An Antarctic ecosystem value index to quantify ecological value across trophic levels and over time

Domain: neuroscience
Published: 2026-02-11
Editorial outcome: minor_revision

Abstract
The Southern Ocean around Antarctica is one of the fastest changing regions on the planet and an emerging resource frontier for fisheries. Here, we present the Antarctic Ecosystem Value Index created by merging ecosystem information across food web trophic levels, from phytoplankton to fish and penguins, to quantify the ecological value of marine areas around the Antarctic continent. We find that coastal polynyas-areas of reduced sea-ice-have Index values 31–72% higher than surrounding areas, suggesting that these areas are biologically valuable hotspots for a number of ice-dependent Antarctic Species. Using output from an Earth system model to generate future projections of the Index, we find that high-value locations, often within polynyas, are likely to continue to be valuable throughout the 21st century despite environmental changes. The Antarctic Ecosystem Value Index indicates that penguins lose importance as their habitat becomes increasingly unsuitable, so protecting high-value habitat areas may be critical for these species. This study also shows that while many high-value Index areas are within existing or proposed Marine Protected Areas, there are several opportunities for adopting additional protection, particularly in East Antarctica and the Amundsen Sea.

Introduction
The Southern Ocean around Antarctica plays a critical role in the global climate system and biogeochemical cycles1 and provides habitat for unique marine biodiversity2. Antarctic species have adapted to unique environmental conditions in the Antarctic, including the presence of strong winds, cold temperatures, months of polar night, and sea-ice. Coastal polynyas are regions along the Antarctic coast with reduced sea-ice. They form repeatedly in particular locations due to strong katabatic winds from the continent blowing ice away from the coast.

Polynyas have specific oceanographic conditions that drive high primary productivity from massive phytoplankton blooms3,4. They are also areas predators, such as seals and penguins, use to access pelagic resources5. The Antarctic ecosystem is facing critical threats from climate change, commercial fishing, and other stressors (e.g., pollution)6–8. Amidst these threats, an international effort is underway towards developing a network of Southern Ocean Marine Protected Areas (MPAs) that is representative of marine biodiversity and ecosystems9. To guide this process, areas of high biodiversity and ecological importance need to be identified for potential inclusion in this network of Southern Ocean MPAs.

Management of the Southern Ocean is the responsibility of the multi-national Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), and CCAMLR’s mandate stipulates an ecosystem and science-based approach10. CCAMLR has shown an increasing emphasis on managing for climate change11, thus necessitating methods that include future changes relevant for regional-scale management. Previous studies of the Southern Ocean have sought to identify regions that are the highest priority for biodiversity. Some have focused on aggregating animal-borne satellite tracking data from bird and mammal predator species to assess habitat importance, high-use areas, and where species are most abundant12–14. These previous studies provide important present-day information for upper trophic levels in the Antarctic ecosystem, but they do not provide information for the future or direct inclusion of lower trophic levels. Other studies have focused on how CO2 emissions scenarios influence future climate risk for marine life, though these often focus on one species (e.g., Emperor penguins, Aptenodytes forsteri)15,16, fewer trophic levels17,18, or are global in scope and provide relatively little coverage of the Southern Ocean19. Additionally, local-scale assessments of valuable ocean habitat have been completed for policy purposes for a particular planning region20. However, a comprehensive evaluation spanning trophic levels, applicable around the Antarctic continent, and incorporating climate change effects in the Antarctic has yet to be conducted.

Here, we present the Antarctic Ecosystem Value (AEV) Index based on several key trophic levels. We focus on areas covered by seasonal sea-ice in five important MPA planning regions-the Weddell Sea, East Antarctic, Ross Sea, Amundsen Sea, and Antarctic Peninsula. These regions include both proposed and existing MPAs. The AEV Index is based on input layers of ecosystem metrics that span trophic levels: net primary productivity, Antarctic krill growth potential, demersal fish biomass potential, and Emperor penguin and Adélie penguin (Pygoscelis adeliae) populations. We use data from an Earth system model (ESM) that includes marine biogeochemistry21,22 for both a historical ice-ocean reconstruction23 and a fully-coupled, free-running model simulation that includes both a historical period and a future projection with upper-middle CO2 emissions (scenario SSP3-7.0)24. The model data are combined with an empirical krill model25, a fish model26, and penguin demographic models15,27–29. The AEV Index, computed by adding normalized values around Antarctica for each input layer and scaling these by the regional maxima, serves as an indicator of the significance of various areas in a management region across trophic levels within the seasonal sea-ice zone.

The AEV Index is useful for three reasons. First, it seeks to comprise the broader ecosystem value by focusing on Antarctic-specific species and integrates across trophic levels (see30). Second, it focuses on management-relevant scales and areas for ecological conservation. And third, it provides future projections of these regions to assess how climate change is likely to affect ecologically valuable regions. When we use the term “valuable” to describe a location, we mean that the location is important for the ecosystem itself, not for any other purpose (e.g., ecosystem services, commercial fishing). When locations of high density or productivity overlap in the same location for multiple AEV Index input layers, it implies species connectivity and richness in this particular location and that this location is likely important for ecosystem function. We demonstrate that areas most ecologically valuable across these trophic levels are also frequently associated with coastal polynyas. We also show that areas valuable in the present-day are projected to stay valuable until the 2090s, suggesting that they may be optimal for inclusion within existing and proposed MPAs.

Results
Present-day AEV Index
We compute a historical reconstruction of the AEV Index that approximates observed conditions from ∽2000-2020. We calculated two versions of the AEV Index by scaling either by the largest summed value in the entire hemisphere (Fig. 1a) or within a regional domain (Fig. 1b). Acknowledging that the magnitude of the AEV Index is not directly comparable between the two panels due to the differences in scaling, we find that the Ross Sea and tip of the Antarctic Peninsula standout as particularly valuable and that many coastal regions have elevated AEV Index values compared to their surroundings in both formulations. It is important to note that the AEV Index values are not directly comparable between the hemispheric and regional indices, nor between separate regions, because each grid point has been scaled differently. To directly compare the most valuable locations, we use three bins-exceptional (top 5th percentile), very high (top 10th percentile), and high (top 25th percentile) value-to describe points based on the distribution of AEV Index values within a particular region. All other points are classified as other (lowest 75th percentile). Grid points classified within the exceptional, very high, or high bins are the most valuable within a particular region (and we often refer to the combination of all three bins as highly valuable areas), and since they are determined from consistent percentiles from regional distributions of the AEV Index they are directly comparable between regions.

In both the hemispheric and regional AEV Indices (Fig. 1c and d) the most valuable areas occur along the Antarctic coasts. There are also some points classified as valuable in the northern parts of the domain in each region, and these are dominated by the Krill Growth Potential (KGP) input layer to the AEV Index (Supplementary Fig. 2). The hemispheric scaling lead to more points with higher value in the Ross Sea and Antarctic Peninsula (Supplementary Fig. 1c), indicating that on a hemispheric basis they have large numbers of penguins and high primary and secondary productivity. However, because so many points in these two regions are classified within the exceptional, very high, or high range using hemispheric scaling it can be difficult to identify and evaluate valuable areas within the CCAMLR MPA planning domains that may be locally critical for the ecosystem. With regional scaling the other regions gain importance as the scalings become regional instead of hemispheric, and local hotspots for biology are emphasized more in the AEV Index bins (Supplementary Fig. 13c). For the rest of this paper we focus on regional AEV Index values to draw attention to regional areas that are biologically valuable and policy relevant.

The most valuable areas occur along the coast (Fig. 1d) and are generally places where there are high values across trophic level inputs to the AEV Index (Supplementary Fig. 2). Specifically, they have high net primary productivity (Fig. 2a) and krill growth potential (Fig. 2b). They also show high demersal fish biomass potential (Fig. 2c). Demersal fish spend part of their life cycle at the seafloor, and present-day Antarctic species include commercially-important Antarctic toothfish. The calculation of demersal fish biomass potential depends on particulate organic carbon (POC) export to the benthos, so the highest concentration of demersal fish biomass potential occurs on the continental shelves below areas with high NPP. Emperor and Adélie penguin colonies are often located in close proximity to the locations with high productivity at lower trophic levels (Fig. 2d, e). The alignment of higher trophic level predators with lower trophic level productivity suggests that the predator colonies may be located so that they can exploit the sea-ice substrate to access essential pelagic food resources.

The AEV Index also shows there is co-location of coastal polynyas and valuable areas across the ecosystem and that the AEV Index values within polynyas are on average 30–70% higher than outside polynyas, though there is higher variability within polynyas as well (Fig. 3a; and Table 1). While polynyas occupy less than 7% of the total area in any region, the total exceptional value area also within polynyas ranges from 14–59% (Table 1). While polynyas occupy a small spatial footprint, they are frequently the most valuable areas within each region. At any given longitude, the total area occupied by the three high value AEV Index bins typically makes up less than 40% of the total area (Fig. 3b), but there are several exceptions where valuable regions comprise over 50% of the total area. These occur in the D’Urville region, the Ross Sea, the Amundsen Sea, and the northern tip of the Antarctic Peninsula.

Future AEV index
To obtain future projections of the AEV Index, we used data from a fully-coupled, free-running model simulation instead of data from an ice-ocean model simulation for the historical reconstruction in the previous section (see Section 10 for details). Since both methods identify the same large valuable areas in all regions for the present day (compare Figs. 4a and 1d), it is reasonable to use the free-running, model-based AEV Index to assess potential future changes. Future projections show that the locations with the highest AEV Index in the seasonal sea ice zone generally remain consistent in the same coastal locations from 2000 to 2090 (Supplementary Figs. 3 and 4). Over the 21st century, the highly valuable coastal points generally increase in value, with notable exceptions in the Weddell Sea and Ross Sea (Fig. 4e–g). The average AEV Index in polynyas remains consistently higher than outside polynyas in both the 2000s and 2090s, and the fraction of the highly valuable regions within polynyas remains high throughout the 21st century (Table 2). In the Ross Sea, the AEV Index decreases very slightly though still remains highly valuable in most areas. The most dramatic decline in high value regions occurs in the Antarctic Peninsula and Weddell Sea as the sea ice zone retreats (Fig. 4). In particular, sections of the Antarctic Peninsula and East Antarctic regions become ice free year round and are no longer optimal habitat for Antarctic species that rely on sea-ice.

Investigation of the contribution of individual inputs to the AEV Index reveal that the importance of penguins to the index is projected to decrease while lower trophic levels become more pronounced (Fig. 5 and Supplementary Fig. 4; Table 3). In the 2000s, the Emperor penguin layer is particularly prominent in the Weddell Sea and Ross Sea, while the Adélie penguin layer is prominent in the Antarctic Peninsula. However, by the 2090s the penguin dominant regions have shrunk dramatically. The shift in which input layer is the primary contribution to the AEV Index reflects large changes occurring throughout the food web and highlights potential climate-change impacts on whole-ecosystem dynamics. Below, we describe changes in each AEV Index input layer both hemispherically and in regional averages.

Net primary productivity is elevated in polynyas, and total polynya area does not change substantially over the 21st century (Fig. 6a–c). In all regions total net primary productivity increases over time (Fig. 7a and Supplementary Fig. 5), likely because ice loss reduces phytoplankton light limitation and extends the growing season. There may also be increased nutrient availability in polynya regions, likely due to increased ocean mixing in these areas, which could lead to increases in productivity as well.

Krill growth potential remains high in polynyas into the 2090s as there is ample food and the summer sea surface temperature is not yet outside the krill’s habitat range (Fig. 6d–f). However, in most regions, regional average krill growth potential levels off, and in the Antarctic Peninsula and East Antarctic regions it starts to decrease by the mid 21st century (Fig. 7 and Supplementary Fig. 5). Since net primary productivity, and thus chlorophyll, increases in these regions, the decrease in krill growth potential is likely driven by rising sea surface temperatures, which increase in all regions and exceed the optimal thermal growth range for adult krill (−1 to 2 °C) and increase thermal stress on krill as they approach the upper bounds of krill’s thermo-tolerance range (3–5 °C).

Demersal fish biomass potential also remains elevated on the continental shelves, in the vicinity of polynyas and is projected to increase given the likely increases in POC flux to the benthos with warming (Fig. 6g–i). This increase in fish biomass is consistent with other studies across multiple models that show increasing fish biomass around Antarctica31. However, present day Antarctic toothfish habitat temperature thresholds (see Section 10) imply that this species would lose habitat since projected bottom temperatures increase from the 2000s to 2090s (Fig. 6g, h, blue contours). As a result, regional projections of Antarctic toothfish potential biomass decline over the 21st century in most regions (Fig. 7e and f and Supplementary Fig. 5) as the ocean bottom temperatures are projected to become increasingly uninhabitable for Antarctic toothfish as they exceed the upper bound of their habitat threshold range of 1 °C32.

Methods
Earth system model simulations
For this analysis we used data from the Community Earth System Model version 2 (CESM2)21. CESM2 includes explicit atmosphere (Community Atmosphere Model version 6; CAM6), terrestrial (Community Land Model version 5; CLM5), ocean (Parallel Ocean Program version 2; POP2), and sea-ice (CICE version 5.1.2) model components run at ~1° horizontal resolution21. Important for this study, CESM2 includes prognostic ocean biogeochemistry (Marine Biogeochemistry Library, MARBL22). The time evolving phytoplankton and zooplankton are represented by functional types and global cycling of oxygen, C, and nutrients (including N, P, Si, and Fe). MARBL also includes specific treatment for light penetration through sea-ice to accurately represent photosynthesis in polar regions62. We use data from two CESM2 experiments for this analysis:

1. For the historical reconstruction data, we use the forced ocean-sea-ice (FOSI) simulation documented in ref. 23 and that is freely available. The FOSI version of CESM2 has prognostic ocean, sea-ice, and marine biogeochemistry components. The experiment is integrated over the 1958-2021 period using reanalysis data from the JRA55-do atmospheric state and runoff dataset63. Thus, the FOSI run provides an estimate of the observed historical period. We use the average CESM2 FOSI output from 2000-2020 when calculating the AEV Index as an estimate of present-day conditions.
2. For the free-running model data, we use an ensemble of 50 simulations from the CESM2 large ensemble (CESM2-LE;24) that are also freely available. Each ensemble member is initialized in 1850 from a pre-industrial control with different initial states. All ensemble members then use identical prescribed forcing for the historical (1850-2014) and future (2015-2100) periods. The historical forcing and SSP3-7.0 (upper-middle) forcing use protocols from the 6th Coupled Model Intercomparison Project (CMIP6;64). Each member of the CESM2-LE is an equally likely realization of a future climate projection. Thus, the ensemble mean captures the response of the system to external forcing alone, while differences between ensemble members demonstrate the internal climate variability65. For each decade shown, we take the average overall 50 ensemble members and the five years surrounding the decade (e.g., 2010 average is taken over 2008–2012) such that there are 250 samples used for decadal statistics. The CESM2-LE is an ideal tool for robustly identifying climate change signals since it has a large number of ensembles from which to sample. By calculating decadal statistics this way, we can focus on the climate change signal and not internal climate variability.

The historical reconstruction AEV Index uses data from the ice-ocean model forced by atmospheric observations to reconstruct observed ocean and sea ice conditions23. On the other hand, an individual fully-coupled, free-running model simulation should not exactly replicate observations, since observations represent one possible response of the climate system to given external forcing in the presence of internal climate variability. Instead, to ensure the AEV Index derived from the fully-coupled, free-running model data24 is robust, we compare the historical reconstruction (average for 2000-2020 conditions) with the fully-coupled simulation data for the 2000s (average over 50 ensemble members from 1998-2002) to ensure they produce similar results in a similar climate state. Indeed, we find that the valuable areas identified by the regional AEV Index historical reconstruction (Fig. 1d) align well with valuable areas identified by the AEV Index calculation using a fully-coupled, free-running model for the 2000s (Fig. 4a). Both methods identify the same large, valuable areas in all regions, though the free-running model has more valuable grid points further north in the Amundsen Sea and the East Antarctic. A major benefit of the AEV Index derived from the fully-coupled model is that it can be used to identify valuable areas into the future. Because the two AEV Index formulations provide similar information in the present day, it is reasonable to use the free-running, model-based AEV Index in the next section of this manuscript to provide information about potential future changes.

Sea-ice data
For the sea-ice data we focus on two variables: sea-ice concentration (SIC) and sea-ice thickness (SIT). These variables are directly output from CESM2 at each grid cell in both the FOSI and CESM2-LE datasets. For model evaluation, we use daily 1979-2020 Climate Data Record (CDR) SIC satellite data product available from the National Snow and Ice Data Center66,67; there are no long term observational datasets of Antarctic SIT available. Antarctic sea-ice in CESM2 has also been well studied and found to be reasonably represented68–71. Supplementary Fig. 6 illustrates that both the CESM2-FOSI and CESM2-LE reasonably represent the mean state and variability of Antarctic sea ice in both winter and summer. Thus, it is appropriate to use the CESM2 model output for analysis of Antarctic climate.

The seasonal sea-ice zone is the maximum extent to which sea-ice expands northward at the end of Antarctic winter (September). We calculate the seasonal sea-ice zone by finding all points where the September mean SIC is 15% or greater; the same method was used for both observational data from 2000–2020 and each decade from the CESM2-LE data.

Polynyas are regions of reduced SIC or SIT surrounded by more extensive or thicker sea-ice and/or coastline. Polynyas form around the Antarctic coasts in specific locations due to strong winds from the continent blowing ice away from the coast. For this study, we assess how the identified ecologically valuable regions correspond to regions where polynyas typically occur but the AEV Index does not directly use environmental data on polynya location or sea-ice state.

We use an algorithm to identify polynyas within gridded data based on satellite threshold methods72. Polynyas are identified as contiguous regions of grid cells that fall below a given threshold and are surrounded by land or ice-covered regions above the threshold. As discussed in detail by ref. 72, different polynya metrics are needed for modeled and satellite observed sea-ice and we use a threshold of 85% SIC and 0.4 m SIT to identify polynyas in the satellite and Earth system model data, respectively. We then calculate the locations of frequent polynyas by identifying regions where a polynya is identified more than 10% of the year over the relevant time period (Supplementary Fig. 7). Over the satellite record, the locations of frequent polynyas in the CESM2 FOSI correspond well with the satellite data. Similarly, the CESM2-LE polynyas in the 2000s correspond well with the CESM2 FOSI polynyas for both SIC and SIT threshold metrics. When comparing the regions where polynyas typically occur with high value AEV regions we use the model SIT threshold methods for comparison since the typical polynyas match better with the satellite observed typical polynyas.

Antarctic ecosystem value (AEV) index inputs
The AEV index uses five inputs from different trophic levels in the Antarctic ecosystem to provide a broader metric across trophic levels (illustrated graphically in Fig. 2). This research prioritizes the use of models with reliance on readily available environmental variables produced by standard ESM data, which is available at monthly temporal resolution. While this limits the biological models we can use, it highlights the models that are capable of linking with an ESM and thus providing a framework for integrating large-scale environmental change to ecological/biological processes. This is a trade off that allows the research to simultaneously make broad projections and highlight pathways for additional research to address.

Net primary productivity (NPP). This input represents primary producers, the base of the food web. As documented in detail in ref. 22, the Marine Biogeochemistry Library (MARBL) component of CESM2 directly calculates time evolving phytoplankton production for several functional types. The phytoplankton growth rate (μi) is a function of the resource-unlimited growth rate (μref) at a reference temperature (30 °C), and the temperature (LimT), nutrient (LimV), and light availability (LimL) functions such that:

μi = μref * Lim(T) * Lim(V) * Lim(L) (1)

The temperature dependence formulation is represented as:

Lim(T) = 1.7 * ((T - 30 °C) / (10 °C)) (2)

where T is the temperature.

Light limitation is calculated as a function of irradiance (I; Wm-2), whereby

Lim(L) = 1 - exp(-αChl_i * θC_i * I / (μref * LimT * LimV)) (3)

where αChl_i (gCm2(gChlorophyll-a Ws)-1) is the initial slope of the Chlorophyll-a-(Chl) specific photosynthesis-irradiance (PI) curve and θC_i is the Chl to C ratio (gChl:gC), and where the demand for growth is represented as the maximum photosynthetic rate constrained by temperature and nutrient limitation (μref * LimT * LimV).

Nutrient limitation is represented as the minimum limitation term of the N, P, Si, and Fe limitation terms for each phytoplankton functional type. Because phytoplankton can assimilate both nitrate (NO3) and ammonium (NH4), the N limitation term (LimN) is represented as:

Lim(N) = (NO3 / (KNO3 + NO3)) + (NH4 / (KNH4 + NH4)) (4)

where KNO3 and KNH4 are the half-saturation constants for NO3 and NH4, respectively.

While we are aware of the complex dynamics of plankton functional types, we chose to use NPP alone to maintain the metric’s broader applicability across different models without requiring detailed assumptions about species-specific responses. NPP is a direct output from CESM2. All configurations of CESM2 calculate phytoplankton growth rates separately for three phytoplankton functional types: diatoms, diazotrophs, and small phytoplankton22. The CESM2-FOSI configuration also includes coccolithophores as a functional type23. None of these functional types are Antarctic specific, but instead parametrized to broadly capture their global distribution. Sea-ice algae are not included. We use the total NPP summed over all phytoplankton types during the Antarctic growing season (months ONDJFM). As seen in Supplementary Fig. 8, the NPP in both the CESM2-FOSI and CESM2-LE captures the observed spatial patterns of a subpolar NPP maximum that decreases within the seasonal sea ice zone, and then areas of higher productivity along the coast that are aligned with polynyas. We compare the model output to three products: the vertically generalized production model [VGPM; 73], the carbon-based productivity model [CbPM; 74], and the carbon, absorption, and fluorescence euphotic-resolving NPP model [CAFE; 75]. These observational products have a wide range in are estimated NPP magnitude, and the model estimates are within this range and thus appropriate for this analysis.

Krill growth potential (KGP). This input represents primary/secondary consumers. We calculate Antarctic krill (Euphausia superba) growth potential over the growing season (months ONDJFM). We use the empirical relationship from 25 that depends only on surface quantities76. This KGP metric has been used at a broad scale across the Southern Ocean in several prior studies43,76–78. While more complex Antarctic krill models that incorporate biomass dynamics and full life cycle processes could provide a more detailed perspective36, such models were not used in this study because of the necessity to use existing coupled ESM data which does not have temporal or spatial resolution needed. A more sophisticated regional modeling study of krill in some of the large scale regions identified by this study would be valuable.

This relatively simple relationship includes contributions due to initial length (KGPlength), food availability (KGPfood), and sea surface temperature (KGPtemp):

KGP = KGPlength + KGPfood + KGPtemp (5)

The length term is calculated as:

KGPlength = -0.066 + (0.002 * L) + (-0.000061 * L^2) (6)

where we use an initial krill length (L) of 40 mm.

The food term is calculated based on the total surface chlorophyll as follows:

KGPfood = 0.385 * (Chl_surf / (0.328 + Chl_surf)) (7)

where the total chlorophyll is summed over all phytoplankton functional types. In reality, krill have been observed to be selective feeders and prefer diatoms to other phytoplankton79. While the MARBL model has a diatom functional type and it generally dominates productivity in polar regions with CESM2, we continue to use total surface chlorophyll from all phytoplankton in our calculation. We do this for consistency with observations from which the equation was derived since they did not take into account different phytoplankton species. However, this use of total chlorophyll should be considered an upper bound on the food term and could lead to an over estimation of krill growth.

We used the KGP model only in a range of sea surface temperature (SST) of -1 to 5 °C, the temperature range over which the model was derived. Within this range, the temperature term depends SST as follows:

KGPtemp = (0.0078 * SST) + (-0.0101 * SST^2) (8)

and has a peak growth rate when SST = 0.386 °C. This reflects that krill are stenothermic, meaning they can live only in a narrow thermal range. Though adult Antarctic krill can survive in waters up to 5 °C, their optimal thermal range lies within 0–3 °C and they experience increasing thermal stress at higher temperatures e.g., ref. 25,42–47. The empirical relationship for KGP was derived for conditions for adult Antarctic krill in summer and does not account for other seasons or growth phases. While Antarctic krill depend on sea ice concentrations for some life stages (e.g., spawning, larval overwintering50), this ice-dependence is not directly taken into account in the KGP modeling. However, because we only use KGP in areas that are within the seasonal sea ice zone, the impact of sea ice availability on other life stages is implicitly included for this input layer. For the AEV Index inputs, CESM2 directly provides the surface chlorphyll and temperature needed to calculate growth rate. The best estimate of krill habitat of which the authors are aware is that provided by AquaMaps80. As seen in Supplementary Fig. 9, KGP calculated from CESM2-FOSI and CESM2-LE data show qualitatively that spatial patterns of maxima in KGP occur in locations where krill are most likely to be found.

Demersal fish biomass potential (DFP). This input represents secondary consumers. The Fisheries Size and Functional Type (FEISTY) model is a mechanistic global size- and trait-based model that resolves the structure of fish communities from environmental forcing26. The CESM-FEISTY framework has been documented in ref. 23. The FEISTY model used in this analysis was run in an “offline” fashion using CESM2 output, i.e., FEISTY is forced by output from the MARBL biogeochemical ocean model without FEISTY impacting fields in MARBL. Forcing variables include water temperature in the top 100 m and at the ocean floor, particulate organic carbon (POC) flux to the ocean floor, and mesozooplankton biomass and loss rates averaged over the top 100 m (as with temperature). Since only one generic zooplankton tracer is simulated in CESM2, we derive mesozooplankton biomass and loss rates using the diatom fraction of phytoplankton biomass following81 and 31.

FEISTY biomass is output as grams of wet weight per square meter (gWW/m2) for each functional type: demersal (“bottom-dwelling”) fish and pelagic fish (forage fish and large pelagic fish). FEISTY simulates three different size classes of fish, from larval to adult, capturing life cycle transitions from one size class to the next (growth), as well as trophic connections between the fish classes and their benthic and pelagic food resources. Once fish reach the adult size class, they can allocate energy towards reproduction to create more of the larval size class (reproduction).

FEISTY is a global model, and the demersal fish functional type in FEISTY represents all types of demersal fish and not a particular species. In the present-day, Antarctic demersal fish include, but are not limited to, species like Antarctic toothfish (Dissostichus mawsoni). We focus on demersal fish biomass potential in the AEV index because Antarctic toothfish are important for fisheries, but FEISTY does not have polar specific modifications to represent this Antarctic species directly. In the absence of considering such Antarctic-specific adaptations, the simulated biomass concentrations in FEISTY should be considered maximum potential biomass. Temperatures above 1 °C become increasingly uninhabitable for Antarctic toothfish32. For the DFP used in the AEV Index, we have summed over each size class of the global FEISTY data. Additionally, we only use values where the bottom temperature is ≤1 °C to account for Antarctic toothfish habitat suitability. When we compare the locations of highest DFP from FEISTY with the best estimate of Antarctic toothfish habitat from AquaMaps80, we see that the spatial patterns of maxima in DFP occur primarily on the continental shelves and continental slopes (Supplementary Fig. 10). These locations are consistent with catch data from East Antarctica of where Antarctic toothfish are likely to be found82. The map of FEISTY biomass is also qualitatively similar to statistical predictions of Antarctic toothfish habitat, which shows that Antarctic toothfish can be particularly well predicted by sea ice thickness, ocean temperature, and depth38.

Penguin population and accessibility. We use two input layers representing different species of penguins and their projected population evolution to represent secondary/tertiary consumers. We include the Emperor penguin (Aptenodytes forsteri) and Adélie penguin (Pygoscelis adeliae), both upper trophic level predators, because these two species experience different future population projection trajectories. In the AEV Index we assign each penguin input layer equal weight as the other layers, though testing shows that using a single penguin layer where each species has a weight of 0.5 results in a similar AEV Index as the results shown here.

The Emperor penguin Population (EPP) accessibility layer is calculated as how many penguins can access each ocean model grid cell by first calculating the distance from each of the 66 Emperor penguin colonies to the center of the model grid cells. Then we calculate how many individuals from a colony can reach each cell by assuming an exponential decay rate of travel from the colony. We determine the decay rate by using an average dispersal distance of 414 km83 and assume half the individuals from a colony can access that distance. Then we sum how many individuals from all colonies can access any given ocean grid cell. The initial colony population is taken from satellite observations from84.

The Adélie penguin Population (APP) accessibility layer is calculated as how many penguins can access each ocean model grid cell by first calculating the distance from each of the 287 Adélie penguin colonies to the center of the model grid cells. Then we calculate how many individuals from a colony can reach each cell by assuming an exponential decay rate of travel from the colony. We determine the decay rate by using maximum dispersal distance of 1933 km85 and assume that 1/1,000,000 of the individuals from a colony can access that distance; we use maximum dispersal distance for Adélie penguins because an average dispersal distance for this species is not known. Then we sum how many individuals from all colonies can access any given ocean grid cell. The initial colony population is taken from satellite observations from86.

Supplementary Fig. 11 shows the penguin colony locations and sizes estimated from satellite data as well as ocean accessibility, calculated for each species as described above. Detailed information about the Emperor penguin and Adélie penguin metapopulation models are found below. Projected penguin populations for each decade are the average over the five years surrounding each decade. This way we can look at the long term signal in the population rather than possible year-to-year variations. For future colony health, we follow15 and define penguin colony health as follows: non-threatened’ penguin colonies are projected to experience either an increase in population or a decline less than 30%; vulnerable’ penguin colonies as those projected to experience a population decline by more than 30%; endangered’ is a likely population decline by more than 50%; quasi-extinct’ is a population decline by more than 90%. We use the penguin populations from the 2000s as a baseline for the penguin colony health.

Antarctic ecosystem value (AEV) index calculation
The AEV index identifies the areas of highest value over multiple trophic levels of the Antarctic food web. We calculate AEV Index values for a historical reconstruction that correlates to conditions experienced from 2000–2020 and for a coupled, free running Earth system model that represents changing climate conditions. Supplementary Table 1 lists the sources for each of the five inputs for both the historical reconstruction and the free running AEV Index calculations.

The metric is computed within the seasonal sea-ice zone south of 60°S and separately for the five regions shown in Fig. 1a: Weddell Sea, East Antarctic, Ross Sea, Amundsen Sea, and Antarctic Peninsula. We calculate the AEV Index by region because Antarctic marine protection is considered by individual planning regions, and we aim to identify the most valuable area for protection in each management region. These regions generally correspond to the Marine Protected Area (MPA) planning domains determined by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR)87: domains 3 and 4 (Weddell Sea), domain 7 (East Antarctic), domain 8 (Ross Sea), domain 9 (Amundsen Sea), and domain 1 (Antarctic Peninsula). For our analysis, we make two important changes to the standard CCAMLR regions. First, we combine planning domains 3 and 4 to form the Weddell Sea region because they both span the Weddell Gyre and the current MPA proposals for the Weddell Sea span both regions. Second, we have shifted the southeastern boundary of the Antarctic Peninsula domain to include some of the Weddell Sea because there are important Adélie penguin colonies on each side of the CCAMLR domain boundary and we have tried to use regions that are more aligned with the known biological connectivity.

We calculate the AEV Index at each grid point at location i (latitude) and j (longitude) as follows. We scale each of the five inputs by the maximum value over the entire Southern Ocean domain and then sum the scaled inputs:

AEVsum_ij = (NPP_ij / NPP_max) + (KGP_ij / KGP_max) + (DFP_ij / DFP_max) + (EPP_ij / EPP_max) + (APP_ij / APP_max) (9)

Each input is weighted equally in the sum of inputs. Then, to get the final regional AEV Index, we scale the summed value at each point by the maximum summed value in each of the five analysis regions:

AEV_ij = AEVsum_ij / AEVsum_max_region (10)

We also performed several sensitivity studies with the AEV Index calculation. We experimented with scaling the index by the median and mean values, instead of the maximum, and it did not impact the AEV Index results significantly. As a result, we use a maximum value for scaling as it is more directly interpretable. Thus, the AEV Index presented in this analysis has values from 0 to 1, with 1 indicating the grid point(s) with the largest sum and therefore has the highest value over all inputs in that region. It is important to note that an AEV Index value within a particular region is not directly comparable to values outside that region as the calculations and scaling are done separately. To highlight the particularly valuable locations within each region, we have binned the AEV Index into four categories: exceptional (AEV Index in the top 5th percentile for that region), very high (AEV Index in the top 10th percentile for that region), high (AEV Index in the top 25th percentile for that region), and other (all other points). Note that the percentile thresholds for these bins are determined separately for each of the regions and decades (see Supplementary Figs. 12 and 13).

A full comparison of biodiversity metrics around Antarctica is beyond the scope of this study. However, to demonstrate that the historical reconstruction AEV Index - both the hemispheric and regionally scaled versions - provides sensible information about valuable biological areas, we qualitatively compare it to the Areas of Ecological Significance (AES) identified by ref. 12 using in-situ tracking data from 17 marine mammal and bird species to identify regions preferred by these predators. Both the AES and AEV Index metrics identify ecologically valuable regions using multiple species, yet it is important to note that while species overlap between these two metrics, the methodology is entirely different and the metrics do not share any overlapping data. This comparison is done primarily to show that a model based metric to identify valuable biological areas is comparable to a similar metric that uses observational data, but a full analysis comparing multiple Antarctic metrics of biodiversity and biological importance is beyond the scope of this work. We find that both metrics identify similar grid cells as valuable (grid cells in the exceptional, very high, and high value bins) as the AES locations, particularly in the coastal areas of the southwest Ross Sea, parts of the East Antarctic, and in the northern Antarctic Peninsula. However, the regionally weighted AEV Index has more overlap with the AES in sectors beyond the Ross Sea and Antarctic Peninsula, thus bolstering our conclusion that the regional weighting is a better way to identify regionally important high-value areas. In other areas, the results do not align as well, which may be due to differences in data availability. In the Amundsen Sea there is less animal tracking data and it is also an area the AES does not identify as a valuable area while the AEV Index does. In other areas, like the Balleny Islands northeast of the D’Urville station in East Antarctica, the AEV Index does not show these as valuable possibly because we didn’t include species inputs that emphasized this area. Thus, we believe that the AEV Index, which is calculated using data on a coarse (~1°) Earth system model grid, is capable of providing high level information about valuable areas around Antarctica, particularly into the future. It is important to note that additional research and monitoring, including with existing observational datasets such as those used in the AES study, could be conducted to further refine locally valuable regions.

Penguin metapopulation models
Two metapopulation models were employed, one for each of the Emperor and Adélie penguins, to forecast the population vector n(t), where each element represents the total population size at each colony at time t. This requires the vector r_i(t) of intrinsic population growth rates at each colony at time t, that is estimated using two approaches: one based solely on Capture-Mark-Recapture (CMR) data and a Structured-non-linear Population model (SPCMR model)88,89 and the other using nest count data from the Antarctic Penguin Biodiversity Project90 and a global empirical population model28,29.

For Emperor penguins, the metapopulation model is also parameterized using new data on dispersal distances, emigration rates, and dispersal behaviors derived from 83,91. Emperor penguins are most likely to disperse through semi-informed dispersal (informed departure but random settlement), characterized by a relatively small mean dispersal distance of 414 km and low emigration rates83. For Adélie the model is parameterized with a mean emigration rate of 0.035 with a semi-informed dispersal assumption. 92 demonstrated that dispersal of breeding Adélie penguins is typically low (<1%), except during years with challenging environmental conditions when movement rates increased, particularly away from the smallest colonies (up to 3.5%). For our analysis, we adopted the highest observed emigration rate (3.5%). Despite incorporating this elevated rate of emigration, the dynamics of the regional and global populations exhibited a trajectory similar to that of a metapopulation without dispersal, suggesting that the higher dispersal rate did not significantly alter overall global and regional population trends for Adélie penguins.

The metapopulation projects the total population size at each colony at time t using:

n(t+1) = D[t, n(t)] * F[t, n(t)] * n(t) (11)

to indicate that the projection interval is divided into two main phases of possibly different duration: the motionless reproduction phase (F) followed by the dispersal phase (D). The projection matrices (F) and (D) depend on both the current population density n(t) and time t because habitat conditions vary among patches and over time. The reproduction matrix F and dispersal projection matrix D are detailed in refs. 91,93, but both depend on r, the vector of colony-specific intrinsic population growth rates at each time step. For Adélie penguin r is calculated using equation B9. For emperor penguin, r is calculated using

r_i,t = log(w_i(t+1) / w_i(t)) (12)

where w(t) is a vector of the number of individuals in each stage for colony i at time t calculated from equations B3.

Emperor penguin model intrinsic growth rates. We used a non-linear, two-sex, stochastic, seasonal, and climate-dependent matrix population model developed by refs. 15,88,89 to calculate the intrinsic population growth rate r_i,t at each colony i and year t. To obtain r_i,t, we projected the population vector w_i as:

w_i(t+1) = A[E(t), w_i(t)] * w_i(t) (13)

where the projection matrix A[E(t), w_i] is determined by vital rates, including survival probabilities, breeding probabilities, breeding success, and mating probabilities, all of which depend on sea ice concentration anomalies (E(t)) around each colony. The functional relationships between vital rates and sea ice concentration anomalies were estimated by ref. 88 using the long-term longitudinal data from Pointe Géologie. In addition, A[E(t), w_i] is influenced by the current population vector (w_i) due to the mating processes that depend on the proportion of males to females available to mate. The population vector w_i distinguishes five stages within the population according to breeding status and sex: male and female prebreeders, male and female nonbreeders, and breeding pairs. A prebreeder is an individual who has yet to breed for the first time. A nonbreeder is an individual that has bred at least once before, but is not breeding in the current year. A breeding pair is a female and a male who mate and cooperate over the breeding season to produce offspring. This sea-ice-dependent two-sex stochastic model has been validated to project the emperor penguin population in previous studies15,16,27,91.

Adélie penguin model intrinsic growth rates. To estimate relationships between long-term average colony growth and long-term average sea ice concentration, this study uses the Adélie penguin global population model from 28 and expanded by ref. 29.

To parameterize the model, we used nest count data from the Antarctic Penguin Biodiversity Project90. The model fitting uses the 38 colonies that have at least 10 years of counts between 1979-2018, as was done in ref. 29. Sea ice concentration for the same years is from satellite observed passive microwave radiation at a 25 x 25 km resolution94 and rescaled to a 1-degree resolution. The sea ice concentration for each colony is identified within a 500 km polygon for March to June each year, which corresponds to the 4-month period following the breeding season.

We modeled true logged nest abundance (lz_i,t) for colony i in year t as:

lz_i,t ~ N(lz_i,t-1 + μ_ri, σ^2_i) (14)

where μ_ri is the average growth rate for colony i, and σ^2_i is process error capturing annual variation in abundance for colony i that is not explained by simple exponential growth. We model μ_ri as a function of the long-term average sea ice concentration (SIC) surrounding each colony:

μ_ri ~ N(β0 + β1 * SIC_i + β2 * SIC_i^2, σ^2_r) (15)

where the β parameters fit a quadratic relationship between long-term average SIC and average colony growth rate, and σ^2_r is process error capturing colony-specific variation in growth rates not explained by its modeled relationship with sea ice. We model σ_i hierarchically using a normal distribution truncated at 0 with hyper-parameters γ and τ:

σ_i ~ N+(γ, τ^2) (16)

We model the logged nest count at colony i in year t as:

y_i,t ~ N(lz_i,t, s^2_i,t) (17)

whose mean is the true logged nest abundance at colony i in year t, and where s^2_i,t is the observation error associated with the nest count. See ref. 28 for details regarding how the observation error for each nest count was determined. We chose weakly informative priors for all parameters in the model.

Projecting long-term populations under climate change. We used output from the Community Earth System Model version 2 (CESM2)21 to forecast and hindcast colony growth rates into years 2100 and 1900 (Emperor penguin) or 1939 (Adélie penguin), respectively. CESM2 is a state-of-the-art Earth system model, run at the nominal 1° resolution with ocean biogeochemistry. A thorough description of the ocean ecosystem model in CESM2 is documented in ref. 22.

We used a large ensemble of fully coupled CESM2 simulations (CESM2-LE)24. We used 50 members of the CESM2-LE which are all forced by the Coupled Model Intercomparison Phase 6 (CMIP6) forcing following shared societal pathway 3-7.0 (SSP3-7.0) but differ slightly in their initial conditions. The small differences at initialization produce a spread in the timing of natural (internal) climate variability of the model (for further description of initial condition large ensembles see ref. 95).

The coupled model configuration produces a different mean state and variance as compared to observed sea ice data. Thus, to make the future and the past coupled model simulations more comparable to passive microwave observations, we applied a bias correction procedure to each of CESM sea ice concentration values of CESM simulations:

δ_cesm * (y_t - θ_cesm) / δ_cesm + θ_obs (18)

where, δ_obs and δ_cesm are standard deviations of the observed and CESM sea ice concentration data for the period 1979 to 2018, respectively; θ_obs and θ_cesm are the means of the observed and CESM sea ice concentration data for the period 1979 to 2018, respectively; y_t is the CESM sea ice concentration value of year t for a given member of CESM simulations. We apply this procedure separately for each of the 66 emperor penguin colonies or 287 Adélie penguin colonies that were used for colony growth projections.

For Adélie penguin, because we use 40-year SIC average to build the empirical model explained above, we used 40-year moving window average of CESM data as well. After bias correction we calculated each year SIC data as the average SIC of the preceding 40 years. So, our colony growth projections range between 1939 and 2100 as the earliest CESM projection we used was 1900. We truncated SIC projections that were beyond the data bounds observed between 1979 and 2018 with the maximum or minimum observed SIC. So, none of the projections were extrapolations.

For the Emperor penguin, our metapopulation models SPCMR employed a time-for-space substitution to project population dynamics for other colonies by assuming that the functional relationships between environmental factors and vital rates estimated at Pointe Géologie are consistent across space, despite potential local adaptive differences or other site-level effects. The model generalizability and predictive performance on new data was conducted by ref. 91 using two approaches: (1) the ability of the model to project year-to-year dynamics at Pointe Géologie; (2) the ability of the model to match the observed global trend and annual fluctuations in emperor penguin abundance between 2009 and 2018.

We compute A[w_i] for all colonies and each year by applying the functional relationships between vital rates and sea ice established at Pointe Géologie to the bias-corrected CESM projections E(t) at each colony for each year. From this, we can then calculate w_i (Equation B3) and r (Equation B2) for the total population. We performed 100 simulations per colony for each climate projection by 1) uniformly sampling the initial population structure from the k-simplex using the broken stick method and 2) sampling the relevant vital rates from their multivariate normal distribution while incorporating both measurement error and environmental stochasticity through explicit error propagation in the observed and process variance-covariance structure. This resulted in a set of intrinsic growth rate vectors, r, that account for different sources of uncertainty included in our metapopulation model, such as measurement error, parameter uncertainty, natural variability in demographic processes, and uncertainties in initial conditions.

For Adélie penguin, we used the fitted relationship in equation 2 coupled with the bias-corrected CESM projections to project average colony growth rates (μ_ri,t) for each colony (i) and year (t), between 1939 and 2100. Because equation 2 only encompasses spatial gradients in sea ice variability, these projections can be considered as space-for-time substitutions96. To employ parameter uncertainty in these projections, we randomly sampled parameters from their respective posterior distributions across 100 iterations. In each iteration, we additionally sampled μ_ri,t from the distribution to employ uncertainty caused by variance in average colony growth unexplained by sea ice. Finally, we projected annual colony growth with spatio-temporal process variance at a given colony and year using:

r_i,t ~ N(μ_ri,t, γ^2) (19)

Where, r_i,t is the projected annual growth at colony i and year t. This resulted in 100 annual colony growth times series for each colony in each CESM member, a total of 1,445,000 times series.

Metapopulation projections. For each metapopulation model, we then projected the annual abundances for all colonies, n(t), using equation B1 by drawing 1000 simulations randomly from the intrinsic population growth rates projections just discussed to parameterize Fm. In addition, for emperor penguins we draw from the posterior distributions of dispersal rates and distances83 to parameterize Dm. For Emperor penguin projections, we used N_0, based on the average population size derived by ref. 84 from satellite data and the stable population distribution computed from average sea ice concentrations during the observation period at each colony91. For Adélie penguin projections we used N_0, based on the median count for each colony from MAPPD.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General Comments\nThe manuscript proposes a novel way to identify ecologically valuable areas in the Southern Ocean, applies this to current and future climate scenarios (up to 2090), and links their geospatial pattern to the occurrence of polynyas. It is novel in that it is the first index to consider multiple trophic levels, and the first to explicitly link them to polynya occurrence. Results highlight distinct areas of high ecological value, some of which continue to exist until 2090. They occur in areas where present and future polynyas occur. Examining area of importance across the ecosystem both now and in the future in this way is relatively novel and the results that indicate many areas of high ecological value don’t substantially change in location are noteworthy and potentially surprising given the scale and magnitude of expected environmental changes. The results are clearly underpinned by a substantial set of earth system and biological models. My main criticism of this work is that there is no mention of any validation to determine how well the present-day biological models perform (and by extension future models will presumably perform), particularly in space, which is the basis upon which the AEV Index is built. Thus, it makes it difficult to determine how robust the AEV spatial patterns are. This particularly concerns me for the krill and demersal fish models- 2/5 of the aggregated index.\nThe underlying biological models would also have uncertainty associated with their predictions, yet this is not presented in the spatial layers of each of the component of the index (i.e. krill growth, demersal fish biomass) and not propagated through to the AEV Index. A sensitivity analysis is conducted on the calculation of the AEV index in terms of whether means or medians etc should be used, but I should imagine these effects would be small for each grid cell compared to propagating biological model/prediction uncertainties.\nThe penguin population models are well described in the appendix and supplementary material, however the krill growth and the demersal fish biomass models are not. The krill model is stated as an empirical relationship between chla and sea surface temperature. The demersal fish are a product of the FIESTY global size-structured model and readers are mostly referred to a reference. More detail is needed about these models to evaluate the utility, without having to read the cited paper. For example, what environmental forcings are used in the FIESTY model. It is a global model- does it have appropriate inputs in the Southern Ocean (many do not). Is this even an appropriate model to use if it doesn’t capture the habitat preferences of the two most common and abundant (and in the case of toothfish high biomass) species (as mentioned in the discussion)? Would a species distribution model for the major species be more appropriate? Again, this highlights the importance of some type of validation to help the reader believe the underlying models are robust.\nSome additional items for consideration include giving greater detail on some of the reasoning behind decisions made, and improving the discussion of results with regards to other studies and polynya dynamics. We also recommend the authors depict both regionally weighted and hemispheric weighted results to aid the reader in distinguishing regional from circumpolar importance\nHaving said the above, we do think that the manuscript addresses an important question in a novel way and is a significant body of work- IF we can be convinced that the spatial patterns and predictions of the models that make up the AEV present-day index, in particular are robust.\nSpecific Comments\nLine 121 onwards: Might be worth being a bit more specific with regards to the dynamics, for your less oceanographically-inclined reader. Also are we looking at just coastal polynyas or open water ones also?\nLines 150-151: “Here, we present the Antarctic Ecosystem Value (AEV) Index that identifies regions of high ecological value around Antarctica.” Maybe consider adding “based on several key trophic levels” to underscore the novelty of your work early on.\nLine 158: Please specify the climate change scenario number, makes it easier for the reader not to have to jump to the methods.\nLine 173: Consider adding a sentence to clarify some MPAs have passed and some have been proposed, otherwise might come as a bit of a surprise in Section 2.\nLines 180 onwards: Consider rephrasing to “Areas with the highest AEV Index occur along the coast in each region (Figure 1a). These high value areas are generally regions where there are high values for all trophic levels considered. Specifically, they have high net primary productivity (Figure 1b)...”\nFigure 1 a): Why are there two black lines for the border between the Antarctic Peninsula and the Weddell Sea? This applies throughout the paper, does this have to do with the shift of the peninsula region? Could one of them be a dashed black line?\nFigure 1 a): Seeing as you used the free-running model going forward, it might be worth depicting this here instead of the historic indices. Also please include text in the legend to indicate the indices have been regionally weighted.\nLine 216: What is the free-running AEV version exactly? How is it model-based exactly? Needs explaining in main body of ms.\nLines 218 onwards: Are you describing the hemispheric (not regionally weighted) areas here? When looking at the regionally weighted ones, the Weddell Sea and East Antarctica offshore regions stand out also. There might be value to you discussing both the hemispheric and the regionally weighted approach – both show slightly different things and are useful for different aspects. In which case you could include the hemispheric map in Figure 1.\nTable 1: If you are making the comparison of “AEV Index (unitless) inside polynyas”, you probably also would need to look at the average AEV Index outside of polynyas.\nTable 1: For the sake of statistical interpretation, we suggest adding a boxplot (at least in the supplementary figures) that shows the potential ranges for the values shown in the table.\nTable 1: At what projection did you carry out the area % changes? (Was it an equal area projection?) Please specify this either in the legend text or in the methods.\nLine 234: If you reference longitudes, please add some reference points to your maps.\nLine 237: “The AEV index was calculated without the direct inclusion of environmental variables”. This is a little confusing/distracting as environmental data is a key input into the models.\nFigure 2: It would be useful to have circumpolar maps oriented in the same way in all figures. This one is different to Figure 1.\nFigure 3: If we are comparing how AEVs have changed over time, we probably might also want to adjust the ecological layers to reflect change over time (i.e. 2090-2000).\nFigure 4 and associated results: Why were averages only taken over a subset of the area of each domain? How were boundaries decided? What is the effect of the choice of boundaries on the results presented?\nLines 260-262: Could you provide a percentage?\nLines 251-253: Perhaps qualify this a bit more, after all some areas like the Peninsula decrease significantly.\nLines 268-270: Presumably things like iron play a role – do you have any insight how these are related to the polynya areas? There is some literature on individual polynyas (like the Amundsen sea one - https://doi.org/10.1016/j.gca.2023.10.029) you could draw on.\nLines 357-360: doesn’t make sense that demersal fish biomass increases over time but fish habiat lost for present day populations??????\nLines 401-409: Might want to consider adding a line to say you targeted species across trophic levels that are core ecosystem components in the Southern Ocean and might be good indicators.\nLines 409 onwards: What are the differences of your study to the AES study – some of the Ross Sea Area only becomes highly valuable in the future in the AES study, while yours finds that it stays the same / increases? Why are those? This is briefly touched upon in the methods, please elaborate on this in the Discussion. Also the hemispheric results differ more from the AES study. This could highlight bias in the way tracking data was selected for the AES study, or limitations in hemispheric applicability of your index. Please discuss.\nLines 416-418: Perhaps rephrase to make this recommendation even stronger, something along the lines of: “This MPA is set to expire in 2052. This work provides evidence the MPA would contain valuable regions through to 2090 and should be considered for extension.”\nLines 421 onwards: Might be worth citing https://doi.org/10.1016/j.marpol.2024.106232 that addresses value of Domain 9 Lines 428 onwards: A bit confusing, suddenly coastal and “large-scale” polynyas are described rather than just polynyas. Line 427: Please consider citing some of the sources that proposed “ecological oases” as a concept (I believe it originates from https://doi.org/10.1371/journal.pone.0023047?)\nLine 433: Was there any patterns or explanations for which polynyas did not have high AEVs?\nLine 437-8: What is the hypothesized link between increased carbon flux and demersal fish??? Many demersal species in this region are carnivores.\nLines 444-448: Gives rise to the question of weighting. Do we care more about some species than others? Are there any areas projected to be suitable for Emperors- should they be targeted more for protection? I’m not necessarily saying that the layers should be weighted differently, this depends on the intended use in terms of conservation outcomes.\nDiscussion starting line 475: While this is an honest discussion around some of the potential issues with demersal fish model, it highlights a significant question around whether this model is actually in any way realistic. Icefish and toothfish are common species and would generally account for a large portion of the biomass. Toothfish are well known to exhibit an ontogenetic shift in habitat with smaller fish in shallow coastal waters and larger/ higher biomass fish in deeper water from around 800m depth. This seems in contrast to the maps presented. Again, it underscores the importance of presenting validation information for the demersal fish model to enable the reader to believe it is robust.\nLine 504: Consider adding a sentence or two about where current anthropogenic activities occur (fishing, but maybe also tourism?) Is there overlap with any of the high AEV areas? Any overlap with high AEV areas that will have decreased by 2090 (i.e. where fishing might contribute to an even faster decline)?\nLines 521 onwards: You are not considering reductions in the ice shelves – your models only extend to the shelf break. Is there a way you could extend your analyses to the areas freeing up in the future?\nLines 663-4: States that the AEV was validated against Areas of ecological significance- which comprise areas of sea bird and mammal use. This is not a validation. At best it is a qualitative comparison. The logic that they are similar therefore the AEV is working well if flawed. Firstly, if they are so similar then why do we need the new AEV index? Secondly, a better comparison would be to quantitatively compare your layers for Emperor penguins and Adelie penguins to the corresponding AES layers which are generated from actual data using tracked animals.\nLines 668 onwards: This is true for the regionally scaled AEV, but more significant differences occur when looking at the hemispheric solution.\nAppendix B- 1214-1217: The penguin population models seem to already include sea ice variability in population size estimates. Is there circularity in using sea-ice again for the projection of important areas?\nAppendix B- 1308-1312: Indicates some validation of the emperor penguin model was conducted, but nothing is reported. Is this a ‘good’ or fit for purpose model?", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "This manuscript presents an interesting and timely approach to identifying ecologically valuable areas in the Southern Ocean by creating the Antarctic Ecosystem Value (AEV) Index. While the study provides valuable insights into current valuable areas and potential changes in them under future climate scenarios, I have some methodological concerns that should be addressed before publication. These include mainly a clarification of the regional standardisation on the text to avoid potential misinterpretation, and further explanation on the choices made for the weighting of ecosystem components. The weighting as is overrepresents penguins, and a justification or adjustment of the model is necessary. I think the authors do a good job on the discussion addressing some of their model limitations, but don’t mention the lack of other components of the food webs of the Southern Ocean, or why penguins are so important. Addressing these issues would strengthen the ecological foundation of the study and improve its utility for conservation planning in the Antarctic region.\nGeneral comments\nI think this study will be of relevance to Southern Ocean studies, and it’s novel enough compared to previous attempts at creating similar indices. The code provided with the article is well documented. I think the work supports most of the conclusions, except for what I raise below.\nSpecific comments\nLines 164-166: “The AEV Index is useful for three reasons. First, it seeks to comprise a holistic ecosystem value by focusing on Antarctic-specific species and integrates across trophic levels (see 28).”\nSee my comments below, but calling it a “holistic” ecosystem value may be too generous when you’re missing some key taxonomic groups (and by consequence overestimating the importance of the ones included) that are very important in regional and global carbon and nutrient cycles.\nLines 156-159: \"We use data from an Earth system model (ESM) that includes marine biogeochemistry [19, 20] for both a historical reconstruction [21] and coupled future projection with upper-middle CO2 emissions [22].\"\nReading this, the methods, and looking at Table 1, you have what it looks like two historical periods; one that you can historical in table 1, and another you name as \"2000s\". I understand one is based on historical reconstructions and the other is model-based and used for future projections, but it's not very clear on the methods which one is which and why you need both. Please make it clearer on the text thinking of the average reader that may not be used to modelling and future projections. This also applies to Table A1: understand the Earth System Model includes the future projections, but I think both the table and the text in general would benefit from clarifying or identifying that the ESM includes future projections. Lines 268-270: \"In all regions total net primary productivity increases over time (Figure 4b), likely because ice loss reduces phytoplankton light limitation and extends the growing season.\" If NPP is coming from the ESM, does the ESM specifically model the relationship between sea ice loss and phytoplankton productivity? While this mechanism is plausible, it would be helpful to confirm whether the model actually simulates these specific processes (light limitation changes due to ice loss) or if this is an interpretation of the results. If the model doesn't explicitly track light limitation, there could be alternative explanations for increased NPP.\nFig. 2 \"a) Model based 2000s locations of high (pink) and exceptional (red) Antarctic Ecosystem Value (AEV) Index indicating ecologically valuable regions\"\nThe regional normalization approach used for the AEV Index creates a potential misinterpretation risk that I think would benefit from being addressed, at least in this figure. By normalizing values within each planning region rather than across the entire Southern Ocean, areas labelled as \"exceptional value\" in different regions are not directly comparable. For example, an \"exceptional value\" area in East Antarctica may have significantly lower absolute ecological importance than an \"exceptional value\" area in the Ross Sea or Antarctic Peninsula. So I would recommend clearly stating in the figure legend and maybe more clearly in the main text that the AEV Index values represent relative importance within each planning region only, not across the entire Southern Ocean. I do understand the reasoning behind this standardization, but making it clear for the interpretation of your results doesn't hurt.\nLines 588-590: This line mentions how CESM2 does not represent specific Antarctic phytoplankton species. It would be nice to know which phytoplankton communities are not included, and how much they are expected to contribute to NPP. I'm not overly familiar with CESM2 and I think a lot of people will not know the specific details, so possibly just a sentence here clarifying how this could impact estimations of NPP would be nice. For instance, does the model capture ice algae communities? Understanding these limitations would help readers better interpret the NPP projections.\nLines 645-655: You gave equal weighting to all five metrics (NPP, KGP, DFB, EPP, APP), and I want to understand the reasoning as this may overrepresent certain trophic levels while undervaluing others. There's two penguin-specific metrics (EPP and APP) but only one primary productivity metric (NPP), which means that the index assigns 40% of the weighting to penguins while allocating just 20% to the trophic level that supports the entire ecosystem, from my understanding of it. This imbalance may distort the identification of high-value areas by overemphasizing regions important to penguins relative to their ecological significance in the broader Antarctic food web. I would like to at least see a justification in the text on why penguins as top predators would justify being 40% of the index. Additionally, the model excludes other key top predators such as whales and seals, which play critical roles in Southern Ocean ecosystem functioning. Their absence further complicates the potential bias created by the penguin-heavy weighting. If it’s not possible to justify this weighting, then I would recommend implementing a more balanced weighting of the different trophic components.", "reviewer_4": "This manuscript introduces a new index to evaluate the value of Antarctic marine ecosystems called the Antarctic Ecosystem Value (AEV) that merges ecosystem information across food web trophic levels. With this index, high value regions in Antarctica were identified for current conditions and for future projections to 2090. They found areas of reduced sea ice (polynyas) to be particularly valuable. They deemed areas that continue to be valuable into the future especially important to preserve. These areas were largely within MPAs, but also in proposed MPA areas or areas not in MPAs, which led to the recommendation to develop protection plans for valuable but currently unprotected regions. The manuscript was well-written and uses the best available models and data to inform the index. I agree that the metrics included in the index together form an important and informative collection to evaluate the value of regions in Antarctica.\nSince this is an introduction of a new index, I would like to see presented as part of the results that the index works. I suggest moving the model validation section into the result section with figures and statistical output, and provide a compelling conclusion from this that the index does indeed correspond with quality habitat/high value ecosystems. The same with the AEV ranges, allow the reader to conclude that the ranges and cut-offs from good to excellent are logically placed. Now that readers have the information to decide if the index can be trusted, results from the index, based on and future conditions, can be presented.\nMinor note on page 5 and on: The hyperlink to S1 doesn’t take you to the S1 figure, but to page 5.", "reviewer_5": "This manuscript provides an aggregated index for considering productivity and accessibility for two predator species at a circumpolar scale in the Southern Ocean. The authors calculate this index for historical, present, and future climate conditions, using output from Earth System Model simulations. The index shows a strong relationship with the location of Antarctic polynyas, which generally persists under future climate conditions.\nThis work is ambitious and is a useful demonstration of an approach to aggregate information about net primary productivity, krill growth potential, demersal fish biomass potential, emperor and Adelie penguin populations, and access to areas for foraging. The authors should be congratulated on the volume of work underpinning the manuscript and the clear presentation of results in figures and tables.\nWe have several concerns regarding the use of this index to consider Antarctic ecosystem value more generally, as well as the methods for calculating krill growth potential and demersal fish biomass potential. Large-scale ecosystem processes and connectivity, together with the key food webs roles of zooplankton groups and mesopelagic fish, are all critical components of ecosystem value in the Southern Ocean that underpin decisions about spatial protection, but are not discussed by the authors or considered as part of the index. There is little recognition that groups other than Antarctic krill drive energy transfer, with their relative influence differing across regions – for example, East Antarctic food webs show a higher number of alternative energy pathways between primary production and large predators (e.g., McCormack et al., 2020, 2021, doi: 10.1002/ece3.7017 & doi: 10.3389/fevo.2021.624763). This has broader implications with respect to the potential use of this index to inform marine spatial protection in the Southern Ocean, because (as we are sure the authors will be aware) Antarctic marine protected areas need to be of sufficient scale to protect ecosystem processes – not just small-scale high productivity areas. Equally, marine spatial protection is important for areas that are at risk of degrading in value due to climate change, because enhanced resilience (through reduction in other threats) provides the best chance of ecological processes and values persisting in these areas.\nWith respect to the methods used by the authors to calculate krill growth potential, we are concerned about the use of the 2006 Atkinson model, which is not designed to address the effects of climate change on physiology, energetics, and fecundity of krill, which are the primary factors governing productivity. The authors do discuss other krill models briefly, but indicate that “they are hindered by substantial uncertainties regarding krill ecology and their interactions with environmental drivers”. It is unclear to us which specific uncertainties prevent the use of more recent and improved krill models in this study. As it stands, the use of a simple empirical relationship between temperature, surface chlorophyll, and krill growth needs to be much more strongly caveated – with implications for interpretation of results explained; the equation for calculating krill growth should also be included for clarity. Consideration, or a caveat, should be given to the variety of krill diets and their dependence on food items other than diatoms, even during the productive summer months. The aggregation of all phytoplankton groups from the NPP model to simulate diatoms is potentially an oversimplification and might lead to an overestimation of krill growth, as krill have been shown to be quite selective feeders (Pauli et al., 2021 https://doi.org/10.1038/s42003-021-02581-5).\nWe are also concerned about the use of projections for demersal fish biomass based on a global model that does not account for the unique physiology of Antarctic fish. In lines 475-485 the authors clearly state that the projected changes for demersal fish are not consistent with expected impacts of temperature increases on key Antarctic species. Future increases in demersal fish biomass due to southward expansion of subpolar and temperate species (presumably not subtropical as the authors state in line 483?) are highly uncertain. As such, we think that the fish projections used in the index are misleading.\nFinally, the usage of the climate model CESM2-LE should be justified in light of its inclusion in an ecosystem index. For example, the sea ice-ocean model ACCESS-OM2 has been specifically reviewed for its application in ecological studies (Fierro-Arcos et al., 2023 https://doi.org/10.1016/j.pocean.2023.103049). Were other ESMs considered?\nSpecific comments:\n1. Line 146 – please see Cavanagh et al. (2021, doi: 10.3389/fmars.2020.615214), who consider more than two trophic levels and have a Southern Ocean focus.\n2. Lines 216-217 – it is unclear to us how Figure S1 compares the historical reconstruction with the model-based index. Could the authors please explain how this comparison is being made?\n3. Methods – the inclusion of equations within the bullet points in the AEV index methods section, on top of the written explanation, would make it clearer for the readers to follow. It would also be useful to see some form of sensitivity analysis regarding which of the inputs for the index is most important in driving the overall value of the index over time.\n4. Figures: Fig. 1 side panels are very hard to read – making them larger would be very helpful. Fig. S1 refers to Fig. 1, but that is not immediately clear; including the historical reconstruction in S1 would make the comparison much easier. Fig. 2: Interestingly, a large swath of East Antarctic waters is considered to be of Medium value. However, no explanation is given in the text; what could be driving this?", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Thanks to all six reviewers for your helpful comments and suggestions. We have addressed all concerns and responded to each reviewer in detail. Our response text is in blue and all line numbers in our responses refer to the revised manuscript. We include the page references, below, to help reviewers navigate our response since it is long.\nReviewer #1 (Remarks to the Author): ....................................................................................... 1\nReviewer #2 (Remarks to the Author): ..................................................................................... 21\nReviewer #3 (Remarks to the Author): ..................................................................................... 22\nReviewer #4 (Remarks to the Author): ..................................................................................... 30\nReviewer #5 (Remarks to the Author): ..................................................................................... 32\nReviewer #6 (Remarks to the Author): ..................................................................................... 46\nReviewer #1 (Remarks to the Author):\nGeneral Comments\nThe manuscript proposes a novel way to identify ecologically valuable areas in the Southern\nOcean, applies this to current and future climate scenarios (up to 2090), and links their geospatial\npattern to the occurrence of polynyas. It is novel in that it is the first index to consider multiple\ntrophic levels, and the first to explicitly link them to polynya occurrence. Results highlight\ndistinct areas of high ecological value, some of which continue to exist until 2090. They occur in\nareas where present and future polynyas occur. Examining area of importance across the\necosystem both now and in the future in this way is relatively novel and the results that indicate\nmany areas of high ecological value don’t substantially change in location are noteworthy and\npotentially surprising given the scale and magnitude of expected environmental changes. The\nresults are clearly underpinned by a substantial set of earth system and biological models.\nMy main criticism of this work is that there is no mention of any validation to determine how\nwell the present-day biological models perform (and by extension future models will presumably\nperform), particularly in space, which is the basis upon which the AEV Index is built. Thus, it\nmakes it difficult to determine how robust the AEV spatial patterns are. This particularly\nconcerns me for the krill and demersal fish models- 2/5 of the aggregated index.\nResponse:\nThanks for pointing out this gap in the methods. We have substantially expanded the methods\nsection to explain and validate the Antarctic sea ice as well as different AEV Index input layers\nin detail. Please see Online Methods Section 4.3 in the revised manuscript (we have not pasted\nthe whole section in this response since the revised text is several pages long).\nThe underlying biological models would also have uncertainty associated with their predictions,\nyet this is not presented in the spatial layers of each of the component of the index (i.e. krill\ngrowth, demersal fish biomass) and not propagated through to the AEV Index. A sensitivity\nanalysis is conducted on the calculation of the AEV index in terms of whether means or medians\netc should be used, but I should imagine these effects would be small for each grid cell compared\nto propagating biological model/prediction uncertainties.\nResponse:\nThanks for pointing out this gap in our discussion. Please see Discussion Section 3.4 in the\nrevised manuscript that focuses on model uncertainties (we have not pasted the whole section in\nthis response since the revised text is several pages long). Additionally, we have also included\nstandard deviation in addition to averages (e.g. Revised Figure 3a, Figure 7, Figure S5, Table 1,\nTable 2) in the manuscript to quantify uncertainties where it is possible to do so.\nThe penguin population models are well described in the appendix and supplementary material,\nhowever the krill growth and the demersal fish biomass models are not. The krill model is stated\nas an empirical relationship between chla and sea surface temperature. The demersal fish are a\nproduct of the FIESTY global size-structured model and readers are mostly referred to a\nreference. More detail is needed about these models to evaluate the utility, without having to read\nthe cited paper. For example, what environmental forcings are used in the FIESTY model. It is a\nglobal model- does it have appropriate inputs in the Southern Ocean (many do not). Is this even\nan appropriate model to use if it doesn’t capture the habitat preferences of the two most common\nand abundant (and in the case of toothfish high biomass) species (as mentioned in the\ndiscussion)? Would a species distribution model for the major species be more appropriate?\nAgain, this highlights the importance of some type of validation to help the reader believe the\nunderlying models are robust.\nResponse:\nAs mentioned above, we have substantially expanded the methods section to explain and validate\nthe Antarctic sea ice as well as different AEV Index input layers in detail. Please see Online\nMethods Section 4.3 in the revised manuscript, where we now include equations for the\ncalculation of NPP, KGP, and explain what inputs the FESITY model needs.\nIn the expanded methods section we explain that FEISTY is a global model and the input\nvariables. FEISTY provides estimates of biomass for three types (demersal, forage, and large\npelagic fish) over three size classes, as illustrated in the diagram, below, from Krumhardt et al.\n2024. The authors are unaware of any species distribution models specific to Antarctic fish. Thus,\nwhile FEISTY is not designed for Antarctic fish species, we believe it is the best current estimate of\npossible fish biomass for the present day and future. To account for habitat suitability\n2\nof Antarctic toothfish (an example of an Antarctic demersal fish species), we have now masked\nthe Demersal Fish Biomass Potential input layer by ocean bottom temperatures and only use\nvalues where the ocean temperature is <1°C, which is an upper limit on habitat suitability for\nAntarctic toothfish (Cheung et al. 2008). In Revised Figure S10 (copied below), we qualitatively\ncompare the locations where FEISTY has highest possible biomass against the Aquamaps\nestimate of Antarctic toothfish habitat (Kaschner et al. 2019) and it compares well. While\nadequate observations of Antarctic toothfish biomass are missing to quantitatively evaluate the\nsimulated biomass fields in FEISTY, this assessment gives us confidence in the simulated spatial\ndistribution and thus in using these fields as an input layer for the AEV index.\nFigure from Krumhardt et al. 2024\nPUBLISHED DATA FIGURE REDACTED\nRevised Figure S10\nbehind decisions made, and improving the discussion of results with regards to other studies and\npolynya dynamics. We also recommend the authors depict both regionally weighted and\nhemispheric weighted results to aid the reader in distinguishing regional from circumpolar\nimportance\nResponse:\nAs the reviewer suggests, we now include both the hemispheric and regionally weighted results\nin the paper. Please see Results Section 2.1 in the revised manuscript that explains in detail why\nwe focus on the regionally weighted AEV Index version for further analysis (we have not pasted\nthe whole section in this response for brevity).\nRevised Figure 1\nnovel way and is a significant body of work- IF we can be convinced that the spatial patterns and\npredictions of the models that make up the AEV present-day index, in particular are robust.\nResponse:\nThank you to the reviewer for your feedback and support of the manuscript. You provided\nvery good suggestions that have improved the manuscript substantially.\nSpecific Comments\nLine 121 onwards: Might be worth being a bit more specific with regards to the dynamics, for\nyour less oceanographically-inclined reader. Also are we looking at just coastal polynyas or open\nwater ones also?\nResponse:\nWe have specified at this line that we are looking only at coastal polynyas. While the model can\nidentify open ocean polynyas, they are generally less common and not the focus of this analysis.\nLine 79: “Coastal polynyas are regions along the Antarctic coast with reduced sea-ice.”\nLines 150-151: “Here, we present the Antarctic Ecosystem Value (AEV) Index that identifies\nregions of high ecological value around Antarctica.” Maybe consider adding “based on several\nkey trophic levels” to underscore the novelty of your work early on.\nResponse:\nWe have modified this sentence as you suggest:\nLine 111: “Here, we present the Antarctic Ecosystem Value (AEV) Index based on\nseveral key trophic levels.”\nLine 158: Please specify the climate change scenario number, makes it easier for the reader not\nto have to jump to the methods.\nResponse:\nWe have included the scenario number here.\nLines 117-121: “We use data from an Earth system model (ESM) that includes marine\nbiogeochemistry [19, 20] for both a historical ice-ocean reconstruction [21] and fully-\ncoupled, free-running model that has a historical period and future projection with upper-\nmiddle CO2 emissions (scenario SSP3-7.0) [22].”\nLine 173: Consider adding a sentence to clarify some MPAs have passed and some have been\nproposed, otherwise might come as a bit of a surprise in Section 2.\nResponse:\nWe have modified the text as you suggested here and in the abstract.\nLine 64: “This study also shows that while many high-value AEV Index areas are within\nexisting or proposed Marine Protected Areas (MPAs),...”\nand proposed MPAs.”\nLines 180 onwards: Consider rephrasing to “Areas with the highest AEV Index occur along the\ncoast in each region (Figure 1a). These high value areas are generally regions where there are\nhigh values for all trophic levels considered. Specifically, they have high net primary\nproductivity (Figure 1b)...”\nResponse:\nWe have modified the text as you suggest and believe that the new wording is clearer.\nLines 179-182: “The most valuable areas occur along the coast (Figure 1d) and are\ngenerally places where there are high values across trophic level inputs to the AEV Index\n(Figure S2). Specifically, they have high net primary productivity (Figure 2a) and krill\ngrowth potential (Figure 2b).”\nFigure 1 a): Why are there two black lines for the border between the Antarctic Peninsula and\nthe Weddell Sea? This applies throughout the paper, does this have to do with the shift of the\npeninsula region? Could one of them be a dashed black line?\nResponse:\nThanks for pointing this out. There was an issue in the plotting scripts that caused this\nartifact. We’ve fixed all figures with the regional divisions so that this is corrected. See\nRevised Figure 1b (pasted above) as an example.\nFigure 1 a): Seeing as you used the free-running model going forward, it might be worth\ndepicting this here instead of the historic indices. Also please include text in the legend to\nindicate the indices have been regionally weighted.\nResponse:\nWe have modified the results section significantly so that the “Present-day” section focuses on\nthe historical reconstruction AEV Index, including comparison of the hemispheric and regional\nweighted indices, but does not present results directly from the free-running model. Instead, the\nfree-running model derived AEV Index is presented only in the “FUture AEV Index” section of\nresults. We have added that we are showing regionally weighted AEV Index results to figure\nlegends.\nLine 216: What is the free-running AEV version exactly? How is it model-based exactly?\nNeeds explaining in main body of ms.\nResponse:\nWe now include a paragraph explaining how the free-running AEV Index differs from the\nhistorical reconstruction.\nLines 720-729: “The historical reconstruction AEV Index uses data from an ice-ocean\nmodel forced by atmospheric observations to reconstruct observed ocean and sea ice conditions\n[23]. On the other hand, an individual fully-coupled, free-running model simulation should not\nexactly replicate observations, since observations represent one possible response of the\nclimate system to given external forcing in the presence of internal climate variability. Instead,\nto ensure the AEV Index derived from the fully-coupled, free- running model data [24] is robust,\nwe compare the historical reconstruction (average for 2000-2020 conditions) with the fully-\ncoupled simulation data for the 2000s (average over 50 ensemble members from 1998-2002) to\nensure they produce similar results in a similar climate state.”\nLines 218 onwards: Are you describing the hemispheric (not regionally weighted) areas here?\nWhen looking at the regionally weighted ones, the Weddell Sea and East Antarctica offshore\nregions stand out also. There might be value to you discussing both the hemispheric and the\nregionally weighted approach – both show slightly different things and are useful for different\naspects. In which case you could include the hemispheric map in Figure 1.\nResponse:\nWe now include results comparing the hemispheric and regionally weighted AEV Index using\nthe historical reconstruction data - Please see Results Section 2.1 in the revised manuscript (we\nhave not pasted the full text in this response for brevity). We also include the hemispheric index\nin Revised Figure 1 (pasted above, in this response).\nTable 1: If you are making the comparison of “AEV Index (unitless) inside polynyas”,\nyou probably also would need to look at the average AEV Index outside of polynyas.\nTable 1: For the sake of statistical interpretation, we suggest adding a boxplot (at least in\nthe supplementary figures) that shows the potential ranges for the values shown in the table.\nResponse (to previous two comments):\nWe now include average AEV Index values inside and outside polynyas in Revised Table\n1 (pasted below) and Table 2, as well as in Revised Figure 3a (pasted below). We also\ninclude standard deviation in these to provide information about the variability and\ntherefore uncertainty in the values.\nRevised Table 1 (example):\nTable 1: At what projection did you carry out the area % changes? (Was it an equal area\nprojection?) Please specify this either in the legend text or in the methods.\nResponse:\nOn the native model grid we know the area of each grid cell (this is essential in an ESM so we\ncan ensure accurate coupling where mass and energy are conserved). Thus, we’re able to\nexactly calculate the area percentages within a given region.\nLine 234: If you reference longitudes, please add some reference points to your maps.\nResponse:\nWe have added longitudes to all spatial plots for reference. See Revised Figure 1 (pasted\nabove) as an example.\nLine 237: “The AEV index was calculated without the direct inclusion of environmental\nvariables”. This is a little confusing/distracting as environmental data is a key input into\nthe models.\nResponse:\nWe meant that the AEV Index uses only input from biological models, which themselves\ninclude environmental data. We have removed this line in the re-written results section as it\nwas unclear and the section has changed substantially.\nFigure 2: It would be useful to have circumpolar maps oriented in the same way in all\nfigures. This one is different to Figure 1.\nResponse:\nWe have ensured all polar stereographic maps are oriented the same way with the top center\nlongitude of 0°, which is labeled on all maps (Please see Revised Figure 3, pasted above). In\nthis figure we start the plots with a longitude of 0° and proceed from L to R as one would go\nclockwise around the polar stereographic projections.\nFigure 3: If we are comparing how AEVs have changed over time, we probably might also want\nto adjust the ecological layers to reflect change over time (i.e. 2090-2000).\nResponse:\nWe have included the layer comparisons in the revised paper. Now we compare the AEV\nIndices over time in Revised Figure 4 and we show the ecological layer differences in Revised\nFigure 6. In both figures we show different plots in addition to the raw fields.\nRevised Figure 4:\ndomain? How were boundaries decided? What is the effect of the choice of boundaries on the\nresults presented?\nResponse:\nWe focused on the most valuable areas and areas that generally coincide with existing or\nproposed MPA boundaries as examples of changes over time. We now show the Ross Sea in\nRevised Figure 7 and other regions in Revised Figure S5 as examples of the change in time.\nRevised Figure 7\nLines 260-262: Could you provide a percentage?\nResponse:\nWe include Revised Table 2 that compares how the percent of area in the different value bins has\nchanged from 2000 to 2090.\nRevised Table 2\nsignificantly.\nResponse:\nWe have clarified in the text that we mean that areas that are still within the sea ice zone,\nwhich the Peninsula is not, so we can’t calculate an AEV Index there.\nLines 219-221: “Future projections show that the locations with the highest AEV\nIndex, and in areas that are still in the seasonal sea ice zone, generally remain consistent\nin the same coastal locations from 2000 to 2090 (Figure S4 and S3).”\nLines 268-270: Presumably things like iron play a role – do you have any insight how these are\nrelated to the polynya areas? There is some literature on individual polynyas (like the\nAmundsen sea one - https://doi.org/10.1016/j.gca.2023.10.029) you could draw on.\nResponse:\ndirectly depends on nutrient availability for Nitrogen (N), Phosphorus (P), Silica (Si), and Iron\n(Fe). Delving into the limitation terms of the NPP is beyond the scope of of this study, but\nbecause our model does not have nutrient input from ice shelf runoff, increased nutrients in\npolynyas areas would most likely be due to increased mixing as turbulent fluxes from ocean-\natmosphere-ice interactions enhance mixing. In the main text we have added that the increase in\nproductivity in the polynyas could also be due to more nutrient availability.\nLines 312-313: “There may also be increased nutrient availability in polynya regions,\nlikely due to increased ocean mixing in these areas, which could lead to increases in productivity\nas well.”\nLines 357-360: doesn’t make sense that demersal fish biomass increases over time but fish habiat\nlost for present day populations??????\nResponse:\nYou are correct that this was confusing. As we explain now in the methods, we have masked the\ndemersal fish biomass potential by habitat temperature thresholds for Antarctic toothfish and the\ndemersal fish biomass potential shows a decrease in time as bottom temperatures increase.\nLines 361-367: “Demersal fish biomass potential also remains elevated on the\ncontinental shelves, in the vicinity of polynyas and would be projected to increase given the\nlikely increases in POC flux to the benthos (Figure 6g-i). However, when we take into\naccount the habitat thresholds for present day Antarctic toothfish, we see that as projected\nbottom temperatures increase from the 2000s to 2090s the projected Antarctic toothfish\nbiomass potential decreases as warming bottom waters start to limit habitat suitability for\nthis species (Figure 7 e and f and S5).”\nLines 917-931: “FEISTY is a global model, and the demersal fish\nfunctional type in FEISTY represents all types of demersal fish and not\na particular species. In the present-day, Antarctic demersal fish include\nspecies like Antarctic toothfish (Dissostichus mawsoni) and Antarctic\nicefish (Chaenocephalus aceratus). We focus on demersal fish in the\nAEV index because Antarctic toothfish are important for fisheries, but\nFEISTY does not have polar specific modifications to represent this\nAntarctic species directly. Temperatures above 1°C become increasingly\nuninhabitable for toothfish [44] and icefish cannot live in temperatures\nabove 2°C [45]. For the DFP used in the AEV Index, we have summed\nover each size class of the global FEISTY data. Additionally, we only\nuse values where the bottom temperature is ≤ 1°C to account for\nAntarctic toothfish habitat suitability. When we compare the locations\nof highest DFP from FEISTY with the best estimate of Antarctic toothfish\nhabitat from AquaMaps [66], we see\n14\nthat the spatial patterns of maxima in DFP occur on the continental\nshelves where Antarctic toothfish are most likely to be found (Figure\nS10).\nLines 401-409: Might want to consider adding a line to say you targeted species across trophic\nlevels that are core ecosystem components in the Southern Ocean and might be good\nindicators. Response:\nWe have included the following text in the first paragraph of the Discussion section.\nLines 434-435: “The ecological input layers we use include some representing\nspecies that are core ecosystem components in the Southern Ocean.”\nLines 409 onwards: What are the differences of your study to the AES study – some of the Ross\nSea Area only becomes highly valuable in the future in the AES study, while yours finds that it\nstays the same / increases? Why are those? This is briefly touched upon in the methods, please\nelaborate on this in the Discussion. Also the hemispheric results differ more from the AES\nstudy. This could highlight bias in the way tracking data was selected for the AES study, or\nlimitations in hemispheric applicability of your index. Please discuss.\nResponse:\nThere are some differences and some similarities in the results between the two studies for\ncurrent and future scenarios. It is important to note that the studies use completely different\ndata sets (as we note in the methods section). Hindell et al. (AES) was limited to using only\ntracking data (and thus had strong bias towards areas that had been well sampled),\nwhereas we\nused both data and models to get at circumpolar information more completely and we worked\nacross trophic levels (rather than just birds and mammals). That said, the results align well in\nsome coastal areas, including the southwest Ross Sea, parts of the East Antarctic and the\nnorthern Antarctic peninsula. The results in our AEV and Hindell’s AES for future change also\nalign well (see, e.g., losses in the East Antarctic and Weddell Sea regions). For the areas that\ndon’t align as well, it is likely due to differences in the data. For example, areas not picked up\nby Hindell et al. - e.g., other parts of the East Antarctic and Weddell Sea, are driven by all\necosystem components, including NPP (net primary productivity) and KGP (krill growth\npotential), rather than penguins. Similarly, some areas emphasized by Hindell et al. (e.g., the\nBalleny Islands) did not emerge as AEVs in our study because none of our variables\nemphasized this region. This is likely because we did not have the array of birds and mammals\nthat utilize the Balleny Islands in our study. To clarify why we provide this comparison to AES\nin the methods section, we now have included the following text.\nLines 1027-1035: “To demonstrate that the historical reconstruction AEV Index - both the\nhemispheric and regionally scaled versions - provides sensible information about valuable\nbiological areas, we qualitatively compare it to the Areas of Ecological Significance (AES)\nidentified by 12 using in-situ tracking data from 17 marine mammal and bird species to identify\nregions preferred by these predators. Both the AES and AEV Index metrics identify ecologically\n15\nvaluable regions though the methodology is entirely different and the metrics do not share\nany overlapping data. This comparison is done primarily to verify that a model based\nmetric to identify valuable biological areas is providing sensible results to a similar metric\nthat uses observational data. We find...”\nLines 416-418: Perhaps rephrase to make this recommendation even stronger, something along\nthe lines of: “This MPA is set to expire in 2052. This work provides evidence the MPA would\ncontain valuable regions through to 2090 and should be considered for extension.”\nResponse:\nWe feel our wording as it currently is makes this point clearly and we have not changed this\nsentence. Additionally, we are unable to imply endorsement of policy positions.\nLines 421 onwards: Might be worth citing https://doi.org/10.1016/j.marpol.2024.106232 that\naddresses value of Domain 9\nResponse:\nThank you for this great suggestion. We have added this citation and text about there\nbeing priority areas in the region.\nLines 553-555: “In management regions with proposed MPAs, CCAMLR’s recent\nlack of progress in adopting these proposed MPAs [32] leaves the species within these areas\nvulnerable to the combination of both climate change and fishing pressure.”\nLines 428 onwards: A bit confusing, suddenly coastal and “large-scale” polynyas are described\nrather than just polynyas.\nResponse:\nWe intended this to mean that the largest polynyas are visible from space, but we understand\nit is confusing. We have changed the text as follows.\nLines 451-454: “Within each management region, coastal polynyas have an AEV Index\n1.4-2 times larger for areas outside polynyas (Table 1), and our analysis indicates that coastal\npolynyas are particularly valuable for Antarctic ecosystems across trophic levels. Given the\nimportance of coastal polynyas, many could be prioritized for protection.”\nLine 427: Please consider citing some of the sources that proposed “ecological oases” as a\nconcept (I believe it originates from https://doi.org/10.1371/journal.pone.0023047?)\nResponse:\nThanks for the suggestion, but we are not clear how this applies in our study (and we\nhave limited space for elaborating).\nLine 433: Was there any patterns or explanations for which polynyas did not have high AEVs?\nResponse:\nthis paper to examine this in detail for each polynya as we are trying to evaluate at a higher\nlevel and suggest in the discussion that further studies at each polynya may be warranted.\nLine 437-8: What is the hypothesized link between increased carbon flux and demersal\nfish??? Many demersal species in this region are carnivores.\nResponse:\nWe believe that the increase in particulate organic carbon (POC) flux to the ocean floor\nwould lead to more prey for Demersal fish. We clarify this connection in the text.\nLines 460 and 527-529 (sequential lines are broken by figures): “More mesozooplankton\nbiomass and increased POC flux to the benthos would provide more prey resources for demersal\nfish. Polynyas are thus potentially advantageous for predators as they provide access to pelagic\nand benthic resources and also allow an easy ability to return to the surface for breathing.”\nLines 444-448: Gives rise to the question of weighting. Do we care more about some species\nthan others? Are there any areas projected to be suitable for Emperors- should they be\ntargeted more for protection? I’m not necessarily saying that the layers should be weighted\ndifferently, this depends on the intended use in terms of conservation outcomes.\nResponse:\nThis is a good question and something we discussed in detail while formulating the AEV Index.\nWe believe there is no \"right\" answer to whether some species “should” be targeted more or\nless for protection as that is more of a values/political question rather than a scientific question\nwhich could be answered with analysis. For example, if you’re concerned about the krill fishing\nindustry, maybe you want krill weighted heavily. If you’re more concerned about Emperor\nPenguins then perhaps they should be targeted more. It depends on one's aims/objectives and it\nisn't something we believe would be appropriate to speak to in this paper, which has a scientific\nfocus, but rather to leave to policy-makers and others.\nDiscussion starting line 475: While this is an honest discussion around some of the potential\nissues with demersal fish model, it highlights a significant question around whether this model\nis actually in any way realistic. Icefish and toothfish are common species and would generally\naccount for a large portion of the biomass. Toothfish are well known to exhibit an\nontogenetic shift in habitat with smaller fish in shallow coastal waters and larger/ higher\nbiomass fish in deeper water from around 800m depth. This seems in contrast to the maps presented.\nAgain, it underscores the importance of presenting validation information for the demersal fish\nmodel to enable the reader to believe it is robust.\nResponse:\nThis is a good point. As stated in our response to one of the reviewer’s general comments, we are\nunaware of any comprehensive species distribution models or biomass estimates for Antarctic\ntoothfish and icefish. We acknowledge that toothfish life history complicates the assessment of\n17\nany simulated future change presented in our manuscript. Yet, we think that accounting for the\nspecific life history of Antarctic toothfish is beyond the scope of this work. That is why, in our\nwork, we focus on a larger scale and total potential DFB. We have significantly expanded the\nmethods section where we describe the FEISTY fish model, including a spatial comparison of the\nFEISTY biomass distribution with the AquaMaps estimate of Antarctic Toothfish Habitat, which\ndemonstrates the suitability of using FEISTY output for our purpose (see Revised Figure S10,\nand Online Methods Section 4.3 in the revised manuscript (we have not pasted the whole section in\nthis response for brevity).\nLine 504: Consider adding a sentence or two about where current anthropogenic activities occur\n(fishing, but maybe also tourism?) Is there overlap with any of the high AEV areas? Any overlap\nwith high AEV areas that will have decreased by 2090 (i.e. where fishing might contribute to an\neven faster decline)?\nResponse:\nWhile we agree that human activities are a major consideration for spatial management in the\nSouthern Ocean, this would be beyond the scope of our study to consider overlap between AEV\nareas and human activities. Nor can we accurately predict if fishing would contribute to a faster\ndecline given the current uncertainty around impacts of fishing on the ecosystem and the lack of\nknowledge about cumulative impacts. Ultimately, as noted above, we feel the discussion of\nhuman activities impacts and responses are discussions that policy-makers will need to have and\nindeed, we hope that our work is considered alongside other information as policy-makers\nconsider management of fishing, tourism and other activities. We are actively working to ensure\nthis work is part of the information considered in policy level discussions.\nLines 521 onwards: You are not considering reductions in the ice shelves – your models only\nextend to the shelf break. Is there a way you could extend your analyses to the areas freeing up in\nthe future?\nResponse:\nThis is a great point, but unfortunately CESM, and no other current CMIP models of which\nwe are aware, have evolving ice shelves to look at how those may change and therefore\nimpact habitat in the future. Thus, extending the analysis as you suggest is beyond the\nscope of our models at this time.\nLines 663-4: States that the AEV was validated against Areas of ecological significance- which\ncomprise areas of sea bird and mammal use. This is not a validation. At best it is a qualitative\ncomparison. The logic that they are similar therefore the AEV is working well if flawed.\nFirstly, if they are so similar then why do we need the new AEV index?\nSecondly, a better comparison would be to quantitatively compare your layers for Emperor\npenguins and Adelie penguins to the corresponding AES layers which are generated from actual\ndata using tracked animals.\nWe have re-worded the methods section to clarify the purpose in comparing to the AES.\nPrimarily we want to verify that the model-based metric is providing sensible results as an index\nthat uses observations. The main benefit, as we state in the introduction, is that a model based\nmetric provides the ability to provide information at all grid points and into the future. A full\nquantitative comparison of the AEV Index and AES (and other types of indices and metrics) is\nbeyond the scope of this paper and is currently being completed by co-author Brooks.\nLines 1027-1035: “To demonstrate that the historical reconstruction AEV Index - both the\nhemispheric and regionally scaled versions - provides sensible information about valuable\nbiological areas, we qualitatively compare it to the Areas of Ecological Significance (AES)\nidentified by 12 using in-situ tracking data from 17 marine mammal and bird species to identify\nregions preferred by these predators. Both the AES and AEV Index metrics identify ecologically\nvaluable regions though the methodology is entirely different and the metrics do not share\nany overlapping data. This comparison is done primarily to verify that a model based metric to\nidentify valuable biological areas is providing sensible results to a similar metric that uses\nobservational data. We find...”\nLines 668 onwards: This is true for the regionally scaled AEV, but more significant differences\noccur when looking at the hemispheric solution.\nResponse:\nThis is a good point. In the methods section where we compare the hemispheric and regionally\nweighted AEV Index with the AES regions, we have included the following text.\nLines 1039-1041: “However, the regionally weighted AEV Index has more overlap with\nthe AES in sectors beyond the Ross Sea and Antarctic Peninsula, thus bolstering our conclusion\nthat the regional weighting is a better way to identify regionally important high value areas.”\nAppendix B- 1214-1217: The penguin population models seem to already include sea ice\nvariability in population size estimates. Is there circularity in using sea-ice again for the\nprojection of important areas?\nResponse:\nWe do not believe that there is circularity here. The AEV Index inputs are only biological and do\nNOT directly include sea ice concentration, polynya area, or any other physical variable as a\ndirect layer. The biological inputs do include environmental information, so environmental\nimpacts on the AEV Index are only considered by their impact on the biological inputs in terms of\ntheir specific relationship with the five input biological layers. The analysis we do on polynyas\nuses a separate dataset and the polynyas are not included in the AEV Index calculation.\nAppendix B- 1308-1312: Indicates some validation of the emperor penguin model was\nconducted, but nothing is reported. Is this a ‘good’ or fit for purpose model?\nResponse:\nhttps://doi.org/10.1016/j.biocon.2025.111037) and detailed in that paper’s Supporting\nInformation. Specifically, Figure B.3 presents a long hindcast (1950–2018) and projection\n(2019–2100) of population percent change relative to 2009–2018. These results demonstrate that\nthe models reproduce historical dynamics before being used for future projections. In addition,\nFigure E.1 reports an out-of-sample validation at Pointe Géologie successfully captures the\nrecent increase in breeding pairs, showing skill under novel conditions despite calibration on a\nprior, non-increasing phase. Validation methods are summarized in Appendix E (and Section 2.7\nin the main text). For reference, the Supporting Information is available here: https://ars.els-\ncdn.com/content/image/1-s2.0-S0006320725000746-mmc1.pdf\nFigure B.3 from Jenouvrier et al. 2025\nPUBLISHED DATA FIGURE REDACTED\nFigure E.1 from Jenouvrier et al. 2025\nReviewer #1 (Remarks on code availability):\nCoding was completed in Python and I am not proficient in this language.\nResponse:\nThanks for confirming our code was accessible and in a widely used language (Python). We have\nupdated all the analysis scripts as part of this revision and the code remains available at:\nhttps://github.com/duvivier/HotSpotsinIce\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nThanks for the thoughtful review and comments. We appreciate your suggestions that\nhave improved this manuscript substantially.\nThis manuscript presents an interesting and timely approach to identifying ecologically valuable\nareas in the Southern Ocean by creating the Antarctic Ecosystem Value (AEV) Index. While the\nstudy provides valuable insights into current valuable areas and potential changes in them under\nfuture climate scenarios, I have some methodological concerns that should be addressed before\npublication. These include mainly a clarification of the regional standardisation on the text to\navoid potential misinterpretation, and further explanation on the choices made for the weighting\nof ecosystem components. The weighting as is overrepresents penguins, and a justification or\nadjustment of the model is necessary. I think the authors do a good job on the discussion\naddressing some of their model limitations, but don’t mention the lack of other components of\nthe food webs of the Southern Ocean, or why penguins are so important. Addressing these issues\nwould strengthen the ecological foundation of the study and improve its utility for conservation\nplanning in the Antarctic region.\nGeneral comments\nI think this study will be of relevance to Southern Ocean studies, and it’s novel enough compared\nto previous attempts at creating similar indices. The code provided with the article is well\ndocumented. I think the work supports most of the conclusions, except for what I raise below.\nResponse:\nThanks for taking the time to review the manuscript. We appreciate your finding that it is timely,\nnovel, and provides valuable insights. Below we describe how we have addressed the three\nmethodological concerns you raise.\n1) You raised a concern about regional standardization of the AEV Index. Based on other reviewer\ncomments, we agree that our previous method of comparing between regions was not\nappropriate. We have changed how we are comparing the valuable areas between regions by\nusing percentiles from the AEV Index distributions in each region to determine Exceptional,\nVery High, and High value points, and then comparing these points between regions. We describe this\nin the main text and methods sections, Revised Figures 1, 3, 5, and include Revised Figures S12\nand S13.\nLines 154-164: “It is important to note that the AEV Index values are not directly\ncomparable between the hemispheric and regional indices, nor between separate regions,\nbecause each grid point has been scaled differently. To directly compare the most valuable\nlocations, we identify use three bins - Exceptional (top 5th percentile), Very High (top 10th\npercentile), and High (top 25th percentile) value - to describe points based on the distribution of\nAEV Index values within a particular region. All other points are classified as Other (lowest 75th\npercentile). Grid points classified within the Exceptional, Very High, or High bins are the most\nvaluable within a particular region (and we often refer to the combination of all three bins as\nhighly valuable areas), and since they are determined from consistent percentiles from regional\ndistributions of the AEV Index they are directly comparable between regions.”\nwe have binned the AEV Index into four categories: Exceptional (AEV Index in the top 5th\npercentile for that region), very high (AEV Index in the top 10th percentile for that region),\nhigh (AEV Index in the top 25th percentile for that region), and other (all other points). Note that the\npercentile thresholds for these bins are determined separately for each of the regions and\ndecades (see Figures S12 and S13).”\nRevised Figure 1\nRevised Figure 3\n23\na)\n1.0\nWhole region\nt 0.8 _ MI INSIDE polynyas\nOUTSIDE polynyas\nS 0.6 -\nx\n'Ig 0.4 -\na 0.2 -\n0.0\nWS\nb) 100\nII\n80\n60\nc ¢ 73 − Other\n,L6 40 − Exceptional\nVery High\niii\n0 100\nIII\n50\n0\nd) EWS EA RS AS `- AP WS-,\nr ==ronal sea ice zone_\nTypical polynyas\n. c V a l u a b l e p o N n y a o u t s i d e M P A\n0° 30°E 151.E 150.W 85°W 50°W\nLongitude (°E)\nRevised Figure 4\nExceptional\nVery High\nHigh\nOther\n— Polynyas\nRevised Figure S12 (and Revised Figure S13, not copied here)\n24\n2) You were concerned that the penguin inputs to the AEV index are overweighted. This is a good\nconcern and something we discussed in detail while formulating the AEV Index. To be sure this\nchoice did not affect our results we did some testing that we show in Figure R1 (below) for both\nthe hemispheric and regionally weighted AEV Index calculations. The “AEV default” maps (left\ncolumn) show the results when both penguin species have a weight of 1, equal to the other input\nlayers. We also tested assigning both penguin species a weight of 0.5 (right column) so that the\ncombined penguin input layer would have the same weight as the other individual layers (e.g.\nKGP). We found that the AEV Index did not substantially change in terms of which locations were\nidentified as most valuable within a region. Thus, weighting each species by half does not\nsubstantively change the results of the paper in terms of identifying valuable biological locations\nwithin the Antarctic. We believe there is no \"right\" answer to whether some species “should” be\ntargeted more or less for protection as that is more of a values/political question, and as such we\ndon’t feel that we should weight any species more or less than others in this Index. We have\nadded text (below) to the methods explaining how the penguin weighting in the AEV Index is done\nand why.\nFigure R1\ntheir projected population evolution to represent secondary/tertiary consumers. We include the\nEmperor Penguin (Aptenodytes forsteri) and Adelie Penguin (Pygoscelis adeliae), both upper\ntrophic level predators, because these two species experience different future population\nprojection trajectories. In the AEV Index we assign each penguin input layer equal weight as\nthe other layers, though testing shows that using a single penguin layer where each species has\na weight of 0.5 results in a similar AEV Index as the results shown here.”\n3) You had concerns about the biological model limitation and lack of representation about other\nspecies in the AEV Index. We agree that this lack of other species is a concern in our method.\nHowever, we used all the species of which the authors were aware that had models for future\n26\nprojections of their population, biomass, or productivity. We would love to include more\nspecies in the future, including seals or whales, but we did not have these models available\nat this time. We think future further expansion of the AEV Index would be valuable as such\ndata becomes available. We have included the following text in the Discussion section to\naddress these concerns.\nLines 570-590: “When possible, the AEV Index uses species-specific future projections, but\nmore detailed species-specific modeling is needed, especially for ice-obligate species. We include\ninput layers for primary producers (NPP), primary/secondary consumers (KGP), secondary\nconsumers (DFP), and secondary/tertiary consumers (EPP/APP) (see Section 4 for details).\nHowever, other key ecosystem species like whales, seals, other penguins and fish, and flighted\nbirds are not included. While it would have been ideal to include a larger range of species, we\nincluded only those species for which we had readily available future projection information\naround the entire Antarctic continent. Unfortunately, the authors are unaware of any other\nspecies for which that level of data is available and which we could include in these calculations. The\nresults presented here show that the AEV Index framework is valuable as a tool to demonstrate the\nutility of such studies across trophic levels, and future work to include more species would help fill\ngaps about the Antarctic ecosystem that this study is unable to address.”\nSpecific comments\nLines 164-166: “The AEV Index is useful for three reasons. First, it seeks to comprise a holistic\necosystem value by focusing on Antarctic-specific species and integrates across trophic levels\n(see 28).”\nSee my comments below, but calling it a “holistic” ecosystem value may be too generous when\nyou’re missing some key taxonomic groups (and by consequence overestimating the importance\nof the ones included) that are very important in regional and global carbon and nutrient cycles.\nResponse:\nThis is a good point and we agree “holistic” is incorrect since we do not include all\nimportant species. We have changed the text to use the wording “broader ecosystem”.\nLines 127-128: “First, it seeks to comprise the broader ecosystem value by focusing\non Antarctic-specific species...”\nLines 156-159: \"We use data from an Earth system model (ESM) that includes marine\nbiogeochemistry [19, 20] for both a historical reconstruction [21] and coupled future projection\nwith upper-middle CO2 emissions [22].\"\nReading this, the methods, and looking at Table 1, you have what it looks like two historical\nperiods; one that you can historical in table 1, and another you name as \"2000s\". I understand\none is based on historical reconstructions and the other is model-based and used for future\nprojections, but it's not very clear on the methods which one is which and why you need both.\nPlease make it clearer on the text thinking of the average reader that may not be used to\nmodelling and future projections. This also applies to Table A1: understand the Earth System\nwould benefit from clarifying or identifying that the ESM includes future projections.\nResponse:\nWe realize how confusing this was. The first section of the Results focuses on the historical\nreconstruction and the second section focuses on the free-running model. We also added a\nparagraph explaining how the free-running model derived AEV Index differs from the historical\nreconstruction AEV Index, and why that means there are two “historical” periods.\nLines 720-739: “The historical reconstruction AEV Index uses data from an ice-ocean\nmodel forced by atmospheric observations to reconstruct observed ocean and sea ice conditions\n[23]. On the other hand, an individual fully-coupled, free-running model simulation should not\nexactly replicate observations, since observations represent one possible response of the\nclimate system to given external forcing in the presence of internal climate variability. Instead,\nto ensure the AEV Index derived from the fully-coupled, free-running model data [24] is robust,\nwe compare the historical reconstruction (average for 2000-2020 conditions) with the fully-coupled\nsimulation data for the 2000s (average over 50 ensemble members from 1998-2002) to ensure\nthey produce similar results in a similar climate state. Indeed, we find that the valuable areas\nidentified by the regional AEV Index historical reconstruction (Figure 1d) align well with\nvaluable areas identified by the AEV Index calculation using a fully-coupled, free-running model\nfor the 2000s (Figure 4a). Both methods identify the same large, valuable areas in all regions,\nthough the free-running model has more valuable grid points further north in the Amundsen Sea\nand the East Antarctic. A major benefit of the AEV Index derived from the fully-coupled model is\nthat it can be used to identify valuable areas into the future. Because the two AEV Index\nformulations provide similar information in the present day, it is reasonable to use the free-\nrunning, model-based AEV Index in the next section of this manuscript to provide information\nabout potential future changes.”\nLines 268-270: \"In all regions total net primary productivity increases over time (Figure 4b),\nlikely because ice loss reduces phytoplankton light limitation and extends the growing season.\"\nIf NPP is coming from the ESM, does the ESM specifically model the relationship between sea ice\nloss and phytoplankton productivity? While this mechanism is plausible, it would be helpful to\nconfirm whether the model actually simulates these specific processes (light limitation changes\ndue to ice loss) or if this is an interpretation of the results. If the model doesn't explicitly track\nlight limitation, there could be alternative explanations for increased NPP.\nResponse:\nYes, the ESM models the relationship between light availability and phytoplankton productivity.\nWhile we feel that delving into the limitation terms of the NPP is beyond the scope of this study,\nwe have substantially expanded the methods section 4.3.1 of the paper to describe how NPP is\ncalculated.\nLines 789-794: “This input represents primary producers, the base of the food web. As\ndocumented in detail in 20, the Marine Biogeochemistry Library (MARBL) component of\ntypes. The phytoplankton growth rate (μi) is a function of the resource-unlimited growth\nrate (μref) at a reference temperature (30◦C), and the temperature (LimT), nutrient (LimV),\nand light availability (LimL) functions...”\nFig. 2 \"a) Model based 2000s locations of high (pink) and exceptional (red) Antarctic\nEcosystem Value (AEV) Index indicating ecologically valuable regions\"\nThe regional normalization approach used for the AEV Index creates a potential\nmisinterpretation risk that I think would benefit from being addressed, at least in this figure.\nBy normalizing values within each planning region rather than across the entire Southern Ocean,\nareas labelled as \"exceptional value\" in different regions are not directly comparable. For\nexample, an \"exceptional value\" area in East Antarctica may have significantly lower absolute\necological importance than an \"exceptional value\" area in the Ross Sea or Antarctic Peninsula.\nSo I would recommend clearly stating in the figure legend and maybe more clearly in the main\ntext that the AEV Index values represent relative importance within each planning region only,\nnot across the entire Southern Ocean. I do understand the reasoning behind this standardization,\nbut making it clear for the interpretation of your results doesn't hurt.\nResponse:\nThis is a very good point and one that we had not fully considered in the previous version of the\npaper. As we described above, we now use percentiles within the distribution of each region to\ndetermine the “Exceptional”, “Very High”, and “High” value regions so that direct comparison\nbetween regions is possible. Thank you very much for bringing this up - we feel that this change\nhas substantially improved the robustness of the results even though the results themselves did not\nchange substantially.\nLines 154-164: “It is important to note that the AEV Index values are not directly\ncomparable between the hemispheric and regional indices, nor between separate regions,\nbecause each grid point has been scaled differently. To directly compare the most valuable\nlocations, we identify use three bins - Exceptional (top 5th percentile), Very High (top 10th\npercentile), and High (top 25th percentile) value - to describe points based on the distribution of\nAEV Index values within a particular region. All other points are classified as Other (lowest 75th\npercentile). Grid points classified within the Exceptional, Very High, or High bins are the most\nvaluable within a particular region (and we often refer to the combination of all three bins as\nhighly valuable areas), and since they are determined from consistent percentiles from regional\ndistributions of the AEV Index they are directly comparable between regions.”\nLines 1021-1026: “To highlight the particularly valuable locations within each region,\nwe have binned the AEV Index into four categories: Exceptional (AEV Index in the top 5th\npercentile for that region), very high (AEV Index in the top 10th percentile for that region),\nhigh (AEV Index in the top 25th percentile for that region), and other (all other points). Note that the\npercentile thresholds for these bins are determined separately for each of the regions and\ndecades (see Figures S12 and S13).”\nLines 588-590: This line mentions how CESM2 does not represent specific Antarctic\nphytoplankton species. It would be nice to know which phytoplankton communities are not\nincluded, and how much they are expected to contribute to NPP. I'm not overly familiar with\nCESM2 and I think a lot of people will not know the specific details, so possibly just a sentence\nhere clarifying how this could impact estimations of NPP would be nice. For instance, does the\nmodel capture ice algae communities? Understanding these limitations would help readers better\ninterpret the NPP projections.\nResponse:\nWe have expanded section 4.3.1 in the methods substantially to describe the calculation of\nNPP. While the CESM model does not include phaeocystis or in-ice phytoplankton, efforts\nare underway to include these and to use additional, polar-specific types of phytoplankton\nand zooplankton. However, to maintain the broader applicability of this model based metric,\nwe use NPP alone within the AEV Index calculation.\nLines 823-831: “While we are aware of the complex dynamics of plankton functional\ntypes, we chose to use NPP alone to maintain the metrics broader applicability across different\nmodels without requiring detailed assumptions about species-specific responses. NPP is a\ndirect output from CESM2. All configurations of CESM2 calculate phytoplankton growth\nrates separately for three phytoplankton functional types: diatoms, diazotrophs, and small\nphytoplankton. The CESM2-FOSI configuration also includes coccolithophores as a\nfunctional type. None of these functional types are Antarctic specific, but instead broadly\napplicable to a global model. Sea-ice algae are not included.”\nLines 645-655: You gave equal weighting to all five metrics (NPP, KGP, DFB, EPP, APP), and I\nwant to understand the reasoning as this may overrepresent certain trophic levels while\nundervaluing others. There's two penguin-specific metrics (EPP and APP) but only one primary\nproductivity metric (NPP), which means that the index assigns 40% of the weighting to penguins\nwhile allocating just 20% to the trophic level that supports the entire ecosystem, from my\nunderstanding of it. This imbalance may distort the identification of high-value areas by\noveremphasizing regions important to penguins relative to their ecological significance in the\nbroader Antarctic food web. I would like to at least see a justification in the text on why\npenguins as top predators would justify being 40% of the index. Additionally, the model\nexcludes other key top predators such as whales and seals, which play critical roles in Southern\nOcean ecosystem functioning. Their absence further complicates the potential bias created by the\npenguin-heavy weighting. If it’s not possible to justify this weighting, then I would recommend\nimplementing a more balanced weighting of the different trophic components.\nResponse:\nWe have clarified above and in the text why we weight each species the same in the AEV\nIndex and the testing we did to ensure this was okay with two penguin species. We also\naddressed why other species like whales and seals were not included (See above response to\nmethodology concern #2 and #3).\nReviewer #3 (Remarks on code availability):\nI haven't tried running the code as it's Python and I'm not super familiar with Python coding, but\nthere's a README file and the code is in a Jupyter notebook and well commented.\nResponse:\nThanks for confirming our code was accessible, in a widely used language (Python), and well\ncommented in the code and with a README. We have updated all the analysis scripts as part of\nthis revision and the code remains available at: https://github.com/duvivier/HotSpotsinIce\nReviewer #4 (Remarks to the Author):\nThis manuscript introduces a new index to evaluate the value of Antarctic marine ecosystems\ncalled the Antarctic Ecosystem Value (AEV) that merges ecosystem information across food\nweb trophic levels. With this index, high value regions in Antarctica were identified for current\nconditions and for future projections to 2090. They found areas of reduced sea ice (polynyas) to\nbe particularly valuable. They deemed areas that continue to be valuable into the future\nespecially important to preserve. These areas were largely within MPAs, but also in proposed\nMPA areas or areas not in MPAs, which led to the recommendation to develop protection plans\nfor valuable but currently unprotected regions. The manuscript was well-written and uses the\nbest available models and data to inform the index. I agree that the metrics included in the\nindex together form an important and informative collection to evaluate the value of regions in\nAntarctica.\nResponse:\nThanks for taking the time to review the manuscript. We are happy you found it informative, and\nwe appreciate your comments that have substantially improved the manuscript and analysis.\nSince this is an introduction of a new index, I would like to see presented as part of the results\nthat the index works. I suggest moving the model validation section into the result section with\nfigures and statistical output, and provide a compelling conclusion from this that the index does\nindeed correspond with quality habitat/high value ecosystems. The same with the AEV ranges,\nallow the reader to conclude that the ranges and cut-offs from good to excellent are logically\nplaced. Now that readers have the information to decide if the index can be trusted, results from\nthe index, based on and future conditions, can be presented.\nResponse:\nWe did not include the comparison to the AES presented by Hindell et al. in the main text due to\nword limit constraints. Unfortunately, this remains an issue in the revised manuscript. However,\nwe have expanded the methods section discussing the qualitative comparison. In the methods\nsection we clarify the purpose in comparing the AES with the AEV Index. Primarily, we want to\nverify that the model-based metric is providing sensible results as an index that uses\nobservations. A full quantitative comparison of the AEV Index and AES (and other types of\nindices and metrics) is beyond the scope of this paper and is currently being completed by co-\nauthor Brooks. Below, is the paragraph in the methods where we validate the AEV Index\nagainst the AES.\nLines 1027-1052: “To demonstrate that the historical reconstruction\nAEV Index - both the hemispheric and regionally scaled versions -\nprovides sensible information about valuable biological areas, we\nqualitatively compare it to the Areas of Ecological Significance (AES)\nidentified by 12 using in-situ tracking data from 17 marine mammal\nand bird species to identify regions preferred by these predators.\nBoth the AES and AEV Index metrics identify ecologically valuable\nregions though the methodology is entirely different and the metrics\ndo not share any overlapping data. This comparison is done\nprimarily to verify that a model based\n31\nmetric to identify valuable biological areas is providing sensible\nresults to a similar metric that uses observational data. We find that\nboth metrics identify similar grid cells as valuable (grid cells in the\nExceptional, Very High, and High value bins) as the AES locations,\nparticularly in the coastal areas of the southwest Ross Sea, parts of\nthe East Antarctic, and in the northern Antarctic Peninsula. However,\nthe regionally weighted AEV Index has more overlap with the AES in\nsectors beyond the Ross Sea and Antarctic Peninsula, thus bolstering\nour conclusion that the regional weighting is a better way to identify\nregionally important high value areas. In other areas, the results\ndon’t align as well, which may be due to differences in data\navailability. In the Amundsen Sea there is less animal tracking data\nand it is also an area the AES does not identify as a valuable area\nwhile the AEV Index does. In other areas, like the Balleny Islands\nnortheast of the D’Urville station in East Antarctica, the AEV Index\ndoes not show these as valuable possibly because we didn’t include\nspecies inputs that emphasized this area. Thus, we believe that the\nAEV Index, which is calculated using data on a coarse (∼1◦) Earth\nsystem model grid, is capable of providing high level information\nabout valuable areas around Antarctica, particularly into the future.\nIt is important to note that additional research and monitoring,\nincluding with existing observational datasets such as those used in\nthe AES study, could be conducted to further refine locally valuable\nregions.”\nMinor note on page 5 and on: The hyperlink to S1 doesn’t take you to the S1 figure, but to page\n5.\nResponse:\nThanks for finding this error. We believe we have fixed the links to particular figures, tables, and\nsections in the revised paper.\nReviewer #5 (Remarks to the Author):\nThis manuscript provides an aggregated index for considering productivity and accessibility for\ntwo predator species at a circumpolar scale in the Southern Ocean. The authors calculate this\nindex for historical, present, and future climate conditions, using output from Earth System\nModel simulations. The index shows a strong relationship with the location of\nAntarctic polynyas, which generally persists under future climate conditions.\nThis work is ambitious and is a useful demonstration of an approach to aggregate information\nabout net primary productivity, krill growth potential, demersal fish biomass potential,\nemperor and Adelie penguin populations, and access to areas for foraging. The authors should\nbe congratulated on the volume of work underpinning the manuscript and the clear\npresentation of results in figures and tables.\nResponse:\nThanks for taking the time to review the manuscript! We appreciate that you found it clear and\nambitious. Thank you for your suggestions, addressed in detail below, which we think have\nhelped improve the manuscript.\nWe have several concerns regarding the use of this index to consider Antarctic ecosystem value\nmore generally, as well as the methods for calculating krill growth potential and demersal fish\nbiomass potential. Large-scale ecosystem processes and connectivity, together with the key food\nwebs roles of zooplankton groups and mesopelagic fish, are all critical components of ecosystem\nvalue in the Southern Ocean that underpin decisions about spatial protection, but are not discussed\nby the authors or considered as part of the index. There is little recognition that groups other than\nAntarctic krill drive energy transfer, with their relative influence differing across regions – for\nexample, East Antarctic food webs show a higher number of alternative energy pathways between\nprimary production and large predators (e.g., McCormack et al., 2020, 2021, doi:\n10.1002/ece3.7017 & doi: 10.3389/fevo.2021.624763). This has broader implications with respect\nto the potential use of this index to inform marine spatial protection in the Southern Ocean,\nbecause (as we are sure the authors will be aware) Antarctic marine protected areas need to be of\nsufficient scale to protect ecosystem processes – not just small-scale high productivity areas.\nEqually, marine spatial protection is important for areas that are at risk of degrading in value due\nto climate change, because enhanced resilience (through reduction in other threats) provides the\nbest chance of ecological processes and values persisting in these areas.\nResponse:\nThank you for bringing up this concern about gaps in which species and components of the\nAntarctic ecosystem are included in this work. We agree that this lack of other species is a\nconcern in our method. However, we used all the species of which the authors were aware that\nhad models for future projections of their population, biomass, or productivity. We would love\nto include more species in the future, including other zooplankton or mesopelagic fish, but we\ndid not have these models available at this time. We think future further expansion of the AEV\nIndex would be valuable as such data becomes available. We have included the following text in\nthe Discussion section to address these concerns and cite the papers you suggest.\nLines 570-592: “When possible, the AEV Index uses species-specific future projections,\nbut more detailed species-specific modeling is needed, especially for ice-obligate species. We\ninclude input layers for primary producers (NPP), primary/secondary consumers (KGP),\nsecondary consumers (DFP), and secondary/tertiary consumers (EPP/APP) (see Section 4 for\ndetails). However, other key ecosystem species like whales, seals, other penguins and fish, and\nflighted birds are not included. Additionally, this study focuses on the species for which future\nprojection data are available from biological models. However, this means that we do not include\nother mesozooplankton or pelagic fish that are important for energy pathways from the primary\nproducers to predators [e.g. 33, 34]. Additionally, excluding all the species means that we may\nnot be capturing the scale needed for protection to protect all ecosystem processes, not just the\nspecies represented here. While it would have been ideal to include a larger range of species, we\nincluded only those species for which we had readily available future projection information\naround the entire Antarctic continent. Unfortunately, the authors are unaware of any other\nspecies for which that level of data is available and which we could include in these calculations.\nThe results presented here show that the AEV Index framework is valuable as a tool to\ndemonstrate the utility of such studies across trophic levels, and future work to include more\nspecies would help fill gaps about the Antarctic ecosystem that this study is unable to address.\nUntil then, the AEV Index is associated with larger uncertainty in regions or at times when\nspecies not included in our assessment play a vital role in the ecosystem. ”\nWith respect to the methods used by the authors to calculate krill growth potential, we are\nconcerned about the use of the 2006 Atkinson model, which is not designed to address the effects\nof climate change on physiology, energetics, and fecundity of krill, which are the primary factors\ngoverning productivity. The authors do discuss other krill models briefly, but indicate that “they\nare hindered by substantial uncertainties regarding krill ecology and their interactions with\nenvironmental drivers”. It is unclear to us which specific uncertainties prevent the use of more\nrecent and improved krill models in this study. As it stands, the use of a simple empirical\nrelationship between temperature, surface chlorophyll, and krill growth needs to be much more\nstrongly caveated – with implications for interpretation of results explained; the equation for\ncalculating krill growth should also be included for clarity. Consideration, or a caveat, should be\ngiven to the variety of krill diets and their dependence on food items other than diatoms, even\nduring the productive summer months. The aggregation of all phytoplankton groups from the\nNPP model to simulate diatoms is potentially an oversimplification and might lead to an\noverestimation of krill growth, as krill have been shown to be quite selective feeders (Pauli et al.,\n2021 https://doi.org/10.1038/s42003-021-02581-5).\nResponse:\nWe understand Atkinson 2006 is a simple model, however it’s important to note that in this study,\nKGP is not used as a mechanistic predictor of krill productivity, but as an indicator of habitat\nsuitability based on temperature and chlorophyll, consistent with prior broad scale applications\n(e.g., Hill et al. 2013; Murphy et al. 2017; Veytia et al 2020; Sylvester et al. 2021). While the\nconcern that aggregating phytoplankton groups may overestimate potential growth, this bias is\nconsistent across our study and does not affect the relative comparisons central to the AEV\nIndex. We now include a comparison of the Atkinson 2006 derived KGP term in our model to\nkrill habitat estimates from AquaMaps (Kaschner et al. 2019) and it compares reasonably well\n(Revised Figure S9, below). Since the authors are unaware of any other more complex model\nthat is suitable for addressing the impacts of climate change on krill growth or secondary\nproductivity and we find the KGP term compares reasonably well to observed habitat we\ncontinue to use the Atkinson 2006 model with NPP from all phytoplankton in this work.\nPlease see Online Methods Section 4.3.2 in the revised manuscript where we have substantially\nexpanded the description of the krill growth potential model (we have not pasted the whole\nsection in this response for brevity) with equations and caveats, and references, as you suggest.\nRevised Figure S9\nWe are also concerned about the use of projections for demersal fish biomass based on a global\nmodel that does not account for the unique physiology of Antarctic fish. In lines 475-485 the\nauthors clearly state that the projected changes for demersal fish are not consistent with expected\nimpacts of temperature increases on key Antarctic species. Future increases in demersal fish\nbiomass due to southward expansion of subpolar and temperate species (presumably not\nsubtropical as the authors state in line 483?) are highly uncertain. As such, we think that the\nfish projections used in the index are misleading.\nResponse:\nAs mentioned above, we have substantially expanded the methods section to explain and validate\nthe Antarctic sea ice as well as different AEV Index input layers in detail. Please see Online\nMethods Section 4.3 in the revised manuscript, where we now include equations for the\ncalculation of NPP, KGP, and explain what inputs the FESITY model needs.\nIn the expanded methods section we explain that FEISTY is a global model and the input\nvariables. FEISTY provides estimates of biomass for three types (demersal, forage, and large\npelagic fish) over three size classes, as illustrated in the diagram, below, from Krumhardt et al.\n2024. The authors are unaware of any species distribution models specific to Antarctic fish. Thus,\nwhile FEISTY is not designed for Antarctic fish species, we believe it is the best current estimate of\npossible fish biomass for the present day and future. To account for habitat suitability\n35\nof Antarctic toothfish (an example of an Antarctic demersal fish species), we have now masked\nthe Demersal Fish Biomass Potential input layer by ocean bottom temperatures and only use\nvalues where the ocean temperature is <1°C, which is an upper limit on habitat suitability for\nAntarctic toothfish (Cheung et al. 2008). In Revised Figure S10 (copied below), we qualitatively\ncompare the locations where FEISTY has highest possible biomass against the Aquamaps\nestimate of Antarctic toothfish habitat (Kaschner et al. 2019) and it compares well. While\nadequate observations of Antarctic toothfish biomass are missing to quantitatively evaluate the\nsimulated biomass fields in FEISTY, this assessment gives us confidence in the simulated spatial\ndistribution and thus in using these fields as an input layer for the AEV index.\nFigure from Krumhardt et al. 2024\nPUBLISHED DATA FIGURE REDACTED\nRevised Figure S10\nthe timeseries of changing average biomass over each region makes sense with increasing\nbottom temperatures over time decreasing habitat for this species. See Revised Figure 7 (below)\n- panel e and the Revised Figure S5, DFP panels (not copied in for space).\nRevised Figure 7 - see panel e\nFinally, the usage of the climate model CESM2-LE should be justified in light of its inclusion in\nan ecosystem index. For example, the sea ice-ocean model ACCESS-OM2 has been specifically\nreviewed for its application in ecological studies (Fierro-Arcos et al., 2023\nhttps://doi.org/10.1016/j.pocean.2023.103049). Were other ESMs considered?\nResponse:\nThe ice-ocean ACCESS-OM2 model you reference is forced using an atmospheric reanalysis, so\nit does not provide information for the future and it is not fully-coupled so it cannot capture the\nfull range of change in the presence of climate variability. We have clarified in the text why we\nuse CESM2 for this study and that we use both a forced ice-ocean configuration (the same type\nof model as ACCESS-OM2) as well as a fully-coupled, free-running configuration. In particular,\nCESM2-LE is an ideal tool for this study because the large number of ensemble members\nprovide a robust signal of forced change due to emissions in the presence of internal climate\nvariability. Importantly, the referenced ACCESS-OM2 simulation does not include any\nrepresentation of ocean biogeochemistry and lower trophic level ecosystem dynamics. While\nFierro-Arcos et al. state that updated ACCESS-OM2 simulations do include these dynamics,\nthey have not been, to the best of our knowledge, thoroughly evaluated for Southern Ocean\napplications yet. As such, ACCESS-OM2 is currently not suitable for use in our work, which\nrequires, e.g., estimates of chlorophyll concentrations or NPP as input layers. CESM2 not only\nincludes an established, well-evaluated biogeochemistry component (MARBL) but has been\nsuccessfully coupled to FEISTY in the past (Krumhardt et al., 2024). We clarify in the Results\nsection why we use both the forced ice-ocean CESM2 output as well as CESM2-LE for the\nfuture. We include in the Discussion section on model uncertainty that other models such as\nCESM2 in the methods to show that it is a reasonable tool to use for this work.\nLines 720-729: “The historical reconstruction AEV Index uses data from an ice-ocean\nmodel forced by atmospheric observations to reconstruct observed ocean and sea ice conditions\n[23]. On the other hand, an individual fully-coupled, free-running model simulation should not\nexactly replicate observations, since observations represent one possible response of the\nclimate system to given external forcing in the presence of internal climate variability. Instead, to\nensure the AEV Index derived from the fully-coupled, free- running model data [24] is robust,\nwe compare the historical reconstruction (average for 2000-2020 conditions) with the fully-\ncoupled simulation data for the 2000s (average over 50 ensemble members from 1998-2002) to\nensure they produce similar results in a similar climate state.”\nLines 655-660: “Moreover, the Earth system model we use has a\ncoarse (∼1◦), but common, grid size for this type of model. Other\nforced and fully-coupled models or high resolution ice-ocean\nmodeling (e.g. ACCESS-OM2 [54] or FESOM-REcoM [18]) could\nelucidate the uncertainty in small-scale ice features, marine heat\nwaves, bathymetric impacts on optimal marine habitat, or intensity\nand locations of warm circumpolar deep water intrusions onto the\nshelf that could impact habitat suitability [18, 55–57].”\nRevised Figure S6\n1. Line 146 – please see Cavanagh et al. (2021, doi: 10.3389/fmars.2020.615214), who consider\nmore than two trophic levels and have a Southern Ocean focus.\nResponse:\nThank you for making us aware of this paper. We have modified the wording in the text and cited\nthis paper.\nLines 103-105: “..though these often focus on one species (e.g., Emperor penguins,\nAptenodytes forsteri) [15, 16], fewer trophic levels [e.g. 17, 18], or are global in scope...”\n2. Lines 216-217 – it is unclear to us how Figure S1 compares the historical reconstruction with\nthe model-based index. Could the authors please explain how this comparison is being made?\nThis figure has been removed. The text has been updated to clarify how to compare the\nhistorical reconstruction with the fully-coupled model for the 2000s.\nLines 720-729: “The historical reconstruction AEV Index uses data from an ice-ocean\nmodel forced by atmospheric observations to reconstruct observed ocean and sea ice conditions\n[23]. On the other hand, an individual fully-coupled, free-running model simulation should not\nexactly replicate observations, since observations represent one possible response of the\nclimate system to given external forcing in the presence of internal climate variability. Instead, to\nensure the AEV Index derived from the fully-coupled, free- running model data [24] is robust,\nwe compare the historical reconstruction (average for 2000-2020 conditions) with the fully-\ncoupled simulation data for the 2000s (average over 50 ensemble members from 1998-2002) to\nensure they produce similar results in a similar climate state.”\n3. Methods – the inclusion of equations within the bullet points in the AEV index methods\nsection, on top of the written explanation, would make it clearer for the readers to follow.\nIt would also be useful to see some form of sensitivity analysis regarding which of the inputs\nfor the index is most important in driving the overall value of the index over time.\nResponse:\nWe have substantially expanded the methods section to include more information about each\ninput layer and validation. Additionally, we thought your suggestion to analyze quantitatively\nwhich input is the largest driver of the AEV Index was a great one. We have now included this\nin the model and it had interesting results about shifts in the ecosystem. We have added text (see\nbelow) in the results section, Revised Figure 5, and Revised Table 3 to show which species\ndominates the AEV Index in the 2000s and 2090s in the free running model. We also include\nRevised Figures S2 and S4 to show the raw contributions by species.\nLines 269-275 & 306-308 (separated by a Figure): “Investigation of individual inputs to the\nAEV Index reveals that the importance of penguins on the index decreases while lower trophic\nlevels becomes more pronounced (Figure 5 and SF4; Table 3). In the 2000s the Emperor penguin\nlayer is particularly prominent in the Weddell Sea and Ross Sea and polynyas, while the Adelie\npenguin layer is prominent in the Antarctic Peninsula. However, by the 2090s the penguin\ndominant regions have shrunk dramatically. The shift in which input layer is the primary\ncontribution to the AEV Index reflects large changes occurring throughout the food web and\nhighlights ecosystem change from climate change pressures. Below, we describe changes in each\nAEV Index input layer both hemispherically and in regional averages.”\nRevised Figure 5\nRevised Figure S4\n42\n4. Figures: Fig. 1 side panels are very hard to read – making them larger would be very helpful.\nResponse:\nThis figure has changed substantially and the input layer panels are now shown\nlarger in Revised Figure 2.\nRevised Figure 2\nin S1 would make the comparison much easier.\nResponse:\nThe figures have changed and this concern is no longer relevant to the updated figures.\nFig. 2: Interestingly, a large swath of East Antarctic waters is considered to be of Medium value.\nHowever, no explanation is given in the text; what could be driving this?\nResponse:\nWe have modified how we quantify the value of grid points based on comments by R3. Instead of\nusing an AEV Index threshold, we now use percentiles within the AEV Index distribution for each\nregion. This makes the “exceptional”, “very high”, and “high” value points between regions\ndirectly comparable. We note this change in the text and as a result of the change in categorizing\nthe value of the grid points we do not believe the swath of waters you mention is still notable.\nLines 154-164: “It is important to note that the AEV Index values are not directly\ncomparable between the hemispheric and regional indices, nor between separate regions,\nbecause each grid point has been scaled differently. To directly compare the most valuable\nlocations, we identify use three bins - Exceptional (top 5th percentile), Very High (top 10th\npercentile), and High (top 25th percentile) value - to describe points based on the distribution of\nAEV Index values within a particular region. All other points are classified as Other (lowest 75th\npercentile). Grid points classified within the Exceptional, Very High, or High bins are the most\nvaluable within a particular region (and we often refer to the combination of all three bins as\nhighly valuable areas), and since they are determined from consistent percentiles from regional\ndistributions of the AEV Index they are directly comparable between regions.”\nLines 1021-1026: “To highlight the particularly valuable locations within each region,\nwe have binned the AEV Index into four categories: Exceptional (AEV Index in the top 5th\npercentile for that region), very high (AEV Index in the top 10th percentile for that region),\nhigh (AEV Index in the top 25th percentile for that region), and other (all other points). Note that the\npercentile thresholds for these bins are determined separately for each of the regions and\ndecades (see Figures S12 and S13).”\n--\nThis manuscript was co-reviewed by and early career researcher and an established researcher.\nReviewer #5 (Remarks on code availability):\nCode not provided - only processes data are available at the link provided.\nResponse:\nWe believe the reviewer missed the URL under the “Declarations” section that provides the\nlink to the code availability. Since R1 and R3 commented that the code was available and\nwell commented, if not in their preferred analysis language, we believe the repository link is\nuniversally accessible. We have updated all the analysis scripts as part of this revision and\nthe data and code remain available at the links provided in the manuscript.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nThanks for taking the time to co-review the manuscript!\nReviewer #6 (Remarks on code availability):\nCode was not included in manuscript.\nResponse:\nWe believe the reviewer missed the URL under the “Declarations” section that provides the\nlink to the code availability. Since R1 and R3 commented that the code was available and\nwell commented, if not in their preferred analysis language, we believe the repository link is\nuniversally accessible. We have updated all the analysis scripts as part of this revision and\nthe data and code remain available at the links provided in the manuscript. (see above for\nscreenshot)\nWe have considered the comments and we believe that we have addressed all the concerns in\ndetail below. All line numbers in responses refer to the clean revised manuscript.\nReviewer #1 (Remarks to the Author):\nThe authors have modified analyses, produced new figures, and substantially re-written\nlarge parts of the manuscript to take into account reviewers’ comments. This has involved a\nlarge amount of work. I believe it is a much-improved manuscript, and the authors should be\ncongratulated.\nA few remaining comments to note. Any line numbers refer to the file with tracked changes.\nThanks for the thoughtful review and comments. We believe we have addressed your\nadditional concerns, as detailed below.\nFish biomass model: Outputs from the global FIESTY model have now been constrained and\ntaken to represent Antarctic Toothfish by truncating potential suitable habitat at <1deg C.\nValidation is qualitative based on Aquamaps which itself is based on the environmental\nenvelope of occurrence data. We still do have some concerns over how well this model actually\nrepresents Antarctic demersal fish. To determine this would require validation with CCAMLR\nfisheries data, which is probably not in scope of this study.\nThanks for this comment. We agree that validation of the model would be helpful to test the\ndegree to which it represents demersal fish. However, as the reviewer notes, this is well\noutside the scope of this study. Getting access to CCAMLR fishing data could help, but this\ndata is unfortunately not publicly available. This would be a great project for a future study!\nIn a related point, Line 511-513 states that there are no SDMs for Antarctic Toothfish. Actually,\na model linking Antarctic Toothfish occurrence to environmental data has been published:\nhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10984185/\nIt shows some similarities and differences to the one presented here.\nThank you for bringing this paper to our attention. It is interesting that this paper has many\nqualitatively similar findings to our results, including that Antarctic Toothfish habitat is\npredicted well by sea ice thickness, ocean temperature, and depth. We are continuing to use the\nFEISTY fish data since FEISTY is a mechanistic model and this SDM is statistical, which we\nthink is important since it is unclear that the statistical relationships that exist in the present day\nmay not be robust into future conditions. We have added text to the paper in the Discussion and\nMethods sections as detailed below to cite this study and how our results compare.\nLines 580-588: Some detailed species-specific models already exist describing todays’\nenvironmental conditions [e.g. 37–39], but these would require substantial work to couple with a\nglobal ESM and to assess potential future changes and if statistical relationships are robust in\ndifferent climate states. For other species (e.g. Antarctic silverfish, Pleuragramma antarctica)\nthere are no existing models of which we are aware, so species-specific model development\nwould be necessary. Our study focuses on the Antarctic-specific and ice-dependent species\nfor which future projection data are available from existing biological models and that use\nthe types of data available from existing ESM experiments.\nLines 923-925: The Fisheries Size and Functional Type (FEISTY) model is a mechanistic\nglobal size- and trait- based model that resolves the structure of fish communities from\nenvironmental forcing [26].\nLines 958-960: The map of FEISTY biomass is also qualitatively similar to statistical predictions\nof Antarctic toothfish habitat, which shows that Antarctic toothfish can be particularly well\npredicted by sea ice thickness, ocean temperature, and depth [39].\nLine 915-916: Actually, Antarctic Toothfish are caught commercially not only on the shelf, but\nin high abundances on the slope 1000-1700m. So hopefully the temperature limits used in this\nms don’t constrain fish biomass to just the shelf.\nhttps://doi.org/10.1016/j.fishres.2019.105338\nWe did not mask the Antarctic Toothfish by the ocean depth, so this statement is a qualitative\nway to describe the locations where FEISTY shows highest demersal fish biomass. We have\nchanged the wording in the manuscript to clarify this and thank you for bringing the paper to\nour attention, which we have now cited.\nLines 953-957: When we compare the locations of highest DFP from FEISTY with the best\nestimate of Antarctic toothfish habitat from AquaMaps [81], we see that the spatial patterns of\nmaxima in DFP occur primarily on the continental shelves and continental slopes (Figure\nS10). These locations are consistent with catch data from East Antarctica of where Antarctic\ntoothfish are likely to be found [83].\nComparison to Areas of Ecologically Significance, Lines 1031-1040: This comparison is\ninteresting in it’s own right and some of the species in this index and the AEV overlap, but I\nstill don’t think it is a validation/justification for a regionally weighted index. Where the\nindices are consistent and different are interesting points for discussion (potentially in the main\nms rather than justifying the index in the supplemental material).\nWe appreciate the reviewers’ perspective that regional weighting is not justified. However,\nbecause CCAMLR management for MPAs is by regional planning domains (see\nhttps://gis.ccamlr.org/ or map, below), we feel that regional analysis is justified and valuable as\nit is consistent with CCAMLR management practices. Thus to be clear, we chose regional\nweighting based on CCAMLRs’ approach, but not on the Areas of Ecological Significance\napproach or overlap.\nauthors are currently working on analysis specifically comparing different Antarctic metrics of\nbiodiversity and ecological importance since that is beyond the scope of this study. Primarily for\nspace and clarity reasons, we have decided to keep this AEV/AES comparison in the methods, as\nwe do for each of the input layers’ validations. We have modified the text to make clear why this\ncomparison is done and the scope.\nLines 1059-1071: A full comparison of biodiversity metrics around Antarctica is beyond the scope\nof this study. However, to demonstrate that the historical reconstruction AEV Index - both the\nhemispheric and regionally scaled versions - provides sensible information about valuable\nbiological areas, we qualitatively compare it to the Areas of Ecological Significance (AES)\nidentified by 12 using in-situ tracking data from 17 marine mammal and bird species to identify\nregions preferred by these predators. Both the AES and AEV Index metrics identify ecologically\nvaluable regions using multiple species, yet it is important to note that while species overlap\nbetween these two metrics the methodology is entirely different and the metrics do not share any\noverlapping data. This comparison is done primarily to show that a model based metric to\nidentify valuable biological areas is comparable to a similar metric that uses observational data,\nbut a full analysis comparing multiple Antarctic metrics of biodiversity and biological\nimportance is beyond the scope of this work.\nMinor points:\nLine 267: The 30-70% higher should include ‘average’. Looking at figure 3a, while the there is a\nconsistent trend for the average AEV index to be higher inside polynyas than outside, the\nvariability inside polynyas is also much higher than outside. This should be acknowledged.\nLines 193-196: The AEV Index also shows there is co-location of coastal polynyas and valuable\nareas across the ecosystem and that the AEV Index values within polynyas are on average 30-\n70% higher than outside polynyas, though there is higher variability within polynyas as well\n(Figure 3a; Table 1).\nLine 314: The decline in AEV in the Ross Sea seems very minimal in Figure\n4g. We have modified the text to make clear the decline is minimal.\nLine 227: In the Ross Sea, the AEV Index decreases very slightly though still remains\nhighly valuable in most areas.\nLine 378-9: Prydz Bay does not appear to have a dramatic decline in projected toothfish\nbiomass in Figure 6i), however the West Antarctic Peninsula does.\nThanks for pointing out that this sentence was confusing. In Figure 6 we show the general\ndemersal fish biomass potential, but it does not include the 1C temperature threshold masking the\ndata. This is consistent with other studies of multiple fish model results. On Figure 6 we overlay\nthe ocean bottom temperatures to show the environmental change that would affect habitat, but\nthe fish contour field is not masked using the temperatures. In contrast, Figure 7 and Figure S5 do\nshow the integrated biomass of demersal fish that includes masking with the 1C temperature\nthreshold for Antarctic Toothfish. We have changed the text to make this clear.\nLines 361-396: Demersal fish biomass potential also remains elevated on the continental\nshelves, in the vicinity of polynyas and is projected to increase given the likely increases in POC\nflux to the benthos with warming (Figure 6g-i). This increase in fish biomass is consistent with\nother studies across multiple models that show increasing fish biomass around Antarctica [32].\nHowever, present day Antarctic toothfish habitat temperature thresholds (see Section 4) imply\nthat this species would lose habitat since projected bottom temperatures increase from the 2000s\nto 2090s (Figure 6g-h, blue contours). As a result, regional projections of Antarctic toothfish\npotential biomass decline over the 21st century in most regions (Figure 7 e and f and S5) as the\nocean bottom temperatures are projected to become increasingly uninhabitable for Antarctic\ntoothfish as they exceed the upper bound of their habitat threshold range of 1 ◦C [33].\nLine 452-453: This statement needs referencing “More mesozooplankton biomass and increased\nPOC flux to the benthos would provide more prey resources for demersal fish”.\nThis statement is related to the FEISTY mechanistic forcing and we have modified the\ntext as follows to make this clear.\nprovide more prey resources for demersal fish through the FEISTY mechanistic forcing [26].\nLine 529-532: Regarding the criticism of krill dynamic models, this is at least equally applicable\nto the parameterisation of the *global* FIESTY model being used to represent Antarctic\ndemersal fish and yet it is part of the AEV index!\nWe understand your concern about the global FEISTY model not being validated for Antarctic\nfish specifically. Unfortunately, since CCAMLR Antarctic toothfish fishing records are not\npublicly available around Antarctica, this data is just not available. However, we still feel there\nis utility in including results from the global fish model because it is based on fish metabolism\nand trophic connections that have been validated in a wide variety of ocean environments\nwhere fishing records allow and extrapolating this model to novel environments is an\ninteresting and insightful exercise. While there is the potential for sensitivity to our choice of\nfish model, all fish models presented in Heneghan et al. (2021)* showed an increase in fish\nbiomass in the Antarctic sea ice zone under climate warming (see their Figure 3). Therefore\nbased on our current understanding of fish physiology, the potential for fish biomass to increase\n(as is shown from FEISTY) with warming appears to be a possibility, albeit it would likely be\nfish that are adapted to warmer conditions, not Antarctic species.\n*https://doi.org/10.1016/j.pocean.2021.102659\nReviewer #1 (Remarks on code availability):\nCode is extensive, but I can't evaluate as I am not a python user.\nThanks for confirming our code was accessible and annotated.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nThanks for re-reading our revised manuscript and contributing to your co-reviewers’ comments.\nReviewer #3 (Remarks to the Author):\nI appreciate the revised version of the manuscript and the substantial work the authors have\nundertaken to address reviewer concerns and improve the manuscript. The main issues I raised\nhave been appropriately addressed, and I am pleased with the improved manuscript.\nThis work represents a valuable contribution to our understanding of climate change impacts on\nAntarctic ecosystems, a research area that, as the authors correctly note, remains\nunderrepresented in the literature (due to a lack of data that the authors have also faced). The\nstudy holds significant conservation value, and the revisions effectively highlight existing\n5\nknowledge gaps while addressing limitations in the proposed index. I particularly appreciate that\nthe authors now clearly identify where their index may miss information, as this transparency not\nonly demonstrates the robustness of their approach in certain contexts but also highlights\nimportant gaps that can inform future research priorities.\nThanks for the thoughtful review and comments. We appreciate your suggestions that\nhave improved this manuscript substantially.\nI have only two minor editorial suggestions:\nLine 168 (clean version): The abbreviation \"KGP\" appears here without prior definition. Please\nintroduce and define this term when it first appears in the text.\nThanks for finding this - we have now defined KGP at this first instance (Line 168).\nLines 173-175: Please specify which planning regions are included in \"other planning regions.\"\nWhile readers could reference the figure, much of this paragraph discusses supplementary\nmaterials that many readers will not examine in detail. For clarity and to avoid relying on\nreaders' memory of figure captions, particularly given the introduction of an \"others\" category in\nyour index, please name these regions explicitly in the text.\nWe are interested in the CCAMLR MPA planning domains (see https://gis.ccamlr.org/ and\nturn on MPA planning domains to see map, below).\nWe have changed the text here to be more precise about these regions:\nLines 174-175: “...within the CCAMLR MPA planning domains that may be locally critical for\nthe ecosystem”\nReviewer #3 (Remarks on code availability):\nResponse:\nThanks for confirming our code was accessible and annotated.\nReviewer #4 (Remarks to the Author):\nThe authors have made significant changes to the manuscript and have addressed my suggestions\nand concerns. I now recommend publication of this manuscript.\nReviewer #4 (Remarks on code availability):\nI have checked the link provided for the code and the code is made available. I am not proficient\nenough in python to provide an evaluation of the code itself. It looks well-organized and\nprovides a README file.\nThanks for confirming our code was accessible and annotated.\nReviewer #5 (Remarks to the Author):\nWe commend the authors for thoroughly responding to a large set of review comments and note\nthat the revisions have improved the rigour of the manuscript. Parts of the methodology have\nbeen modified following the initial review, including improvements in the treatment of fish\nhabitat to reflect polar fish requirements, validation of the sea ice environmental variable and a\nsensitivity analysis.\nThanks for the thoughtful review and comments. We believe we have addressed your\nadditional concerns, as detailed below.\nMajor points:\na)While the authors have much more clearly caveated the scope of their Antarctic Ecosystem\nValue Index in the revised manuscript – including an explanation of their use of the term\n“valuable” at the end of the Introduction and a new subsection of the Discussion on “Gaps in\nEcosystem Representation” – we feel that this scope is still overstated in some parts of the\nmanuscript. Notably, in the abstract line 059 “biologically valuable hot spots for the Antarctic\necosystem” would be more accurately stated as “biologically valuable hot spots for the groups\nconsidered, and potentially for the ecosystem more broadly” (as this has not been assessed in the\nstudy), and at the beginning of the Discussion where the authors claim they have answered the\nquestion “Where are the most ecologically valuable marine areas around Antarctica and will\nthese locations change in the future” we do not think is accurate for the reasons outlined in our\nprevious review (i.e. key trophic groups are excluded from the index).\necosystem, though we did choose to focus on Antarctic-specific, ice-dependent species for which\nwe had the ability to use standard coupled ESM data with biological models to evaluate present\nand future conditions. To address your concern about the scope, we have modified the text as\nfollows.\nLines 58-59: ...these areas are biologically valuable hot spots for a number of ice-\ndependent Antarctic Species.\nLines 409-435: Where are the most ecologically valuable marine areas around Antarctica and\nwill these locations change in the future? To answer this question, our study presents the AEV\nIndex, a model-based integrative tool that synthesizes information across trophic levels for\nseveral species within the seasonal sea ice zone to investigate the present and future ecological\nvalue of broader regions. The ecological input layers we use include some representing ice-\ndependent species that are core ecosystem components in the Southern Ocean.\nb)Both in the revised manuscript and in the response to review comments the authors state that\nthey “are unaware of any other species for which that models linking environmental conditions\nto the occurrence of a species is available and which we could include in these calculations at\nthis time” (note there seems to be a slight issue with the formulation of this sentence). We think\nit is important that the authors provide an explanation as to why they are unable to use other\navailable models, for example SEAPODYM (micronekton) (Green 2022), KRILLPODYM\n(Green et al. 2023), or MICE models available for baleen whales (Tulloch et al. 2017, 2019). If\na set of criteria have been used to determine model suitability for the index could the authors\nplease specify these? There is of course no problem with making particular choices about model\nsuitability – such as using habitat suitability models rather than mechanistic models for krill and\nfish (but metapopulation models for penguins), but we think this needs to be explained in the\nMethods.\nThanks for bringing up this concern. Our model selection was driven by a pragmatic set of\ncriteria focused on data availability (e.g., does the ESM output the necessary variables at\nthe right frequency?), model complexity, and the specific objectives of our AEV Index.\nWe have clarified this in the text as follows.\nLine 795-804: The AEV index uses five inputs from different trophic levels in the Antarctic\necosystem to provide a broader metric across trophic levels (illustrated graphically in Figure 2).\nThis research prioritizes the use of models with reliance on readily available environmental\nvariables produced by standard ESM data, which is available at monthly temporal resolution.\nWhile this limits the biological models we can use, it highlights the models that are capable of\nlinking with an ESM and thus providing a framework for integrating large-scale environmental\nchange to ecological/biological processes. This is a tradeoff that allows our research to\n8\nsimultaneously make broad projections and highlight pathways for additional\nresearch to address.\nWe are familiar with the SEAPODYM and KRILLPODYM models. While KRILLPODYM offers a\nmechanistic, spatially resolved model of Antarctic krill distribution and abundance, its detailed\nlife history requirements, such as 291 age classes each lasting one week, would necessitate a\nlevel of temporal and spatial resolution in environmental data that was not compatible with our\nbroader framework of using existing ESM model data (usually at monthly resolution) and\ncomputational availabilities since we could not re-run large ensemble experiments to increase\ntemporal frequency. While ESMs are suitable for our broader regional analysis, their output do\nnot adequately support the fine-grained advection and life-stage tracking required by models like\nKRILLPODYM, which has data requirements and inherent complexities, particularly concerning\nfine-scale spatial and temporal dynamics, that were not compatible with the scope and available\ndata from our ESM simulations for a comprehensive, circumpolar assessment across multiple\ntrophic levels and future projections. We have modified the text as follows to clarify why these\nmodels were not used in this analysis.\nLine 609-614: While more complex Antarctic krill models that incorporate biomass dynamics\nand full life cycle processes could provide a more detailed perspective [e.g. 41], such models\nwere not used in this study because of the necessity to use existing coupled ESM data which does\nnot have temporal or spatial resolution needed. A more sophisticated regional modeling study of\nkrill in some of the large scale regions identified by this study would be valuable.\nWe were unfamiliar with the MICE baleen whale model and we very much appreciate the\nreviewer bringing this to our attention! We have read the paper you reference and while we think\nthat including this model would be interesting, we feel that including it at this stage is beyond the\nscope of the present study. Some major questions arose, including how to account for the sea ice\nimportance of a species that migrates far from the ice edge when the AEV Index is calculated only\nin the seasonal ice zone. Additionally, we felt that using MARBL mesozooplankton output for the\nkrill and copepod components of MICE would require careful consideration and substantial\ntesting. Thus, we are not including the whales in the present study, though we would be interested\nin integrating MICE with the CESM and MARBL in the future, so if you are the MICE developer\nand interested in collaborating please feel free to reach out. Since we feel that integrating MICE\noutput at this time is beyond the scope of this present study, we have clarified in the text that we\nare focusing on ice-dependent species with existing models in text.\nLine 574-600: When possible, the AEV Index uses species-specific future projections, but more\ndetailed species-specific modeling is needed, especially for ice-obligate species. We include\ninput layers for primary producers (NPP), primary/secondary consumers (KGP), secondary\nconsumers (DFP), and secondary/tertiary consumers (EPP/APP) (see Section 4 for details).\nbirds are not included. Some detailed species models already exist [e.g. 37–39] though may\nrequire substantial work to couple with a global ESM, but in other cases more detailed species\nspecific model development may be required. This study focuses on the Antarctic-specific and ice-\ndependent species for which future projection data are available from biological models and that\nuse the types of data available from existing ESM experiments. However, this means that we do\nnot include other mesozooplankton or pelagic fish that are important for energy pathways from\nthe primary producers to predators [e.g. 40, 41]. Additionally, excluding species means that we\nmay not be capturing the scale needed for protection of all critical ecosystem processes, and we\ncan only assess processes relevant to the species represented as AEV Index inputs. While it would\nhave been ideal to include a larger range of species, we included only those species for which we\nhad readily available future projection information around the entire Antarctic continent. The\nresults presented here show that the AEV Index framework is valuable as a tool to demonstrate\nthe utility of such studies across trophic levels, and future work to include more species would\nhelp fill gaps about the Antarctic ecosystem that this study is unable to address. Until then, the\nAEV Index is associated with larger uncertainty in regions or at times when species not included\nin our assessment play a vital role in the ecosystem.\nc) If policy recommendations are being included in the Discussion (Section 3.2) then we think it\nis important that the authors make it clear that this is an initial/exploratory study in the application\nof the AEV Index. While much effort has clearly been put into this manuscript, there are multiple\nkey ecosystem processes that are not captured by the approach and choice of models (e.g. krill\ngrowth model not capturing sea ice conditions and the key trophic roles of groups such as\nmesopelagic fish, (McCormack et al. 2020)).\nWe agree that our study alone should not be the basis of policy recommendations. Additionally, a\nnumber of the authors are prohibited from giving policy recommendations or advocating for\nparticular policies by our institutions. Thus, we intentionally do not include explicit policy\nrecommendations. We have changed the text as follows to make clear the exploratory nature of\nthis study as well as the need for additional work and to ensure we are not recommending a\npolicy decision.\nLines 540-554: The primary outcomes of this work are the identification of highly valuable regions\nacross the marine ecosystem in the present-day and the assessment to what extent they remain\nvaluable into the future. It is important to note that this is an exploratory scale that uses standard\nESM spatial and temporal resolution data to identify locations valuable to the marine ecosystem at\na large scale, but that more detailed studies for these regions is necessary for specific policy\ndecisions. Still, this initial study identifies some important patterns. In most management regions.\nIn most management regions, the majority (55-100%) of exceptional value areas are within\ncurrent proposed or existing MPA boundaries; the exception is the Amundsen Sea (discussed\nbelow). The existing Ross Sea region MPA protects the majority of exceptional,\n10\nvery high, and high value areas in this region in the present day (Table 1), and is projected\nto keep protecting the majority of these high value areas (Table 2). While the MPA is set to\nexpire in 2052, this study suggests that keeping the MPA in effect would likely continue to\nprotect most of these valuable regions through 2090.\nMinor points:\na)Lines 605-606: “we were able to investigate two important environmental variables known to\nimpact Antarctic krill: SST and sea ice availability”. The sentence suggests that there is a\nmodelled linkage between krill and sea ice, which is not the case as lines 597-598 state that\n“(krill growth potential) reflects adult krill growth and uses only surface chlorophyll and sea\nsurface temperature (SST) as inputs”. It should be clarified that the modelled krill metric is not\naffected by sea ice, with appropriate limitations/caveats about result interpretation and potential\nfuture effects of sea ice decline (also see Meyer et al. 2017 for more details about krill habitat\nusage that might be relevant to the effects of sea ice loss on krill populations).\nThanks for bringing up that this is a confusing sentence. We have modified the\nsentence as follows to clarify how we consider sea ice availability.\nLines 615-619: Nonetheless, we were able to investigate two important environmental variables\nknown to impact Antarctic krill: SST and sea ice availability. By assessing KGP only within the\nsea ice zone we implicitly consider only locations where sea ice is available during the year,\nthough not the seasonality or direct impact of sea ice on the krill lifecycle. Additionally, krill are\nstenothermic...\nb)Line 857 (KGP methods): Initial krill length (L) is specified as 40cm – is this correct?\nThis is a typo, thank you for catching it - it should read 40 mm and we have fixed this (Line 884).\nReferences\n⚫ Green, DB (2022). The missing link : pelagic prey field prediction for Southern Ocean\npredators. University of Tasmania. Thesis. https://doi.org/10.25959/23973123.v1\n⚫ Green et al. (2023) KRILLPODYM: a mechanistic, spatially resolved model of\nAntarctic krill distribution and abundance\nhttps://www.frontiersin.org/journals/marine-\nscience/articles/10.3389/fmars.2023.1218003 /full#B52\n⚫ McCormack SA, Melbourne-Thomas J, Trebilco R, Blanchard JL, Raymond B,\nConstable A. Decades of dietary data demonstrate regional food web structures in the\nSouthern Ocean. Ecol Evol. 2021; 11: 227–241. https://doi.org/10.1002/ece3.7017\n⚫ Meyer, B., U. Freier, V. Grimm, J. Groeneveld, B. P. V. Hunt, S. Kerwath, R. King, C.\nKlaas, E. Pakhomov, K. M. Meiners, J. Melbourne-Thomas, E. J. Murphy, S. E. Thorpe, S.\nStammerjohn, D. Wolf-Gladrow, L. Auerswald, A. Gamp, L. Halbach, S. Jarman, S.\nKawaguchi, T. Krumpen, G. Nehrke, R. Ricker, M. Sumner, M. Teschke, R. Trebilco, and\nfor larval Antarctic krill. Nature Ecology & Evolution 1:1853–1861.\n⚫ Tulloch VJD, Plagányi ÉE, Matear R, Brown CJ, Richardson AJ. Ecosystem modelling\nto quantify the impact of historical whaling on Southern Hemisphere baleen whales. Fish\nFish. 2018; 19: 117–137. https://doi.org/10.1111/faf.12241\n⚫ Tulloch VJD, Plagányi ÉE, Brown C, Richardson AJ, Matear R. Future recovery of\nbaleen whales is imperiled by climate change. Glob Change Biol. 2019; 25: 1263–\n1281. https://doi.org/10.1111/gcb.14573\nReviewer #5 (Remarks on code availability):\nApologies we had previously missed the link provided for the Python code. We have not\nreviewed the code as we are not experts in Python coding.\nThanks for confirming our code was accessible.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts!\nThanks for re-reading our revised manuscript and contributing to your co-reviewers’ comments!\n12"}]

Decision letter