Over a century of global decline in the growth performance of marine fishes

Domain: neuroscience
Published: 2026-02-10
Editorial outcome: minor_revision

Abstract
Human-driven pressures are causing large-scale changes in the ecologies and life histories of fishes. Growth performance is a composite life-history trait that captures the trade-off between two fundamental traits: growth and body size. Here, we assess the impacts of fishing and temperature on the growth performance of marine teleost fishes globally over the last century. Using 7683 growth curves encompassing 1479 species, we find a global pattern of decline in growth performance from 1908 onwards, with the greatest declines in commercially valuable fishes. Indeed, managed fisheries experienced a 9% decline in growth performance over the last century, which can equate to an average decline of up to 23% in asymptotic body size or a 45% decline in the von Bertalanffy growth coefficient (K). Despite relatively consistent increases in ocean temperatures globally, we only detect a decline in the growth performance of fishes in temperate regions, which is probably indicative of an overrepresentation of commercially valuable fishes at higher latitudes. The declines in growth performance likely reflect legacy effects, shaped by overfishing, on changes in the underlying size structure and demographic processes of fished stocks. Therefore, the potential impacts of warmer temperatures on growth performance may be overwhelmingly masked by the impacts of overfishing.

Introduction
We are currently in an era of global change. The oceans of the Anthropocene are becoming increasingly impacted by destructive, human-driven activities, threatening critical ecosystem functions and services. Undoubtedly, the two greatest threats to ocean biodiversity are overexploitation and biophysical changes due to climate change. The extinction risk of many of the world’s most iconic and culturally valuable fishes is increasingly elevated by overfishing. Although fisheries management regimes can improve the status of fished stocks when implementation is strong, many of the world’s managed fisheries are still overfished. This paradigm is likely an artifact of the temporal mismatch between overfishing and management: strong management is typically triggered once stocks have been seriously depleted. Consequently, large declines in stock abundances have not only led to declines in fisheries production but have also led to demographic changes in the life histories of many commercially valuable species.

Changes in fishes’ life histories may be further exacerbated by changes in environmental conditions due to climate change. The impacts of climate change are manifesting throughout the world’s oceans, with increasing temperatures and decreasing dissolved oxygen levels driving changes in relatively plastic critical life-history traits. There is substantial evidence that the expression of life histories differs between conspecifics found across different temperature regimes, with faster life-history traits (e.g. faster growth rates, earlier ages at maturity, smaller body sizes) typically coinciding with warmer temperatures. Climate change can thus push the physiological limits of fishes towards decreases in body size or range expansions into deeper waters and/or higher latitudes. The potentially compounding synergism between fishing and climate change may disproportionately impact the population dynamics of the world’s fishes more than any one stressor alone, driving changes in their ecologies and life histories. The impacts of climate change and overfishing have now extended to all reaches of the ocean, yet we are lacking a global evaluation of their respective impacts on the life histories of fishes.

To address these issues, we use a global dataset comprising 7683 growth curves of marine teleost fishes, which encompasses 1479 species over 113 years. We then implement a suite of Bayesian state-space models, which separate the underlying population state from observation deviances, to reveal global trends in growth performance over the last century (from 1908 to 2021). Growth performance (ϕ; sensu ref. 30) is a composite life-history trait that captures the trade-offs between asymptotic body size and the von Bertalanffy growth coefficient (K), two dominant and fundamentally linked life-history traits. Specifically, growth performance standardises estimates of instantaneous growth coefficients against a single unit of body size. Using the temporal trends of growth performance, we can then determine if changes coincide with increasing temperatures and/or different fisheries management regimes. First, we assess the global growth performance of fishes from 1908 to 2021. Second, we adapt a management classification scheme to disaggregate these patterns by managed fisheries, unmanaged fisheries, and unfished species. Managed fisheries represent families for which formal stock assessments are available, whereas unmanaged fisheries encompass families that have global catch landings reported or reconstructed but lack formal stock assessments; the remaining families are classified as “unfished”. We then assess the spatial patterns in growth performance by separating the time series into temperate, subtropical, and tropical locations, to account for potentially different geographic patterns. Finally, we directly assess the combined impacts of fishing and temperature on growth performance, after accounting for phylogenetic relatedness.

Results
Globally, we found that the average growth performance across 1479 focal species had declined from 1908 to 2021 by 7.9% (90% credible interval: 4.4% to 11.4%). The median predicted growth performance in 1908 was 3.3 (3.1–3.4) compared to 3.0 (2.9–3.1) in 2021, which could be indicative of declines in K, L∞, or both (see Methods). For instance, if L∞ remained unchanged, this would indicate a 44.9% (29.0–60.0%) decline in K; if K remained unchanged, this decline in growth performance would represent a 22.8% (13.2–32.2%) decline in L∞. Notably, in both cases (i.e. a change in L∞ or a change in K), the size for a given age is reduced. This global decline is most likely driven by fishes that are growing to relatively smaller sizes and/or at slower rates (i.e. intraspecific changes), and less likely driven by compositional shifts in the species sampled (i.e. interspecific changes). Given the spatial disparities in fishing pressure and ocean warming around the world, we can further disaggregate these global patterns across different management regimes.

When we disaggregated the global time series by management regime, we found that fishes from managed fisheries exhibited the greatest declines in growth performance over the last century. We found a steady decline in growth performance of managed fishes from 1908 onwards, declining by 9.1% (5.0–13.2%). This resulted in a decline of a median fitted growth performance of 3.5 (3.4–3.7) in 1908 to 3.2 (3.1–3.3) in 2021, corresponding to an average asymptotic body size decline of 27.2% (16.2–38.8%) when holding K constant or a 51.9% (34.5–68.8%) decrease in the growth coefficient K when L∞ is constant over the last century. Unmanaged and unfished species exhibited no change in their respective growth performances through time. We found that the growth performance of unmanaged fishes, although lower than most managed fishes at a mean of 3.0, exhibited no net change across the time series (−11.0–21.6%). Similarly, the growth performance of unfished species remained relatively stable as well (−20.7–44.6%).

The decline in growth performance in managed fisheries is not indicative of the role of management per se; it is likely a reflection of the legacy of historically intense fishing mortality experienced by commercially valuable fishes. Indeed, declines in the fished biomass of commercially valuable fisheries were similarly matched with increases in fishing effort until the mid-1990s, which can be an indication of overfishing. Similarly, the total reported and reconstructed catches of managed fisheries had increased steadily until the mid-1980s before plateauing, whereas the number of stock assessments only started to increase after the 1990s. Although the catches of unmanaged fishes followed a similar pattern to managed fishes, the catch of unmanaged fisheries is approximately half that of managed fisheries. Many commercially valuable fishes had been heavily exploited well before management began, indicating that management is often a reaction to decline as opposed to a preventative measure. The presence of management alone is not an indication of effective management; some of the most heavily regulated fisheries in the world still resulted in significant overfishing and population collapse. Additionally, some stocks that are effectively managed may not be overfished but may experience prolonged fishing pressures (i.e. maximally fished), which can similarly lead to fishing-induced demographic changes. Although effective fisheries management practices can improve stocks that are in decline, it may take longer to achieve similar levels of recovery in the life histories of historically exploited fishes.

The declining growth performance of commercially valuable fishes is likely a legacy effect of size-based targeting via fisheries. Fishers are incredibly effective hunters and tend to target the largest (and typically oldest) individuals of a stock. Unsurprisingly, intense fishing pressure has truncated populations towards smaller average sizes and younger ages. The resulting populations characterised by younger individuals (i.e. population juvenescence) not only express faster life histories, such as smaller body sizes and/or faster growth rates, but also exhibit less stability through time. The dynamic instability of juvenescent populations is likely driven by changes in demographic compensatory mechanisms, such as increased somatic growth or fecundity, or potentially through the loss of social learning. An alternative but not mutually exclusive mechanism for the observed declines in growth performance could be due to the ‘Rosa Lee’ phenomenon. In short, larger, faster-growing individuals are targeted first by size-selective fisheries, allowing the smaller, slower-growing individuals of a population to survive. Consequently, for a given age, the remaining individuals are smaller and exhibit slower growth rates than would be expected in the absence of size-selective fishing, potentially resulting in declines in growth performance. Therefore, the declines in growth performance of commercially valuable fishes over time could represent the interplay between two synergistic processes. First, an unfinished, residual stock is remaining with smaller individuals from size-selective fishing, which may be dominated by slow-growing individuals (via the ‘Rosa Lee’ phenomenon). Second, these demographic changes are then further reinforced and amplified by fisheries-induced selection against larger body sizes, which would manifest as lower growth performance values in subsequent generations.

Declines in global growth performance can be indicative of both intraspecific changes and interspecific compositional changes. Despite considerable overlap in the families and species that were sampled through time, which suggests relative homogeneity in the annual species composition, there were also relatively distinct compositional shifts between specific time periods. While it is plausible that declines in growth performance could be due to potential sampling biases (i.e. shifting from species with higher growth performance values toward those with lower growth performance through time), it is unlikely that this is the primary mechanism of the observed declines. Indeed, there is considerable overlap between the early (pre-1960) and late (post-2000) compositional assemblages, which have the most leverage on temporal trend estimates. Additionally, species compositions explained very little variation across growth performance studies. It is therefore unlikely that the observed declines in growth performance were solely due to changes in the species sampled through time. Irrespective of the underlying mechanism, given the inherent temporal lag between management implementation and stock recovery, the legacy of size-based fishing could manifest throughout populations as declines in growth performance, even when abundances are stable or increasing. Regardless of whether overfishing is the proximate or distal cause for the decline in fish growth performance, changes in the life histories of fishes are likely to be slow to reverse (or potentially irreversible) and can have repercussions on the biomass and productivity of capture fisheries.

Disaggregating the time series by geographic location largely reflected patterns in fisheries management. Growth performance in temperate locations declined by 8.7% (90% credible interval: 4.2–13.2%). Although the decline in growth performance does coincide with an increase in average ocean temperature of 0.7 °C over the same time period (from 4.7 °C in 1908 to 5.4 °C in 2021), we found generally no change in growth performance in subtropical regions (−17.5–4.9%), which experienced an even greater level of warming (from 21.5 °C in 1908 to 22.5 °C in 2021). Similarly, despite stronger temperature oscillations in the tropics, we also found no change in the growth performance of tropical fishes (−10.3–20.8%). The decline in growth performance of fishes in the temperate region is likely a manifestation of the dominance of managed fisheries in higher latitudes, particularly in the northern hemisphere. Indeed, nearly 90% of the studies in the temperate region comprises managed fisheries and the strongest evidence of decline was found when separate analyses were run for managed fisheries in temperate and subtropical regions. The dominance of commercially valuable, and thus managed, fisheries is likely the primary factor driving the decline in the growth performance of fishes in the temperate region, which appear to have extended to some subtropical fishes as well.

The impacts of commercial size-based fishing are likely confounding and masking the potential physiological impacts of rising temperatures on the growth performance of fishes. After accounting for phylogenetic relatedness, we found strong evidence of a positive relationship between temperature and growth performance in both unmanaged and unfished species (median estimate [90% credible interval]: 0.023 [0.0099 to 0.036] and 0.040 [−0.00076–0.081], respectively). For unmanaged fishes, a 1 °C increase in temperature translated to, at most, a 7–15% median increase in L∞ or an 18–37% increase in K across the majority of the range of observed temperatures (smaller estimates calculated from 0 to 1 °C and larger estimates from 28 to 29 °C). For unfished species, a similar increase in temperature resulted in a maximum increase of 14–48% in L∞ or a 34–147% increase in K. Across managed fishes, however, we found no evidence of an effect of temperature on growth performance (−0.00011 [−0.0045–0.0050]). It appears that larger temperature gradients can be a strong driver of growth across fishes; however, a single degree of warming may be too small to lead to detectable changes in growth performance over an entire century. Indeed, temperature has little visible impact on commercially valuable, managed fishes, further reinforcing size-selective fisheries as a dominant driver of declines in growth performance.

Across all three regions (i.e. temperate, subtropics, and tropics), the temperature has increased steadily by roughly 1 °C across the entire time series. Increases in ocean temperatures, paired with declines in dissolved oxygen, can push the physiological limits of ectotherms, such as fishes, towards faster metabolic rates. Given the inherent correlation between metabolic rates and growth, models of energy assimilation predict that under a high-emissions climate scenario, the asymptotic body sizes of fishes may decrease by 14–24% globally in the next 30 years due to changes in both physiology and species assemblages. Empirically, while not directly comparable, species’ average body sizes have been shown to both decrease and increase with warming temperatures, indicating that changes in body size can occur relatively quickly (over only a few decades), but in potentially polarising directions. Nevertheless, fishing and warmer temperatures may act synergistically to drive further changes in fishes’ body sizes. Declines in body size across commercially valuable fishes would not only have demographic consequences with potential flow-on effects on the functioning of marine systems, but would also impact fisheries yields and global protein supplies.

Unmanaged fisheries are typically exposed to unsustainable fishing pressures, therefore we might expect that growth performance would follow, if not surpass, the same magnitude of decline found in commercially valuable fishes. However, we found that the growth performance of unmanaged fishes was relatively stable throughout the time series. Although an underwhelming pattern of stagnation could be indicative of resilient demographic processes (i.e. resilience to fishing and temperature changes) or lagged responses, it is also possible that the focus on unmanaged fishes is disproportionately higher in tropical regions and may have biased the current literature on growth across marine fishes. The tropics are dominated by small-scale coral reef fisheries, where management is typically regulated by local communities rather than national-level policies. Consequently, many coral reef nations lack the resources or funding to conduct stock assessments and such unmanaged stocks are thus misrepresented in global fisheries census data. Given that growth studies can be tightly linked to conventional fisheries management practices, the lack of stock assessments in the tropics may have led to an under-representation of unmanaged fishes in the growth literature. Indeed, unmanaged fishes may already be operating on a shifted baseline, whereby stocks are severely depleted to the point of mainly comprising individuals with lower growth performance. Care is needed, therefore, as these systematic biases could distort or completely omit any realised changes in growth performance across unmanaged fishes.

We uncovered global patterns of decline in the growth performance of marine fishes over the last century. The decline in growth performance of temperate fishes is most likely the by-product of an overrepresentation of commercially valuable, managed fishes in this region. Specifically, in the temperate region, managed fishes experienced the strongest magnitude of decline compared to unmanaged and unfished species, which exhibited relative stability through time. Although we found evidence of an effect of temperature on the growth performance of unmanaged and unfished species, the effect of one degree was not sufficiently strong to produce detectable changes over an entire century. We therefore found stronger evidence that commercial size-based fishing practices, not temperature, are the primary drivers of the global pattern of declining growth performance in marine fishes. The pervasiveness of size-selective fisheries can both change the demographic dynamics of fish populations and trigger relatively strong selection pressures toward smaller body sizes, both of which could manifest as declines in growth performance across ocean basins. Adaptive fisheries management protocols, such as the holistic implementation of both minimum and maximum size restrictions (i.e. harvest slots), could provide localised levers to managers to prevent further declines in fishes’ life histories. In turn, these localised mechanisms could potentially offset the global-scale negative impacts of climate change on the life histories of marine fishes.

Methods
Data collation and growth performance
To calculate growth performance, we used the von Bertalanffy growth parameters L∞ (cm), which is the expected asymptotic size for an individual in a given population, and K (year−1), which is the instantaneous rate at which an individual approaches its asymptotic size. We used the growth data generated in ref. 28, but excluded hagfishes (family Myxinidae) to retain only ray-finned fishes in our analyses. In short, we first generated a list of growth studies from FishBase and manually corrected all detected typological/rounding errors by individually assessing each reference; observations were removed when growth parameters could not be found. We then conducted a systematic literature search using the ISI Web of Science database to find all published articles using von Bertalanffy growth parameters for marine fishes and cross-checked the list with that from FishBase to remove any duplicates (conducted 21 May 2022). We used the search terms ‘fish* AND (growth OR von Bertalanffy)’, which yielded 141649 articles. Articles were rejected based on the titles and abstracts. We excluded all artificially reared individuals (including aquaculture species), as well as all freshwater species and euryhaline species collected in freshwater habitats. For each study, we recorded the growth parameters L∞ and K, the length measurement type (i.e. total length, standard length, or fork length), the method used for aging (i.e. mark recapture, otolith rings, other growth rings [e.g. scale rings, vertebrae rings], length frequency, or unknown), the year for which individuals were surveyed, and the geographic location for which individuals were collected. Articles missing any of the above-mentioned information were excluded. Following ref. 58, we only included studies where the discrete geographic locations were provided. For example, studies conducted in Chesapeake Bay were included, but those conducted across the entire Atlantic were excluded. In total, we retained 2886 articles, resulting in 7683 growth curves encompassing 1479 species over 113 years (from 1908 to 2021). To make studies comparable, we used published length-length conversion coefficients from FishBase to convert estimates of L∞ recorded as standard or fork length to total length in cm using the following equation:
Total length = a + b × standard or fork length (1)
Here, a and b are the length-length conversion factors from FishBase. When species-level coefficients were not available, we used genus or family means.

Using estimates of L∞ and K, we are able to calculate growth performance as defined by ref. 30. Growth performance (ϕ) is a composite life-history trait that captures the trade-offs between the von Bertalanffy growth coefficient and body size. Specifically, it standardises for body size and allows the comparison of growth coefficients across species and/or populations. For example, species that have higher growth performance values tend to exhibit higher resting metabolic rates for a given body size. Growth parameters were converted to estimates of growth performance using the equation:
ϕ = log10 K + SL × log10 L∞ (2)
Here, K and L∞ are the growth parameters and SL is the slope between log10 L∞ and log10 K for the population. The parameter SL can also be defined as
SL = −m × b (3)
Where m is the anabolic term exponent from the von Bertalanffy growth model and b is the length-weight regression exponent.

Following refs. 58, 59, we used the metabolic scaling exponent in place of the anabolic exponent m, which was found to be, on average, 0.761 for fishes. To account for the uncertainty of ϕ for different SL values, we re-ran the global time series model but calculated ϕ using empirically derived SL values. We calculated SL by regressing log10 K against log10 L∞ for all species-level observations that had more than three observations per aging method (adapted from refs. 33, 58). This resulted in 714 species with empirically measured SL values. We used the ‘constant’ SL value for all species without empirically measured estimates by multiplying the mean anabolic term by the length-weight regression exponent (as denoted by Eq. 3). While we found a relatively positive association between both methods of calculating growth performance (i.e. constant vs. varying SL values; Fig. S8), using varying SL values produced much more extreme values of growth performance compared to using constant values (−15.43–41.07 vs. 0.087–6.5, respectively), which exceeded the estimates of previous interspecific growth studies. Despite these extreme values, we found a steeper, albeit more uncertain pattern of decline in the global time series (median decline [90% credible interval]: 21.0% [−39.3–1.53%]; Fig. S9). We therefore present the conservative estimates generated using the ‘constant’ SL values in the main text. See supplementary methods for model specifications.

We used Eq. (2) to generate hypothetical scenarios depicting how changes in growth performance (Δϕ) across time steps (t) would result in changes in either L∞ and K (while holding K and L∞ constant, respectively; Fig. S2). However, we used the “constant” SL value (b=3.03).
Δϕ = ϕt − ϕt−1 = (log10 Kt + SL × log10 L∞,t) − (log10 Kt−1 + SL × log10 L∞,t−1) (4)
Δϕ = log10 (Kt / Kt−1) + SL × log10 (L∞,t / L∞,t−1) (5)
Solving Eq. (5) to find changes in K while keeping L∞ constant removes the entire L∞ term by setting SL × log10 (1) = 0.
Kt = Kt−1 × 10Δϕ (6)
Conversely, solving Eq. (5) to find changes in L∞ produces:
L∞,t = L∞,t−1 × 10Δϕ / SL (7)

Spatial and management regimes
Using the geographic locations of each growth curve, we were able to (1) separate the locations into different regions and (2) extract estimates of sea surface temperature from each location. We used the latitude of each location to assign locations into different regions (i.e. temperate, subtropical, and tropical). Tropical locations were chosen between −23° and 23°, subtropical were from 23° and 34° in the northern hemisphere and −23° and −34° in the southern hemisphere, and the remaining locations were classified as temperate and included polar regions. Across all three regions, the number of observations noticeably increased from the 1950s to the early 2000s, before steadily decreasing after 2010 (Fig. S10). Sea surface temperature raster files were calculated from annual temperature means at a 1° × 1° resolution from https://psl.noaa.gov/data/gridded/data.cobe.html. Using mean annual temperature raster files and the geographic location of each observation, we created a 2° buffer around each point and calculated mean sea surface temperature per location per sampling year.

To reflect different fishing pressures across our samples, we classified all families for which a formal stock assessment existed from the RAM Legacy Database was considered a ‘managed fishery’; conversely, families that had reported or reconstructed catches on the Sea Around Us database, but no formal stock assessment, were then considered ‘unmanaged fisheries’. All remaining species were classified as ‘unfished’. Although we acknowledge the potential biases that not all species belonging to a family will be formally managed; however, this is likely to make our results more conservative because species belonging to the same family exhibit similar ecologies and are likely to be similarly vulnerable to fishing.

Time series analyses
We developed separate Bayesian state-space models to assess the growth performance of fishes from 1908 to 2021 globally, under different management regimes (i.e. managed, unmanaged, and unfished), and different regions (i.e. temperate, subtropics, and tropics), after accounting for environmental temperature. To assess the changes in growth performance through time, we used a state-space modelling approach treating each growth performance value as an individual observation (taken from a population/stock’s calculated growth parameters). Each model estimates the trend in a single underlying population state of growth performance of fishes (x) through time, while accounting for observation error distribution by specifying the gamma shape (τ) and rate parameters (λi,t).
Gamma(yi,t | τ, λi,t) (8)
λi,t = τ / μi,t (9)
log μi,t = αt + αfam + βXi (10)
Here, the term αfam is the varying effect of family on the growth performance of fishes. The term β is the estimated population-level effect of temperature and X is the extracted temperature for each specified location for each growth performance value yi. We set the following weakly informative priors:
λ ∼ Gamma(0.01, 0.01) (11)
β ∼ Normal(0, 2) (12)
The term αt (t=1908,…2021) is the expected mean growth performance for each year. Each annual growth performance estimate is considered the observed estimate of an underlying, unobserved population state xt. The underlying population state xt changes from year-to-year following a first-order autoregressive process, where
xt = xt−1 + u + wt (13)
αt = xt + vt (14)
wt ∼ Normal(0, σq) (15)
vt ∼ Normal(0, σr) (16)
Here, wt represents the random annual deviations (i.e. process deviance) in growth performance resulting from demographic processes of the population and is drawn from a normal distribution with a mean of zero and a process standard deviation of σq (variance). The drift term u allows the model to estimate if there is a consistent trend upwards or downwards over time. The ‘observed’ growth performance of a given year represents the underlying state xt plus some observation error vt (i.e. observation deviance), which is drawn from a normal distribution with a mean of zero and its respective measurement standard deviation σr. To estimate the state at xt=1908, we estimated the initial state x0 following the same equation as above.
xt=1 = x0 + u + wt=1 (17)
The initial state x0 and the drift term u used the following weakly informative priors:
x0 ∼ Normal(0, 5) (18)
u ∼ Normal(0, 5) (19)

Individual aging methods (i.e. mark recapture, otolith rings, other rings [e.g. scales, vertebrae], length frequency analyses, and unknown) each contain their own mechanistic biases, which can lead to different values of L∞ and K (and thus, different growth performance values ϕ). Indeed, aging method accounts for roughly 5% of the variation in growth coefficients across fishes. To account for these inherent methodological biases quantitatively, each method was modelled with its own respective observation deviance. Annual estimates of expected growth performance of the same underlying population state xt may have constant differences between different aging methods. Therefore, we added scalar terms for four of the aging methods (i.e. mark recapture [MR], otolith rings [OR], length frequency [LF], and unknown [UN]). All scalar terms were specified with weakly informative normal priors:
cMR, cOR, cLF, cUN ∼ Normal(0, 5) (20)
For each aging method i, we specified the following weakly informative priors
vi,t ∼ Normal(0, σr,i) (22)
σr,i ∼ Half-normal(0, 1) (23)
Each model was run with four Markov Chain Monte-Carlo chains for 6000 iterations with an initial warmup phase of 3000 iterations. We visually inspected traceplots and ensured the chains were consistent with convergence for all estimated coefficients by checking that the potential scale reduction factor Rhat < 1.01 and the effective sample size ESS > 1500 (Supplementary Data 1). Models were run in Stan using the rstan v2.21.8 package in R v4.2.0.

To assess whether changes in growth performance through time were due to species compositional changes, we used a Principal Coordinate Analysis (PCoA) and separated our observations into five distinct time slices: before 1940 (inclusive), 1941–1960, 1961–1980, 1981–2000, and after 2000. We used a Jaccard index based on presence/absence and visualised the results using a bivariate ordination plot. Although there were significant differences between the centroids of different time slices based on a PERMANOVA using 999 permutations (p < 0.05), this was driven by heterogeneity in group dispersion (i.e. time slices significantly differed in variance; p < 0.05). We ran the PCoA analysis using the vegan v2.6−10 package.

Phylogenetic analyses
To build a fish phylogeny, we followed the methods set forth by ref. 67. In short, we used the chronogram constructed by Rabosky et al. (www.fishtreeoflife.org), which has been time calibrated with the fossil record, as the backbone for the imputation of missing species based on taxonomy. We assigned taxonomic ranks for all species for which we have empirically measured growth performance values and applied the TACT stochastic polytomy resolution algorithm to impute missing species onto the backbone tree. The TACT algorithm uses models incorporating birth and death rates to calculate the diversification for taxonomic ranks. Species are then imputed within the most restrictive ranks according to their respective calculated diversification rates. We implemented the TACT algorithm using Python 3.12.7.

We used a Bayesian generalised mixed-effects model with a phylogenetic random effect to assess the interacting effects of fisheries management (i.e. managed, unmanaged, unfished) and sea surface temperature on the growth performance of marine fishes. The growth performance (yi; where i corresponds to a single population/stock) was modelled according to a gamma error distribution by specifying the gamma shape (τ) and rate parameters (λi).
Gamma(yi | τ, λi) (24)
λi = τ / μi (25)
log μi = β0 + αspecies + αmethod + αyear + bphylo + βfish + βsst + βint × xsst (26)
Here, β0 is the overall intercept, βfish and βsst are the estimated effects of fisheries management and sea surface temperature, respectively; βint is the interaction term between fisheries management and sea surface temperature. We specified random intercepts for repeated species-level observations (αspecies), different aging methods (αmethod), and the sampling year (αyear). The phylogenetic random effect is denoted with bphylo. Because observations are taken at the species/stock level, the phylogenetic random effect bphylo accounts for the evolutionary non-independence among the residuals for interspecific contrasts (i.e. it allows for the fact that closely related species cannot be treated as independent samples; their evolutionary association makes them more likely to be similar). We specified the following weakly informative priors:
λ ∼ Gamma(0.01, 0.01) (27)
β0 ∼ Normal(0, 5) (28)
βfish,sst,int ∼ Normal(0, 2) (29)
We visually inspected traceplots and simulated residuals from the DHARMa v0.4.6 package. Models were run for 4000 iterations with an initial warmup phase of 2000 iterations using four Markov Chain Monte-Carlo chains. Phylogenetic models were run in R v4.4.0 using the ape v5.8 and brms v2.21.0 packages.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper uses a very large database for its inference that the growth of exploited marine fishes has declined in the last decades and there is no arguing with this.\nHowever, there are at least three ways that fish can and have become smaller, which are not really distinguished in this otherwise OK paper:\nA) the first is that fishing tends to target the large individuals of a population. This leads to truncated size distributions and thus different growth parameters;\nB) the second is that decade-long fishing as in A modifies the genetics of a population and induces a selection for small sizes. Fisheries scientists have real problems accepting this, but there is no reason to assume that evolutionally processes are different in fish than, e.g., in bighorn sheep, which hunters targeting the biggest-horned ones have turned into 'smallhorn sheep.' Indeed, there is a large body of recent literature on this negative selection in fishes;\nC) fishes are getting smaller at higher temperature, which is described phenomenologically by Arkinson's 'temperature-size rule' (TSR) and mechanistically by, e.g., Pauly's gill-oxygen limitation theory.\nWithout a extensive section dealing and quantifying the possible contributions of A, B and C, the data presented in the paper cannot be properly evaluated, especially as the differentiation made now, between 'managed' and 'unmanaged' stocks are probably not as important as the authors think.", "reviewer_2": "I have already reviewed this study for Nature Ecology & Evolution about a year ago. My review for NEE was generally positive; I considered the study valuable and important, both for the results it provided and for the revised collection of life-history data (but see my comment below requesting more details on this). I therefore recommended that the study be published but asked for some clarifications regarding the methodology and for the authors to ensure that the code used for analyses was publicly available. My current view remains unchanged—it is a valuable contribution, but the authors should provide further methodological clarifications.\nThe current manuscript appears very similar to what I reviewed previously, but the authors now provide the code and include a section on modelling performance in a phylogenetic relatedness context. It is unfortunate that they did not provide a previous set of reviews and responses, as this would have made it easier to assess what has been changed and how previous comments were addressed. In general, such repeated submissions waste reviewers’ and editors’ time and should not be encouraged.\nIt is good to see that the authors are now providing access to the code through Figshare, but it does not appear to be complete, as it remains unclear how growth performance was calculated—something I specifically asked about in my previous review. Several other comments and questions from my earlier review were also not addressed and remain relevant (see below). Additionally, another reviewer from the NEE submission raised important concerns, which should be addressed or at least acknowledged in this manuscript. The main issue raised was confusion between intra- and interspecific processes: it is unclear whether growth performance declined within species or whether species with higher growth performance were replaced by sampling those with lower growth performance. I personally do not think that such systematic bias in sampling is likely. And if higher growth performance species were replaced with lower performance ones the final effect is the same - growth performance appears to be declining in the ocean. However, the authors must clarify this point explicitly in the discussion. They should also explain precisely what the phylogenetic models account for, as they do not capture intra- versus interspecific contrasts. Finally, a previous reviewer could not understand why ageing method was included in the models. I believe it is essential to include this variable – the authors did the right thing but please add a couple of sentences clearly explaining this. Different ageing methods produce different L∞ and K values.\nRelevant comments from my earlier review:\n- The authors state that they individually assessed each reference of growth curves to check for errors. This would require reviewing thousands of publications (approximately 8,000 published curves in the final analysis) manually, which seems staggering. How was this accomplished? More importantly, what criteria were used to identify errors? Was the approach similar to that of Prince et al. (2023, ICES Journal of Marine Science), who proposed a specific methodology to assess the quality of published VB growth values available through FishBase? Which publications were identified as containing errors? This information alone would be an invaluable resource, as many researchers struggle with the low quality of published VB parameters on FishBase.\n- Line 261: The conversion from fork/standard length to total length L∞ estimates is now clearer, but please provide the equations used for this transformation.\n- Line 265: It is unclear how growth efficiency was estimated. A more detailed explanation of growth performance should be included in the main text. Please correct me if I am mistaken, but based on my understanding, the equation on line 272 would yield the following:\nφ = log₁₀K + SL × log₁₀L∞\nwhere SL = -m × b, with m = 0.76 (line 253) and b ≈ 3 (length-weight regression exponent).\nIf I use K = 0.1, L∞ = 100, and SL = -2.28 (-0.76 × 3), I obtain φ = -5.58.\nIf K = 0.5 and L∞ = 40, then φ = -3.9, and if K decreases to 0.4 (instead of 0.5), then φ = -4.05.\nHowever, the φ values reported in Fig. 3 range from 2.6 to 3.4, which I do not understand based on the provided methodology. The methods section does not allow these values to be reproduced.\nAnother point that was raised in the earlier review and which does not seem to be addressed is the fact that it is not clear whether the observed declines in growth performance over time are due to changes in performance within species or because species with lower growth performances were systematically sampled closer to present day (and higher growth performance species were sampled in the early 1900s. Some species were sampled multiple times, but the species composition through time does seem to differ. Without the information on when fish populations were sampled, it is impossible to disentangle whether growth performance has indeed declined with time due to fishing (as stated by the authors), or whether declines in growth performance are due to sampling different species at different time periods. In my view both aspects are interesting, but they should be clearly addressed.\nFurther, one reviewer in the previous review made a comment that this study is drawing intraspecific inferences from what are largely interspecific comparisons. The dataset combines measures from across many different species to draw its conclusions, i.e. the study is still drawing conclusions about changes within populations (due to evolution or plasticity) without actually examining changes in Φ through time within populations. The study also uses the interspecific scaling coefficient of 0.761 (Savage et al. 2004) to calculate the growth coefficient (Φ), which is a composite life history trait based on intraspecific measures of asymptotic size (L∞) and the instantaneous rate at which an individual approaches its asymptotic size (K) – both parameters are intra-specific properties. Intraspecific metabolic scaling ranges from 0.40 to 1.29, and this aspect should be addressed and explored. Moreover, the study assumes a constant metabolic scaling exponent across the entire study period, which is not necessarily the case with fishing and warming. It is possible that L∞, K, and metabolic scaling could all change over time so that Φ actually remains constant through time. Exploring how different values of L∞, K, and metabolic scaling interact to produce Φ could be a useful exercise for exploring how robust Φ is to changes in L∞, K, or metabolic scaling, or all three", "reviewer_3": "Dear authors and Editor,\nThank you very much for the opportunity to review “Over a century of global decline in growth performance of commercially valuable fishes”. I think it’s a fantastic manuscript that presents a much needed global-scale empirical assessment of trends in growth/size (there are a few model-based predictions). The data set collated is impressive and the modelling approach makes that justice. To understand how robust these conclusions are, I would like to see some additional explanations of the growth performance index, how composition of species/life histories has changed over time, and the assumption that managed species have had a higher exploitation history.\nHere are my comments in order of appearance:\nTitle: Perhaps its somewhat of a missed opportunity to limit the title to “commercially valuable fishes”. I understand it’s because that’s where you saw the decline, however you’ve also looked a few non-commercial species, and I think that’s an important feature since most fishery data are biased towards commercial species. I don’t have any good suggestions though, but it there is one that keeps the title short and to the point it could be considered.\nLine 24: Perhaps a detail, but are you looking at global impacts, or impacts globally? Since you extracted relatively “local” temperatures I’m thinking global is the scale of the analysis, not the impacts.\nLine 28: You have a way of converting growth performance to size that I don’t really follow. I’ll get back to that. In this summary section, I wonder though if you could specify which sizes we are talking about here that decline with 28%. Juvenile, maturation size, typical adult size, asymptotic size etc could all have different rates of declines for a given growth trajectory. Just thinking it’s good to be specific! You cite Cheung et al 2013 for example, where the size decline is often mis-interpreted.\nLine 46: I think you can remove the “(but see13)” here, because you start the sentence by saying management can improve the status of fished stocks, then say however many are still overfished (and then again point to the paper showing managed stocks are in better shape).\nLine 89: Can you elaborate on how the growth performance allows you to calculate a size and a growth rate? I don’t really follow that step form the growth performance equation. At which ages are you looking? Related: on line 92 you indicate that the decline is likely because fish grow to smaller asymptotic sizes. Depending on how this translation works, could there be a situation where say young fish actually become bigger for a given age?\nLine 72: I’m not very familiar with this growth index, but is this independent of life history strategy? You mention metabolic rate, for instance. I understand it corrects for mass, but are there still life history relationships in it? Are there systematic differences between say pelagic/demersal, mobile/passive species in this index? I’m curious because if this relationship exists it would be important to ensure that there is a consistency in which lifestyles / life history strategies are used over time.\nLine 119: A key explanation of the results in this manuscript is that managed fisheries have a legacy of intense fishing. An argument is that species likely are managed because they have been exploited. However, 1) there are other factors influencing whether a species is managed (read: assessed), not least a regional pattern linked to how fisheries operate, how species-rich the fish community is, possibility to age easily with otoliths etc (Hilborn et al 2020). 2) effective management can improve stock status, and managed stocks have better status today, because they have appropriate fishing exploitation rates. Many un-managed stocks are in worse shapes because they experience too high fishing pressure. This also makes the heading questionable; is it really overfishing that drives these results, if managed stocks may be less overfished than fish-but-unmanaged stocks? I understand you argue there may be a delay here, but can you in some way show that fish in the category “managed fish” have had a higher cumulative fishing impact than fished-but-un-assessed fish? I think this is a critical assumption to support.\nLine 127: Isn’t approximately half of the catches from managed fisheries? Or am I misunderstanding the graph or the “well below those” part?\nLine 140: Expresses => express?\nFigure 4: consider some transformation of the x-axis. Perhaps a square-root transformation would be gentle enough to not distort the relationship but still allow to see how many observations you have for the unfished category.\nFigure 4: Related to my previous comment; could you make a similar plot over time as well? How does the proportion temperate, subtropical and tropics change over time\nLine 196: Can you put these estimates in a size context (which you so nicely did for the global trend). I get a feeling from looking at the predictions that these are very small! Given a change in the growth index of 0.025 when the temperature increases one degree (I hope that’s a correct interpretation), how big effect does that have on the size?\nLine 206: In this context, where you look at growth potential and not community reorganisation, I think it’s important to state that approximately half of this 14-24 % decline in asymptotic size is due to physiology and the other half due to changes in species composition. In that sense it also better aligns with your findings.\nLine 276: I’m a bit puzzled here. Do you use S_L as the slope (Fig. S1) or the anabolic term? Seems you used the average 0.761 value based on metabolic arguments, but why not the slope if you already have that available?\nLine 322: I think here and possibly elsewhere it’s important to be more explicit that you are looking at the effect of temperature over space, since each growth performance metric is paired with a semi-local temperature. I.e., as far as I understand, you are not looking at a given population with multiple observations through time, how warming has affected its growth potential. You mention “climate change” on line 25 for example, but this sort of analysis is if I’m correct not necessarily looking at climate warming, only under the assumption that spatial and temporal effects are similar.", "reviewer_4": "In the Anthropocene, the two greatest threats to ocean biodiversity are overexploitation and climate change, and these two factors are interacting. The impacts of overfishing are not clear, because impacts of climate change on the growth performance of fishes act as confounding factors. The authors directly assessed the combined impacts of fishing and temperature on growth performance, accounting for phylogenetic relatedness. They used a Bayesian generalised mixed-effects model with a phylogenetic random effect to assess these interacting factors.\nThe methods they used are sound, and the results are highly interesting and important. Therefore, I would like to recommend publication of paper.\nIt might be difficult for general readers to understand why phylogeny must be taken into account, and I suggest to add a short explanation for this point.\nJournal name is lacking for Ref. 48 (White et al. 2022)\nScience 2022 Aug 19;377(6608):834-839."}, "author_response": "RESPONSE: We thank the reviewer for their comments and agree that these three mechanisms can simultaneously impact the growth performance of marine fishes. Unfortunately, we are unaware of any data that is currently available for which these mechanisms can be scrutinised and evaluated at global scales. To reconcile this, we have included additional text throughout the manuscript detailing each of the points. Responses to specific points are as follows:\nA) Truncation of size distributions\nWe conducted additional multivariate analyses to show that the species composition through time have remained relative homogenous, highlighting that declines in growth performance are likely mainly driven by the size-selective nature of fisheries. This section reads as:\nDeclines in global growth performance can be indicative of both intraspecific changes and interspecific compositional changes. Despite considerable overlap in the families and species that were sampled through time, which suggests relative homogeneity in the annual species composition, there were also relatively distinct compositional shifts between specific time periods (Fig. S2). While it is plausible that declines in growth performance could be due to potential sampling biases (i.e. shifting from species with higher growth performance values towards those with lower growth performance through time), it is unlikely that this is the primary mechanism of the observed declines. Indeed, there is considerable overlap between the early (pre-1960) and late (post 2000) compositional assemblages, which have the most leverage on temporal trend estimates (Fig. S2). Additionally, species compositions explained very little variation across growth performance studies (Fig. S2). It is therefore unlikely that the observed declines in growth performance were solely due to changes in the species sampled through time.\nB) Fishing-induced evolutionary processes\nWe agree and have discussed how the truncation of populations can lead to the selection against larger body sizes. This reads as:\nTherefore, the declines in growth performance of commercially valuable fishes could represent the interplay between two synergistic processes. First, an unfished, residual stock is remaining with smaller, younger individuals from size-selective fishing. Second, these demographic changes are then further reinforced and amplified by fisheries-induced selection against larger body sizes, which would manifest as lower growth performance values in subsequent generations45–48.\nIn the same aforementioned paragraph, we have now added additional text introducing the Rosa Lee phenomenon as a possible explanation for our results, which describes how the size-selective nature of fisheries can lead to differences in the expression of life-history traits in fishes. This section now reads as:\nAn alternative but not mutually exclusive mechanism for the observed declines in growth performance could be due to the “Rosa Lee” phenomenon43. In short, larger, faster-growing individuals are targeted first by size-selective fisheries, allowing the smaller, slower-growing individuals of a population to survive44. Consequently, for a given age, the remaining individuals are smaller and exhibit slower growth rates than would be expected in the absence of size-selective fishing44, potentially resulting in declines in growth performance.\nC) Temperature-size rule and GOLT\nThis is an interesting comment that points to a multifaceted problem. The relationship between temperature and growth is complex as it encompasses both physiological and ecological limitations. Although Pauly initially suggested that gills present a physiological limitation to growth, work by Bigman et al. (2023ab) has shown that gill surface area, an improved unbiased predictor compared to Pauly’s original gill area index, was only weakly related to growth and body size in fishes. The underlying mechanisms driving the temperature-size rule has been heavily debated, with multiple hypotheses (including Pauly’s GOLT and the metabolic theory of ecology) presented as co-occurring mechanisms that are not mutually exclusive (reviewed by Audzijonyte et al. 2019). This is a complex debate and outside the scope of our current study, but presents a very fruitful research avenue for the future.\n\nRESPONSE: Thank you for your attention with our manuscript, we greatly appreciate your comments and repeated assessment of our work.\nRESPONSE: We agree and apologise. We have now incorporated all of the reviewer’s comments and believe that they have greatly improved our manuscript.\nRESPONSE: We apologise for the oversight and have updated the scripts available to include the lines of code showing how growth performance was calculated.\nRESPONSE: This is a good point, thank you for bringing it to our attention again. While both mechanisms are plausible (i.e. demographic changes in the size/age distributions of fishes versus changes in the species that are sampled through time), we initially controlled for the latter by including a family-level grouping factor in our models, which accounts for repeated sampling within families. This random effect term means that the global mean for each year can be interpreted as the expected mean growth performance if all families were sampled in that year. Provided there is at least some continuity in the families observed from year-to-year (i.e. there is not a completely new set of families included in later years than in earlier years), this modelling approach can resolve issues related to biased sampling over time (e.g. if families with lower growth performances were oversampled in later years).\nImportantly, there are many families that are present throughout the entirety of the time series (e.g. Clupeidae, Pleuronectidae, Labridae). Additionally, each of these families comprise species that are also present throughout the entire time series (e.g. Sprattus sprattus, Pleuronectes platessa, Ctenolabrus rupestris), indicating that there is also some compositional homogeneity within families through time and further reinforcing our confidence that our results are not being unduly influenced by compositional changes.\nHowever, to more directly explore this issue, we now further explore changes in species sampled through time using Jaccard distances of the species per year to show how years clustered in multivariate space using a Principal Coordinate Analysis. In short, while there are some time periods that are relatively distinct from others, there was considerable overlap as well (as denoted by their respective 90% ellipses; Fig. S2), which suggests that species compositions have not markedly or consistently shifted through time. Notably, the median annual growth performance did not display any obvious clustering in multivariate space, with some of the lowest recorded values found in earlier time slices. If declines in growth performance were due to changes in species composition alone, we would expect to find distinct shifts in median annual growth performance values to match the clusters of each time slice. Therefore, the observed declines in growth performance are unlikely to be due primarily to changes in species composition through time. To address this, we have added a paragraph to the discussion, additional text to the methods, and a new supplemental figure (Fig. S2). The new discussion paragraph reads as:\nDeclines in global growth performance can be indicative of both intraspecific changes and interspecific compositional changes. Despite considerable overlap in the families and species that were sampled through time, which suggests relative homogeneity in the annual species composition, there were also relatively distinct compositional shifts between specific time periods (Fig. S2). While it is plausible that declines in growth performance could be due to potential sampling biases (i.e. shifting from species with higher growth performance values towards those with lower growth performance through time), it is unlikely that this is the primary mechanism of the observed declines. Indeed, there is considerable overlap between the early (pre-1960) and late (post 2000) compositional assemblages, which have the most leverage on temporal trend estimates (Fig. S2). Additionally, species compositions explained very little variation across growth performance studies (Fig. S2). It is therefore unlikely that the observed declines in growth performance were solely due to changes in the species sampled through time. Irrespective of the underlying mechanism, given the inherent temporal lag between management implementation and stock recovery12, the legacy of size-based fishing could manifest throughout populations as declines in growth performance, even when abundances are stable or increasing. Regardless of whether overfishing is the proximate or distal cause for the decline in fish growth performance, changes in the life histories of fishes are likely to be slow to reverse (or potentially irreversible) and can have repercussions on the biomass and productivity of capture fisheries45,47.\nRESPONSE: The phylogeny accounts for the evolutionary non-independence among the residuals of the generalised linear mixed-effects model at the species level and therefore captures interspecific contrasts. We have now added text to the Methods explicitly stating this, which now reads as:\nBecause observations are taken at the species/stock level, the phylogenetic random effect b accounts for the evolutionary non-independence among the residuals for interspecific phylo contrasts (i.e. it allows for the fact that closely related species cannot be treated as independent samples; their evolutionary association makes them more likely to be similar).\nRESPONSE: We have now added more explicit text to the methods, which reads as:\nIndividual aging methods (i.e. mark recapture, otolith rings, other rings (e.g. scales, vertebrae), length frequency analyses, and unknown) each contain their own mechanistic biases, which can lead to different values of L and K (and thus, different growth performance values )58,62. Indeed, aging method accounts for roughly 5% of the variation in growth rates across fishes28. To account for these inherent methodological biases quantitatively, each method was modelled with its own respective observation deviance.\nRESPONSE: Collating this dataset was a dominant component of the lead author’s doctoral thesis work. Indeed, she spent almost a year going through the publications. This work has been used in a recently published article (ref. 28 in the main text). The original search yielded 141,649 articles, which were manually evaluated following the steps outlined in the first paragraph of the Methods. Regrettably, we did not record which growth curves from FishBase contained errors, but they were mainly typological or rounding errors.\nRESPONSE: We have now included the equation in the methods. This section reads as:\nTo make studies comparable, we used published length-length conversion coefficients from FishBase55 to convert estimates of L recorded as standard or fork length to total length in cm using the following equation:\n𝑇𝑜𝑡𝑎𝑙 𝑙𝑒𝑛𝑔𝑡ℎ = 𝑎+𝑏×𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑜𝑟 𝑓𝑜𝑟𝑘 𝑙𝑒𝑛𝑔𝑡ℎ\nHere, a and b are the length-length conversion factors from FishBase55. When species-level coefficients were not available, we used genus or family means.\nRESPONSE: Thank you for bringing this technical misspecification to our attention. You are correct that S is generally negative because it describes the slope between log K and log L . The double-log relationship can be represented by the following equation:\nlog K = S × log L +β (R1)\nHere,  is the general intercept. Growth performance () is the specific  calculated where log L = 0 (i.e. L = 1 cm). Rearranging the formula to solve for the intercept  produces:\n = log K− S × log L (R2)\nWe attempted to simplify the equation due to the double negative produced by the subtraction and the negative S slope. However, you are correct that this is inaccurate because S is generally negative, so we have rewritten the formula in the main text to reflect equation (R2), which matches the code that is now provided. With these corrections, your theoretical examples would produce  = 3.56 and 3.35, respectively, which are within the range of our observed  values.\nRESPONSE: This has now been addressed. Please see our specific responses above.\nRESPONSE: This is a good point, thank you for carrying it over from our previous submission. We have now re-run the global time series model but with varying S values. In short, we calculated S for all species that had at least three growth curves – we could not calculate S per species within each year because there were not enough repeated observations per method. Mainly, we still find evidence of a decline in growth performance throughout the time series (now shown in Figure S8). We note, however, that varying S at the species level produced more extreme values of growth performance than using the constant scaling exponent from Savage et al. (2004), which is now shown in Figure S7. Indeed, the values of growth performance produced exceeded both the upper and lower bounds of both Pauly (1979) and Morais and Bellwood (2018), who also calculated interspecific growth performance values (note that growth performance from Pauly (1979) is based on W instead of L ).\nThese extreme growth performance values are likely produced by the high leverage of individual growth curves, which can be affected by methodological biases (e.g. underrepresentation of body size ranges) or environmental factors. These factors that are methodologically difficult to account for likely explain why previous studies have chosen to use a common S value to normalise extreme observations towards a unified auximetric relationship, grounded in metabolic theory (Morais and Bellwood 2018; Pauly 1979).\nAdditionally, the time series using a variable S resulted in a larger magnitude of decline with greater uncertainty: 21% [90% credible interval: -39.3% to 1.53%] decline compared to 7.9% [4.4% to 11.4%] using a constant S . We have therefore retained the conservative analyses using a “constant” S in the main text (i.e. the product of the anabolic term and the length-weight regression exponent) but present the global time series using variable S values in the supplementary materials (Fig. S8). These changes have been detailed in the Methods, which reads as:\nTo test the sensitivity of  to different S values, we re-ran the global time series model but calculated  using empirically derived S values. We calculated S by regressing log (K) against log (L ) for all species-level observations that had more than three observations per aging method (adapted from ref.32,58). This resulted in 714 species with empirically measured S values. We used the “constant” S value for all species without empirically measured estimates by multiplying the mean anabolic term by the length-weight regression exponent (as denoted by equation 3). While we found a relatively positive association between both methods of calculating growth performance (i.e. constant vs. varying S values; Fig. S7), using varying S values produced much more extreme values of growth performance compared to using constant values (-15.43 to 41.07 vs. 0.087 to 6.5, respectively), which exceeded the estimates of previous interspecific growth studies58,60. Despite these extreme values, we found a steeper, albeit more uncertain pattern of decline in the global time series (median decline [90% credible interval]: 21.0% [-39.3% to 1.53%]; Fig. S8). We therefore present the conservative estimates generated using the “constant” S values in the main text.\nSee supplementary methods for model specifications.\n\nRESPONSE: Thank you for your constructive and positive feedback on our manuscript. We greatly appreciate your suggestions and believe that they have strengthened our work.\nRESPONSE: In light of the comments that you have provided below, we agree and have now changed the title to exclude commercially valuable fishes. It now reads: Over a century of global decline in the growth performance of marine fishes.\nRESPONSE: We have changed the text accordingly. This sentence now reads as:\nHere, we assessed the impacts of fishing and temperature on the growth performance of marine teleost fishes globally over the last century.\nRESPONSE: Thank you for highlighting our need for clarity. We previously extrapolated body sizes and growth trajectories from the raw data based on the range of modelled growth performance values, which was likely to lead to confusion. We have now replaced these estimates with modelled values. Because growth performance is intrinsically capturing the trade-off between L and K, we converted changes in growth performance into changes in either L or K by holding the other parameter constant (i.e. holding K and L constant, respectively). We have added extra text and the mathematical formulations of these calculations in the Methods, which reads as:\nWe used equation (2) to generate hypothetical scenarios depicting how changes in growth performance () across time steps (t) would result in changes in either L and K (while holding K and L constant, respectively). Here, we used the “constant” S value (see above) with the mean length-weight regression exponent across species (b = 3.03).\nBecause these are modelled estimates based on general patterns, we are unable to specify the ontogenetic stage for which these changes are occurring. Please note that we have removed the citations from the Abstract to match the journal style.\nRESPONSE: Done.\nRESPONSE: This is now clarified. Please see specific response above.\nRESPONSE: We have corrected this sentence to indicate that growth performance standardises for body size not mass. This sentence now reads as:\nSpecifically, growth performance standardises estimates of instantaneous growth rates against a single unit of body size33.\nExploring differences in growth performance across different ecological traits and/or lifestyles would be very interesting; unfortunately, very few interspecific studies have been conducted across marine fishes outside aquaculture or laboratory settings. For example, Morais and Bellwood (2018) showed that K , another standardisation of growth in marine fishes, was marginally affected by ecological traits, such as position in the water column. The uncorrelated nature of K and growth performance (see Fig. 4b from Morais and Bellwood 2018) however, prohibits the extrapolation of a priori expectations across metrics. The main studies that evaluate growth performance in interspecific contexts are relating growth to metabolic rate (e.g. Wong et al. 2021) or proxies (e.g. gill surface area; Bigman et al., 2023; Pauly 2021). Exploring systematic differences between ecological lifestyles is outside the scope of the work herein, but would be a very interesting avenue of research to explore.\nRESPONSE: This is a really good point, thank you for bringing it up. Given the lack of management support for unassessed fishes, we are unlikely able to empirically capture the full breadth of fisheries exploitation that this group of fishes has received relative to managed fishes. As illustrated by Fig. 4b, the majority of unmanaged fishes are concentrated in the warm-water, tropical regions. The tropics are typically dominated by small-scale, coral reef fisheries that are reliant on locally managed fisheries, which are often controlled by local communities rather than federal agencies. While local management practices can be incredibly effective (e.g. Cinner et al. 2019), they are also misrepresented in national census measures and typically lack the financial resources to conduct formal stock assessments (Pauly et al. 2013). Due to these inherent biases in global fisheries data, it is possible that the steady level of growth performance that we observed was driven by two likely co-occurring mechanisms: (1) growth studies on unmanaged species are underprioritised by scientists and are thus inadequately represented in the current growth literature and/or (2) many fisheries in these regions have been severely fished to the point that they are already operating on a shifted baseline of generally lower growth performance. Although we are unable to distil which mechanism is driving our results with the data at hand, we have included an additional discussion paragraph highlighting these biases, which reads as:\nUnmanaged fisheries are typically exposed to unsustainable fishing pressures, therefore we might expect that growth performance would follow, if not surpass, the same magnitude of decline found in commercially valuable fishes. However, we found that the growth performance of unmanaged fishes was relatively stable throughout the time series. Although an underwhelming pattern of stagnation could be indicative of resilient demographic processes (i.e. resilience to fishing and temperature changes) or lagged responses, it is also possible that the focus on unmanaged fishes is disproportionately higher in tropical regions and may have biased the current literature on growth across marine fishes. The tropics are dominated by small-scale coral reef fisheries, where management is typically regulated by local communities rather than national-level policies53. Consequently, many coral reef nations lack the resources or funding to conduct stock assessments54 and such unmanaged stocks are thus misrepresented in global fisheries census data10. Given that growth studies can be tightly linked to conventional fisheries management practices13, the lack of stock assessments in the tropics may have led to an under-representation of unmanaged fishes in the growth literature. Indeed, unmanaged fishes may already be operating on a shifted baseline, whereby stocks are severely depleted to the point of mainly comprising individuals with lower growth performance. Care is needed, therefore, as these systematic biases could distort or completely omit any realised changes in growth performance across unmanaged fishes.\nRESPONSE: You are correct, thank you for pointing that out. We have modified the text to read:\nAlthough the catches of unmanaged fishes followed a similar pattern to managed fishes, the catch of unmanaged fisheries is approximately half that of managed fisheries35 (Fig. S3).\nRESPONSE: Changed accordingly.\nRESPONSE: We have recreated the figure using the square root transformation as suggested. However, we found the distortion of the x axis reduced the interpretability of the values considerably, while using another transformation (i.e. log) distorted the relationship too drastically. We therefore chose to keep the original scaling in the main text but have provided a supplemental figure with a square root transformation in the supplementary materials (Fig. S4).\nRESPONSE: We have now added a supplemental figure (Fig. S9) showing the changing proportions through time and added text to the Methods highlighting this new figure. The text reads as:\nAcross all three regions, the number of observations noticeably increased from the 1950s to the early 2000s, before steadily decreasing after 2010 (Fig. S9).\nRESPONSE: Although we agree that visualising the size equivalence of these modelled results would be useful, the structure of this model means that the effect of a single degree is different depending on its value. The phylogenetic models use a gamma error distribution and the estimates provided are based on a log link, meaning that the effect of temperature on growth performance is exponential and a one-degree difference will have different effects along the range of temperatures. We therefore followed a similar formulation produced by equations 4-4.3 and calculated the proportional changes in L and K with a 1ºC change in the lower and upper ranges of our data. This section now reads as:\nFor unmanaged fishes, a 1ºC increase in temperature translated to, at most, a 7-15% median increase in L or an 18-37% increase in K across the majority of the range of observed temperatures (smaller estimates calculated from 0-1ºC and larger estimates from 28-29ºC). For unfished species, a similar increase in temperature resulted in a maximum increase of 14-48% in L or a 34-147% increase in K.\nRESPONSE: Done. This sentence now reads as:\nGiven the inherent correlation between metabolic rates and growth30,51, models of energy assimilation predict that under a high-emissions climate scenario, the asymptotic body sizes of fishes may decrease by 14-24% globally in the next 30 years due to changes in both physiology and species assemblages25.\nRESPONSE: We apologise for the confusion. Based on Pauly (1979) and Morais and Bellwood (2018), we used S as the slope, but it is calculated based on the anabolic term (0.761) from Savage et al. (2004) using the following equation: S = -m * b where m is the anabolic term and b is the length-weight regression parameter (now Equation 3 in the main text).\nWe have re-run the global time series model but with varying S values by regressing log (K) against log (L ). In short, we calculated S for all species that had at least three growth curves – we could not calculate S per species within each year because there were not enough repeated observations per method. Mainly, we still find evidence of a decline in growth performance throughout the time series (now shown in Figure S8). We note, however, that varying S at the species level produced more extreme values of growth performance than using the constant scaling exponent from Savage et al. (2004), which is now shown in Figure S7. Indeed, the values of growth performance produced exceeded both the upper and lower bounds of both Pauly (1979) and Morais and Bellwood (2018), who also calculated interspecific growth performance values (note that growth performance from Pauly (1979) is based on W instead of L ).\nAdditionally, the time series using a variable S resulted in a larger magnitude of decline with greater uncertainty: 21% [90% credible interval: -39.3% to 1.53%] decline compared to 7.9% [4.4% to 11.4%] using a constant S . We have therefore retained the conservative analyses using a common S in the main text but present the global time series using variable S values in the supplementary materials (Fig. S8). These changes have been detailed in the Methods, which reads as:\nTo test the sensitivity of  to different S values, we re-ran the global time series model but calculated  using empirically derived S values. We calculated S by regressing log (K) against log (L ) for all species-level observations that had more than three observations per aging method (adapted from ref.32,58). This resulted in 714 species with empirically measured S values. We used the “constant” S value for all species without empirically measured estimates by multiplying the mean anabolic term by the length-weight regression exponent (as denoted by equation 3). While we found a relatively positive association between both methods of calculating growth performance (i.e. constant vs. varying S values; Fig. S7), using varying S values produced much more extreme values of growth performance compared to using constant values (-15.43 to 41.07 vs. 0.087 to 6.5, respectively), which exceeded the estimates of previous interspecific growth studies58,60. Despite these extreme values, we found a steeper, albeit more uncertain pattern of decline in the global time series (median decline [90% credible interval]: 21.0% [-39.3% to 1.53%]; Fig. S8). We therefore present the conservative estimates generated using the “constant” S values in the main text.\nSee supplementary methods for model specifications.\nRESPONSE: This is a good point, thank you for bringing it up. We have specified throughout the text that we are evaluating the effect of temperature over space.\nRESPONSE: Thank you for your positive evaluation of our work.\nRESPONSE: We have added additional text to the Methods describing why we included a phylogenetic random effect. This new sentence reads as:\nBecause observations are taken at the species/stock level, the phylogenetic random effect b accounts for the evolutionary non-independence among the residuals for interspecific phylo contrasts (i.e. it allows for the fact that closely related species cannot be treated as independent samples; their evolutionary association makes them more likely to be similar).\nRESPONSE: Thank you for finding this error, we have added the journal name to the aforementioned citation."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I have reviewed this manuscript twice already and I think it has improved substantially. I do not have any further comments, except for requesting the authors and editors to ensure that the compiled data and code are publicly available in a permanent repository. While the paper itself is a valuable and interesting contribution, the dataset of growth records will be used in many other future studies if it is made available.\nAll the best and congrats on a great study.\nAsta Audzijonyte", "reviewer_3": "Dear authors and Editor,\nThank you very much for the opportunity to review “Over a century of global decline in growth performance of commercially valuable fishes” for a second time. Seeing the responses to my own comments, and those of other reviewers (especially R#1 whose comments I was asked to look at specifically), I still think it’s a great manuscript building on a massive data collection effort that presents a much needed global-scale empirical assessment of trends in growth/size.\nHere are my comments. Apologies if they are scattered, I’m replying both to your replies, R#1 replies, and new things in the text!\n1. Line 352: I appreciate the effort to translate growth performance into something more relatable, and I fully understand it is inherently challenging given the same change can be due to a change in K, Linf, or both. You chose to hold one constant to solve for the remaining. I wonder if you could add a figure of how those changes in growth curves would look like. If Linf or K matters for the growth trajectory, so a change in growth performance alone doesn’t say much about which growth trajectories become more common under fishing (as an example). One thing they have in common though is that for a given age, the size is reduced. Perhaps you could add a figure if you have room for that, like the one below, to help show that while there are differences (depending on if Linf or K flexible), there are commonalities in the growth trajectory. See the figure below (code at the end of the document).\n2. Reply to R#1 comment 1: Just a note; in A) I believe that the reviewer mean size-truncation at the population level, not changes in species composition. That said, it’s not immediately clear to me how fishing removing large and old individuals affects growth parameters if there’s no genetic effect (mechanism B). I don’t think I’ve seen a simulation study on that; how sampling young fish leads to biased growth parameters. The Rosa Lee effect could lead to that, but I that’s what the reviewer had in mind because it’s not about truncation per se. And you already discuss the Rosa Lee effect, which I think is the most relevant here given your findings. It also doesn’t invoke criticised mechanisms or genotypic change.\n3. Line 29: I would refrain from calling K a growth rate. Rather say von Bertalanffy growth coefficient (K) at the first mention, and then just growth coefficient. This because it has unit per time (not size per time).\n4. Line 93: I don’t really follow the sentence starting with “Therefore”, perhaps check the grammar!\n5. Line 133: This is true! I’d argue also that even managed fisheries without overfishing (but maximally fished) still have exploitation rates that would give rise to fishing induced evolution and/or Rosa Lee effects (just to strengthen your argument; it’s not overfishing per se, but the magnitude of prolonged fishing that would lead to demographic changes).", "reviewer_4": "I found, in the revised MS, my comments have been fully taken into account.\nTherefore, I recommend publication of this paper."}, "author_response": "RESPONSE: Thank you for your constructive comments, which we believe have greatly improved our manuscript.\nRESPONSE: Thank you for your careful attention to our work.\nRESPONSE: Thank you for the great suggestion and associated R code. We have now added an additional figure to the supplemental materials (now denoted as Fig. S2) and the following text:\nNotably, in both cases (i.e. a change in L or a change in K), the size for a given age is reduced (Fig. S2).\nThis is my only follow up on the points I raised in my first review. I buy all the other changes and appreciate the effort but into this major review.\nRESPONSE: Thank you for clarifying the comments provided by R1. We agree that the two aforementioned mechanisms that can affect a population’s growth performance – size truncations via size-selective fisheries (including the Rosa Lee phenomenon) and subsequent selective pressures – are not mutually exclusive, with the latter a likely consequence of the former. Size-selective fisheries that remove the largest and oldest individuals from a stock can affect growth parameters in two ways: (1) the remaining population exhibits smaller body sizes, which can bias estimated growth performance values (Figure S1) and/or (2) fast-growing young individuals recruit into the fishery earlier and are selectively removed alongside older fishes, leaving a residual population disproportionally composed of slow-growing individuals (Rosa Lee phenomenon). These patterns can then be reinforced by genetic effects arising from natural selection, which could manifest as long-term declines in growth performance (i.e. over multiple generations). Note that we have discussed these mechanisms as plausible in the previously revised manuscript, but have also modified one of the sentences in this section to clarify these points. It now reads as:\nTherefore, the declines in growth performance of commercially valuable fishes over time could represent the interplay between two synergistic processes. First, an unfished, residual stock is remaining with smaller individuals from size-selective fishing, which may be dominated by slow-growing individuals (via the “Rosa Lee” phenomenon43). Second, these demographic changes are then further reinforced and amplified by fisheries-induced selection against larger body sizes, which would manifest as lower growth performance values in subsequent generations45–48.\nRESPONSE: Done.\nRESPONSE: We have modified this sentence. It now reads as:\nThis global decline is most likely driven by fishes that are growing to relatively smaller sizes and/or at slower rates (i.e. intraspecific changes), and less likely driven by compositional shifts in the species sampled (i.e. interspecific changes; Fig. S3).\nRESPONSE: We have added an additional sentence to include the potential impact of prolonged fishing exposure. This sentence now reads as:\nAdditionally, some stocks that are effectively managed may not be overfished but may experience prolonged fishing pressures (i.e. maximally fished), which can similarly lead to fishing-induced demographic changes20.\nRESPONSE: Thank you for your assessment of our work."}]

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