries production due to changes in freshwater nutrient inputs.\nI have provided some additional, very minor comments that are typographical in nature below. Once addressed, I believe that this paper is suitable for publication in Nature Communications.\nMinor points\nLine 164: As ‘F’ has not been previously defined, please amend this sentence to include the word ‘fishing mortality’ as follows: ‘…when fishing mortality (F) is set to 0…’.\nLine 230: The latter part of sentence does not make sense because it is missing a linking word. Please therefore insert the word ‘and’ before ‘…may have contributed…’.\nLines 236-237: Delete ‘Fulton and Smith, 2004’, so the citation is just the number ‘39’.\nLine 671: Ensure that the words ‘Highly Migratory Species’ are all in lowercase font.\n(Remarks on code availability)\nI reviewed the R code and it looked eloquently compiled, reproducible, and straightforward to run."}, "author_response": "Igal Berenshtein\nDepartment of Marine Biology\nCharney School of Marine Sciences\nUniversity of Haifa\nAbba Khoushy Ave 199, Haifa, 3498838\niberensht@univ.haifa.ac.il\nMobile phone +972-54-533-8837\nPoint-by-point response to the reviewer’s comments\nREVIEWERS' COMMENTS\nReviewer #3 (Remarks to the Author):\nThis paper examines the relationship between drought conditions and fisheries production in the\nGulf of Mexico using an Ecopath with Ecosim modelling framework. They have put effort into\naddressing the comments of all reviewers in multiple rounds of review. They have addressed and\nclarified all the questions I had (clarifying methods, acknowledging limitations and pointing to\nopportunities for future work) and I think the paper is now ready for publication.\nWe thank the reviewer for the positive assessment and appreciation of the revisions. We\nare pleased that the manuscript is now considered ready for publication.\nReviewer #3 (Remarks on code availability):\nI have not tried to execute the code, but I have read over it and the logic and content makes sense\nto me with no immediately obvious issues.\nWe thank the reviewer for reviewing the code and for the positive feedback.\nReviewer #4 (Remarks to the Author):\nIn the revised paper, the authors have satisfactorily addressed the comments and suggestions\npreviously provided by myself. A more comprehensive description of the mechanisms\nunderlying the impacts of drought on marine fisheries production reported in the scientific\nliterature has been provided. In addition, the authors have also included further details on the\nprojected impacts of future droughts on the functional groups in the Gulf of Mexico.\nCongratulations to the authors for taking onboard the feedback in the revised paper and for\nproducing a laudable study that advances our understanding of climatic impacts on marine\nfisheries production due to changes in freshwater nutrient inputs.\nI have provided some additional, very minor comments that are typographical in nature below.\nOnce addressed, I believe that this paper is suitable for publication in Nature Communications.\nWe thank the reviewer for the encouraging remarks and for identifying the minor issues\nlisted below. All have now been corrected.\nMinor points\nLine 164: As ‘F’ has not been previously defined, please amend this sentence to include the word\n‘fishing mortality’ as follows: ‘…when fishing mortality (F) is set to 0…’.\nCorrected as requested. The sentence now reads:\n“…when fishing mortality (F) is set to 0…”\nLine 230: The latter part of sentence does not make sense because it is missing a linking word.\nPlease therefore insert the word ‘and’ before ‘…may have contributed…’.\nCorrected. The sentence now includes the linking word “and”.\nLines 236-237: Delete ‘Fulton and Smith, 2004’, so the citation is just the number ‘39’.\nDone. The citation now appears solely as numerical reference 39.\nLine 671: Ensure that the words ‘Highly Migratory Species’ are all in lowercase font.\nCorrected to lowercase (“highly migratory species”).\nReviewer #4 (Remarks on code availability):\nI reviewed the R code and it looked eloquently compiled, reproducible, and straightforward to\nrun.\nWe thank the reviewer for taking the time to evaluate the code and for the positive\ncomments."}]

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