Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea

5708 | Glob Change Biol. 2022;28:5708–5725.wileyonlinelibrary.com/journal/gcb Received: 18 February 2022 | Revised: 10 May 2022 | Accepted: 15 May 2022 DOI: 10.1111/gcb.16301 RESEARCH ARTICLE Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea Joaquim Garrabou1,2 | Daniel Gómez- Gras1,3 | Alba Medrano3 | Carlo Cerrano4,5 | Massimo Ponti6,7 | Robert Schlegel8 | Nathaniel Bensoussan1,2 | Eva Turicchia6,7 | Maria Sini9 | Vasilis Gerovasileiou10,11 | Nuria Teixido8,12 | Alice Mirasole12 | Laura Tamburello12 | Emma Cebrian13 | Gil Rilov14 | Jean- Baptiste Ledoux1,15 | Jamila Ben Souissi16,17 | Faten Khamassi16 | Raouia Ghanem17 | Mouloud Benabdi18 | Samir Grimes19 | Oscar Ocaña20 | Hocein Bazairi21 | Bernat Hereu3 | Cristina Linares3 | Diego Kurt Kersting3 | Graciel la Rovira3 | Júlia Ortega3 | David Casals3 | Marta Pagès- Escolà3 | Núria Margarit3 | Pol Capdevila22 | Jana Verdura23 | Alfonso Ramos24 | Andres Izquierdo25 | Carmen Barbera24 | Esther Rubio- Portillo26 | Irene Anton25 | Paula López- Sendino1 | David Díaz27 | Maite Vázquez- Luis27 | Carlos Duarte28,29 | Nuria Marbà29 | Eneko Aspillaga29 | Free Espinosa30 | Daniele Grech31 | Ivan Guala31 | Ernesto Azzurro12,32 | Simone Farina33 | Maria Cristina Gambi34 | Giovanni Chimienti7,35 | Monica Montefalcone36 | Annalisa Azzola36 | Torcuato Pulido Mantas4 | Simonetta Fraschetti7,37 | Giulia Ceccherelli38 | Silvija Kipson39,40 | Tatjana Bakran- Petricioli40 | Donat Petricioli41 | Carlos Jimenez42,43 | Stelios Katsanevakis9 | Inci Tuney Kizilkaya44 | Zafer Kizilkaya45 | Stephane Sartoretto46 | Rouanet Elodie47 | Sandrine Ruitton2 | Steeve Comeau8 | Jean- Pierre Gattuso8,48 | Jean- Georges Harmelin47 1Institut de Ciències del Mar- CSIC, Barcelona, Spain 2Université de Toulon, CNRS, IRD, MIO, Aix Marseille Univ, Marseille, France 3Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Institut de Recerca de la Biodiversitat (IRBIO), Universitat de Barcelona, Barcelona, Spain 4Dept of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy 5Fano Marine Centre, Fano, Italy 6Department of Biological, Geological and Environmental Sciences, University of Bologna, Ravenna, Italy 7CoNISMa, Rome, Italy 8Laboratoire d'Océanographie de Villefranche, Sorbonne, Université, CNRS, Villefranchesur- mer, France 9Department of Marine Sciences, University of the Aegean, Mytilene, Greece 10Department of Environment, Faculty of Environment, Ionian University, Zakynthos, Greece 11Hellenic Centre for Marine Research (HCMR), Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Heraklion, Greece 12Stazione Zoologica Anton Dohrn, Department of Integrative Marine Ecology, Ischia Marine Centre, Naples, Italy This is an open access article under the terms of the Creative Commons Attribution- NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd. | 5709 GARRABOU et Al. 13Centre d'Estudis Avançats de Blanes (CEABCSIC), Girona, Spain 14National Institute of Oceanography, Israel Oceanographic and Limnological Research (IOLR), Haifa, Israel 15CIIMAR- Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Matosinhos, Portugal 16National Agronomic Institute of Tunisia, Tunis University of Carthage, Tunis, Tunisia 17Biodiversity, Biotechnology and Climate Change Laboratory- LR11ES09, University of Tunis El Manar, Tunis, Tunisia 18Abyss Environmental Services, Algiers, Algeria 19Ecole Nationale Supérieure des Sciences de la Mer et de l'Aménagement (ENSSMAL), Alger, Algeria 20MMC (Museo del Mar de Ceuta), Ceuta, Spain 21Laboratory 'Biodiversity, Ecology and Genome', Faculty of Sciences, Mohamed V University in Rabat, Rabat, Morocco 22School of Biological Sciences, University of Bristol, Bristol, UK 23Université Côte d'Azur, CNRS, Nice, France 24Departamento de Ciencias del Mar y Biología Aplicada, Universidad de Alicante, Alicante, Spain 25CIMAR— Universidad de Alicante, Alicante, Spain 26Dpt. Physiology, Genetics and Microbiology, Alicante, Spain 27Centro Oceanográfico de Baleares (IEOCSIC), Palma de Mallorca, Spain 28Red Sea Research Center, King Abudllah University of Science and Technology, Thuwal, Saudi Arabia 29Institut Mediterrani d'Estudis Avançats, Mallorca, Spain 30Laboratorio de Biología Marina, Universidad de Sevilla, Sevilla, Spain 31IMC— International Marine Centre, Oristano, Italy 32CNR- IRBIM, NRIRBIM, National Research Council, Institute of Biological Resources and Marine Biotechnologies, Ancona, Italy 33Stazione Zoologica Anton Dohrn, Deptartment of Integrative Marine Ecology, Genoa Marine Centre, Genoa, Italy 34National Institute of Oceanography and Applied Geophysics, OGS, Trieste, Italy 35Department of Biology, University of Bari Aldo Moro, Bari, Italy 36Department of Earth, Environment and Life Sciences, University of Genoa, Genoa, Italy 37Department of Biology, University of Naples Federico II, Naples, Italy 38Dipartimento di Chimica e Farmacia, University of Sassari, Sassari, Italy 39SEAFAN, Zagreb, Croatia 40Faculty of Science, Department of Biology, University of Zagreb, Zagreb, Croatia 41D.I.I.V. Ltd for Marine, Freshwater and Subterranean Ecology, Sali, Croatia 42Enalia Physis Environmental Research Centre, Nicosia, Cyprus 43The Cyprus Institute Energy Environment and Water Research Center, Aglantzia, Cyprus 44Department of Biology, Faculty of Science, Ege University, Izmir, Turkey 45Mediterranean Conservation Society, Izmir, Turkey 46IFREMER, Brest, France 47GIS Posidonie, OCEANOMED— MIO— Case 901, Marseille, France 48Institute for Sustainable Development and International Relations. Sciences Po, Paris, France Abstract Climate change is causing an increase in the frequency and intensity of marine heatwaves (MHWs) and mass mortality events (MMEs) of marine organisms are one of their main ecological impacts. Here, we show that during the 2015– 2019 period, the Mediterranean Sea has experienced exceptional thermal conditions resulting in the onset of five consecutive years of widespread MMEs across the basin. These MMEs affected thousands of kilometers of coastline from the surface to 45 m, across a range of marine habitats and taxa (50 taxa across 8 phyla). Significant relationships were found between the incidence of MMEs and the heat exposure associated with MHWs observed both at the surface and across depths. Our findings reveal that the Mediterranean Sea is experiencing an acceleration of the ecological impacts of MHWs which poses an unprecedented threat to its ecosystems' health and functioning. Correspondence Joaquim Garrabou, Institut de Ciències del Mar- CSIC, Passeig Marítim de la Barceloneta 37- 49, 08003 Barcelona, Spain. Email: [email protected] Funding information French National Research Agency, Grant/ Award Number: ANR- 17- MPGA- 0001; H2020 Environment, Grant/Award Number: SEP- 210597628; Interreg Med, Grant/Award Number: 1MED15_3.2_M2_337; Marie Sklodowska- Curie, Grant/Award Number: 801370; Beatriu de Pinós funded by the Secretary of Universities and Research (Government of Catalonia); Ministerio de Ciencia, Innovación y Universidades, 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 5710 | GARRABOU et Al. 1 | INTRODUCTION Anthropogenic climate change has become a major environmental driver, strongly affecting marine biodiversity and ecosystem functioning, often interacting with other humaninduced stressors (Gissi et al., 2021; Smale et al., 2019). Besides gradual ocean warming, acidification, deoxygenation, and sealevel rise (IPCC, 2019), human activities have caused a rapid increase in the frequency and intensity of extreme climatic events, such as marine heatwaves (MHWs; Collins et al., 2019; Darmaraki, Somot, Sevault, & Nabat, 2019; Oliver et al., 2018). MHWs are discrete periods of prolonged anomalously warm water at a particular location (Hobday et al., 2016). In the last 20 years, MHWs have globally doubled in frequency and have become longerlasting, more intense and more extensive (Collins et al., 2019). This increase in the frequency of MHWs has been associated with the rapid increase in mass mortality events (MMEs) worldwide (Garrabou et al., 2009; Hughes et al., 2017; Thomson et al., 2015; Wernberg et al., 2016). Understanding the emergent properties and dynamics of these recurrent climatic disturbances is critical for predicting spatial refuges and cumulative ecological responses, and for managing longterm impacts on ecosystems and the provision of services they provide (Hughes et al., 2021; Smith et al., 2021). The Mediterranean Sea is generally considered a relevant model to assess the ecological effects of climate change on marine biodiversity and to test potential adaptation and mitigation strategies that may be scaled up (Cramer et al., 2018). Although the Mediterranean Sea represents only 0.32% of the total volume of the oceans, its unique geomorphological history led to a markedly high level of biodiversity with 7%– 10% of all known marine species and a large proportion of endemic species (Bianchi & Morri, 2000; Coll et al., 2010). While barely recognized by society at large, marine MMEs are one of the most predominant biological impacts of climate change in the Mediterranean Sea (Cramer et al., 2018; Marbà et al., 2015; Rivetti et al., 2014). Indeed, Mediterranean MHWs have triggered unprecedented climatedriven MMEs during the last decades and their occurrence is expected to increase in the coming decades (Darmaraki, Somot, Sevault, Nabat, Cabos- Navaez, et al., 2019; Garrabou et al., 2019). The most dramatic reported events in terms of geographic extent and number of Grant/Award Number: RTI2018- 095441- B- C21; Spanish Ministries of Economy and Competitiveness, Grant/ Award Number: CGL2015- 71809- P and CTM2012- 32603; Greenpeace Italy; National Geographic Society, Grant/ Award Number: EC- 176R- 18; Ente Parco Nazionale del Gargano; Italian Ministry of Education, University and Research, Grant/Award Number: AIM 1807508– 1 and PON 2014– 2020; Tropical Signals Program of CIESM; Fondation Albert 2 Monaco (MIMOSA Project); MITERD, Grant/Award Number: CTM2016- 77027- R; Departament de Territori i Sostenibilitat of the Generalitat de Catalunya; FCT— Foundation for Science and Technology, Grant/ Award Number: UIDP/04423/2020 and UIDB/04423/2020; FCT/ MCTES (PIDDAC); European Regional Development Fund (ERDF); RD Unit, Grant/Award Number: UID/ Multi/04423/2019; Regione Autonoma Sardegna, Grant/Award Number: CUP 87G17000070002; Public Investments Programme (PIP) of the Hellenic Republic; EEA GRANTS; MARISCA Project; European Union (European Regional Development Fund, Cohesion Fund); Hellenic Government (Ministry of Development and Investments); CIESM “Tropical Signals,” Stelios Katsanevakis; European Commission; MAVA Fondation; Israel Ministry of Environmental Protection; Stazione Zoologica Anton Dohrn; MCIU/AEI/FEDER, Grant/Award Number: RTI2018- 095346- B- I00; Severo Ochoa Centre of Excellence, Grant/ Award Number: CEX2019- 000928- S; Euromarine Overall, we show that increasing the resolution of empirical observation is critical to enhancing our ability to more effectively understand and manage the consequences of climate change. KEYWORDS climate change, coralligenous habitats, foundation species, habitatforming species, impact assessment, marine conservation, marine heatwaves, temperate reefs 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5711 GARRABOU et Al. affected species occurred in 1999 and 2003 along the Northwestern Mediterranean Sea (Cerrano et al., 2000; Garrabou et al., 2009; Perez et al., 2000). These two events affected more than 40 species from various taxa (e.g., Porifera, Cnidaria, Bivalvia, Bryozoa, Ascidiacea) across thousands of kilometers of coastline. The vast majority of studies on MMEs in the Mediterranean have focused on foundation species, such as habitatforming gorgonians and seaweeds (Chimienti et al., 2021; Garrabou et al., 2019; Verdura et al., 2021). Some species associated with MMEs have exhibited mortality rates of up to 80% of the studied populations (Cerrano et al., 2000; Garrabou et al., 2009) with major consequences for the structure and functioning of benthic ecosystems (Gómez- Gras et al., 2021; Verdura et al., 2019). Yet, MMEs occurring within other Mediterranean populations and communities remains poorly understood, offering an incomplete picture of ecosystemwide impacts. Likewise, MMEs were mainly documented in the western Mediterranean Sea (Garrabou et al., 2019). However, given that the eastern basin is warming faster (Pisano et al., 2020), our knowledge may be biased from the extensive and longterm sampling efforts in the western compared to the eastern basin. Indeed, the southeastern basin is the trailing edge for many native Mediterranean species, including molluscs and sea urchins, which have collapsed in the past decades (Albano et al., 2021; Rilov, 2016; Yeruham et al., 2015). Linking the timing and dynamics of these collapses to gradual ocean warming or to past, undocumented MHWs is hampered by a lack of historical baselines. Since the observation of the 1999 and 2003 MMEs, different research teams have been monitoring climatedriven mortalities across the Mediterranean basin. Given the apparent increase of mortality events and concomitant extreme heat conditions during the 2015– 2019 period, we sought to investigate patterns and trends in MHWs and MMEs over temporal, spatial, and depth scales. Specifically, we characterize the exceptional Mediterranean wide thermal conditions recorded between 2015 and 2019 using highresolution, satellitederived sea surface temperature (SST) and subsurface coastal seawater temperature data (from 5 to 40 m depth). Then, we quantify the occurrence of MMEs during this period using observational data across the Mediterranean basin and explore potential relationship with MHW activity. By assessing and integrating sea surface and in situ temperature data with mass mortality records across the basin, our study provides the most up- to- date account of the impacts of extreme warming events on Mediterranean marine organisms and ecosystems. 2 | MATERIAL AND METHODS 2.1 | Characterizing 2015– 2019 temperature conditions 2.1.1 | Temperature data sets Satellitederived SST across the Mediterranean Sea was obtained from CMEMS (https://resou rces.marine.coper nicus.eu/?optio n= com_csw&view=d e t a i l s & p r o d u c t _ i d =SST_MED_SST_L4_REP_ O B S E R V A T I O N S _ 0 1 0 _ 0 2 1 ). The data consist of daily (nighttime), gap free, optimally interpolated foundation SST at ~4 km resolution from AVHRR (Advanced Very- High- Resolution Radiometer) with improved accuracy and stability over the 1982– 2019 period (Pisano et al., 2016, 2020). The global ecoregion analyses were performed with the NOAA OISST daily 25 km resolution product (Banzon et al., 2016, 2020; Reynolds et al., 2007). This optimally interpolated remotely sensed product is a gap free level 4 operational product with records from late 1981 to a 1- or 2- day lag from the current date. Subsurface coastal seawater temperature data were obtained from the T- MEDNet temperature database. T- MEDNet is a longterm collaborative initiative devoted to building a pan- Mediterranean observation network on climate change effects in marine coastal ecosystems (www.tmednet.org). The T- MEDNet temperature database consists of highfrequency (hourly) time series obtained using HOBO data loggers (accuracy ±0.21°C) setup at standard depths along rocky walls by divers, generally every 5 m from the surface to 40 m depth. To characterize the 2015– 2019 thermal conditions across depths, from the 75 sites currently in the network we retained seven sites that had time series over the full depth range with a minimum length of 10 years of available data including the 2015– 2019 period. The selected sites were located in the Western Mediterranean ecoregion (sensu Spalding et al., 2007), and within this area the sites where located in the European coasts, namely in the Columbretes islands Natural Park (Spain), El Montgrí, Les Illes Medes I El Baix Ter Natural Park (Spain), northern and southern areas of the Cap De Creus Natural Park (Spain), Calanques National Park (France), Port- Cros National Park (France) and Scandola Natural Reserve (France). From the hourly data series, daily average seawater temperature was calculated. Missing data surrounded by shallower and deeper observations were interpolated vertically using linear interpolation. The obtained daily data series presented high temporal coverage for both the 2015– 2019 period (97.0 ± 5.3%, mean ± SD) and 2010– 2019 period (95.4 ± 6.6%, mean ± SD). Overall, the in situ seawater temperature observations span a range of hydroclimatic conditions and seasonal temperature stratification patterns (Figure S1; Bensoussan et al., 2010, 2019). 2.1.2 | Temperature data analysis The temperature analysis for each year focused on the warm hydrological period when thermal maxima were observed, defined hereafter as the period from June 1 to November 30 [JJASON]. This includes the period of maximum heat for the near surface (usually in July or August) and for the subsurface coastal ecosystems (after thermocline deepening in Fall, Figure S1). Individual SST analyses were conducted per pixel. Statistics at the ecoregion and Mediterranean basin levels were obtained by averaging the per pixel statistics within the given region. Spatial SST analysis was conducted both at Mediterranean basin and ecoregion levels. For the ecoregion level, to cope with north– south heterogenous temperature conditions, the vast Western Mediterranean was 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 5712 | GARRABOU et Al. subdivided in two at the 39°N parallel limit. Hence, spatial analysis of SST was conducted on a total of eight marine ecoregions as follows: Alboran, Northwestern Mediterranean, Adriatic, Ionian, Aegean, Levantine, Tunisian, and southwestern Mediterranean (Figure 1). The seasonally varying climatology for each SST pixel was determined by first finding the average daily temperatures from 1982 to 2011 and applying a moving average with an 11- day window to them. A second pass of smoothing was then given using a 31- day window. The same method was used to create the 90th percentile thresholds that were used to detect MHWs. MHWs, and their categories, were detected in this study using the standard methodology outlined in Hobday et al. (2016, 2018) and implemented in R (R Core Team, 2021; Schlegel & Smit, 2018). Whenever SST values exceeded the 90th percentile threshold determined for each pixel for five or more continuous days over the JJASON period per year this was flagged as an MHW. The duration of a MHW is the count of the number of days from the beginning to the end of the event. The annual duration values were determined by adding up the duration of all MHWs over the JJASON period. The annual chronic heat stress of organisms during the JJASON period was quantified as the number of MHW days a site was exposed to. To provide a general quantitative understanding of the acute heat stress experienced by many different taxa the categories of MHWs were used. These are determined by the number of times the temperature anomaly is in excess of the distance between the seasonally expected temperature and the 90th percentile threshold. A weak positive anomaly will be flagged as a category I “Moderate” event. Whereas an anomaly twice as high as the aforementioned distance will be II “Strong,” three times is III “Severe,” and four times or greater is IV “Extreme.” The MHW duration and category values used for the global comparisons were extracted from the MHW Tracker (http:// www.marin eheat waves.org/track er.html; Schlegel 2020; Hobday et al., 2016, 2018). To determine how rapidly the surface of the Mediterranean Sea may be warming, the average SST values from the first 5 years of the satellite period (1982– 1986) were subtracted from the average values over the study period (2015– 2019). These differences were calculated for the Mediterranean Basin, per ecoregion, and per pixel. Similar 5- year average comparisons were performed for the annual average MHW days detected. Because the presence of moderate MHWs are relatively common, both in the Mediterranean basin and globally, a metric for the change in the category of MHWs over time required more nuance. It was decided that because category I “Moderate” MHWs are consistently present throughout the satellite record, they would not be included in comparisons of the first and last 5- year periods. To determine how much the occurrence of MHWs increased over the surface of the Mediterranean Sea, the highest category event per pixel in the first 5- year period (1982– 1986) was checked against the highest category event in the current study period (2015– 2019). If the highest category of MHW in the study period was not in excess of the highest category of MHW in the first 5- year period, then it was considered that the intensity of MHWs on that pixel had not increased. There were no pixels in which the category in the first 5- year period was greater than the current period. The MHW days and categories experienced per year (not only the JJASON period) in the Mediterranean were compared against the global averages by extracting these statistics from the MHW Tracker. Considering the strong vertical temperature gradients across a few tens of meters during the seasonal stratification in the nearshore monitoring areas (Figure S1), we further investigated how the warming conditions at the sea surface are being transmitted to the subsurface layers taking advantage of highresolution in situ temperature series for decadal trends and MHW analysis. From the seven sites retained for analysis, the colocated satellite SST data were retrieved to allow their comparison with the in situ data. The decadal [2010– 2019] warming trend was calculated from the linear regression of monthly anomalies versus time. As for the SST, MHWs were detected using the standard methodology outlined in Hobday et al. (2016) and implemented in Matlab (Zhao & Marin, 2019) using the 2010– 2019 period for climatology and thresholds calculation. As above, but focusing on the years 2015– 2019, the annual duration values were determined by summing the duration of all MHW days over the JJASON period. To determine the warming and MHW patterns across the vertical dimension, the statistics for the seven sites were pooled for the calculation of depth average and standard error values. 2.2 | Assessment of MMEs during 2015– 2019 across the Mediterranean 2.2.1 | Field surveys Data were obtained through benthic surveys conducted by 33 research teams from 11 Mediterranean countries. Surveys covered thousands of kilometers of coastline, spanning 13° of latitude (32°S to 45°N) and 40° of longitude (−5° W to 35° E) in the Mediterranean Sea. The data set represents the most comprehensive inventory of MME records for benthic species between 2015 and 2019 in the region. This database, as with the depth temperature monitoring, were undertaken as part of the T- MEDNet initiative (www.tmednet.org). The surveys were conducted in 142 monitoring areas. Monitoring areas were considered as geographic areas (10– 25 km coastline, for example, a marine protected area and the nearby coast) sharing common environmental features. The data set provides quantitative information to assess the patterns of MMEs regarding the following: (i) species and habitats; (ii) geographic and temporal patterns; and (iii) depth distribution (0– 45 m depth; Garrabou et al., 2022). 2.2.2 | Quantification of MMEs Mass mortality records were obtained from quantitative benthic surveys assessing the mortality impact of local populations at specific times. Here, we consider a local population as a group of colonies/individuals or cover of the same species (ranging from tens to 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5713 GARRABOU et Al. thousands colonies/individuals depending on the species) dwelling in a specific geographic location defined by spatial coordinates and depth range. Note that from the same geographic location we can obtain several mass mortality records obtained from surveys on different species and/or for the same species at different depth ranges and years. The surveys quantified the percentage of affected colonies/ individuals for each species either directly in the field or from photographic/video sampling. In this study, we considered affected colonies/individuals displaying signs of recent mortality such as denuded skeletons in gorgonians, sponges, and scleractinian corals and empty valves in bivalves attached to the substratum. The observations on mortality across taxonomic groups may depend on the timing of the surveys and the duration of the specimens' structures (e.g., soft vs. hard skeletons) attached to the substratum after their death. In the case of clonal species (e.g., gorgonians, bryozoans, sponges; Figure 2; Video S1), we considered an individual/colony to be affected by mortality when it showed recent tissue necrosis over 10% of its surface (Garrabou et al., 2009, 2019). In the case of Posidonia oceanica and habitat forming macroalgal species, increase in shoot mortality or a sharp decline on thallus densities, respectively, served as indicators (Marbà & Duarte, 2010; Verdura et al., 2021). In the case of mussel beds, decline in cover was considered. Depending on the abundance of the surveyed species/taxa in the monitored locations, the total number of surveyed colonies/individuals ranged from 10 for the less abundant (e.g., sponges) to hundreds of colonies/individuals for the most abundant (e.g., gorgonians), or in the case of mussels to 100% cover of the rock. 2.2.3 | MME severity Four severity classes were considered based on the values of the percentage of affected colonies/individuals reported in each survey: No mortality (<10%), Low (≥10, <30%), Moderate (≥30, <60%), and Severe (≥60%) MME. The same categories were applied for species for which we monitored decline of cover (e.g., mussels). 2.2.4 | Species and habitats affected MMEs was assessed by field surveys mostly on species already affected in previous climatedriven mortality events in the Mediterranean Sea (Cerrano et al., 2000; Garrabou et al., 2009, 2019). These species are mainly easily identifiable macrobenthic species, and often abundant at the surveyed locations. The majority of surveys identified benthic taxa (n = 50) to the species (90%) and genus levels (6%) while 4% were identified at level of broad taxonomic group (e.g., calcareous algae; Table S1). Each MME record was assigned to three broad habitat categories, namely hard substrates, mobile substrates, and seagrasses, which represent the three principal aspects of the coastal Mediterranean seascape. Hard substrates refer to all types of natural or artificial rigid surfaces, mainly including rocks of varying physiognomic characteristics in terms of geological origin, inclination, and biotic features. Based on their overall structure and degree of exposure to natural or human disturbances, hard substrates were further subdivided into three distinct habitat subcategories: (a) the upper sublittoral zone, which is overall more exposed to light, thermal changes, wave action, and anthropogenic disturbances (for simplicity reasons in this subcategory we included the mediolittoral zone since there were very few observations), (b) the lower sublittoral zone, which is generally characterized by more stable physical conditions, and within the Mediterranean basin it is mainly associated with the precoralligenous and coralligenous assemblages (Ballesteros, 2006), and (c) marine caves, including both semi- and entirely submerged sea caves (Gerovasileiou & Bianchi, 2021). Mobile substrates include types ranging from mud, well sorted fine sand and gravel, to rhodolith and maërl beds, while the habitat category of seagrasses relates to observations of the endemic species P. oceanica. 2.2.5 | Spatial and temporal mortality impact patterns The surveys were mainly conducted in summer and fall seasons when the effects of climatedriven mortality events usually occur (Garrabou et al., 2009). The surveys were conducted in 142 Monitoring areas (see Section 2.2.1 for the definition) encompassing a total of 352 surveyed locations (minimum of one and a maximum of 15 survey locations per Monitoring area and year), resulting in a total of 985 survey records. Observations covered all the eight Mediterranean ecoregions considered in this study (see above). 2.2.6 | Depth related patterns of MMEs The depth distribution of mortality occurrences was analyzed based on the lower and upper depths of mortality reported in each single record and assuming continuity of the phenomenon in this depth interval. The vertical profile was divided into levels of 5 m each, from the surface to the maximum recorded depth, and for each depth level the number of occurring records belonging to the different species and different severity classes (low, moderate, and severe) was counted. For instance, a record reporting Paramuricea clavata damaged from 0 to 16 m, was included in the counting in each of the following depth levels: 0– 5, 5– 10, 10– 15, and 15– 20 m. Records were analyzed year by year and for the entire study period. Species data were aggregated by phyla. In the plots, data were reported in terms of percentage of the total mortality records in the considered period. The main information gathered for each MME record is provided in Table S2. 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 5714 | GARRABOU et Al. 2.3 | Relationships between MHWs and MMEs To explore to what extent the onset of the MMEs observed during the 2015– 2019 were related with heat exposure (using both SST and in situ temperature data), we focused on the Northwestern Mediterranean since this ecoregion has most of the mortality records as well as the most comprehensive monitoring of both the biology and temperature series (65% mortality records and 100% of the monitoring areas with at least 10 years of in situ temperature time series) allowing for comprehensive coverage across years and depths. We conducted a twolevel analysis. At the first level, we explored if the temporal patterns in surface heat exposure observed in the basin generally matched the patterns of ecological impacts observed during the 2015– 2019 period. For this analysis, we first obtained the annual regional average of MHWs days calculated over the JJASON period with SST data per year corresponding to pixels (or the closest ones) in which mortality surveys were conducted (see above); and then jointly plotted the resulting MHW patterns with the annual mortality incidence at the ecoregion level by calculating the proportion of surveyed records displaying mass mortality impacts (including all different categories of impact Low, Moderate, or Severe). For the second level, and to further explore the potential cause– effect relationship between heat exposure conditions and mortality incidence, we fitted two generalized linear models (GLMs) with binomial error structure and with either SST or in situ temperature data as the independent variables. In the first model, we used per pixel MHW days obtained from SST as the explanatory variable and the mass mortality incidence obtained at the Monitoring areas level as the dependent variable. In this case, MHWs days over the JJASON period were averaged across all pixels covering each Monitoring area (usually 1 to 10 pixels, but more than 20 for Cap de Creus and El Montgrí). For this analysis, only Monitoring areas with at least 2 years of monitoring over the 5- year study period were selected, and within these areas, only years with at least three or more mortality records of recurrently monitored populations were considered to quantify the mortality incidence. These criteria allowed us to perform the analysis using the most consistent data in which mortality patterns could be unequivocally dated within the study period based on strong local ecological knowledge from the different research teams. This is critical to ensure an accurate assessment of the relationship between ecological data and the corresponding MHW patterns. Moreover, because the independent variable was surface temperature, we only included the shallowest mass mortality records in the analysis when multiple records were reported for the same species at the same location but at different depths. Finally, to avoid under estimation of MMEs, records from species that were not affected at any time during the study period across the region (e.g., Paracentrotus lividus) were not included in the analysis. Finally, the GLM was weighted by the sampling size in each year and monitored area to account for potential sampling biases. A second GLM with in situ (across depth) data was conducted on monitored areas with both longterm, in situ temperature monitoring (at least 10 years of in situ temperature data) and robust ecological data (at least three survey records for a given depth and year). Considering these criteria, only three monitored areas from the Catalan Sea could be included in the analyses. Within these areas, the number of in situ MHW days per year at each depth was considered as the explanatory variable and the corresponding mass mortality incidence at different depths was set as dependent variable. As for the first GLM, weights were also used to account for potential sampling biases across the three monitored areas, years and depths. The data sets used for the analysis performed are available at Garrabou et al. (2022). 3 | RESULTS AND DISCUSSION 3.1 | 2015– 2019: The warmest years on record in the Mediterranean Sea Every location in the Mediterranean Sea has experienced considerable increases in annual mean temperatures in recent decades (Figure 1a). Over the entire Mediterranean basin, the 2015– 2019 period has been the warmest since the beginning of satellite records in 1982 (Figure 1c), with average temperature now 1.2 ± 0.23°C (mean ± SD of absolute annual anomalies) higher compared with the 1982– 1986 period. In fact, the average Mediterranean SST for three of the 5 years in the study period (2015, 2016, and 2018) were higher than in 2003, when the most damaging MHW on record occurred in the Mediterranean (Garrabou et al., 2009). However, different warming rates were observed across the Mediterranean ecoregions. In the Aegean and Levantine Sea ecoregions the average temperatures over the study period (2015 to 2019) were 1.4 ± 0.25°C and 1.3 ± 0.23°C warmer than the first 5 years of the satellite record (1982 to 1986; Figure 1a). Although the Mediterranean Sea is not warming as rapidly everywhere, the lowest average increase in temperature between the first and last 5- year periods (Alboran Sea at 1.0 ± 0.32°C) is still greater than the global average. The average warming rate in the Mediterranean Sea from 1982 to 2019 was 0.38°C per decade, more than three times higher than the global average of 0.11°C per decade (IPCC, 2019). Considering that SST in the Mediterranean Sea is increasing so rapidly, it is not surprising that there has been a dramatic rise in the occurrence and severity of MHWs. Nearly the whole Mediterranean basin experienced much more intense events during the 2015– 2019 period than during the first 5 years of the satellite record (1982 to 1986; Figure 1b). During the first 5 years considered (1982– 1986), there were very few MHWs with a category greater than I “Moderate,” (10% for II “Strong,” 0.05% for III “Severe,” and 0% for IV “Extreme”), whereas during 2015– 2019, 99.99% of the surface of the basin was affected by at least category II “Strong” events, 55% by category III “Severe” events, and while they remained rare at only 3%, IV “Extreme” MHWs occurred on an annual basis (Figure 1d; MHW Category I “Moderate” not shown). For the full satellite record of 1982 to 2019, there have been only 11 years when extreme MHWs were detected, with five of these 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5715 GARRABOU et Al. being the years of our study period (2015– 2019). As with the differing rates of warming, Mediterranean ecoregions have also experienced different increases to the categories of MHWs detected therein. The Aegean Sea was one of the most impacted regions by MHWs, but it was also the region that experienced the most intense MHWs during the first 5- year period (1982 to 1986), making the overall upward trend in MHWs here less steep than other ecoregions. The region that has seen the most dramatic increase FIGURE 1 Patterns of warming and marine heatwaves (MHWs) across the Mediterranean Sea. (a) The difference in temperature between the mean of 2015 to 2019 minus the mean of 1982 to 1986. Green lines and blue dots have been added to visualize, respectively, the Mediterranean ecoregions and the monitoring areas with insitu (along depth) temperature monitoring (also in panel b). (b) The highest category of MHW experienced from 2015 to 2019 at locations where only a category I MHW was experienced in 1982 to 1986. Light pink areas show when a category II or greater MHW occurred in the earlier period. (c) Annual sea surface temperature (SST) anomalies from 1982 to 2019 (climatology period is 1982 to 2019). Horizontal black bars show 5- year averages over the time series. (d) The annual surface area (%) of the Mediterranean affected by category II or greater MHWs from 1982 to 2019. The color of the bar shows what proportion relates to the MHWs category. Horizontal black bars show roughly 5- year averages in % of surface affected by category II or greater MHWs; finally the black dots show the average global ocean cover (%) per year for category II or greater MHWs. Category I moderate events are excluded here to allow for a better understanding of the increasing severity of MHWs. (e) The average per depth strata decadal warming trend for 7 nearshore sampling areas from the Northwestern Mediterranean sea. Error bars indicate the standard deviation. The asterisk (*) indicate that satellite data were used for the surface whereas subsurface data originate from in situ monitoring every 5 m from 5 to 40 m depth. (f) The average along depth number of MHW days for the months from June to November over the study period (2015– 2019) for the seven Northwestern Mediterranean sampling areas. Error bars indicate the standard error. The asterisk (*) indicate that satellite data were used for the surface, whereas subsurface data originate from in situ monitoring every 5 m from 5 to 40 m depth. The map lines in panels (a) and (b) delineate study areas and do not necessarily depict accepted national boundaries. 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 5716 | GARRABOU et Al. FIGURE 2 Ecological impacts of the 2015– 2019 mass mortality events (MMEs). (a) A red gorgonian Paramuricea clavata colony exhibiting partial mortality (brown areas overgrown by epibionts) contrasting with live tissue (red areas; 35 m depth in Tavolara Island, Sardinia, Italy; photo by: M. Munaretto). (b) A colony of the reefbuilding coral Cladocora caespitosa with signs of partial mortality (bare skeleton in white areas) contrasting with live tissue (brown areas; 3– 4 m depth in Tabarca Island, Alicante, Spain; photo by A. Izquierdo). (c) A colony of the bryozoan Myriapora truncata showing mortality (white parts overgrown by epibionts; 15 m depth in Ibiza, Balearic Islands, Spain; photo by J. Garrabou), (d) An individual of the mollusk Spondylus gaederopus affected by mortality (white; 25 m depth in Ullastres, Catalan coast; photo by P. López- Sendino). (e) The massive sponge Agelas oroides exhibiting mortality (brown spongin network free of cellular material) contrasting with the alive orange tissue (16 m depth, Chalkidiki peninsula, North Greece; photo by V. Gerovasileiou). (f) Almost completely dead individual of Spongia officinalis showing brown spongin network free of cellular material (10 m depth in Portofino, Liguria, Italy; photo by F. Betti). (g) Partially dead rhodophyte Mesophyllum sp. (10 m depth, Kalymnos Island, South Greece; photo by M. Sini). (h) Arbacia lixula exhibiting mortality with the loss of spines (3 m depth in Cap de Creus, NE Spain; photo by J. Garrabou). (i) Percentage (%) of MME records per phylum across the entire data set. (j) Number of taxa affected per phylum over depth. (k) Percentage (%) of MME records per phylum over depth. (l) Number of affected taxa per phylum and habitat category (barplot), and percentage (%) of affected habitat category across the overall mortality data set (pie chart). 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5723 GARRABOU et Al. REFERENCES Albano, P. 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How to cite this article: Garrabou, J., Gómez- Gras, D., Medrano, A., Cerrano, C., Ponti, M., Schlegel, R., Bensoussan, N., Turicchia, E., Sini, M., Gerovasileiou, V., Teixido, N., Mirasole, A., Tamburello, L., Cebrian, E., Rilov, G., Ledoux, J.-B., Souissi, J. B., Khamassi, F., Ghanem, R. … Harmelin, J.-G. (2022). Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea. Global Change Biology, 28, 5708– 5725. https://doi.org/10.1111/gcb.16301 13652486, 2022, 19, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16301 by Universidad De Sevilla, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License