Fishing and Warming Reshape Size Spectra of Commercial Species in the Mediterranean Sea

Fishing and Warming Reshape Size Spectra of Commercial Species in the Mediterranean Sea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fishing and Warming Reshape Size Spectra of Commercial Species in the Mediterranean Sea Vojsava Gjoni, Germana Garofalo, Fabio Fiorentino, Vincent Goerges, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7565528/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Anthropogenic pressures, particularly fishing effort and ocean warming are reshaping marine ecosystems and influencing the population dynamics of key fisheries species. While these stressors have been widely studied in isolation, their interactive effects across taxonomically distinct groups remain poorly understood. Here, we examine how fishing pressure and increasing temperature jointly affect the size spectra of nine commercial species (three bony fish, three crustaceans, and three cephalopods) in the central Mediterranean Sea. Using size-spectrum analyses applied to fishery-independent survey data from 2000 to 2023, we evaluate population-level responses to these stressors. Our findings reveal taxon-specific patterns: under high fishing effort and high temperatures, fish populations exhibit a higher proportion of smaller individuals, consistent with fishing-induced truncation and temperature-driven metabolic constraints. In contrast, crustaceans and cephalopods show different responses, reflecting their greater physiological plasticity and shorter life cycles, which may buffer against environmental changes. These results suggest that the combined effects of fishing and climate change could disproportionately reduce fish biomass while allowing more flexible taxa to persist or even thrive. Our results emphasize the need for adaptive management strategies that incorporate both environmental change and fishing pressure projections to maintain sustainable yields and ecosystem resilience in the face of ongoing climate-driven shifts. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences size spectra temperature fishing effort commercial species climate change impacts fisheries management Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Marine ecosystems are constantly subject to multiple anthropogenic pressures, with climate change and fishing pressure acting as key drivers of biodiversity loss and shifts in community structure 1 , 2 . Rising ocean temperatures alters metabolic rates, growth and reproduction patterns and species distributions, while intense fishing pressure modifies population structures and trophic interactions 3 , 4 . However, the combined effects of temperature changes and fishing pressure on marine populations remain poorly understood, particularly across taxonomically distinct groups such as fish, crustaceans and cephalopods. Trawling and other high-intensity fishing methods selectively remove individuals based on size and life history traits, often leading to faster-growing and smaller-bodied populations 5 . This can interact with rising sea temperature as effect of warming oceans, which generally favor smaller individuals due to increased metabolic demands and oxygen limitations 6 , 7 . Such combined pressures may cause unexpected, non-additive effects, where species’ responses vary due to physiological differences in thermal tolerance, growth rates and reproductive strategies 8 . Size spectra modelling has traditionally been applied at the community level to assess the distribution of individual body sizes within ecological communities, offering key insights into the effects of environmental and anthropogenic pressures 9 – 11 . The size spectrum is typically described by a power law, N ~ M λ , where N is the abundance of individuals and M is their body mass 12 . The exponent λ of this relationship is known to be sensitive to both temperature-induced changes 11 , 13 and fishing pressures 14 – 16 , making it a valuable metric for tracking changes in ecosystem structure and function. Owing to the apparent consistency of the size spectra across systems, body size distributions have even been proposed as a “universal indicator” of ecological status 17 . By capturing shifts in body size distributions, size spectra provide a mechanistic link between changes in natural or human-induced factors and population dynamics. However, their application at the population level remains underexplored, despite their potential to reveal the effects of various biotic and abiotic drivers. These include, for example, temperature-induced shifts in adult body size and the impacts of size-selective fishing, both of which are crucial for understanding population-level responses 16 . Indeed, at the population level, size spectra offer valuable insights into demographic processes, capturing species-specific responses to environmental stressors such as ocean warming and fishing pressure. Given that size spectra at the community level are known to respond to external drivers such as temperature and to human-induced pressures like fishing mortality 18 , it is plausible to expect similar responses at the population level. This expectation aligns with previous findings demonstrating that warming trends and fishing pressure can drive reductions in adult body size and shifts in life history traits 19 , 20 . Therefore, testing size spectra at the population level may provide a more mechanistic understanding of species-specific responses to global change—an approach particularly valuable for commercially exploited species, which tend to be more sensitive as they are already exposed to overfishing. This, in turn, can improve predictions of population resilience and contribute to more effective strategies for maintaining ecosystem stability. Crustaceans and cephalopods, for instance, exhibit greater plasticity in response to environmental drivers than many fish species, often displaying faster life cycles and flexible growth rates 21 , 22 . Some studies suggest that trawling may temporarily enhance shrimp productivity by resuspending nutrients and reducing predator abundance 23 , 24 . Among commercially exploited species, cephalopods—due to their short generation times and rapid turnover—often show a higher capacity for population recovery following disturbances 25 . In contrast, many fish species exhibit longer life spans and experience more pronounced size-selective mortality, which can hinder their recovery and make them particularly vulnerable to the combined pressures of overfishing and climate change 4 . Understanding these taxon-specific responses is essential for ecosystem-based fisheries management, as shifts in size spectra can affect food web dynamics and ultimately impact fisheries yields and ecosystem stability 14 , 16 . Here, we examine how temperature and fishing pressure interactively influence the size spectra of fish, crustaceans, and cephalopods in the central Mediterranean Sea. By focusing on size structure, we gain critical insights into fundamental biological processes such as growth rates, reproductive output, and population mortality, particularly under the combined influence of size-selective fishing and temperature-driven environmental change. A recent study documented both elevated sea temperatures and intense fishing pressure are associated with shifts in the size distribution of exploited fish stocks toward smaller individuals 26 . These shifts may reduce the reproductive potential and resilience of populations, with significant implications for fisheries productivity and ecosystem sustainability. Based on this evidence, we assume that warming and fishing will increase the proportion of smaller individuals, particularly among fish, whereas taxa like crustaceans and cephalopods may exhibit more variable responses due to their greater life-history plasticity. Therefore, we have used 20 years of fishery-independent data from the MEDITS bottom trawl survey in the Strait of Sicily (SoS) to assess long-term trends in size spectra to understand how environmental and anthropogenic pressures shape population structures over time. In particular, three commercially important crustacean species were considered in this study (the deep water shrimp Aristaeomorpha foliacea (Risso 1827), the rose shrimp Parapenaeus longirostris (Lucas 1846), and the Norway lobster Nephrops norvegicus (Linnaeus 1758), as well as three bony fish species, the European hake Merluccius merluccius (Linnaeus 1758), and the mullets Mullus surmuletus (Linnaeus 1758) and Mullus barbatus (Linnaeus 1758)and three cephalopods, the squids Loligo vulgaris (Lamark 1798) and Illex coindetii (Vérany 1839), and the octopus Octopus vulgaris (Cuvier 1797). These nine species where selected because they constitute a valuable portion of the fish market in the Mediterranean Sea (FAO 2022) and represent a wide range of life-history strategies, thermal preferences, and responses to exploitation. Given that both temperature and fishing pressure are increasing in many regions, understanding their combined effects on commercial stocks is essential for developing more adaptive and sustainable fisheries management strategies that can be applied to inform the ecosystem-based approach at Mediterranean scale. Results The spatial and temporal variability of environmental conditions and fishing pressure across the south-central Mediterranean Sea is evident from both the compiled dataset and the accompanying spatial maps (Fig. 1 ; Supplementary Table S1 ). The dataset comprises a total of 26,856 (across all years) individual records, reflecting observations collected across a broad range of depths and geographic locations, as indicated by the MEDITS trawl haul positions (Fig. 1 A). The thermal landscape (Fig. 1 B) reveals a marked gradient in mean sea surface temperature, with warmer waters along the eastern coastal areas and cooler conditions toward the deeper offshore zones. This variability in temperature is paralleled by heterogeneity in fishing effort (Fig. 1 C), which is most intense along the western shelf and central portions of the basin. Despite the limited spatial resolution of some data layers, Supplementary Table S1 highlights substantial interannual variation in both temperature and fishing effort, underscoring the dynamic nature of environmental and anthropogenic pressures across the region. Although presented within a limited space, the dataset (summarized in Supplementary Table S1 ) clearly illustrates a high degree of variability in both sea surface temperature and fishing effort, not only across different sites but also across years, highlighting the dynamic and heterogeneous nature of the marine environment and human exploitation patterns over time. The contour plots (Figs. 2 – 4 ; Table 1 ) illustrate the interactive effects of temperature (°C) and fishing effort (fishing hours/year) on the size spectrum exponent (λ) across the selected nine commercially important species. Warmer colors (yellow) represent more negative λ values, suggesting a higher proportion of smaller individuals, while cooler colors (purple) indicate less declines in larger size classes, reflecting a shift toward larger individuals. Crustaceans In P. longirostris , higher temperatures are associated with more negative λ values, indicating a shift toward smaller individuals primarily driven by temperature-induced physiological constraints, while fishing pressure appears to have little or no effect (Fig. 2 A; Table 1 ). In this species, the probability of a shift toward smaller size classes exceeds 80% as temperatures increase. Similarly, A. foliacea shows a trend toward smaller individuals under warmer conditions, reflected by more negative λ values (Fig. 2 B). However, in this case, the trend is more pronounced at low to moderate fishing pressures, suggesting a slight interactive effect between temperature and fishing effort on individual size. The probability of a shift toward smaller individuals in A. foliacea exceeds 85% with rising temperatures. In contrast, N. norvegicus exhibits an opposite pattern, with a higher proportion of larger individuals at higher temperatures (Fig. 2 C; Table 1 ). At lower temperatures, fishing effort seems to have a modest impact, increasing the proportion of smaller individuals as fishing intensity rises. The probability of an increase in larger individuals surpasses 75% under warming scenarios, particularly when fishing pressure is low, suggesting that warming conditions may favor size persistence or even growth in this species, potentially due to physiological adaptations to temperature fluctuations. Bony fish M. merluccius shows a strong response to both temperature and fishing, with a higher proportion of smaller individuals under increased fishing effort and warmer waters (Fig. 3 A; Table 1 ). The probability of this shift is above 85% at temperatures exceeding 14°C with fishing pressure above 30 hours/year. M. surmuletus and M. barbatus also follow this trend but with slight differences, in fact while M. surmuletus experiences a size reduction at high temperatures and intense fishing (Fig. 3 B; Table 1 ), with a probability of 70% at temperatures above 16°C, M. barbatu s shows a steeper decline in λ at lower fishing pressure (Fig. 3 C; Table 1 ), suggesting that temperature alone may drive size shifts in this species, with a 65% probability of smaller individuals at temperatures above 16°C. Cephalopods L. vulgaris displays a more complex interaction between temperature and fishing pressure, where moderate temperatures (16–18°C) and fishing pressure above 40 hours year - 1 result in the more negative λ values, suggesting a balanced size structure (Fig. 4 A; Table 1 ). At higher temperatures and fishing intensity, the probability of a shift toward smaller individuals reaches 75%. Differently, I. coindetii exhibits a pronounced reduction in size structure at both high fishing pressure and warmer temperatures (Fig. 4 B; Table 1 ), with a 90% probability of smaller individuals above 18°C. Finally, O. vulgaris shows the most pronounced decline in size under sea warming and high fishing pressure (Fig. 4 C; Table 1 ), with a 95% probability of smaller individuals at temperatures above 16°C. This strong response suggests that temperature directly influences physiological constrains, leading to an overall shift in smaller sizes of the population. Table 1 Table 1 . Parameter estimates +/- 95% CrI of the relationship between lambda estimates (ISD’s λ) of the Bayesian analysis. Figures 2 , 3 and 4 are based on this model and incorporate the effects of fishing effort (hours/year) and temperature (°C). We restricted the model to these terms based on a priori hypotheses and performed no model selection steps thereafter. Parapenaeus longirostris’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -0.26 (0.01) -0.01 (0.02) -0.50 (0.02) -0.01 (0.01) -0.27 to -0.29 0.00 to -0.03 -0.51 to -0.49 -0.02 to -0.00 Aristaeomorpha foliacea’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -1.64 (0.01) -0.01 (0.01) -1.77 (0.02) -0.01 (0.01) -1.66 to -1.60 0.00 to -0.02 -1.76 to -1.79 -0.00 to -0.02 Nephrops norvegicus’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -0.71 (0.02) -0.01 (0.01) -0.32 (0.03) 0.01 (0.01) -0.72 to -0.69 -0.02 to -0.02 -0.33 to -0.30 -0.01 to -0.02 Merluccius merluccius’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -1.51 (0.01) -0.01 (0.01) -0.40 (0.01) -0.01 (0.01) -1.62 to -1.64 -0.03 to -0.00 -3.13 to -3.16 -0.02 to -0.00 Mullus surmiuletus’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -1.61 (0.01) -0.01 (0.01) -3.15 (0.01) -0.01 (0.01) -1.62 to -1.64 -0.02 to 0.00 -3.13 to -3.16 -0.02 to -0.01 Mullus barbatus’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -0.66 (0.02) -0.01 (0.03) -0.30 (0.01) -0.01 (0.01) -0.68 to -0.64 -0.02 to 0.00 -0.33 to -0.29 -0.02 to-0.01 Logico vulgaris’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -0.97 (0.01) -0.01 (0.02) -0.02 (0.02) -0.01 (0.02) -0.98 to -0.95 0.00 to-0.02 0.01 to-0.04 -0.02 to-0.00 Ilex coindetii’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -1.49 (0.03) -0.01 (0.02) -0.13 (0.01) -0.01 (0.01) -1.51 to -1.48 -0.03 to 0.01 -0.11 to-0.14 -0.02 to-0.03 Octopus vulgaris’s lambda Predictors Estimates 95% CrI (Intercept) fishing effort temperature fishing effort*temperature -0.27 (0.01) -0.01 (0.02) -0.50 (0.01) -0.01 (0.01) -0.28 to -0.28 -0.00 to-0.02 -0.51 to–0.49 -0.02 to-0.00 Discussions This study explores for the first time the specie-specific response in size spectra of commercially important species in the central Mediterranean Sea. Our results reveal clear but taxon-specific shifts in body size distributions under the combined influence of water warming and fishing pressure in the central Mediterranean Sea. Across the nine studied species, belonging to bony fish, crustaceans, and cephalopods, higher temperatures were generally associated with a shift toward smaller adult sizes. This broad trend confirms the physiological constraints imposed by warming, which accelerates metabolic rates, promotes faster growth, but leads to earlier maturation at smaller sizes 27 , 28 . Additionally, rising temperatures are known to increase natural mortality rates in marine organisms, further contributing to size structure changes 29 . While temperature emerged as a pervasive driver across taxa, the role of fishing pressure on size spectra was more variable. In general, fishing selectively removed larger individuals, amplifying the shift toward smaller body sizes. However, species differed in their sensitivity to this pressure: crustaceans such as P. longirostris and A. foliacea exhibited size reductions primarily driven by temperature, with fishing playing a minor role for P. longirostris . In contrast, N. norvegicus maintained or even increased adult size under warming conditions, suggesting adaptive physiological traits. In contrast, fish species ( M. merluccius, M. surmuletus, M. barbatus ) and the cephalopod O. vulgaris seems to strongly respond to both combined effects (warming and fishing pressure) that reinforced a shift toward smaller adults. These findings highlight the importance of life-history traits and thermal tolerances in mediating species-specific responses to environmental change and stress the need for management strategies that consider the synergistic effects of climate and exploitation pressures. Crustaceans All three crustacean species appear largely unaffected by fishing pressure (Fig. 1 ), this result is likely driven by two indirect effects of bottom trawling. On one hand, trawling disturbs the seabed, resuspending sediments and increasing nutrient availability in the water column. This process can enhance primary production, leading to higher food availability for benthic organisms, including shrimp prey 30 . Increased organic matter can stimulate microbial activity and boost the production of benthic invertebrates that serve as shrimp food 31 . Indeed, bottom trawling can modify habitat structures by flattening seabed features and removing complex structures, therefore creates soft-bottom habitats that are preferred by burrowing shrimp species 23 . In addition, fishing can alter trophic interactions by reducing predator abundance, leading to community change where prey species experience reduced predation pressure 32 , 33 . Reductions in population density due to fishing can lead to compensatory responses, where reduced competition for food and space allows remaining individuals to grow faster and attain larger sizes 34 . This density-dependent effect has been documented in various exploited crustacean populations, where fishing pressure results in increased per capita resource availability, promoting faster individual growth 35 . Our results suggest that temperature exerts a strong influence on all three species, with P. longirostris and A. foliacea exhibiting a higher proportion of smaller individuals as temperatures rise, while N. norvegicus shows an increased proportion of larger individuals under warming conditions. This difference in the response to increasing temperature could be related to the fact that while this factor enhance the spatial distribution and population dynamics of P. longirostris and A. foliacea as has been previously suggested (i.e. for P. longirostris) warmer waters have led to expanded spatial patches and depth ranges 3 , as well as increased abundance trends 36 . Differently for A. foliacea populations that show seasonal migrations related to temperature, with winter movements to upper slopes coinciding with male maturity and mating 37 this could represent a limiting factor. Indeed, the effect of bottom temperature, together with other environmental factors such as particulate organic matter may explain the spatial distribution of A. foliacea 38 . Both species exhibit short-term spatio-temporal variations in population dynamics and biology, influenced by environmental factors such as temperature, productivity, and seafloor characteristics 39 . While these studies do not directly address changes in the number of small individuals, they indicate that warming temperatures are affecting the distribution, abundance, and life cycles of these species, potentially impacting population structure and recruitment patterns in the Mediterranean Sea. In contrast, N. norvegicus exhibits notable resilience to warming waters, a trait that can be attributed to several biological and physiological factors. Studies have demonstrated that this species possesses metabolic plasticity, allowing it to adjust its metabolic rates in response to temperature changes 40 . Specifically, N. norvegicus has been observed to decrease its standard metabolic rate with increased acclimation temperatures while maintaining its aerobic metabolic scope, thereby optimizing energy expenditure under elevated temperatures 41 . Additionally, the burrowing behavior of N. norvegicus offers a refuge from environmental fluctuations, including temperature variations. By inhabiting muddy substrates and constructing burrows, these lobsters can mitigate the impacts of external temperature changes, contributing to their resilience in warming conditions 42 . However, it is important to note that while N. norvegicus displays certain adaptive capacities to cope with rising temperatures, some studies have reported potential negative effects. For instance, exposure to simulated climate change conditions, including elevated temperatures, has been associated with immune suppression and protein damage in this species 40 . However, this apparent resilience may come at a cost. Prolonged exposure to elevated temperatures could lead to physiological trade-offs, such as diminished aerobic capacity, reduced reproductive success, or inefficient energy use. Although some studies have suggested that warming might enhance productivity in certain marine species, such benefits remain debated—especially in the absence of a clear link to changes in trophic interactions. For N. norvegicus , evidence points in the opposite direction: lower temperatures have been associated with better physiological performance, increased catch rates, and more favorable growth conditions 43 . These findings imply that, without buffering mechanisms through the food web, sustained warming is more likely to challenge than benefit this cold-adapted, economically important crustacean. Bony fish Our results show that M. merluccius size spectra is negatively influenced by both rising temperatures and fishing pressure, highlighting the vulnerability of this long-lived, slow-growing demersal species. This species has a complex life cycle, characterized by fast growth in early stages and a prolonged adult phase, with individuals reaching sexual maturity between 2 and 4 years of age 44 . It inhabits a broad bathymetric range (from shallow waters to over 1000 m deep) but shows a preference for temperatures between 10°C and 14°C. Recruits of age 0 prefer habitat characterized by stable bottom temperature (11.8–15.0°C), low bottom currents (< 0.034 m s - 1 ) and a frequent occurrence of productive fronts in low chlorophyll-a areas (0.1– 0.9 mg m - 3 ), mainly between 50 and 200 m depth 45 . Temperature plays a critical role in regulating M. merluccius population dynamics. Warmer conditions have been associated with increased juvenile growth rates, but they may also disrupt recruitment success by altering larval dispersal and prey availability 46 . Furthermore, higher metabolic demands under warmer temperatures can lead to earlier maturation at smaller sizes, reducing the overall size structure of the population 20 . Fishing pressure exacerbates these trends by selectively removing larger individuals, which disproportionately affects M. merluccius due to its late maturity and strong size-dependent reproductive success 47 . As a result, populations subjected to both intense fishing and warming conditions exhibit a shift toward smaller individuals with faster turnover rates. This size truncation can have cascading effects on trophic interactions and stock productivity, potentially increasing M. merluccius ’s vulnerability to further environmental changes 5 . All three fish species, M. surmuletus, and M. barbatus , exhibit a broadly similar pattern of decreasing size with increasing temperature and fishing pressure. However, the two red mullet species show notable differences in the steepness of their response gradients, reflecting their distinct ecological preferences despite their shared life-history traits that make them sensitive to environmental stressors. Both species are short-lived, fast-growing, and exhibit relatively early maturation, making them more responsive to environmental fluctuations 48 . While M. barbatus is more commonly associated with muddy seabed, with the maximum of abundance between 50 and 100 m, M. surmuletus prefers rocky and sandy substrates with the maximum of abundance at deeper level between 100 and 200 m 49 . Temperature exerts a direct influence on the physiological constraints of both species, with warming conditions accelerating growth rates, advancing maturation, and shifting spawning periods and habitat suitability 49 . However, recent studies suggest that M. surmuletus , due to its broader environmental tolerance, is better able to expand into newly favorable habitats under warming conditions, whereas M. barbatus , with more restrictive habitat requirements, may experience range contractions in some areas 50 . High fishing pressure further compounds these effects by disproportionately targeting larger individuals, leading to a decrease in average body size. This pattern is particularly evident in M. barbatus , where size-selective fishing reduces the reproductive potential of the population, potentially limiting recruitment success 51 . The combined effects of warming and fishing may thus accelerate demographic shifts in these species, favoring smaller, faster-reproducing individuals while potentially reducing overall stock resilience. Over time, fishing and warming may induce evolutionary shifts toward earlier maturation at smaller sizes. When larger individuals are consistently removed, natural selection favors fish that reproduce at younger ages and smaller body sizes 5 , 52 . High temperatures can further reinforce this shift by increasing growth rates early in life while limiting maximum size 6 . A shift toward smaller individuals under high temperature and fishing effort raises concerns about a lower reproductive output and reduced resilience of population (Marshall and White 2019). Smaller fish generally produce fewer and lower-quality eggs, which could lead to a lower recruitment, contributing to a productivity decline of stocks over time 53 . Cephalopods Loligo vulgaris and Illex coindetii show an opposite pattern with L. vulgaris is more vulnerable to fishing pressure under low temperature while I. coindetii under high temperature. This is consistent with the fact that these two species exhibit ecological and behavioral differences that influence their thermal tolerance. L. vulgaris is a benthopelagic species with a relatively narrow bathymetric range, typically found between 20 and 250 m, with a preference for temperatures between 13°C and 20°C, and an optimum around 18°C 54 . This species is more closely associated with the seabed, suggesting a more restricted thermal range. In contrast, I. coindetii is a semipelagic species that undertakes extensive horizontal and vertical migrations, making it more adaptable to temperature variations 55 . Its migratory behavior likely allows for greater tolerance to environmental changes, although it is still believed to be favored by warmer temperatures. However, reports of I. coindetii in cooler waters, such as the Baltic Sea, indicate some degree of thermal plasticity, yet further research is needed to clarify the species’ physiological limits 56 . These ecological and behavioral differences shape how L. vulgaris and I. coindetii respond to the combined effects of temperature and fishing pressure. Under high fishing pressure and warmer conditions, L. vulgaris appears to maintain a relatively stable population structure, suggesting a limited response to these environmental and anthropogenic drivers. However, at lower temperatures, fishing disproportionately removes smaller individuals, potentially skewing the population toward larger individuals that are less vulnerable to capture either due to behavioral avoidance or their ability to escape through net meshes 57 . In contrast, I. coindetii populations seem to experience higher natural and fishing-induced mortality rates under intense fishing pressure and elevated temperatures, resulting in a decline in older, larger individuals 58 . This pattern suggests that I. coindetii may have shorter lifespans under such conditions, with most individuals reproducing before reaching large sizes, ultimately leading to a population-wide shift toward smaller body sizes 59 . The response of O. vulgaris to the combined effects of fishing pressure and warming further highlights the influence of species-specific life history traits on size structure dynamics. O. vulgaris appears to exhibit a reduction in body size under intense fishing pressure and elevated temperatures, a pattern that aligns with its biological and ecological characteristics. As a fast-growing, short-lived species with high plasticity, O. vulgaris has a lifespan of approximately 12 to 18 months and reaches maturity quickly, often within a year 60 . This rapid life cycle makes it highly responsive to environmental changes, including fluctuations in temperature and exploitation rates. Temperature plays a crucial role in the growth, metabolism, and reproductive cycles of O. vulgaris . Higher temperatures shorten embryonic development and the planktonic dispersal phase, potentially increasing survival rates 61 and ultimately favoring recruitment 62 . However, this can come at the cost of smaller, less robust paralarvae, with potential long-term consequences on adult size and reproductive capacity 22 . Warmer conditions have been shown to accelerate growth rates but may also lead to earlier maturation and smaller adult sizes due to increased metabolic demands 63 . Studies indicate that O. vulgaris prefers temperatures ranging between 15°C and 22°C, with optimal growth occurring at intermediate temperatures 64 . However, prolonged exposure to higher temperatures can impose physiological stress, reducing survival rates and potentially leading to shifts in population structure toward smaller individuals 65 . High fishing pressure exacerbates these effects by selectively removing larger individuals, intensifying size truncation within the population. Due to its benthic lifestyle and reliance on coastal habitats, O. vulgaris is particularly vulnerable to overexploitation, with heavy fishing pressure leading to a demographic shift favoring smaller, faster-reproducing individuals 66 . This combination of warming and intense fishing effort may therefore drive an overall decrease in size within O. vulgaris populations, with potential consequences for reproductive output and population resilience. Overall Importantly, these patterns are not driven by fluctuations in early recruitment stages ( see Methods). Consequently, the shifts we observed in size structure reflect changes occurring within the adult and near-reproductive segments of the populations. This distinction is critical, as it indicates that the trends toward smaller sizes are not merely an artifact of episodic recruitment events but represent genuine alterations in adult body size likely linked to environmental pressures and fishing impacts. Overall, our findings highlight the strongly species-specific nature of size-based responses to environmental change. Temperature consistently emerges as a dominant driver, promoting smaller adult body sizes across most taxa through mechanisms related to metabolic constraints and accelerated growth rates. Fishing pressure, while more variable in its effects across species, generally acts by selectively removing larger individuals from the population. Although the magnitude of fishing impacts differs among groups, its cumulative effect invariably reinforces a bias toward smaller body sizes. Particularly among fish species, the interaction between warming and fishing appears synergistic, with both stressors jointly amplifying the truncation of adult size distributions. Conclusions This study explores for the first time the specie-specific response in size spectra of commercially important species in the central Mediterranean Sea. Our study reveals that intense fishing efforts and rising temperatures interact to influence the size structure of commercial marine species, with responses varying across different species. Intense fishing pressure on size spectra often removes larger individuals, either through direct selection via gear designed to capture bigger fish or indirect selection where high mortality rates reduce the lifespan of fish before they attain larger sizes. This selective pressure leads to a phenomenon known as fishing-induced truncation, resulting in populations dominated by smaller individuals. Our findings emphasize the importance of considering the combined influence of warming and anthropogenic drivers when predicting shifts in population structure, stock productivity, and the broader resilience of exploited marine ecosystems under future environmental change. From a management perspective, this underscores the need for adaptive fisheries policies that incorporate size limits, temperature-driven catch adjustments, and ecosystem-based approaches to buffer against long-term population declines 67 . By integrating size distribution indicators, our research advocates for a comprehensive approach to fisheries management that considers size-structured changes in marine populations under future climate scenarios. Such insights are vital for implementing the Marine Stategy Framework Directive, particularly Descriptor 3, which aims to maintain commercially exploited stocks within safe biological limits. Indeed, the analysis of size spectra across species and taxonomic groups provides quantitative metrics that can serve as early warning indicators of population stress before it is detected by traditional abundance-based approaches. Ultimately, our findings provide a scientific foundation for adaptive policies that incorporate climate change projections into fisheries management, ensuring the sustainability of species that hold both ecological and economic importance in a rapidly changing Mediterranean Sea. Materials and Methods Study area and Data collection The SoS, located in the south-central Mediterranean Sea, serves as a transitional zone connecting the western and eastern Mediterranean basins. It is recognized as one of the most productive and intensively exploited fishing grounds in the Mediterranean, supporting fleets from both European Union (EU) and North African countries 68 . The region features a highly complex seafloor topography and dynamic hydrodynamic processes that regulate water mass exchanges between the two Mediterranean sub-basins 69 . Along the southern coast of Sicily (southern Italy), the continental shelf presents two wide and shallow banks—the Adventure Bank in the west and the Malta Bank in the east—separated by a narrower central shelf. In contrast, the North African shelf is considerably broader, particularly off the eastern and southern Tunisian coasts 70 . The study area encompasses one Geographical Sub-Areas (GSA) as defined by the General Fisheries Commission for the Mediterranean (GFCM): GSA 16 (South of Sicily) (FAO-GFCM 2009). This study focuses on georeferenced biomass data collected within GSA 16 (South of Sicily) from 2000 to 2023. Data were obtained from the MEDITS (Mediterranean International Trawl Survey) program, a long-term fishery-independent survey carried out annually under the European Data Collection Framework (DCF) 71 . All datasets used in this study are available at the project repository: https://github.com/gjoniv/Medits_ISDbayes_commercial_species . The MEDITS survey operates each year during late spring and early summer across multiple Mediterranean regions. In GSA 16, sampling involved bottom trawl surveys conducted using a standardized trawl net with a vertical opening of 2.5–3 meters and a cod-end diamond mesh opening of 20-mm. Hauls were distributed according to a stratified random sampling design across five depth strata: 10–50 m, 51–100 m, 101–200 m, 200–500 m, and 500–800 m, with the number of hauls proportional to the surface area of each stratum (Fig. 1 ; Supplementary Table S1 ). At each sampling station, fish species were sorted, counted, weighed, and measured. Relative abundance data, expressed in number of individuals/km², were obtained from a total of 1090 trawl hauls conducted between 2000 and 2023. The analysis focuses on the temporal trends and spatial distribution of commercially important species across this time frame. Environmental data Mean bottom temperature climatology (2000–2023) was extracted from the Bio-Oracle Database Version 3.0 72 at a native resolution of 0.05 decimal degrees for which the values were assigned to each haul based on mid-haul location. Fishing effort was extracted into a 0.05 dd raster layer using the Global fishing watch data 73 , expressed as fished hours per year from trawlers, on the 2012–2020 period. the layer’s resolution (4 x 5 at our latitude) is consistent with mean haul length (approx. 3–5 km) and thus accounts for geolocalisation error. Model Fitting and Prior Choice We used a Bayesian likelihood approach for our analysis because it is flexible, accommodates negative log-transformed values, and allows for accurate modeling through adaptable functions and prior distributions. All scripts for data processing and statistical analyses, along with documentation to reproduce the results, are openly available at our GitHub repository: https://github.com/gjoniv/Medits_ISDbayes_commercial_species . To examine how size spectra varied as a function of temperature and resources, we used a Bayesian generalized linear mixed model with a truncated Pareto likelihood. We applied this approach to nine commercially important marine species—three fish (i.e. Merluccius merluccius, Mullus barbatus, and Mullus sirmuletus) , three crustaceans (i.e. Aristaeomorpha foliacea , Parapenaeus longirostris , Nephrops norvegicus ), and three cephalopods (i.e. Loligo vulgaris , Illex coindetii , and Octopus vulgaris — to evaluate their population-level responses to key environmental and human-induced drivers, temperature and fishing pressure, as fixed effects. In addition, to ensure that our analysis focused on adult population dynamics rather than variability in juvenile abundance, we excluded individuals smaller than 2 cm in crustaceans, 5–14 cm in fish, and 2–3 cm in cephalopods. These size thresholds were selected based on known species-specific growth and maturation patterns, effectively filtering out recently settled juveniles and early life stages. A detailed description and justification of this modelling approach for size spectra is given in 10 . The model structure was: $$\:{x}_{ijkl}\:\sim\:f\left({x}_{jkl};\:{\lambda\:}_{jkl},{x}_{{min}_{jkl}},{x}_{{max}_{jkl}},{counts}_{ijkl}\right)$$ $$\:{\lambda\:}_{jkl}\:=\:a\:+\:\varvec{\beta\:}\mathbf{Z}\:+\:{\gamma\:}_{j}+\:{\gamma\:}_{k}\:+\:{\gamma\:}_{l}$$ where \(\:{x}_{ijkl}\) is the i th body size from sample j in site k and year l . The likelihood f(…) is a truncated Pareto with a single free parameter \(\:\:{\lambda\:}_{jkl}\) , the exponent of the ISD. \(\:{x}_{{min}_{jkl}}\) and \(\:{x}_{{max}_{jkl}}\) are the minimum and maximum body sizes in each sample, site, and year. \(\:{x}_{{min}_{jkl}}\) was chosen as the smallest body size in each sample for which the data most closely match a power law using the estimate_xmin function poweRlaw package 74 . This method ensures that estimates of λ are not biased from undersampling of small individuals (Virkar and Clauset 2014). Each body size has a corresponding density in units of number per m 2 , represented by counts ijkl 9 . \(\:{\lambda\:}_{jkl}\:\) is modeled as a linear function of an intercept \(\:\alpha\:\) and \(\:\varvec{\beta\:}\mathbf{Z}\) represents the single, two-way interactions of the Z predictors of mean annual temperature and fishing effort. All predictors were standardized as z-scores prior to fitting. Varying intercepts are included for individual sample ( \(\:{\gamma\:}_{j}\) ), site \(\:{(\gamma\:}_{k})\) , and year \(\:\left({\gamma\:}_{l}\right)\) . To improve sampling efficiency, the varying intercepts were modeled using non-centered parameterization, which is excluded here for clarity, but is present in the Stan model code: https://github.com/gjoniv/Medits_ISDbayes_commercial_species Priors for the intercept were Normal(-2, 0.5), chosen based on theoretical predictions 12 . Priors for each β parameter were set to Normal(0, 0.2). Priors for the q varying intercepts were Normal(0, s q ), with each s q hyperprior set to Exponential(7). These priors were chosen based on prior predictive simulation 10 to center the prior probabilities of λ between about − 2.5 to -1 while still allowing probabilities at very large (e.g. 1) or small values (e.g. -4). We fit all models in rstan via the brms and isdbayes packages in R. Each model had 4 chains with 2000 iterations, where the first 1000 were discarded as warm-up. Model fit was checked using posterior predictive checks and prior influence was checked using prior predictive simulation. Declarations Funding: This work was supported by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 – Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU Author Contribution V.G. performed the statistical analysis. V.G., V.L., F.F., G.G., and V.G. performed theliterature search, wrote the manuscript, and made edits. V.G., F.F., G.G. and V.L. were mainly responsiblefor the interpretation of the data and preparing the final version. V.G. created the figures. All authors providedcritical feedback and contributed to the final manuscript. Correspondence and requests for materials should beaddressed to V.G. Data Availability All datasets, data processing and data analyses of the manuscript are available at the GITHUB project repository: https:/github.com/gjoniv/Medits_ISDbayes_commercial_species References Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Fishing Down Marine Food Webs. Science 279 , 860–863 (1998). Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308 , 1912–1915 (2005). Cheung, W. W. L. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7565528","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":517215556,"identity":"9e859828-c787-4734-89bd-3e4aee8f1d0c","order_by":0,"name":"Vojsava Gjoni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYHCCBDDJ2MDAcOADiMVMipaDM8BaiNADB8w8DERYY3C84fGHn212ds3tvQcP29TcyWdg5z+AX8uZA2mSvW3JyY095xIO5xx7ZtlAyGGSMxLSGHjOMCczzsgxOJzbcNiAoF+AWpI//jlTD9FiSYwWfomEBGmeisN2YC2MRGnhOZAmLVNxPIGx54zBwZ5jhw3YmJkN8GphY+9J/vjGoNresL3H+MOPmsMG/PwHH+C3hoEnAUQmbmyAGUJAPRCwHwCR9vKEVY6CUTAKRsFIBQAdB0SpbS6hiAAAAABJRU5ErkJggg==","orcid":"","institution":"National Research Council","correspondingAuthor":true,"prefix":"","firstName":"Vojsava","middleName":"","lastName":"Gjoni","suffix":""},{"id":517215557,"identity":"a6a1b8d7-b148-4f8b-bc53-1b2b9b1ecb3f","order_by":1,"name":"Germana Garofalo","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Germana","middleName":"","lastName":"Garofalo","suffix":""},{"id":517215558,"identity":"d176c497-5b9a-4434-8ad5-2aa196570a78","order_by":2,"name":"Fabio Fiorentino","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Fabio","middleName":"","lastName":"Fiorentino","suffix":""},{"id":517215559,"identity":"cf666cf2-4959-4270-a385-c9a1532d5c38","order_by":3,"name":"Vincent Goerges","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Goerges","suffix":""},{"id":517215560,"identity":"075330ab-5a1e-411c-b32b-e43f6deadc29","order_by":4,"name":"Valentina Lauria","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Lauria","suffix":""}],"badges":[],"createdAt":"2025-09-08 14:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7565528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7565528/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91683962,"identity":"a8d258f1-6645-4fe1-9ee5-2c96be6d29eb","added_by":"auto","created_at":"2025-09-19 07:10:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1773885,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of bathymetry, sea surface temperature, and fishing effort in the south-central Mediterranean Sea. (A) Bathymetric map showing the seafloor topography with isobaths ranging from 0 to -1,500 meters. Black crosses indicate the locations of MEDITS (Mediterranean International Trawl Survey) haul positions, demonstrating the spatial extent of sampling efforts across various depth zones. (B) Mean annual sea surface temperature (°C) illustrating a clear thermal gradient, with cooler temperatures in deeper offshore areas and warmer waters concentrated in shallower, coastal zones. (C) Annual fishing effort intensity (expressed in hours per year), highlighting areas of concentrated fishing activity, particularly along the western shelf and central basin.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7565528/v1/1d2b6d6556b42de7151ffef3.png"},{"id":91683198,"identity":"57827223-031c-4690-8ad4-db8f27971329","added_by":"auto","created_at":"2025-09-19 07:02:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105450,"visible":true,"origin":"","legend":"\u003cp\u003eThe contour plots illustrate the relationship among temperature (°C), fishing effort (hours of fishing/year), and λ (lambda), the exponent of the size spectra, for three crustacean species: \u003cstrong\u003e(A)\u003c/strong\u003e\u003cem\u003eParapenaeus longirostris, \u003c/em\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e Aristaeomorpha foliacea, \u003c/em\u003eand\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003cem\u003eNephrops norvegicus\u003c/em\u003e. The size spectrum exponent (λ) describes how the abundance of organisms’ changes with body size, where more negative values indicate a steeper decline in abundance with increasing size. Warmer colors (yellow) represent more negative λ values, while cooler colors (purple) indicate less negative λ values.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7565528/v1/77b40111aa86880aa421f354.png"},{"id":91683963,"identity":"575df463-39bf-4a3d-a6cc-304bc808a1ef","added_by":"auto","created_at":"2025-09-19 07:10:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116631,"visible":true,"origin":"","legend":"\u003cp\u003eThe contour plots illustrate the relationship between temperature (°C), fishing effort (hours/year), and λ (lambda), the exponent of the size spectra, for three bony fish species: \u003cstrong\u003e(A)\u003c/strong\u003e\u003cem\u003eMerluccius merluccius, \u003c/em\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e Mullus surmuletus, \u003c/em\u003eand \u003cstrong\u003e(C)\u003c/strong\u003e\u003cem\u003e Mullus barbatus\u003c/em\u003e. The size spectrum exponent (λ) describes how the abundance of organisms changes with body size, where more negative values indicate a steeper decline in abundance with increasing size. Warmer colors (yellow) represent more negative λ values, while cooler colors (purple) indicate less negative λ values.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7565528/v1/520768ff72a6c02465fc3e46.png"},{"id":91683196,"identity":"bf2f8b5b-e46c-46c2-ab9d-3f2849196ed1","added_by":"auto","created_at":"2025-09-19 07:02:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97531,"visible":true,"origin":"","legend":"\u003cp\u003eThe contour plots depict the relationship between temperature (°C), fishing effort (hours/year), and λ (lambda), the exponent of the size spectra, for three cephalopod species:\u003cstrong\u003e(A)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eLoligo vulgaris, \u003c/em\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e Illex coindetii, \u003c/em\u003eand\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(C)\u003c/strong\u003e \u003cem\u003eOctopus vulgaris\u003c/em\u003e. The size spectrum exponent (λ) describes how the abundance of organisms changes with body size, where more negative values indicate a steeper decline in abundance with increasing size. Warmer colors (yellow) represent more negative λ values, while cooler colors (purple) indicate less negative λ values.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7565528/v1/8bc006d1362447f4d37bd045.png"},{"id":91685094,"identity":"ea4ea0a6-c982-401c-981f-e8a9da534934","added_by":"auto","created_at":"2025-09-19 07:26:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3115701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7565528/v1/0a2604a6-8be7-4718-8967-247efeb437fe.pdf"},{"id":91683206,"identity":"d7a1cb10-b231-4b98-93ff-a920cde64a9b","added_by":"auto","created_at":"2025-09-19 07:02:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":240067,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialSF16102025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7565528/v1/df8c48f54ff47c593ebe2085.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFishing and Warming Reshape Size Spectra of Commercial Species in the Mediterranean Sea\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMarine ecosystems are constantly subject to multiple anthropogenic pressures, with climate change and fishing pressure acting as key drivers of biodiversity loss and shifts in community structure\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Rising ocean temperatures alters metabolic rates, growth and reproduction patterns and species distributions, while intense fishing pressure modifies population structures and trophic interactions\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, the combined effects of temperature changes and fishing pressure on marine populations remain poorly understood, particularly across taxonomically distinct groups such as fish, crustaceans and cephalopods.\u003c/p\u003e\u003cp\u003eTrawling and other high-intensity fishing methods selectively remove individuals based on size and life history traits, often leading to faster-growing and smaller-bodied populations\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This can interact with rising sea temperature as effect of warming oceans, which generally favor smaller individuals due to increased metabolic demands and oxygen limitations\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Such combined pressures may cause unexpected, non-additive effects, where species\u0026rsquo; responses vary due to physiological differences in thermal tolerance, growth rates and reproductive strategies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSize spectra modelling has traditionally been applied at the community level to assess the distribution of individual body sizes within ecological communities, offering key insights into the effects of environmental and anthropogenic pressures\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The size spectrum is typically described by a power law, \u003cem\u003eN\u0026thinsp;~\u0026thinsp;M\u003c/em\u003e\u003csup\u003e\u003cem\u003eλ\u003c/em\u003e\u003c/sup\u003e, where \u003cem\u003eN\u003c/em\u003e is the abundance of individuals and \u003cem\u003eM\u003c/em\u003e is their body mass\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The exponent λ of this relationship is known to be sensitive to both temperature-induced changes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and fishing pressures\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, making it a valuable metric for tracking changes in ecosystem structure and function. Owing to the apparent consistency of the size spectra across systems, body size distributions have even been proposed as a \u0026ldquo;universal indicator\u0026rdquo; of ecological status\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. By capturing shifts in body size distributions, size spectra provide a mechanistic link between changes in natural or human-induced factors and population dynamics. However, their application at the population level remains underexplored, despite their potential to reveal the effects of various biotic and abiotic drivers. These include, for example, temperature-induced shifts in adult body size and the impacts of size-selective fishing, both of which are crucial for understanding population-level responses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIndeed, at the population level, size spectra offer valuable insights into demographic processes, capturing species-specific responses to environmental stressors such as ocean warming and fishing pressure. Given that size spectra at the community level are known to respond to external drivers such as temperature and to human-induced pressures like fishing mortality\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, it is plausible to expect similar responses at the population level. This expectation aligns with previous findings demonstrating that warming trends and fishing pressure can drive reductions in adult body size and shifts in life history traits\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, testing size spectra at the population level may provide a more mechanistic understanding of species-specific responses to global change\u0026mdash;an approach particularly valuable for commercially exploited species, which tend to be more sensitive as they are already exposed to overfishing. This, in turn, can improve predictions of population resilience and contribute to more effective strategies for maintaining ecosystem stability.\u003c/p\u003e\u003cp\u003eCrustaceans and cephalopods, for instance, exhibit greater plasticity in response to environmental drivers than many fish species, often displaying faster life cycles and flexible growth rates\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Some studies suggest that trawling may temporarily enhance shrimp productivity by resuspending nutrients and reducing predator abundance\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Among commercially exploited species, cephalopods\u0026mdash;due to their short generation times and rapid turnover\u0026mdash;often show a higher capacity for population recovery following disturbances \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In contrast, many fish species exhibit longer life spans and experience more pronounced size-selective mortality, which can hinder their recovery and make them particularly vulnerable to the combined pressures of overfishing and climate change\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Understanding these taxon-specific responses is essential for ecosystem-based fisheries management, as shifts in size spectra can affect food web dynamics and ultimately impact fisheries yields and ecosystem stability\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we examine how temperature and fishing pressure interactively influence the size spectra of fish, crustaceans, and cephalopods in the central Mediterranean Sea. By focusing on size structure, we gain critical insights into fundamental biological processes such as growth rates, reproductive output, and population mortality, particularly under the combined influence of size-selective fishing and temperature-driven environmental change. A recent study documented both elevated sea temperatures and intense fishing pressure are associated with shifts in the size distribution of exploited fish stocks toward smaller individuals\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These shifts may reduce the reproductive potential and resilience of populations, with significant implications for fisheries productivity and ecosystem sustainability. Based on this evidence, we assume that warming and fishing will increase the proportion of smaller individuals, particularly among fish, whereas taxa like crustaceans and cephalopods may exhibit more variable responses due to their greater life-history plasticity.\u003c/p\u003e\u003cp\u003eTherefore, we have used 20 years of fishery-independent data from the MEDITS bottom trawl survey in the Strait of Sicily (SoS) to assess long-term trends in size spectra to understand how environmental and anthropogenic pressures shape population structures over time. In particular, three commercially important crustacean species were considered in this study (the deep water shrimp \u003cem\u003eAristaeomorpha foliacea\u003c/em\u003e (Risso 1827), the rose shrimp \u003cem\u003eParapenaeus longirostris\u003c/em\u003e (Lucas 1846), and the Norway lobster \u003cem\u003eNephrops norvegicus\u003c/em\u003e (Linnaeus 1758), as well as three bony fish species, the European hake \u003cem\u003eMerluccius merluccius\u003c/em\u003e (Linnaeus 1758), and the mullets \u003cem\u003eMullus surmuletus\u003c/em\u003e (Linnaeus 1758) and \u003cem\u003eMullus barbatus\u003c/em\u003e (Linnaeus 1758)and three cephalopods, the squids \u003cem\u003eLoligo vulgaris\u003c/em\u003e (Lamark 1798) and \u003cem\u003eIllex coindetii\u003c/em\u003e (V\u0026eacute;rany 1839), and the octopus \u003cem\u003eOctopus vulgaris\u003c/em\u003e (Cuvier 1797). These nine species where selected because they constitute a valuable portion of the fish market in the Mediterranean Sea (FAO 2022) and represent a wide range of life-history strategies, thermal preferences, and responses to exploitation.\u003c/p\u003e\u003cp\u003eGiven that both temperature and fishing pressure are increasing in many regions, understanding their combined effects on commercial stocks is essential for developing more adaptive and sustainable fisheries management strategies that can be applied to inform the ecosystem-based approach at Mediterranean scale.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe spatial and temporal variability of environmental conditions and fishing pressure across the south-central Mediterranean Sea is evident from both the compiled dataset and the accompanying spatial maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The dataset comprises a total of 26,856 (across all years) individual records, reflecting observations collected across a broad range of depths and geographic locations, as indicated by the MEDITS trawl haul positions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The thermal landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) reveals a marked gradient in mean sea surface temperature, with warmer waters along the eastern coastal areas and cooler conditions toward the deeper offshore zones. This variability in temperature is paralleled by heterogeneity in fishing effort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), which is most intense along the western shelf and central portions of the basin. Despite the limited spatial resolution of some data layers, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e highlights substantial interannual variation in both temperature and fishing effort, underscoring the dynamic nature of environmental and anthropogenic pressures across the region.\u003c/p\u003e\u003cp\u003eAlthough presented within a limited space, the dataset (summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) clearly illustrates a high degree of variability in both sea surface temperature and fishing effort, not only across different sites but also across years, highlighting the dynamic and heterogeneous nature of the marine environment and human exploitation patterns over time. The contour plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrate the interactive effects of temperature (\u0026deg;C) and fishing effort (fishing hours/year) on the size spectrum exponent (λ) across the selected nine commercially important species. Warmer colors (yellow) represent more negative λ values, suggesting a higher proportion of smaller individuals, while cooler colors (purple) indicate less declines in larger size classes, reflecting a shift toward larger individuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCrustaceans\u003c/h2\u003e\u003cp\u003eIn \u003cem\u003eP. longirostris\u003c/em\u003e, higher temperatures are associated with more negative λ values, indicating a shift toward smaller individuals primarily driven by temperature-induced physiological constraints, while fishing pressure appears to have little or no effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this species, the probability of a shift toward smaller size classes exceeds 80% as temperatures increase. Similarly, \u003cem\u003eA. foliacea\u003c/em\u003e shows a trend toward smaller individuals under warmer conditions, reflected by more negative λ values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). However, in this case, the trend is more pronounced at low to moderate fishing pressures, suggesting a slight interactive effect between temperature and fishing effort on individual size. The probability of a shift toward smaller individuals in \u003cem\u003eA. foliacea\u003c/em\u003e exceeds 85% with rising temperatures. In contrast, \u003cem\u003eN. norvegicus\u003c/em\u003e exhibits an opposite pattern, with a higher proportion of larger individuals at higher temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At lower temperatures, fishing effort seems to have a modest impact, increasing the proportion of smaller individuals as fishing intensity rises. The probability of an increase in larger individuals surpasses 75% under warming scenarios, particularly when fishing pressure is low, suggesting that warming conditions may favor size persistence or even growth in this species, potentially due to physiological adaptations to temperature fluctuations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBony fish\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eM. merluccius\u003c/em\u003e shows a strong response to both temperature and fishing, with a higher proportion of smaller individuals under increased fishing effort and warmer waters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The probability of this shift is above 85% at temperatures exceeding 14\u0026deg;C with fishing pressure above 30 hours/year. \u003cem\u003eM. surmuletus\u003c/em\u003e and \u003cem\u003eM. barbatus\u003c/em\u003e also follow this trend but with slight differences, in fact while \u003cem\u003eM. surmuletus\u003c/em\u003e experiences a size reduction at high temperatures and intense fishing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a probability of 70% at temperatures above 16\u0026deg;C, \u003cem\u003eM. barbatu\u003c/em\u003es shows a steeper decline in λ at lower fishing pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that temperature alone may drive size shifts in this species, with a 65% probability of smaller individuals at temperatures above 16\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCephalopods\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eL. vulgaris\u003c/em\u003e displays a more complex interaction between temperature and fishing pressure, where moderate temperatures (16\u0026ndash;18\u0026deg;C) and fishing pressure above 40 hours year\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e result in the more negative λ values, suggesting a balanced size structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At higher temperatures and fishing intensity, the probability of a shift toward smaller individuals reaches 75%. Differently, \u003cem\u003eI. coindetii\u003c/em\u003e exhibits a pronounced reduction in size structure at both high fishing pressure and warmer temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a 90% probability of smaller individuals above 18\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, \u003cem\u003eO. vulgaris\u003c/em\u003e shows the most pronounced decline in size under sea warming and high fishing pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a 95% probability of smaller individuals at temperatures above 16\u0026deg;C. This strong response suggests that temperature directly influences physiological constrains, leading to an overall shift in smaller sizes of the population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Parameter estimates +/- 95% CrI of the relationship between lambda estimates (ISD\u0026rsquo;s λ) of the Bayesian analysis. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e are based on this model and incorporate the effects of fishing effort (hours/year) and temperature (\u0026deg;C). We restricted the model to these terms based on a priori hypotheses and performed no model selection steps thereafter.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eParapenaeus longirostris\u0026rsquo;s\u003c/em\u003e lambda\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.26 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.02)\u003c/p\u003e\u003cp\u003e-0.50 (0.02)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.27 to -0.29\u003c/p\u003e\u003cp\u003e0.00 to -0.03\u003c/p\u003e\u003cp\u003e-0.51 to -0.49\u003c/p\u003e\u003cp\u003e-0.02 to -0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAristaeomorpha foliacea\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.64 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003cp\u003e-1.77 (0.02)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.66 to -1.60\u003c/p\u003e\u003cp\u003e0.00 to -0.02\u003c/p\u003e\u003cp\u003e-1.76 to -1.79\u003c/p\u003e\u003cp\u003e-0.00 to -0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNephrops norvegicus\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.71 (0.02)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003cp\u003e-0.32 (0.03)\u003c/p\u003e\u003cp\u003e0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.72 to -0.69\u003c/p\u003e\u003cp\u003e-0.02 to -0.02\u003c/p\u003e\u003cp\u003e-0.33 to -0.30\u003c/p\u003e\u003cp\u003e-0.01 to -0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMerluccius merluccius\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.51 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003cp\u003e-0.40 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.62 to -1.64\u003c/p\u003e\u003cp\u003e-0.03 to -0.00\u003c/p\u003e\u003cp\u003e-3.13 to -3.16\u003c/p\u003e\u003cp\u003e-0.02 to -0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMullus surmiuletus\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.61 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003cp\u003e-3.15 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.62 to -1.64\u003c/p\u003e\u003cp\u003e-0.02 to 0.00\u003c/p\u003e\u003cp\u003e-3.13 to -3.16\u003c/p\u003e\u003cp\u003e-0.02 to -0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMullus barbatus\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.66 (0.02)\u003c/p\u003e\u003cp\u003e-0.01 (0.03)\u003c/p\u003e\u003cp\u003e-0.30 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.68 to -0.64\u003c/p\u003e\u003cp\u003e-0.02 to 0.00\u003c/p\u003e\u003cp\u003e-0.33 to -0.29\u003c/p\u003e\u003cp\u003e-0.02 to-0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLogico vulgaris\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.97 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.02)\u003c/p\u003e\u003cp\u003e-0.02 (0.02)\u003c/p\u003e\u003cp\u003e-0.01 (0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.98 to -0.95\u003c/p\u003e\u003cp\u003e0.00 to-0.02\u003c/p\u003e\u003cp\u003e0.01 to-0.04\u003c/p\u003e\u003cp\u003e-0.02 to-0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIlex coindetii\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.49 (0.03)\u003c/p\u003e\u003cp\u003e-0.01 (0.02)\u003c/p\u003e\u003cp\u003e-0.13 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.51 to -1.48\u003c/p\u003e\u003cp\u003e-0.03 to 0.01\u003c/p\u003e\u003cp\u003e-0.11 to-0.14\u003c/p\u003e\u003cp\u003e-0.02 to-0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOctopus vulgaris\u0026rsquo;s\u003c/b\u003e \u003cb\u003elambda\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95% CrI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003etemperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efishing effort*temperature\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.27 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.02)\u003c/p\u003e\u003cp\u003e-0.50 (0.01)\u003c/p\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.28 to -0.28\u003c/p\u003e\u003cp\u003e-0.00 to-0.02\u003c/p\u003e\u003cp\u003e-0.51 to\u0026ndash;0.49\u003c/p\u003e\u003cp\u003e-0.02 to-0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis study explores for the first time the specie-specific response in size spectra of commercially important species in the central Mediterranean Sea. Our results reveal clear but taxon-specific shifts in body size distributions under the combined influence of water warming and fishing pressure in the central Mediterranean Sea. Across the nine studied species, belonging to bony fish, crustaceans, and cephalopods, higher temperatures were generally associated with a shift toward smaller adult sizes. This broad trend confirms the physiological constraints imposed by warming, which accelerates metabolic rates, promotes faster growth, but leads to earlier maturation at smaller sizes \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Additionally, rising temperatures are known to increase natural mortality rates in marine organisms, further contributing to size structure changes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile temperature emerged as a pervasive driver across taxa, the role of fishing pressure on size spectra was more variable. In general, fishing selectively removed larger individuals, amplifying the shift toward smaller body sizes. However, species differed in their sensitivity to this pressure: crustaceans such as \u003cem\u003eP. longirostris\u003c/em\u003e and \u003cem\u003eA. foliacea\u003c/em\u003e exhibited size reductions primarily driven by temperature, with fishing playing a minor role for \u003cem\u003eP. longirostris\u003c/em\u003e. In contrast, \u003cem\u003eN. norvegicus\u003c/em\u003e maintained or even increased adult size under warming conditions, suggesting adaptive physiological traits. In contrast, fish species (\u003cem\u003eM. merluccius, M. surmuletus, M. barbatus\u003c/em\u003e) and the cephalopod \u003cem\u003eO. vulgaris\u003c/em\u003e seems to strongly respond to both combined effects (warming and fishing pressure) that reinforced a shift toward smaller adults. These findings highlight the importance of life-history traits and thermal tolerances in mediating species-specific responses to environmental change and stress the need for management strategies that consider the synergistic effects of climate and exploitation pressures.\u003c/p\u003e\n\u003ch3\u003eCrustaceans\u003c/h3\u003e\n\u003cp\u003eAll three crustacean species appear largely unaffected by fishing pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), this result is likely driven by two indirect effects of bottom trawling. On one hand, trawling disturbs the seabed, resuspending sediments and increasing nutrient availability in the water column. This process can enhance primary production, leading to higher food availability for benthic organisms, including shrimp prey \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Increased organic matter can stimulate microbial activity and boost the production of benthic invertebrates that serve as shrimp food\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Indeed, bottom trawling can modify habitat structures by flattening seabed features and removing complex structures, therefore creates soft-bottom habitats that are preferred by burrowing shrimp species\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, fishing can alter trophic interactions by reducing predator abundance, leading to community change where prey species experience reduced predation pressure\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Reductions in population density due to fishing can lead to compensatory responses, where reduced competition for food and space allows remaining individuals to grow faster and attain larger sizes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This density-dependent effect has been documented in various exploited crustacean populations, where fishing pressure results in increased per capita resource availability, promoting faster individual growth\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur results suggest that temperature exerts a strong influence on all three species, with \u003cem\u003eP. longirostris\u003c/em\u003e and \u003cem\u003eA. foliacea\u003c/em\u003e exhibiting a higher proportion of smaller individuals as temperatures rise, while \u003cem\u003eN. norvegicus\u003c/em\u003e shows an increased proportion of larger individuals under warming conditions. This difference in the response to increasing temperature could be related to the fact that while this factor enhance the spatial distribution and population dynamics of \u003cem\u003eP. longirostris\u003c/em\u003e and \u003cem\u003eA. foliacea\u003c/em\u003e as has been previously suggested (i.e. for \u003cem\u003eP. longirostris)\u003c/em\u003e warmer waters have led to expanded spatial patches and depth ranges\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, as well as increased abundance trends\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Differently for \u003cem\u003eA. foliacea\u003c/em\u003e populations that show seasonal migrations related to temperature, with winter movements to upper slopes coinciding with male maturity and mating\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e this could represent a limiting factor. Indeed, the effect of bottom temperature, together with other environmental factors such as particulate organic matter may explain the spatial distribution of \u003cem\u003eA. foliacea\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Both species exhibit short-term spatio-temporal variations in population dynamics and biology, influenced by environmental factors such as temperature, productivity, and seafloor characteristics\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. While these studies do not directly address changes in the number of small individuals, they indicate that warming temperatures are affecting the distribution, abundance, and life cycles of these species, potentially impacting population structure and recruitment patterns in the Mediterranean Sea.\u003c/p\u003e\u003cp\u003eIn contrast, \u003cem\u003eN. norvegicus\u003c/em\u003e exhibits notable resilience to warming waters, a trait that can be attributed to several biological and physiological factors. Studies have demonstrated that this species possesses metabolic plasticity, allowing it to adjust its metabolic rates in response to temperature changes\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Specifically, \u003cem\u003eN. norvegicus\u003c/em\u003e has been observed to decrease its standard metabolic rate with increased acclimation temperatures while maintaining its aerobic metabolic scope, thereby optimizing energy expenditure under elevated temperatures\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Additionally, the burrowing behavior of \u003cem\u003eN. norvegicus\u003c/em\u003e offers a refuge from environmental fluctuations, including temperature variations. By inhabiting muddy substrates and constructing burrows, these lobsters can mitigate the impacts of external temperature changes, contributing to their resilience in warming conditions\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, it is important to note that while \u003cem\u003eN. norvegicus\u003c/em\u003e displays certain adaptive capacities to cope with rising temperatures, some studies have reported potential negative effects. For instance, exposure to simulated climate change conditions, including elevated temperatures, has been associated with immune suppression and protein damage in this species\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, this apparent resilience may come at a cost. Prolonged exposure to elevated temperatures could lead to physiological trade-offs, such as diminished aerobic capacity, reduced reproductive success, or inefficient energy use. Although some studies have suggested that warming might enhance productivity in certain marine species, such benefits remain debated\u0026mdash;especially in the absence of a clear link to changes in trophic interactions. For \u003cem\u003eN. norvegicus\u003c/em\u003e, evidence points in the opposite direction: lower temperatures have been associated with better physiological performance, increased catch rates, and more favorable growth conditions\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These findings imply that, without buffering mechanisms through the food web, sustained warming is more likely to challenge than benefit this cold-adapted, economically important crustacean.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBony fish\u003c/h2\u003e\u003cp\u003eOur results show that \u003cem\u003eM. merluccius\u003c/em\u003e size spectra is negatively influenced by both rising temperatures and fishing pressure, highlighting the vulnerability of this long-lived, slow-growing demersal species. This species has a complex life cycle, characterized by fast growth in early stages and a prolonged adult phase, with individuals reaching sexual maturity between 2 and 4 years of age\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. It inhabits a broad bathymetric range (from shallow waters to over 1000 m deep) but shows a preference for temperatures between 10\u0026deg;C and 14\u0026deg;C. Recruits of age 0 prefer habitat characterized by stable bottom temperature (11.8\u0026ndash;15.0\u0026deg;C), low bottom currents (\u0026lt;\u0026thinsp;0.034 m s\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and a frequent occurrence of productive fronts in low chlorophyll-a areas (0.1\u0026ndash; 0.9 mg m\u003csup\u003e-\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e), mainly between 50 and 200 m depth\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTemperature plays a critical role in regulating \u003cem\u003eM. merluccius\u003c/em\u003e population dynamics. Warmer conditions have been associated with increased juvenile growth rates, but they may also disrupt recruitment success by altering larval dispersal and prey availability\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Furthermore, higher metabolic demands under warmer temperatures can lead to earlier maturation at smaller sizes, reducing the overall size structure of the population\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFishing pressure exacerbates these trends by selectively removing larger individuals, which disproportionately affects \u003cem\u003eM. merluccius\u003c/em\u003e due to its late maturity and strong size-dependent reproductive success\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. As a result, populations subjected to both intense fishing and warming conditions exhibit a shift toward smaller individuals with faster turnover rates. This size truncation can have cascading effects on trophic interactions and stock productivity, potentially increasing \u003cem\u003eM. merluccius\u003c/em\u003e\u0026rsquo;s vulnerability to further environmental changes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAll three fish species, \u003cem\u003eM. surmuletus, and M. barbatus\u003c/em\u003e, exhibit a broadly similar pattern of decreasing size with increasing temperature and fishing pressure. However, the two red mullet species show notable differences in the steepness of their response gradients, reflecting their distinct ecological preferences despite their shared life-history traits that make them sensitive to environmental stressors. Both species are short-lived, fast-growing, and exhibit relatively early maturation, making them more responsive to environmental fluctuations\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. While \u003cem\u003eM. barbatus\u003c/em\u003e is more commonly associated with muddy seabed, with the maximum of abundance between 50 and 100 m, \u003cem\u003eM. surmuletus\u003c/em\u003e prefers rocky and sandy substrates with the maximum of abundance at deeper level between 100 and 200 m\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTemperature exerts a direct influence on the physiological constraints of both species, with warming conditions accelerating growth rates, advancing maturation, and shifting spawning periods and habitat suitability\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. However, recent studies suggest that \u003cem\u003eM. surmuletus\u003c/em\u003e, due to its broader environmental tolerance, is better able to expand into newly favorable habitats under warming conditions, whereas \u003cem\u003eM. barbatus\u003c/em\u003e, with more restrictive habitat requirements, may experience range contractions in some areas\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHigh fishing pressure further compounds these effects by disproportionately targeting larger individuals, leading to a decrease in average body size. This pattern is particularly evident in \u003cem\u003eM. barbatus\u003c/em\u003e, where size-selective fishing reduces the reproductive potential of the population, potentially limiting recruitment success\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The combined effects of warming and fishing may thus accelerate demographic shifts in these species, favoring smaller, faster-reproducing individuals while potentially reducing overall stock resilience.\u003c/p\u003e\u003cp\u003eOver time, fishing and warming may induce evolutionary shifts toward earlier maturation at smaller sizes. When larger individuals are consistently removed, natural selection favors fish that reproduce at younger ages and smaller body sizes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. High temperatures can further reinforce this shift by increasing growth rates early in life while limiting maximum size\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A shift toward smaller individuals under high temperature and fishing effort raises concerns about a lower reproductive output and reduced resilience of population (Marshall and White 2019). Smaller fish generally produce fewer and lower-quality eggs, which could lead to a lower recruitment, contributing to a productivity decline of stocks over time\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCephalopods\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eLoligo vulgaris\u003c/em\u003e and \u003cem\u003eIllex coindetii\u003c/em\u003e show an opposite pattern with \u003cem\u003eL. vulgaris\u003c/em\u003e is more vulnerable to fishing pressure under low temperature while \u003cem\u003eI. coindetii\u003c/em\u003e under high temperature. This is consistent with the fact that these two species exhibit ecological and behavioral differences that influence their thermal tolerance. \u003cem\u003eL. vulgaris\u003c/em\u003e is a benthopelagic species with a relatively narrow bathymetric range, typically found between 20 and 250 m, with a preference for temperatures between 13\u0026deg;C and 20\u0026deg;C, and an optimum around 18\u0026deg;C\u003csup\u003e54\u003c/sup\u003e. This species is more closely associated with the seabed, suggesting a more restricted thermal range. In contrast, \u003cem\u003eI. coindetii\u003c/em\u003e is a semipelagic species that undertakes extensive horizontal and vertical migrations, making it more adaptable to temperature variations\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Its migratory behavior likely allows for greater tolerance to environmental changes, although it is still believed to be favored by warmer temperatures. However, reports of \u003cem\u003eI. coindetii\u003c/em\u003e in cooler waters, such as the Baltic Sea, indicate some degree of thermal plasticity, yet further research is needed to clarify the species\u0026rsquo; physiological limits\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese ecological and behavioral differences shape how \u003cem\u003eL. vulgaris\u003c/em\u003e and \u003cem\u003eI. coindetii\u003c/em\u003e respond to the combined effects of temperature and fishing pressure. Under high fishing pressure and warmer conditions, \u003cem\u003eL. vulgaris\u003c/em\u003e appears to maintain a relatively stable population structure, suggesting a limited response to these environmental and anthropogenic drivers. However, at lower temperatures, fishing disproportionately removes smaller individuals, potentially skewing the population toward larger individuals that are less vulnerable to capture either due to behavioral avoidance or their ability to escape through net meshes \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. In contrast, \u003cem\u003eI. coindetii\u003c/em\u003e populations seem to experience higher natural and fishing-induced mortality rates under intense fishing pressure and elevated temperatures, resulting in a decline in older, larger individuals\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. This pattern suggests that \u003cem\u003eI. coindetii\u003c/em\u003e may have shorter lifespans under such conditions, with most individuals reproducing before reaching large sizes, ultimately leading to a population-wide shift toward smaller body sizes\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe response of \u003cem\u003eO. vulgaris\u003c/em\u003e to the combined effects of fishing pressure and warming further highlights the influence of species-specific life history traits on size structure dynamics. \u003cem\u003eO. vulgaris\u003c/em\u003e appears to exhibit a reduction in body size under intense fishing pressure and elevated temperatures, a pattern that aligns with its biological and ecological characteristics. As a fast-growing, short-lived species with high plasticity, \u003cem\u003eO. vulgaris\u003c/em\u003e has a lifespan of approximately 12 to 18 months and reaches maturity quickly, often within a year\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. This rapid life cycle makes it highly responsive to environmental changes, including fluctuations in temperature and exploitation rates.\u003c/p\u003e\u003cp\u003eTemperature plays a crucial role in the growth, metabolism, and reproductive cycles of \u003cem\u003eO. vulgaris\u003c/em\u003e. Higher temperatures shorten embryonic development and the planktonic dispersal phase, potentially increasing survival rates\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003eand ultimately favoring recruitment\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. However, this can come at the cost of smaller, less robust paralarvae, with potential long-term consequences on adult size and reproductive capacity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Warmer conditions have been shown to accelerate growth rates but may also lead to earlier maturation and smaller adult sizes due to increased metabolic demands\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Studies indicate that \u003cem\u003eO. vulgaris\u003c/em\u003e prefers temperatures ranging between 15\u0026deg;C and 22\u0026deg;C, with optimal growth occurring at intermediate temperatures\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. However, prolonged exposure to higher temperatures can impose physiological stress, reducing survival rates and potentially leading to shifts in population structure toward smaller individuals\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHigh fishing pressure exacerbates these effects by selectively removing larger individuals, intensifying size truncation within the population. Due to its benthic lifestyle and reliance on coastal habitats, \u003cem\u003eO. vulgaris\u003c/em\u003e is particularly vulnerable to overexploitation, with heavy fishing pressure leading to a demographic shift favoring smaller, faster-reproducing individuals \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. This combination of warming and intense fishing effort may therefore drive an overall decrease in size within \u003cem\u003eO. vulgaris\u003c/em\u003e populations, with potential consequences for reproductive output and population resilience.\u003c/p\u003e\n\u003ch3\u003eOverall\u003c/h3\u003e\n\u003cp\u003eImportantly, these patterns are not driven by fluctuations in early recruitment stages (\u003cem\u003esee\u003c/em\u003e Methods). Consequently, the shifts we observed in size structure reflect changes occurring within the adult and near-reproductive segments of the populations. This distinction is critical, as it indicates that the trends toward smaller sizes are not merely an artifact of episodic recruitment events but represent genuine alterations in adult body size likely linked to environmental pressures and fishing impacts.\u003c/p\u003e\u003cp\u003eOverall, our findings highlight the strongly species-specific nature of size-based responses to environmental change. Temperature consistently emerges as a dominant driver, promoting smaller adult body sizes across most taxa through mechanisms related to metabolic constraints and accelerated growth rates. Fishing pressure, while more variable in its effects across species, generally acts by selectively removing larger individuals from the population. Although the magnitude of fishing impacts differs among groups, its cumulative effect invariably reinforces a bias toward smaller body sizes. Particularly among fish species, the interaction between warming and fishing appears synergistic, with both stressors jointly amplifying the truncation of adult size distributions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study explores for the first time the specie-specific response in size spectra of commercially important species in the central Mediterranean Sea. Our study reveals that intense fishing efforts and rising temperatures interact to influence the size structure of commercial marine species, with responses varying across different species. Intense fishing pressure on size spectra often removes larger individuals, either through direct selection via gear designed to capture bigger fish or indirect selection where high mortality rates reduce the lifespan of fish before they attain larger sizes. This selective pressure leads to a phenomenon known as fishing-induced truncation, resulting in populations dominated by smaller individuals. Our findings emphasize the importance of considering the combined influence of warming and anthropogenic drivers when predicting shifts in population structure, stock productivity, and the broader resilience of exploited marine ecosystems under future environmental change. From a management perspective, this underscores the need for adaptive fisheries policies that incorporate size limits, temperature-driven catch adjustments, and ecosystem-based approaches to buffer against long-term population declines\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBy integrating size distribution indicators, our research advocates for a comprehensive approach to fisheries management that considers size-structured changes in marine populations under future climate scenarios. Such insights are vital for implementing the Marine Stategy Framework Directive, particularly Descriptor 3, which aims to maintain commercially exploited stocks within safe biological limits. Indeed, the analysis of size spectra across species and taxonomic groups provides quantitative metrics that can serve as early warning indicators of population stress before it is detected by traditional abundance-based approaches. Ultimately, our findings provide a scientific foundation for adaptive policies that incorporate climate change projections into fisheries management, ensuring the sustainability of species that hold both ecological and economic importance in a rapidly changing Mediterranean Sea.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStudy area and Data collection\u003c/h2\u003e\u003cp\u003eThe SoS, located in the south-central Mediterranean Sea, serves as a transitional zone connecting the western and eastern Mediterranean basins. It is recognized as one of the most productive and intensively exploited fishing grounds in the Mediterranean, supporting fleets from both European Union (EU) and North African countries\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The region features a highly complex seafloor topography and dynamic hydrodynamic processes that regulate water mass exchanges between the two Mediterranean sub-basins\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Along the southern coast of Sicily (southern Italy), the continental shelf presents two wide and shallow banks\u0026mdash;the Adventure Bank in the west and the Malta Bank in the east\u0026mdash;separated by a narrower central shelf. In contrast, the North African shelf is considerably broader, particularly off the eastern and southern Tunisian coasts\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The study area encompasses one Geographical Sub-Areas (GSA) as defined by the General Fisheries Commission for the Mediterranean (GFCM): GSA 16 (South of Sicily) (FAO-GFCM 2009).\u003c/p\u003e\u003cp\u003eThis study focuses on georeferenced biomass data collected within GSA 16 (South of Sicily) from 2000 to 2023. Data were obtained from the MEDITS (Mediterranean International Trawl Survey) program, a long-term fishery-independent survey carried out annually under the European Data Collection Framework (DCF)\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. All datasets used in this study are available at the project repository: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gjoniv/Medits_ISDbayes_commercial_species\u003c/span\u003e\u003cspan address=\"https://github.com/gjoniv/Medits_ISDbayes_commercial_species\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe MEDITS survey operates each year during late spring and early summer across multiple Mediterranean regions. In GSA 16, sampling involved bottom trawl surveys conducted using a standardized trawl net with a vertical opening of 2.5\u0026ndash;3 meters and a cod-end diamond mesh opening of 20-mm. Hauls were distributed according to a stratified random sampling design across five depth strata: 10\u0026ndash;50 m, 51\u0026ndash;100 m, 101\u0026ndash;200 m, 200\u0026ndash;500 m, and 500\u0026ndash;800 m, with the number of hauls proportional to the surface area of each stratum (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt each sampling station, fish species were sorted, counted, weighed, and measured. Relative abundance data, expressed in number of individuals/km\u0026sup2;, were obtained from a total of 1090 trawl hauls conducted between 2000 and 2023. The analysis focuses on the temporal trends and spatial distribution of commercially important species across this time frame.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental data\u003c/h2\u003e\u003cp\u003eMean bottom temperature climatology (2000\u0026ndash;2023) was extracted from the Bio-Oracle Database Version 3.0\u003csup\u003e72\u003c/sup\u003e at a native resolution of 0.05 decimal degrees for which the values were assigned to each haul based on mid-haul location. Fishing effort was extracted into a 0.05 dd raster layer using the Global fishing watch data\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, expressed as fished hours per year from trawlers, on the 2012\u0026ndash;2020 period. the layer\u0026rsquo;s resolution (4 x 5 at our latitude) is consistent with mean haul length (approx. 3\u0026ndash;5 km) and thus accounts for geolocalisation error.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eModel Fitting and Prior Choice\u003c/h2\u003e\u003cp\u003eWe used a Bayesian likelihood approach for our analysis because it is flexible, accommodates negative log-transformed values, and allows for accurate modeling through adaptable functions and prior distributions. All scripts for data processing and statistical analyses, along with documentation to reproduce the results, are openly available at our GitHub repository: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gjoniv/Medits_ISDbayes_commercial_species\u003c/span\u003e\u003cspan address=\"https://github.com/gjoniv/Medits_ISDbayes_commercial_species\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTo examine how size spectra varied as a function of temperature and resources, we used a Bayesian generalized linear mixed model with a truncated Pareto likelihood. We applied this approach to nine commercially important marine species\u0026mdash;three fish (i.e. \u003cem\u003eMerluccius merluccius, Mullus barbatus, and Mullus sirmuletus)\u003c/em\u003e, three crustaceans (i.e. \u003cem\u003eAristaeomorpha foliacea\u003c/em\u003e, \u003cem\u003eParapenaeus longirostris\u003c/em\u003e, \u003cem\u003eNephrops norvegicus\u003c/em\u003e), and three cephalopods (i.e. \u003cem\u003eLoligo vulgaris\u003c/em\u003e, \u003cem\u003eIllex coindetii\u003c/em\u003e, and \u003cem\u003eOctopus vulgaris\u003c/em\u003e \u0026mdash; to evaluate their population-level responses to key environmental and human-induced drivers, temperature and fishing pressure, as fixed effects. In addition, to ensure that our analysis focused on adult population dynamics rather than variability in juvenile abundance, we excluded individuals smaller than 2 cm in crustaceans, 5\u0026ndash;14 cm in fish, and 2\u0026ndash;3 cm in cephalopods. These size thresholds were selected based on known species-specific growth and maturation patterns, effectively filtering out recently settled juveniles and early life stages. A detailed description and justification of this modelling approach for size spectra is given in\u003csup\u003e10\u003c/sup\u003e. The model structure was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{x}_{ijkl}\\:\\sim\\:f\\left({x}_{jkl};\\:{\\lambda\\:}_{jkl},{x}_{{min}_{jkl}},{x}_{{max}_{jkl}},{counts}_{ijkl}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\lambda\\:}_{jkl}\\:=\\:a\\:+\\:\\varvec{\\beta\\:}\\mathbf{Z}\\:+\\:{\\gamma\\:}_{j}+\\:{\\gamma\\:}_{k}\\:+\\:{\\gamma\\:}_{l}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{ijkl}\\)\u003c/span\u003e\u003c/span\u003e is the \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e body size from sample \u003cem\u003ej\u003c/em\u003e in site \u003cem\u003ek\u003c/em\u003e and year \u003cem\u003el\u003c/em\u003e. The likelihood \u003cem\u003ef(\u0026hellip;)\u003c/em\u003e is a truncated Pareto with a single free parameter\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\lambda\\:}_{jkl}\\)\u003c/span\u003e\u003c/span\u003e, the exponent of the ISD. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{{min}_{jkl}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{{max}_{jkl}}\\)\u003c/span\u003e\u003c/span\u003e are the minimum and maximum body sizes in each sample, site, and year. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{{min}_{jkl}}\\)\u003c/span\u003e\u003c/span\u003e was chosen as the smallest body size in each sample for which the data most closely match a power law using the \u003cem\u003eestimate_xmin\u003c/em\u003e function \u003cem\u003epoweRlaw\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. This method ensures that estimates of λ are not biased from undersampling of small individuals (Virkar and Clauset 2014). Each body size has a corresponding density in units of number per m\u003csup\u003e2\u003c/sup\u003e, represented by counts\u003csub\u003e\u003cem\u003eijkl\u003c/em\u003e\u003c/sub\u003e \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{jkl}\\:\\)\u003c/span\u003e\u003c/span\u003eis modeled as a linear function of an intercept \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\mathbf{Z}\\)\u003c/span\u003e\u003c/span\u003e represents the single, two-way interactions of the \u003cb\u003eZ\u003c/b\u003e predictors of mean annual temperature and fishing effort. All predictors were standardized as z-scores prior to fitting. Varying intercepts are included for individual sample (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e), site \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{(\\gamma\\:}_{k})\\)\u003c/span\u003e\u003c/span\u003e, and year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\gamma\\:}_{l}\\right)\\)\u003c/span\u003e\u003c/span\u003e. To improve sampling efficiency, the varying intercepts were modeled using non-centered parameterization, which is excluded here for clarity, but is present in the Stan model code: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gjoniv/Medits_ISDbayes_commercial_species\u003c/span\u003e\u003cspan address=\"https://github.com/gjoniv/Medits_ISDbayes_commercial_species\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ePriors for the intercept were Normal(-2, 0.5), chosen based on theoretical predictions\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Priors for each \u003cem\u003eβ\u003c/em\u003e parameter were set to Normal(0, 0.2). Priors for the \u003cem\u003eq\u003c/em\u003e varying intercepts were Normal(0, s\u003csub\u003eq\u003c/sub\u003e), with each s\u003csub\u003eq\u003c/sub\u003e hyperprior set to Exponential(7). These priors were chosen based on prior predictive simulation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e to center the prior probabilities of λ between about \u0026minus;\u0026thinsp;2.5 to -1 while still allowing probabilities at very large (e.g. 1) or small values (e.g. -4). We fit all models in \u003cem\u003erstan\u003c/em\u003e via the \u003cem\u003ebrms\u003c/em\u003e and \u003cem\u003eisdbayes\u003c/em\u003e packages in R. Each model had 4 chains with 2000 iterations, where the first 1000 were discarded as warm-up. Model fit was checked using posterior predictive checks and prior influence was checked using prior predictive simulation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 \u0026ndash; Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union \u0026ndash; NextGenerationEU\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eV.G. performed the statistical analysis. V.G., V.L., F.F., G.G., and V.G. performed theliterature search, wrote the manuscript, and made edits. V.G., F.F., G.G. and V.L. were mainly responsiblefor the interpretation of the data and preparing the final version. V.G. created the figures. All authors providedcritical feedback and contributed to the final manuscript. Correspondence and requests for materials should beaddressed to V.G.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll datasets, data processing and data analyses of the manuscript are available at the GITHUB project repository: https:/github.com/gjoniv/Medits_ISDbayes_commercial_species\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePauly, D., Christensen, V., Dalsgaard, J., Froese, R. \u0026amp; Torres, F. Fishing Down Marine Food Webs. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e279\u003c/b\u003e, 860\u0026ndash;863 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerry, A. L., Low, P. J., Ellis, J. R. \u0026amp; Reynolds, J. D. Climate change and distribution shifts in marine fishes. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e308\u003c/b\u003e, 1912\u0026ndash;1915 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheung, W. W. L. et al. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. \u003cem\u003eGlob. Change Biol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 24\u0026ndash;35 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinsky, M. L. \u0026amp; Fogarty, M. Lagged social-ecological responses to climate and range shifts in fisheries. \u003cem\u003eClim. Change\u003c/em\u003e. \u003cb\u003e115\u003c/b\u003e, 883\u0026ndash;891 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharpe, D. M. T. \u0026amp; Hendry, A. P. SYNTHESIS: Life history change in commercially exploited fish stocks: an analysis of trends across studies. \u003cem\u003eEvol. Appl.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 260\u0026ndash;275 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaufresne, M., Lengfellner, K. \u0026amp; Sommer, U. Global warming benefits the small in aquatic ecosystems. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 106, 12788\u0026ndash;12793 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAudzijonyte, A. et al. Fish body sizes change with temperature but not all species shrink with warming. \u003cem\u003eNat. Ecol. Evol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 809\u0026ndash;814 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeck, M. A., Reglero, P., Takahashi, M. \u0026amp; Catal\u0026aacute;n, I. A. Life cycle ecophysiology of small pelagic fish and climate-driven changes in populations. \u003cem\u003eProg. 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(2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"size spectra, temperature, fishing effort, commercial species, climate change impacts, fisheries management","lastPublishedDoi":"10.21203/rs.3.rs-7565528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7565528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnthropogenic pressures, particularly fishing effort and ocean warming are reshaping marine ecosystems and influencing the population dynamics of key fisheries species. While these stressors have been widely studied in isolation, their interactive effects across taxonomically distinct groups remain poorly understood. Here, we examine how fishing pressure and increasing temperature jointly affect the size spectra of nine commercial species (three bony fish, three crustaceans, and three cephalopods) in the central Mediterranean Sea. Using size-spectrum analyses applied to fishery-independent survey data from 2000 to 2023, we evaluate population-level responses to these stressors. Our findings reveal taxon-specific patterns: under high fishing effort and high temperatures, fish populations exhibit a higher proportion of smaller individuals, consistent with fishing-induced truncation and temperature-driven metabolic constraints. In contrast, crustaceans and cephalopods show different responses, reflecting their greater physiological plasticity and shorter life cycles, which may buffer against environmental changes. These results suggest that the combined effects of fishing and climate change could disproportionately reduce fish biomass while allowing more flexible taxa to persist or even thrive. Our results emphasize the need for adaptive management strategies that incorporate both environmental change and fishing pressure projections to maintain sustainable yields and ecosystem resilience in the face of ongoing climate-driven shifts.\u003c/p\u003e","manuscriptTitle":"Fishing and Warming Reshape Size Spectra of Commercial Species in the Mediterranean Sea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 07:02:13","doi":"10.21203/rs.3.rs-7565528/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T18:40:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T21:18:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57044623412736109337305636879444621477","date":"2026-03-20T13:04:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254765300959827011975140390808036327729","date":"2026-01-14T03:39:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318360359479006726580491085218481516533","date":"2025-11-28T06:43:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T22:04:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105204435920509960624950759225947012612","date":"2025-10-31T19:35:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158466304690695708368004289093384588418","date":"2025-10-24T00:05:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T21:15:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T09:40:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T16:19:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T11:03:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-17T10:58:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6de3d395-4585-4128-a4c5-b63a92dec9eb","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":54991717,"name":"Biological sciences/Ecology"},{"id":54991718,"name":"Earth and environmental sciences/Ecology"},{"id":54991719,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-04-08T18:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 07:02:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7565528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7565528","identity":"rs-7565528","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}