Marine Heat Waves Are Transforming Western Mediterranean Marine Ecosystems

Marine Heat Waves Are Transforming Western Mediterranean Marine Ecosystems | 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 Marine Heat Waves Are Transforming Western Mediterranean Marine Ecosystems Camila Artana, Andrea Kaplan, Francisco Ramirez, Miquel Ortega, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7573187/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract We investigated ecosystem impacts of marine heatwaves (MHWs) in the Western Mediterranean Sea using a spatially explicit food-web model. Our simulations suggest that declining biomass trends observed over the past decades in the Western Mediterranean driven by long-term ocean warming and fishing pressure have been exacerbated by the occurrence of MHWs. A north–south dipole in biomass rate of change emerged in response to MHWs: while the northern region displayed positive or neutral responses due to MHWs, the south, particularly the Alboran and Algerian seas, experienced negative impacts. Benthic producers and commercially important species (pelagic and demersal fish and invertebrates) were particularly affected, leading to catch reductions exceeding 10%. We identify previously unreported vulnerable groups and regions, supporting the use of ecosystem models in guiding adaptive management for the future. The increase in intensity and extension of MHW with time suggests that larger effects may be expected in the future. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Ocean sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Marine heatwaves (MHWs) are discrete and prolonged periods of anomalously high sea temperatures that deviate significantly from historical seasonal means [ 1 ] . These extreme events have attracted considerable scientific and public attention in recent years due to their dramatic consequences on marine ecosystems and their substantial socio-economic impacts [ 2 , 3 ] . Due to their abrupt nature, extreme events can have more severe impacts than those emanating from progressive global warming [ 4 , 5 ] . Given their disruptive potential, understanding how ecosystems respond to MHWs has become a pressing scientific and management challenge. However, the ability to manage marine ecosystems under these extreme events in a sustainable, effective and holistic manner is limited by the lack of information and analytical capabilities regarding the effect of MHWs on the ecosystem structure and functioning [ 6 ] . The Mediterranean Sea is particularly vulnerable to MHWs. Over the past three decades, the region has experienced a sustained and rapid increase of sea surface temperature (SST) [ 7 ] . Concurrently, the frequency and intensity of MHWs have dramatically increased, with an unprecedented acceleration rate in recent years [ 8 ] . These extreme thermal anomalies pose a substantial threat to the rich and diverse marine biodiversity of the Mediterranean Sea [ 9 , 10 ] , as well as to the multiple ecosystem services it supports [ 11 ] . Local extinctions, reductions in the rate of natural carbon sequestration, loss of essential habitats, and decreased socioeconomic value due to reductions in potential catch are only a few of the reported effects of recent MHWs in the Mediterranean Sea (e.g. [ 12 , 13 , 14 ] ). Such impacts compromise the long-term resilience of the ecosystem and erode its ability to provide services upon which human populations depend, cascading to the socioeconomic system [ 15 ] . Although several studies have investigated species responses to MHWs in the Mediterranean (e.g. [ 13 ] ), a holistic and integrated understanding of their impacts on the entire marine food web remains limited. Here, we used a food-web modelling approach encompassing the whole ecosystem instead rather than a single species to investigate the marine ecosystem response to past MHWs. Our approach enabled the quantification of spatial and temporal patterns in biomass change and the assessment of shifts in key ecological indicators, reflecting alterations in trophic dynamics and community composition driven by MHWs occurring on top of the long-term ocean warming and fishing activities. We focused on the Western Mediterranean Sea, considering thermal anomalies at multiple depths. Our study contributes to a more comprehensive understanding of how extreme ocean warming events affect complex marine ecosystems, and provides insight into potential vulnerabilities and pathways for resilience in the face of future climate extremes. Results MHWs in the Western Mediterranean over the last decades (1995–2022) The evolution of MHW spatial extent over the 27-year period (1995–2022) (Fig. 1 a) highlights important temporal changes. After the dramatic MHW of 2003, MHWs began to consistently affect more than 60% of the surface basin area. Over the past decade, this threshold was exceeded 12 times, indicating an increasing spatial extent of surface MHWs. In contrast, subsurface MHWs (i.e., those occurring at 150 m) and bottom MHW did not exceed a spatial extent of 20% before 2016. However, after 2016, subsurface and bottom MHWs began to cover more than 40% of the basin, suggesting a deeper penetration of heat anomalies in recent years. The mean intensity of absolute temperatures associated with surface MHWs shows that on average the southern part of the basin remained consistently warmer than the northern region during MHWs (Fig. 1 b). Indeed, during MHW events, averaged absolute surface temperatures in the south reached values around 24°C, whereas they generally remained below 20°C in the north. A similar latitudinal gradient is observed at the subsurface, where temperatures in the south averaged around 15°C, decreasing to approximately 13°C in the northern region (Fig. 1 c). Further MHWs characteristics are presented in supplementary material (Figure S1 ). Temporal changes of the mean rate of change for the Western Mediterranean In order to assess the effect on MHWs on the ecosystem of the Western Mediterranean, we conducted two simulations: one simulation forced with temperature time series including past MHWs and another without MHWs (see Methods). The simulation without MHWs was defined as a control simulation. Time series of biomass for each run (with and without MHWs) were computed for each, individual and aggregated modelled groups, by averaging biomass across the entire basin (Figure S2 and S3). The long-term trends of these time series were assessed for each simulation (Fig. 2 , blue and yellow bars in right panels). They indicate, overall, long-term declines in biomass for most of the functional groups except for small phytoplankton. Overall, the inclusion of MHWs in the simulations resulted in a stronger negative trend across all aggregated groups. This is also observed when considering individual groups (Figure S4, S5). Ecosystem-wide changes in biomass induced by MHWs were assessed by computing the rate of change (RC) in biomass for each functional group represented in our food-web model through comparisons between both simulations (with and without MHWs, see Methods for more details). The RC reflects the direct effects resulting from the thermal stress induced by MHWs occurring on top of the long-term trends in temperature and fishing. It also captures the cascading impacts of these effects on the ecosystem resulting from ecological changes such as trophic interactions, species distributions and changes in metabolic rates. The time series of the spatially averaged RC underscore the diverse timing, duration, and intensity of ecological responses to MHWs across trophic levels (Fig. 2 ). They reveal a clear inflection point in RC for all groups, marking the onset of MHWs effects. For most groups, this inflection point occurred around 2003–2004 (Fig. 2 ). Throughout the series, the RC oscillated between positive and negative values after the inflection points. Benthic producers stand out as the only group showing consistently negative values after their inflection point, suggesting a sustained negative impact of MHWs. However, by the end of the time series, all groups exhibited negative RC values, indicating a widespread decline in biomass due to the cumulative effect of historic MHWs (right panel in Fig. 2 , green bar). When comparing the time series across all groups (Fig. 2 ), we observe that higher trophic levels tended to respond more slowly to MHWs than lower trophic levels. Zooplankton and phytoplankton, for instance, responded on intra-annual timescales with the largest magnitudes in RC, while top predators exhibited much slower dynamics and, often responding over several years. Time series of selected ecological and fisheries indicators (Figure S6) were used to compute their long-term trend and RC across the Western Mediterranean basin (Fig. 3 ). The majority of these indicators experienced a long-term decline as well, with the exception of TL community and Total Catch (blue and yellow bars in Fig. 3 ). The inclusion of MHWs amplified the long-term trends of commercial biomass, pelagic catch, fish catch, and the community trophic level (TL), whereas it weakened the trends for total catch, the mean trophic index (MTI), and Kempton’s Q diversity index. Similarly to the biomass RC time series, the indicators’ RC time series experienced varying responses and most of them also showed a marked change after 2003–2004. Afterwards, the majority of biomass and catch indicators experienced a period with increasing RC reaching positive values, peaking around 2008–2010. However, after this period, the RC of the majority of these indicators began to decline, and by the last year of the simulation they presented consistently negative RC. The trophic level indicators, namely TLcatch (trophic level of the catch), TLcommunity (trophic level of the community, including all species) and MTI (mean trophic index), and the Kempton’s Q diversity index, all exhibited a neutral and stable RC at the beginning of the time series until 2003–2004, after which their RC oscillates until the end of the period. Spatial changes of mean rate of change In order to distinguish the different vertical and temporal behaviour of MHWs we selected 3 five-year periods with markedly different MHW patterns (Fig. 1 a): 1995–2000 (mainly surface MHWs not exceeding 40% of coverage), 2003–2008 (mainly surface MHWs exceeding 40% of coverage) and 2017–2022 (surface MHWs exceeding 60% of coverage and subsurface and bottom MHWs exceeding 40%) (see figures S7, S8 and S9 for more details on MHW behaviour during these periods). During the period of 1995–2000, seabirds exhibited consistently negative RC values across the entire region. In contrast, most other groups showed either near-zero or positive RC values over the same period (Fig. 4 ). During the 2003–2008 interval, a broader range of functional groups began to exhibit declines. Top predators, seabirds, elasmobranchs, fish, and cephalopods all showed predominantly negative RC values. In contrast, other invertebrates and small phytoplankton showed the highest positive RC values during this time. Notably, several groups (including crustaceans, other invertebrates, zooplankton, and large phytoplankton) exhibited a pronounced spatial dipole, with positive RC values in the northern part of the basin and negative values in the south. This north-south dipole became even more prominent during the 2017–2022 period for nearly all functional groups. Maps of RC in biomass (figure S10) reveal the spatial heterogeneity of the RC and region-specific nature of ecosystem responses summarized in Fig. 4 . When considering spatial changes by periods averaged in each geographic subarea (GSA, Fig. 4 ) we observed that the southern GSAs (Northern Alboran Sea, Southern Alboran Sea, and Algeria) suffered the most pronounced changes in RC over time. In these regions, the majority of functional groups displayed a clear shift from positive RC values during the 1995–2000 period to significantly negative values by 2017–2022. Notably, in the most recent period, top predators, seabirds, and elasmobranchs, the highest trophic levels, showed the strongest negative RC values, exceeding 5%. In contrast, the functional groups in northern GSAs (Gulf of Lion, Corsica, Eastern Sardinia, and the Southern and Central Tyrrhenian Sea) showed lower fluctuations in RC. During the 2017–2022 period, many functional groups in these areas continued to exhibit positive or near-neutral RC values due to MHWs. The Balearic Islands and Western Sardinia GSAs showed a more moderate pattern, with relatively small fluctuations in RC across all periods for most groups. An exception is seabirds, which experienced a decrease larger than 5%. Notably, changes observed for the highest trophic levels were in general significant over the last period, with relatively small errors (Figure S11). When analysing spatial changes in indicators by periods and GSA (Figure S12 and S13), we observe the most prominent changes in the southern GSAs. In summary, the model indicates that the recent increase in MHW frequency, intensity, and depth of penetration over the past five years has tended to induce a contraction of the food web, characterized by larger negative RC in higher trophic levels in the southern part of the modelled region, particularly in the Alboran and Algerian seas. Commercial species were strongly affected, resulting in reductions of over 10% in total catch due to MHWs, including pelagic, demersal, fish, and invertebrate groups in these areas (Figure S12). Benthic producers showed the most consistent decline in RC over the basin and over time, with negative RC exceeding 15% along most of the western Mediterranean coastline (except in the northern Tunisian Sea) by the end of the simulation. Discussion This study presents a comprehensive modelling exercise aimed at understanding the impacts of MHWs over the past decades on the western Mediterranean ecosystem, occurring on top of long-term effects of climate change and fisheries. It offers a temporal and spatial assessment of changes in biomass and key ecosystem indicators resulting from the introduction of realistic MHW events in a marine ecosystem model. MHWs have significantly intensified in frequency, intensity, and vertical extent in recent years. The model suggests that as a consequence, MHWs have had an overall negative effect on the spatially averaged biomass on top of the effect induced by the long-term warming for most of the functional groups included in the ecosystem model. It is worth noting that the RC quantified by these simulations reflects the impacts attributable only to MHWs. These impacts can be direct, as MHWs act as thermal stressors on individual species, and indirect, as the resulting ecological changes (e.g., in species distributions, metabolic rates or trophic interactions) cascade through the food web. The gradual warming trend and the impact of fishing is implicitly accounted for in the baseline runs but their direct effects are not considered in the RC computation (Fig. 5 ). Indeed, the RC quantified the change in biomass due to MHWs occurring on top of those due to long-term warming and fishing activities. The negative sign of the long-term trends and of RC in biomass averaged over the entire basin and over the last period is in accordance with extensive literature detailing observed negative effects in Mediterranean species, like seagrasses, corals and many other benthic invertebrates 12, [ 16 ] . Although MHW impacts on groups like marine mammals, seabirds, fish, elasmobranchs and crustaceans have not been widely reported in the Mediterranean, they are common in the global literature [ 17 – 20 ] . The model results therefore provide insights on the responses of those functional groups for which MHW effects have not yet been clearly reported in the Mediterranean Sea specifically. They evidence the heterogeneous spatial responses obtained over the entire basin and over the entire period based on a detailed quantification of changes triggered by MHWs. In this sense, our study highlights how ecosystem models can serve as useful tools for informing decision-making processes and developing effective management that addresses the potential effects of climate change on marine ecosystems and fisheries. When considering MHWs induced spatial patterns we observed the emergence of a pronounced spatial dipole in the RC of biomass of marine organisms and ecological indicators, with overall neutral or positive RC values in the northern part of the region due to the impact of MHWs and negative values in the south, especially at the end of the study period. This suggests that the southern region of the Mediterranean Sea is particularly vulnerable to MHWs, agreeing with findings that species at their warmer edges of their thermal range experience stronger negative responses to such events [ 2 ] . The model suggests a negative impact on biomass due to MHWs at high trophic levels in the southern part of the region; however, trophic-based indicators did not experience significant changes. This is consistent with modelled ecosystem results from the Northern California Current, where biomass shifts induced by MHWs did not lead to changes in the mean trophic level [ 21 ] . As suggested by the authors, this may imply that the efficiency of the food web (defined as the rate of energy transfer to the production of new biomass as metabolic rates increase) is robust in the face of disruptions induced by MHWs. The dipole in RC may result from either increased species mortality and growth or species mobility. However, maps of RC computed for the individual species included in the model (not shown) revealed that the dipole pattern is present across the majority of species, including those with relatively low dispersal capacity. This suggests that the dipole is primarily driven by a net increase or decrease in total biomass, rather than by a redistribution of species within the basin. The spatial dipole could be attributed to the latitudinal gradient observed in the mean absolute temperatures during MHWs. Maps of maximum temperatures reached during MHW events (Figure S9) further suggest that this meridional gradient in absolute temperature can temporarily (or permanently) collapse in the near future, since in extreme cases, the northern part of the domain reached peak temperatures similar to those typically observed in the south (on the order of 30°C). In this context, it is worth mentioning that the final year in our time series (2022) featured an exceptionally severe MHW (Fig. 1 ). Given that many marine species exhibit a delayed response to thermal stress (particularly those with long life cycles and higher up the trophic web) it is likely that the ecosystem changes driven by this extreme MHW were not fully captured in the model results because simulations ended in 2022. Future iterations of the study can be used to investigate the impacts of these recent MHW and develop future simulations following previous studies [ 22 ] . The higher intensity and larger extension of MHW in recent years may indicate larger ecosystem effects in recent times and to be seen in the future. One important limitation of our study lies in the model’s strong dependence on species-specific thermal performance curves. Currently, there is no scientific consensus on the precise shape of these curves or whether they vary across space and time for each species [ 23 – 26 ] . Consequently, we acknowledge that our results may be conservative and that further empirical research is needed to increase the robustness of our findings. We recommend long-term monitoring of species population dynamics, along with targeted mesocosm experiments, to better characterize species’ and population’s thermal tolerance and performance responses. Additionally, our model does not account for the potential adaptive mechanisms that species might employ in response to long-term environmental change. This omission is particularly relevant, as some species may be capable of adjusting physiologically or behaviourally to altered thermal regimes over time [ 27 , 28 ] . In addition, adaptations can be extremely local as suggested by a recent study [ 29 ] , showing that sardines from the South of the Mediterranean have adapted differently compared to those of the North to environmental pressures. This issue deserves further investigation to provide a more comprehensive understanding of marine ecosystem resilience and vulnerability under climate stress. Overall, this study offers insights into the impact that MHWs may pose to the ensemble of functional groups of the western Mediterranean Sea and informs on the most vulnerable areas, providing valuable information to develop pathways for resilience and future research priorities in a hotspot of marine biodiversity and climate change. Methods Ocean reanalysis We used daily means of 27 years (1993–2022) of high-resolution (1/12°) global Mercator Ocean reanalysis (hereafter, GLORYS12) from Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/ ) [ 30 ] . GLORYS12 reanalysis uses the reprocessed atmospheric forcing coming from the global atmospheric reanalysis ERA5. The model has 50 vertical levels with 22 levels in the upper 100 m leading to a vertical resolution of 1 m in the upper levels and 450 m at 5,000 m depth. The physical component of the model is the Nucleus for European Modelling of the Ocean platform (NEMO). The model assimilates observations using a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the background error and a 7-days assimilation cycle [ 31 ] . Along-track satellite altimetric data from CMEMS [ 32 ] , satellite sea surface temperature from NOAA, sea-ice concentration, and in situ temperature and salinity vertical profiles from the latest CORA in situ databases [ 33 , 34 ] ) are jointly assimilated. A 3D-VAR scheme provides an additional 3-D correction for the slowly evolving large-scale biases in temperature and salinity when enough observations are available [ 29 ] . The model has been extensively used in different ocean regions to study MHWs at the surface and at the subsurface [ 35 , 36 , 37 ] . Marine Heat Wave Detection Following the definition proposed by [ 1 ] , we identified Marine Heatwaves as events during which ocean temperatures are anomalously warm, specifically exceeding the seasonally-varying 90th percentile for at least five consecutive days. This definition was applied using the freely available ‘m_mhw’ MATLAB toolbox developed by [ 38 ] . Our study focuses on the period 1995–2022 as that is when ecological information is available to fit the model, however MHWs were detected using the full time series (1993–2022) as a baseline, with no removal of long-term trends. This approach allows for a more comprehensive assessment of the impacts of MHWs on the broader marine ecosystem. In addition to event detection, we also calculated key characteristics of the MHWs, including the mean absolute temperature reached during each event and the maximum absolute temperature observed. The difference between the mean temperature with and without MHWs was considered a proxy for the mean MHW-induced anomaly. The frequency of MHWs was defined as the number of events occurring per year. To evaluate the spatial extent of these events, we computed the percentage of area affected by MHWs for each month and at each depth level analyzed. The total area considered corresponds to the western Mediterranean which comprises 12 Geographical Subareas: Northern Spain, Gulf of Lion, Corsica, Ligurian and Northern Tyrrhenian Sea, Balearic Islands, Western Sardinia, Eastern Sardinia, Southern Central Tyrrhenian Sea, Southern Alboran Sea, northern Alboran Sea and Algeria. Food web modelling approach We used the spatiotemporal food web model of the western Mediterranean marine ecosystem [ 39 ] developed with the Ecopath with Ecosim desktop software (EwE v6.7.0.19431 Beta on Windows 11, https://ecopath.org ) [ 40 , 41 , 42 ] . The temporal dynamic module, Ecosim, uses a previously defined Ecopath model to simulate changes in biomass, production, consumption and diets of species or functional groups over time. Ecospace, the spatially explicit dynamic module, applies the Ecosim module to the species in each cell of a grid of cells and accounts for species movement in or out of a cell depending on its suitability. Western Mediterranean Ecosystem Model We used the ecosystem model fitted run for the period 1995 to 2020. The model includes a total of 93 species which were aggregated into 11 groups: top predators, seabirds, elasmobranchs, fish, cephalopods, crustaceans, other invertebrates, zooplankton, benthic producers, large phytoplankton and small phytoplankton (Table S1 ). The top predators’ group is a mixed category that includes high trophic level species for illustrative purposes, some of which are also included in the seabirds, elasmobranchs or fish groups. Each functional group was assigned a temperature layer of influence. The considered temperature layers were the surface (5 m), subsurface (150 m), or the bottom temperature (Table S1 ), meant to represent the thermal conditions of the sea surface, the pelagic and the benthic realm, respectively. Layers were linked to functional groups according to their main ecological traits, including feeding grounds. To isolate the changes due to sea warming, we included a climatology spatial layer of the primary production and salinity that did not change with time. Functional groups, including primary production groups, changed due to spatial-temporal changes in temperature and food web interactions. The model also included temporal drivers of fishing effort from 1995 to 2020 as the original configuration [ 39 ] . Mechanistic link between sea water temperature and consumption rate Temperature is one of the most influential environmental factors affecting species physiology, primarily due to its central role in regulating metabolic processes and overall energy balance [ 43 ] . Most marine species have evolved to operate within a specific thermal window, in which physiological performance - such as growth, reproduction, and feeding - is optimized [ 44 , 45 ] . When ocean temperatures exceed this optimal range, as often occurs during marine heatwaves, organisms may experience thermal stress that compromises key biological functions and reduces individual fitness [ 46 , 47 ] . Under suboptimal thermal conditions, metabolic efficiency is impaired, leading to reduced consumption rates, especially when ambient temperatures surpass species-specific physiological thresholds [ 48 , 49 ] . These disruptions can have cascading effects on population dynamics, potentially causing short-term declines in biomass and altering community structure, species interactions, and food web stability [ 21 , 50 ] . To incorporate species-specific responses to thermal variability into our model, we implemented thermal performance curves as environmental response functions using the habitat foraging capacity model [ 51 , 10 ] . These thermal performance curves describe how each species or functional group's consumption rate varies with temperature and were obtained from AquaMaps [ 52 ] . Consumption peaks at the species’ optimal temperature, reflecting maximum physiological efficiency, and declines progressively as temperatures deviate above or below this optimum. This approach allows for a realistic representation of both the beneficial and detrimental effects of temperature variability within a spatial-temporal food web approach. MHWs Simulations We conducted two simulations in order to calculate the rate of change in species' biomass caused by MHWs (Fig. 5.1). First, the simulation referred to as S, where the model was run using temperature time series from GLORYS12 as the spatial-temporal environmental driver. Temperature time series from three levels, the surface, subsurface (150m), and the bottom, were used. Then, the "control" simulation was conducted using temperature time series from GLORYS12 at the same depths, with MHWs removed. MHWs were eliminated from each temperature time series by replacing the temperature values during MHW events with climatological values (Fig. 5.2). For each case, the Ecospace model was run under three trophic assumptions: vulnerability = 2, the vulnerability obtained from the model fitting procedure to minimize differences between predicted and observed historical time series of data (v = fitting), and vulnerability = 10 (Fig. 5.3), which were used to compute uncertainty (see next section). The model outputs were then used to compute long-term trends in biomass and to calculate the rate of change in biomass (RC) for each species due to the occurrence of MHWs (Fig. 5.4). This was achieved by comparing the biomass of the S run with respect to the biomass of the control run. The calculation of RC is defined as follows: $$\:RC=\frac{Biomass\left(S\right)-Biomass\left(control\right))}{Biomass\left(control\right)}*100$$ Ecological indicators To describe the effect of MHWs on the environmental status of the ecosystem, we used the ECOIND plugin in Ecopath with Ecosim , which calculates standardized ecological indicators from the biomass and catch outputs of EwE food web models [ 53 ] . We selected key biomass-, catch- and trophic-based ecological indicators which give information of the status of a marine ecosystem and biodiversity in relation to impacts of fisheries and climate change. We used the following ecological and catch indicators, further detailed in [ 53 ] : Biomass-based indicators, which depend on species abundance in the food web: commercial species biomass and Kempton’s Q Biodiversity Index, an ecosystem diversity index commonly used in marine ecosystems with emphasis on median abundance species [ 54 , 55 ] . A decrease in the Kempton’s Q index reflects a decline in functional group diversity in the ecosystem represented in the model. Catch-based indicators, based on the removal of species from the ecosystem due to fisheries activity: total catch, demersal species catch, pelagic species catch, fish catch (all fish species) and invertebrate species catch. These represent the extraction of organisms from the ecosystem due to fishing activities, and are therefore fisheries-dependent metrics, but they can still be used as an estimate of the abundance of these groups of organisms [53, 56] . Trophic-based indicators, based on the concept of trophic level (TL) which positions a species in the food web depending on its sources of energy [ 57 , 58 ] : Trophic Level of the catch (TLcatch) [ 57 , 59 ] , Trophic Level of all the organisms in the community (TLcommunity) and Marine Trophic Index (MTI), which is the mean TL of all the organisms with TL \(\:\ge\:\) 3.25 [ 60 ] . These indicators give an estimation of whether an ecosystem is more dominated by top predators (higher TL) or primary producers (lower TL), therefore a decrease in top predators or an increase in lower TL organisms will result in a lower mean trophic level of the community [ 61 ] . For each of the indicators we computed the long-term trend and the RC using the same methodology described above for the biomass results. Uncertainty for species biomass and indicators and statistical tests To quantify the uncertainty in the long-term trend and the RC, the model was run using different values of the vulnerability parameter [ 62 , 63 ] . In the model, this parameter represents the predation mortality experienced by prey; higher values indicate stronger trophic control by predators. The simulation presented in this study corresponds to the run using vulnerability values derived from model fitting, i.e., estimated by calibrating the model to observed time series of biological or fisheries indicators for one or more functional groups. To assess the sensitivity of the results, additional simulations were performed with fixed vulnerability values set to 2 and 10. Uncertainty was then quantified by calculating the standard deviation of the biomass long-term trend and RC across these three simulations. In addition to uncertainty analysis, we tested whether the simulated biomass and indicator time series significantly differed from the control simulation in terms of their mean RC values. This was done using Student’s t-test with a 95% confidence level. Furthermore, to examine whether changes were particularly pronounced during specific time periods, we compared the mean biomass and indicator values during 1995–2000, 2003–2008, and 2017–2022 to the mean over the entire study period (1995–2022) using the same statistical test. Declarations Acknowledgements and Funding Information: This study is a contribution to the Spanish funded ProOceans (Ministerio de Ciencia e Innovación, Proyectos de I+D+I, RETOS-PID2020-118097RB-I00) and GES4SEAS (European Union's Horizon 2020 research under grant agreement no. 101059877) projects. CA acknowledges institutional support from the Institut de Recherche pour le Développement (IRD) and LOCEAN. AK was supported by an FPU grant from the Spanish Ministry of Science, Innovation and Universities (FPU22/02668) and by the predoctoral research mobility scholarship granted by the Embassy of France in Spain and the Institut Français of Spain. AK, MC, FR, MO acknowledge institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC). Data availability: Glorys12 reanalysis is available at http://marine.copernicus.eu/. Ecopath with Ecosim desktop software at EwE v6.7.0.19431 Beta on Windows 11, https://ecopath.org . Model outputs from the ecosystem model will be made available on the CSIC Digital platform upon acceptance of the manuscript Contributions: C.A. and A.K. contributed equally. They were involved in conceptualisation, methodology, investigation, formal analysis, visualization and writing original draft. M.C. and J.S. run the ecological models. M.C., F.R., M.O. and J.S. involved in conceptualisation, methodology, formal analysis, review and editing. All authors contributed critically to the drafts and gave final approval for publication. Additional information: Competing interests: The authors declare no competing interests. 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1","display":"","copyAsset":false,"role":"figure","size":158014,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Temporal evolution of spatial extent of MHWs, expressed as the percentage of pixels experiencing a MHW at the surface, subsurface (150 m), and bottom depths. (b–d) Mean absolute temperature during MHW events at the surface, subsurface (150 m), and bottom depths over the period 1995–2022.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/00b6efd2a8357e7d8da3ce90.png"},{"id":92591674,"identity":"928970a4-7b8d-4202-92e7-68717d1a1829","added_by":"auto","created_at":"2025-10-01 12:02:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":402185,"visible":true,"origin":"","legend":"\u003cp\u003e(a-k) Left axis: Time series of the rate of change (RC%) in biomass for each aggregated functional group (note the different scales for each panel). Titles indicate the name of the aggregated functional group. Markers (asterisks and dashes) refer to the results of the statistical test: asterisks (*) denote periods where the mean biomass is significantly different from the control simulation at the 95% confidence level, while dashes (–) indicate non-significant periods. The position of each marker corresponds to a specific time period: the first position refers to the entire period (1995–2022), the second to 1995–2000, the third to 2003–2008, and the fourth to 2017–2022. Right axis: Spatial extent of MHWs, expressed as the percentage of domain pixels experiencing a MHW at the surface, subsurface, and bottom depths as in Figure 1. Panels on the right indicate the historical biomass trends expressed as a percentage for the simulations with (yellow bars) and without (blue bars) MHWs. The RC, averaged over the final year of the simulation, is represented by a green bar. The final panel (l) shows all time series superimposed for scale comparison.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/95a47ca2d223c962ebb28b52.png"},{"id":92592574,"identity":"4a860b5d-0ac7-4a7c-b7f4-a183703ff320","added_by":"auto","created_at":"2025-10-01 12:18:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431739,"visible":true,"origin":"","legend":"\u003cp\u003e(a-j).\u003cstrong\u003e \u003c/strong\u003eLeft axis: time series of the rate of change (RC%) in ecological indicators (note the different scales for each panel). Titles indicate the indicator. Markers (asterisks and dashes) refer to the results of the statistical test: asterisks (*) denote periods where the mean indicator is significantly different from the control simulation at the 95% confidence level, while dashes (–) indicate non-significant periods. The position of each marker corresponds to a specific time period: the first position refers to the entire period (1995–2022), the second to 1995–2000, the third to 2003–2008, and the fourth to 2017–2022. Left axis: Spatial extent of MHWs, expressed as the percentage of domain pixels experiencing a MHW at the surface, subsurface, and bottom depths as in Figure 1. Panels on the right indicate the historical trend of each indicator expressed as a percentage for the simulations with (yellow bars) and without (blue bars) MHWs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/e112b6f2cd5f1cf8f82186d7.png"},{"id":92592575,"identity":"00866654-cb5d-4c5e-aa73-a9aa737ceddf","added_by":"auto","created_at":"2025-10-01 12:18:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229942,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the location of each considered GSA (Geographical Sub-Area) represented with a particular colour alongside lollipop plots illustrating the rate of change in biomass for each functional group within each GSA. Coloured dots represent mean values of the rate of change for three distinct periods: 1995–2000, 2003–2008, and 2017–2022. Asterisks (*) indicate values that are significantly different from the mean over the entire period (1995–2022) at the 95% confidence level. The position of each asterisk corresponds to a specific period: first position for 1995–2000, second for 2003–2008, and third for 2017–2022.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/822558523f007407d296ca67.png"},{"id":92591678,"identity":"5302bd91-a11d-4dfc-8670-1f5a99f23ce6","added_by":"auto","created_at":"2025-10-01 12:02:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148250,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the methodological approach used in this study. (1) Detection and characterization of MHW in the Western Mediterranean basin between 1995-2022 (2) Preparation of forcing scenarios with and without MHWs (3) Driving of the ecosystem model, run with three trophic assumptions (vulnerabilities) used for the parameterization of the predator–prey interactions (4) Calculation of the rate of change due to MHW of species' biomass and ecological indicators.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/9f6a26378d4106ffc0889efd.png"},{"id":101151818,"identity":"55ab4ef6-4b71-4cc6-9f6d-afae2717c88f","added_by":"auto","created_at":"2026-01-26 16:06:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1934849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/167e41e0-1187-43e5-b755-7393786bd7b8.pdf"},{"id":92591693,"identity":"31836928-139c-49ae-829a-35e1b317e440","added_by":"auto","created_at":"2025-10-01 12:02:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20728005,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7573187/v1/4e48341025ab41f849a9fa88.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Marine Heat Waves Are Transforming Western Mediterranean Marine Ecosystems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMarine heatwaves (MHWs) are discrete and prolonged periods of anomalously high sea temperatures that deviate significantly from historical seasonal means \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. These extreme events have attracted considerable scientific and public attention in recent years due to their dramatic consequences on marine ecosystems and their substantial socio-economic impacts \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Due to their abrupt nature, extreme events can have more severe impacts than those emanating from progressive global warming \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Given their disruptive potential, understanding how ecosystems respond to MHWs has become a pressing scientific and management challenge. However, the ability to manage marine ecosystems under these extreme events in a sustainable, effective and holistic manner is limited by the lack of information and analytical capabilities regarding the effect of MHWs on the ecosystem structure and functioning \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Mediterranean Sea is particularly vulnerable to MHWs. Over the past three decades, the region has experienced a sustained and rapid increase of sea surface temperature (SST) \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Concurrently, the frequency and intensity of MHWs have dramatically increased, with an unprecedented acceleration rate in recent years \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. These extreme thermal anomalies pose a substantial threat to the rich and diverse marine biodiversity of the Mediterranean Sea \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, as well as to the multiple ecosystem services it supports \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Local extinctions, reductions in the rate of natural carbon sequestration, loss of essential habitats, and decreased socioeconomic value due to reductions in potential catch are only a few of the reported effects of recent MHWs in the Mediterranean Sea (e.g. \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e). Such impacts compromise the long-term resilience of the ecosystem and erode its ability to provide services upon which human populations depend, cascading to the socioeconomic system \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough several studies have investigated species responses to MHWs in the Mediterranean (e.g. \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e), a holistic and integrated understanding of their impacts on the entire marine food web remains limited. Here, we used a food-web modelling approach encompassing the whole ecosystem instead rather than a single species to investigate the marine ecosystem response to past MHWs. Our approach enabled the quantification of spatial and temporal patterns in biomass change and the assessment of shifts in key ecological indicators, reflecting alterations in trophic dynamics and community composition driven by MHWs occurring on top of the long-term ocean warming and fishing activities. We focused on the Western Mediterranean Sea, considering thermal anomalies at multiple depths. Our study contributes to a more comprehensive understanding of how extreme ocean warming events affect complex marine ecosystems, and provides insight into potential vulnerabilities and pathways for resilience in the face of future climate extremes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMHWs in the Western Mediterranean over the last decades (1995\u0026ndash;2022)\u003c/h2\u003e\u003cp\u003eThe evolution of MHW spatial extent over the 27-year period (1995\u0026ndash;2022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) highlights important temporal changes. After the dramatic MHW of 2003, MHWs began to consistently affect more than 60% of the surface basin area. Over the past decade, this threshold was exceeded 12 times, indicating an increasing spatial extent of surface MHWs. In contrast, subsurface MHWs (i.e., those occurring at 150 m) and bottom MHW did not exceed a spatial extent of 20% before 2016. However, after 2016, subsurface and bottom MHWs began to cover more than 40% of the basin, suggesting a deeper penetration of heat anomalies in recent years.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe mean intensity of absolute temperatures associated with surface MHWs shows that on average the southern part of the basin remained consistently warmer than the northern region during MHWs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Indeed, during MHW events, averaged absolute surface temperatures in the south reached values around 24\u0026deg;C, whereas they generally remained below 20\u0026deg;C in the north. A similar latitudinal gradient is observed at the subsurface, where temperatures in the south averaged around 15\u0026deg;C, decreasing to approximately 13\u0026deg;C in the northern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Further MHWs characteristics are presented in supplementary material (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTemporal changes of the mean rate of change for the Western Mediterranean\u003c/h3\u003e\n\u003cp\u003eIn order to assess the effect on MHWs on the ecosystem of the Western Mediterranean, we conducted two simulations: one simulation forced with temperature time series including past MHWs and another without MHWs (see Methods). The simulation without MHWs was defined as a control simulation. Time series of biomass for each run (with and without MHWs) were computed for each, individual and aggregated modelled groups, by averaging biomass across the entire basin (Figure S2 and S3). The long-term trends of these time series were assessed for each simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, blue and yellow bars in right panels). They indicate, overall, long-term declines in biomass for most of the functional groups except for small phytoplankton. Overall, the inclusion of MHWs in the simulations resulted in a stronger negative trend across all aggregated groups. This is also observed when considering individual groups (Figure S4, S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEcosystem-wide changes in biomass induced by MHWs were assessed by computing the rate of change (RC) in biomass for each functional group represented in our food-web model through comparisons between both simulations (with and without MHWs, see Methods for more details). The RC reflects the direct effects resulting from the thermal stress induced by MHWs occurring on top of the long-term trends in temperature and fishing. It also captures the cascading impacts of these effects on the ecosystem resulting from ecological changes such as trophic interactions, species distributions and changes in metabolic rates.\u003c/p\u003e\u003cp\u003eThe time series of the spatially averaged RC underscore the diverse timing, duration, and intensity of ecological responses to MHWs across trophic levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). They reveal a clear inflection point in RC for all groups, marking the onset of MHWs effects. For most groups, this inflection point occurred around 2003\u0026ndash;2004 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThroughout the series, the RC oscillated between positive and negative values after the inflection points. Benthic producers stand out as the only group showing consistently negative values after their inflection point, suggesting a sustained negative impact of MHWs. However, by the end of the time series, all groups exhibited negative RC values, indicating a widespread decline in biomass due to the cumulative effect of historic MHWs (right panel in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, green bar).\u003c/p\u003e\u003cp\u003eWhen comparing the time series across all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we observe that higher trophic levels tended to respond more slowly to MHWs than lower trophic levels. Zooplankton and phytoplankton, for instance, responded on intra-annual timescales with the largest magnitudes in RC, while top predators exhibited much slower dynamics and, often responding over several years.\u003c/p\u003e\u003cp\u003eTime series of selected ecological and fisheries indicators (Figure S6) were used to compute their long-term trend and RC across the Western Mediterranean basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The majority of these indicators experienced a long-term decline as well, with the exception of TL community and Total Catch (blue and yellow bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The inclusion of MHWs amplified the long-term trends of commercial biomass, pelagic catch, fish catch, and the community trophic level (TL), whereas it weakened the trends for total catch, the mean trophic index (MTI), and Kempton\u0026rsquo;s Q diversity index.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilarly to the biomass RC time series, the indicators\u0026rsquo; RC time series experienced varying responses and most of them also showed a marked change after 2003\u0026ndash;2004. Afterwards, the majority of biomass and catch indicators experienced a period with increasing RC reaching positive values, peaking around 2008\u0026ndash;2010. However, after this period, the RC of the majority of these indicators began to decline, and by the last year of the simulation they presented consistently negative RC.\u003c/p\u003e\u003cp\u003eThe trophic level indicators, namely TLcatch (trophic level of the catch), TLcommunity (trophic level of the community, including all species) and MTI (mean trophic index), and the Kempton\u0026rsquo;s Q diversity index, all exhibited a neutral and stable RC at the beginning of the time series until 2003\u0026ndash;2004, after which their RC oscillates until the end of the period.\u003c/p\u003e\n\u003ch3\u003eSpatial changes of mean rate of change\u003c/h3\u003e\n\u003cp\u003eIn order to distinguish the different vertical and temporal behaviour of MHWs we selected 3 five-year periods with markedly different MHW patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea): 1995\u0026ndash;2000 (mainly surface MHWs not exceeding 40% of coverage), 2003\u0026ndash;2008 (mainly surface MHWs exceeding 40% of coverage) and 2017\u0026ndash;2022 (surface MHWs exceeding 60% of coverage and subsurface and bottom MHWs exceeding 40%) (see figures S7, S8 and S9 for more details on MHW behaviour during these periods). During the period of 1995\u0026ndash;2000, seabirds exhibited consistently negative RC values across the entire region. In contrast, most other groups showed either near-zero or positive RC values over the same period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). During the 2003\u0026ndash;2008 interval, a broader range of functional groups began to exhibit declines. Top predators, seabirds, elasmobranchs, fish, and cephalopods all showed predominantly negative RC values. In contrast, other invertebrates and small phytoplankton showed the highest positive RC values during this time. Notably, several groups (including crustaceans, other invertebrates, zooplankton, and large phytoplankton) exhibited a pronounced spatial dipole, with positive RC values in the northern part of the basin and negative values in the south. This north-south dipole became even more prominent during the 2017\u0026ndash;2022 period for nearly all functional groups. Maps of RC in biomass (figure S10) reveal the spatial heterogeneity of the RC and region-specific nature of ecosystem responses summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen considering spatial changes by periods averaged in each geographic subarea (GSA, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) we observed that the southern GSAs (Northern Alboran Sea, Southern Alboran Sea, and Algeria) suffered the most pronounced changes in RC over time. In these regions, the majority of functional groups displayed a clear shift from positive RC values during the 1995\u0026ndash;2000 period to significantly negative values by 2017\u0026ndash;2022. Notably, in the most recent period, top predators, seabirds, and elasmobranchs, the highest trophic levels, showed the strongest negative RC values, exceeding 5%. In contrast, the functional groups in northern GSAs (Gulf of Lion, Corsica, Eastern Sardinia, and the Southern and Central Tyrrhenian Sea) showed lower fluctuations in RC. During the 2017\u0026ndash;2022 period, many functional groups in these areas continued to exhibit positive or near-neutral RC values due to MHWs. The Balearic Islands and Western Sardinia GSAs showed a more moderate pattern, with relatively small fluctuations in RC across all periods for most groups. An exception is seabirds, which experienced a decrease larger than 5%. Notably, changes observed for the highest trophic levels were in general significant over the last period, with relatively small errors (Figure S11). When analysing spatial changes in indicators by periods and GSA (Figure S12 and S13), we observe the most prominent changes in the southern GSAs.\u003c/p\u003e\u003cp\u003eIn summary, the model indicates that the recent increase in MHW frequency, intensity, and depth of penetration over the past five years has tended to induce a contraction of the food web, characterized by larger negative RC in higher trophic levels in the southern part of the modelled region, particularly in the Alboran and Algerian seas. Commercial species were strongly affected, resulting in reductions of over 10% in total catch due to MHWs, including pelagic, demersal, fish, and invertebrate groups in these areas (Figure S12). Benthic producers showed the most consistent decline in RC over the basin and over time, with negative RC exceeding 15% along most of the western Mediterranean coastline (except in the northern Tunisian Sea) by the end of the simulation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a comprehensive modelling exercise aimed at understanding the impacts of MHWs over the past decades on the western Mediterranean ecosystem, occurring on top of long-term effects of climate change and fisheries. It offers a temporal and spatial assessment of changes in biomass and key ecosystem indicators resulting from the introduction of realistic MHW events in a marine ecosystem model. MHWs have significantly intensified in frequency, intensity, and vertical extent in recent years. The model suggests that as a consequence, MHWs have had an overall negative effect on the spatially averaged biomass on top of the effect induced by the long-term warming for most of the functional groups included in the ecosystem model.\u003c/p\u003e\u003cp\u003eIt is worth noting that the RC quantified by these simulations reflects the impacts attributable only to MHWs. These impacts can be direct, as MHWs act as thermal stressors on individual species, and indirect, as the resulting ecological changes (e.g., in species distributions, metabolic rates or trophic interactions) cascade through the food web. The gradual warming trend and the impact of fishing is implicitly accounted for in the baseline runs but their direct effects are not considered in the RC computation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Indeed, the RC quantified the change in biomass due to MHWs occurring on top of those due to long-term warming and fishing activities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe negative sign of the long-term trends and of RC in biomass averaged over the entire basin and over the last period is in accordance with extensive literature detailing observed negative effects in Mediterranean species, like seagrasses, corals and many other benthic invertebrates\u003csup\u003e12, [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Although MHW impacts on groups like marine mammals, seabirds, fish, elasmobranchs and crustaceans have not been widely reported in the Mediterranean, they are common in the global literature\u003csup\u003e[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The model results therefore provide insights on the responses of those functional groups for which MHW effects have not yet been clearly reported in the Mediterranean Sea specifically. They evidence the heterogeneous spatial responses obtained over the entire basin and over the entire period based on a detailed quantification of changes triggered by MHWs. In this sense, our study highlights how ecosystem models can serve as useful tools for informing decision-making processes and developing effective management that addresses the potential effects of climate change on marine ecosystems and fisheries.\u003c/p\u003e\u003cp\u003eWhen considering MHWs induced spatial patterns we observed the emergence of a pronounced spatial dipole in the RC of biomass of marine organisms and ecological indicators, with overall neutral or positive RC values in the northern part of the region due to the impact of MHWs and negative values in the south, especially at the end of the study period. This suggests that the southern region of the Mediterranean Sea is particularly vulnerable to MHWs, agreeing with findings that species at their warmer edges of their thermal range experience stronger negative responses to such events \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The model suggests a negative impact on biomass due to MHWs at high trophic levels in the southern part of the region; however, trophic-based indicators did not experience significant changes. This is consistent with modelled ecosystem results from the Northern California Current, where biomass shifts induced by MHWs did not lead to changes in the mean trophic level \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. As suggested by the authors, this may imply that the efficiency of the food web (defined as the rate of energy transfer to the production of new biomass as metabolic rates increase) is robust in the face of disruptions induced by MHWs.\u003c/p\u003e\u003cp\u003eThe dipole in RC may result from either increased species mortality and growth or species mobility. However, maps of RC computed for the individual species included in the model (not shown) revealed that the dipole pattern is present across the majority of species, including those with relatively low dispersal capacity. This suggests that the dipole is primarily driven by a net increase or decrease in total biomass, rather than by a redistribution of species within the basin. The spatial dipole could be attributed to the latitudinal gradient observed in the mean absolute temperatures during MHWs.\u003c/p\u003e\u003cp\u003eMaps of maximum temperatures reached during MHW events (Figure S9) further suggest that this meridional gradient in absolute temperature can temporarily (or permanently) collapse in the near future, since in extreme cases, the northern part of the domain reached peak temperatures similar to those typically observed in the south (on the order of 30\u0026deg;C). In this context, it is worth mentioning that the final year in our time series (2022) featured an exceptionally severe MHW (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Given that many marine species exhibit a delayed response to thermal stress (particularly those with long life cycles and higher up the trophic web) it is likely that the ecosystem changes driven by this extreme MHW were not fully captured in the model results because simulations ended in 2022. Future iterations of the study can be used to investigate the impacts of these recent MHW and develop future simulations following previous studies \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The higher intensity and larger extension of MHW in recent years may indicate larger ecosystem effects in recent times and to be seen in the future.\u003c/p\u003e\u003cp\u003eOne important limitation of our study lies in the model\u0026rsquo;s strong dependence on species-specific thermal performance curves. Currently, there is no scientific consensus on the precise shape of these curves or whether they vary across space and time for each species \u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Consequently, we acknowledge that our results may be conservative and that further empirical research is needed to increase the robustness of our findings. We recommend long-term monitoring of species population dynamics, along with targeted mesocosm experiments, to better characterize species\u0026rsquo; and population\u0026rsquo;s thermal tolerance and performance responses.\u003c/p\u003e\u003cp\u003eAdditionally, our model does not account for the potential adaptive mechanisms that species might employ in response to long-term environmental change. This omission is particularly relevant, as some species may be capable of adjusting physiologically or behaviourally to altered thermal regimes over time \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In addition, adaptations can be extremely local as suggested by a recent study \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, showing that sardines from the South of the Mediterranean have adapted differently compared to those of the North to environmental pressures. This issue deserves further investigation to provide a more comprehensive understanding of marine ecosystem resilience and vulnerability under climate stress.\u003c/p\u003e\u003cp\u003eOverall, this study offers insights into the impact that MHWs may pose to the ensemble of functional groups of the western Mediterranean Sea and informs on the most vulnerable areas, providing valuable information to develop pathways for resilience and future research priorities in a hotspot of marine biodiversity and climate change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eOcean reanalysis\u003c/h2\u003e\n \u003cp\u003eWe used daily means of 27 years (1993\u0026ndash;2022) of high-resolution (1/12\u0026deg;) global Mercator Ocean reanalysis (hereafter, GLORYS12) from Copernicus Marine Environment Monitoring Service (CMEMS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://marine.copernicus.eu/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. GLORYS12 reanalysis uses the reprocessed atmospheric forcing coming from the global atmospheric reanalysis ERA5. The model has 50 vertical levels with 22 levels in the upper 100 m leading to a vertical resolution of 1 m in the upper levels and 450 m at 5,000 m depth. The physical component of the model is the Nucleus for European Modelling of the Ocean platform (NEMO). The model assimilates observations using a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the background error and a 7-days assimilation cycle \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Along-track satellite altimetric data from CMEMS \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, satellite sea surface temperature from NOAA, sea-ice concentration, and in situ temperature and salinity vertical profiles from the latest CORA in situ databases \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e) are jointly assimilated. A 3D-VAR scheme provides an additional 3-D correction for the slowly evolving large-scale biases in temperature and salinity when enough observations are available \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The model has been extensively used in different ocean regions to study MHWs at the surface and at the subsurface \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMarine Heat Wave Detection\u003c/h3\u003e\n\u003cp\u003eFollowing the definition proposed by \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, we identified Marine Heatwaves as events during which ocean temperatures are anomalously warm, specifically exceeding the seasonally-varying 90th percentile for at least five consecutive days. This definition was applied using the freely available \u0026lsquo;m_mhw\u0026rsquo; MATLAB toolbox developed by \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Our study focuses on the period 1995\u0026ndash;2022 as that is when ecological information is available to fit the model, however MHWs were detected using the full time series (1993\u0026ndash;2022) as a baseline, with no removal of long-term trends. This approach allows for a more comprehensive assessment of the impacts of MHWs on the broader marine ecosystem. In addition to event detection, we also calculated key characteristics of the MHWs, including the mean absolute temperature reached during each event and the maximum absolute temperature observed. The difference between the mean temperature with and without MHWs was considered a proxy for the mean MHW-induced anomaly. The frequency of MHWs was defined as the number of events occurring per year. To evaluate the spatial extent of these events, we computed the percentage of area affected by MHWs for each month and at each depth level analyzed. The total area considered corresponds to the western Mediterranean which comprises 12 Geographical Subareas: Northern Spain, Gulf of Lion, Corsica, Ligurian and Northern Tyrrhenian Sea, Balearic Islands, Western Sardinia, Eastern Sardinia, Southern Central Tyrrhenian Sea, Southern Alboran Sea, northern Alboran Sea and Algeria.\u003c/p\u003e\n\u003ch3\u003eFood web modelling approach\u003c/h3\u003e\n\u003cp\u003eWe used the spatiotemporal food web model of the western Mediterranean marine ecosystem \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e developed with the Ecopath with Ecosim desktop software (EwE v6.7.0.19431 Beta on Windows 11, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ecopath.org\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. The temporal dynamic module, Ecosim, uses a previously defined Ecopath model to simulate changes in biomass, production, consumption and diets of species or functional groups over time. Ecospace, the spatially explicit dynamic module, applies the Ecosim module to the species in each cell of a grid of cells and accounts for species movement in or out of a cell depending on its suitability.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eWestern Mediterranean Ecosystem Model\u003c/h2\u003e\n \u003cp\u003eWe used the ecosystem model fitted run for the period 1995 to 2020. The model includes a total of 93 species which were aggregated into 11 groups: top predators, seabirds, elasmobranchs, fish, cephalopods, crustaceans, other invertebrates, zooplankton, benthic producers, large phytoplankton and small phytoplankton (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The top predators\u0026rsquo; group is a mixed category that includes high trophic level species for illustrative purposes, some of which are also included in the seabirds, elasmobranchs or fish groups. Each functional group was assigned a temperature layer of influence. The considered temperature layers were the surface (5 m), subsurface (150 m), or the bottom temperature (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), meant to represent the thermal conditions of the sea surface, the pelagic and the benthic realm, respectively. Layers were linked to functional groups according to their main ecological traits, including feeding grounds. To isolate the changes due to sea warming, we included a climatology spatial layer of the primary production and salinity that did not change with time. Functional groups, including primary production groups, changed due to spatial-temporal changes in temperature and food web interactions. The model also included temporal drivers of fishing effort from 1995 to 2020 as the original configuration \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eMechanistic link between sea water temperature and consumption rate\u003c/h2\u003e\n \u003cp\u003eTemperature is one of the most influential environmental factors affecting species physiology, primarily due to its central role in regulating metabolic processes and overall energy balance \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Most marine species have evolved to operate within a specific thermal window, in which physiological performance - such as growth, reproduction, and feeding - is optimized \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. When ocean temperatures exceed this optimal range, as often occurs during marine heatwaves, organisms may experience thermal stress that compromises key biological functions and reduces individual fitness \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eUnder suboptimal thermal conditions, metabolic efficiency is impaired, leading to reduced consumption rates, especially when ambient temperatures surpass species-specific physiological thresholds \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. These disruptions can have cascading effects on population dynamics, potentially causing short-term declines in biomass and altering community structure, species interactions, and food web stability \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eTo incorporate species-specific responses to thermal variability into our model, we implemented thermal performance curves as environmental response functions using the habitat foraging capacity model \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. These thermal performance curves describe how each species or functional group\u0026apos;s consumption rate varies with temperature and were obtained from AquaMaps \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Consumption peaks at the species\u0026rsquo; optimal temperature, reflecting maximum physiological efficiency, and declines progressively as temperatures deviate above or below this optimum. This approach allows for a realistic representation of both the beneficial and detrimental effects of temperature variability within a spatial-temporal food web approach.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eMHWs Simulations\u003c/h2\u003e\n \u003cp\u003eWe conducted two simulations in order to calculate the rate of change in species\u0026apos; biomass caused by MHWs (Fig.\u0026nbsp;5.1). First, the simulation referred to as S, where the model was run using temperature time series from GLORYS12 as the spatial-temporal environmental driver. Temperature time series from three levels, the surface, subsurface (150m), and the bottom, were used. Then, the \u0026quot;control\u0026quot; simulation was conducted using temperature time series from GLORYS12 at the same depths, with MHWs removed. MHWs were eliminated from each temperature time series by replacing the temperature values during MHW events with climatological values (Fig.\u0026nbsp;5.2). For each case, the Ecospace model was run under three trophic assumptions: vulnerability\u0026thinsp;=\u0026thinsp;2, the vulnerability obtained from the model fitting procedure to minimize differences between predicted and observed historical time series of data (v\u0026thinsp;=\u0026thinsp;fitting), and vulnerability\u0026thinsp;=\u0026thinsp;10 (Fig.\u0026nbsp;5.3), which were used to compute uncertainty (see next section).\u003c/p\u003e\n \u003cp\u003eThe model outputs were then used to compute long-term trends in biomass and to calculate the rate of change in biomass (RC) for each species due to the occurrence of MHWs (Fig.\u0026nbsp;5.4). This was achieved by comparing the biomass of the S run with respect to the biomass of the control run. The calculation of RC is defined as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:RC=\\frac{Biomass\\left(S\\right)-Biomass\\left(control\\right))}{Biomass\\left(control\\right)}*100$$\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eEcological indicators\u003c/h2\u003e\n \u003cp\u003eTo describe the effect of MHWs on the environmental status of the ecosystem, we used the ECOIND plugin in \u003cem\u003eEcopath with Ecosim\u003c/em\u003e, which calculates standardized ecological indicators from the biomass and catch outputs of \u003cem\u003eEwE\u003c/em\u003e food web models \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. We selected key biomass-, catch- and trophic-based ecological indicators which give information of the status of a marine ecosystem and biodiversity in relation to impacts of fisheries and climate change. We used the following ecological and catch indicators, further detailed in \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eBiomass-based indicators, which depend on species abundance in the food web: commercial species biomass and Kempton\u0026rsquo;s Q Biodiversity Index, an ecosystem diversity index commonly used in marine ecosystems with emphasis on median abundance species \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. A decrease in the Kempton\u0026rsquo;s Q index reflects a decline in functional group diversity in the ecosystem represented in the model.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCatch-based indicators, based on the removal of species from the ecosystem due to fisheries activity: total catch, demersal species catch, pelagic species catch, fish catch (all fish species) and invertebrate species catch. These represent the extraction of organisms from the ecosystem due to fishing activities, and are therefore fisheries-dependent metrics, but they can still be used as an estimate of the abundance of these groups of organisms \u003csup\u003e[53, 56]\u003c/sup\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTrophic-based indicators, based on the concept of trophic level (TL) which positions a species in the food web depending on its sources of energy \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e: Trophic Level of the catch (TLcatch) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e, Trophic Level of all the organisms in the community (TLcommunity) and Marine Trophic Index (MTI), which is the mean TL of all the organisms with TL\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e3.25 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. These indicators give an estimation of whether an ecosystem is more dominated by top predators (higher TL) or primary producers (lower TL), therefore a decrease in top predators or an increase in lower TL organisms will result in a lower mean trophic level of the community \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eFor each of the indicators we computed the long-term trend and the RC using the same methodology described above for the biomass results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eUncertainty for species biomass and indicators and statistical tests\u003c/h2\u003e\n \u003cp\u003eTo quantify the uncertainty in the long-term trend and the RC, the model was run using different values of the vulnerability parameter \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. In the model, this parameter represents the predation mortality experienced by prey; higher values indicate stronger trophic control by predators. The simulation presented in this study corresponds to the run using vulnerability values derived from model fitting, i.e., estimated by calibrating the model to observed time series of biological or fisheries indicators for one or more functional groups. To assess the sensitivity of the results, additional simulations were performed with fixed vulnerability values set to 2 and 10. Uncertainty was then quantified by calculating the standard deviation of the biomass long-term trend and RC across these three simulations.\u003c/p\u003e\n \u003cp\u003eIn addition to uncertainty analysis, we tested whether the simulated biomass and indicator time series significantly differed from the control simulation in terms of their mean RC values. This was done using Student\u0026rsquo;s t-test with a 95% confidence level. Furthermore, to examine whether changes were particularly pronounced during specific time periods, we compared the mean biomass and indicator values during 1995\u0026ndash;2000, 2003\u0026ndash;2008, and 2017\u0026ndash;2022 to the mean over the entire study period (1995\u0026ndash;2022) using the same statistical test.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements and Funding Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a contribution to the Spanish funded ProOceans (Ministerio de Ciencia e Innovaci\u0026oacute;n, Proyectos de I+D+I, RETOS-PID2020-118097RB-I00) and GES4SEAS (European Union\u0026apos;s Horizon 2020 research under grant agreement no. 101059877) projects. CA acknowledges institutional support from the Institut de Recherche pour le D\u0026eacute;veloppement (IRD) and LOCEAN. AK was supported by an FPU grant from the Spanish Ministry of Science, Innovation and Universities (FPU22/02668) and by the predoctoral research mobility scholarship granted by the Embassy of France in Spain and the Institut Fran\u0026ccedil;ais of Spain. AK, MC, FR, MO acknowledge institutional support of the \u0026lsquo;Severo Ochoa Centre of Excellence\u0026rsquo; accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlorys12 reanalysis is available at http://marine.copernicus.eu/. Ecopath with Ecosim desktop software at EwE v6.7.0.19431 Beta on Windows 11, https://ecopath.org . Model outputs from the ecosystem model will be made available on the CSIC Digital platform upon acceptance of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.A. and A.K. contributed equally. They were involved in conceptualisation, methodology, investigation, formal analysis, visualization and writing original draft. M.C. and J.S. run the ecological models. M.C., F.R., M.O. and J.S. involved in conceptualisation, methodology, formal analysis, review and editing. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHobday, A. J et al., A hierarchical approach to defining marine heatwaves. \u003cem\u003eProgress in Oceanography\u003c/em\u003e, 141, 227\u0026ndash;238. (2016).\u003c/li\u003e\n \u003cli\u003eSmith, K. 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W., et al., Calibrating ecosystem models to support marine Ecosystem-based Management. \u003cem\u003eICES Journal of Marine Science\u003c/em\u003e 81(2): 260-275 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7573187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7573187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated ecosystem impacts of marine heatwaves (MHWs) in the Western Mediterranean Sea using a spatially explicit food-web model. Our simulations suggest that declining biomass trends observed over the past decades in the Western Mediterranean driven by long-term ocean warming and fishing pressure have been exacerbated by the occurrence of MHWs. A north\u0026ndash;south dipole in biomass rate of change emerged in response to MHWs: while the northern region displayed positive or neutral responses due to MHWs, the south, particularly the Alboran and Algerian seas, experienced negative impacts. Benthic producers and commercially important species (pelagic and demersal fish and invertebrates) were particularly affected, leading to catch reductions exceeding 10%. We identify previously unreported vulnerable groups and regions, supporting the use of ecosystem models in guiding adaptive management for the future. The increase in intensity and extension of MHW with time suggests that larger effects may be expected in the future.\u003c/p\u003e","manuscriptTitle":"Marine Heat Waves Are Transforming Western Mediterranean Marine Ecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 12:02:39","doi":"10.21203/rs.3.rs-7573187/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-24T07:40:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T21:17:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T10:03:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T08:52:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T07:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68685684178802672905068328557664581393","date":"2025-09-26T08:04:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272301852485296069463902950312428389970","date":"2025-09-25T10:48:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94744957955919049556826616503178321959","date":"2025-09-22T19:02:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321603614651723191076280606543765136789","date":"2025-09-22T08:24:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-20T10:21:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-17T12:10:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T07:11:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T12:11:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-09T11:08:14+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":"95a94eb3-4c04-4d6e-b3a6-a9cf53a8efac","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55570877,"name":"Earth and environmental sciences/Climate sciences"},{"id":55570878,"name":"Biological sciences/Ecology"},{"id":55570879,"name":"Earth and environmental sciences/Ecology"},{"id":55570880,"name":"Earth and environmental sciences/Environmental sciences"},{"id":55570881,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-01-26T16:02:17+00:00","versionOfRecord":{"articleIdentity":"rs-7573187","link":"https://doi.org/10.1038/s41598-026-35813-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-20 15:58:59","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-10-01 12:02:39","video":"","vorDoi":"10.1038/s41598-026-35813-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35813-x","workflowStages":[]},"version":"v1","identity":"rs-7573187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7573187","identity":"rs-7573187","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}