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From traits to life-history strategies: Deconstructing fish community composition across European seas
Pécuchet, Lauréne; Lindegren, Martin ; Hidalgo, Manuel; Delgado, Marina; Esteban, Antonio; Fock, Heino O.; Gil de Sola, Luis; Punzón, Antonio; Sólmundsson, Jón; Payne, Mark R.
Published in: Global Ecology and Biogeography
Link to article, DOI: 10.1111/geb.12587
Publication date: 2017
Document Version Early version, also known as pre-print
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Citation (APA): Pécuchet, L., Lindegren, M., Hidalgo, M., Delgado, M., Esteban, A., Fock, H. O., Gil de Sola, L., Punzón, A., Sólmundsson, J., & Payne, M. R. (2017). From traits to life-history strategies: Deconstructing fish community composition across European seas. Global Ecology and Biogeography, 26(7), 812-822. https://doi.org/10.1111/geb.12587
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Global Ecology and Biogeography
From traits to life hi story strategies: deconstructing fish community composition across European Seas
Journal: Global Ecology and Biogeography
ManuscriptFor ID GEB-2016-0281 Peer Review
Manuscript Type: Research Papers
Date Submitted by the Author: 11-Jul-2016
Complete List of Authors: Pecuchet, Laurene; Danmarks Tekniske Universitet, Centre for Ocean Life, National Institute of Aquatic Resources (DTU-Aqua) Lindegren, Martin; Danmarks Tekniske Universitet, Centre for Ocean Life, National Institute of Aquatic Resources (DTU-Aqua) Hidalgo, Manuel; Instituto Espanol de Oceanografia Centro Oceanografico de Baleares, Ecosystems Oceanography Delgado, Marina; Instituto Español de Oceanografía, Centro Oceanográfico de Cádiz, Marine Resources Esteban, Antonio; uto Español de Oceanografía, Centro Oceanográfico de Murcia, Marine Resources Fock, Heino; Johann Heinrich von Thünen Institute, Institute of Sea Fisheries Gil De Sola, L; Centro Oceanográphico de Málaga, (IEO) Punzón, Antonio; Instituto Español de Oceanografía, Centro Oceanográfico de Santander, Marine Resources Sólmundsson, Jón; Marine and Freshwater Research Institute, Marine and Freshwater Research Institute Payne, Mark; Danmarks Tekniske Universitet, Centre for Ocean Life, National Institute of Aquatic Resources (DTU-Aqua)
traits, fish, community composition, life history strategies, trade-offs, Keywords: temperature, depth, fecundity, offspring survival, size
Page 1 of 47 Global Ecology and Biogeography
1 2 3 4 1 From traits to life history strategies: deconstructing fish community composition 5 6 2 across European Seas 7
8 1 1 2 3 4 9 3 Laurene Pecuchet *, Martin Lindegren , Manuel Hidalgo , Marina Delgado , Antonio Esteban , Heino O. 10 4 Fock 5, Luis Gil de Sola 6, Antonio Punzón 7, Jón Sólmundsson 8 and Mark Payne 1 11 12 5 1 Centre for Ocean Life, National Institute of Aquatic Resources (DTU-Aqua), Technical University of 13 14 6 Denmark, Charlottenlund Castle, DK-2920 Charlottenlund, Denmark 15 16 7 2Instituto Español de Oceanografía, Centro Oceanográfico de Baleares, Muelle de Poniente, s/n, Apdo. 291, 17 18 8 07015 Palma de Mallorca,For Spain Peer Review 19 3 20 9 Instituto Español de Oceanografía, Centro Oceanográfico de Cádiz, Muelle de Levante, 11006 Cádiz, Spain 21 22 10 4Instituto Español de Oceanografía, Centro Oceanográfico de Murcia, Varadero 1, 30740 San Pedro del 23 11 Pinatar, Spain. 24
25 5 26 12 Thuenen Institute of Sea Fisheries, Hamburg, Germany 27 7 28 13 Instituto Español de Oceanografía, Centro Oceanográfico de Santander, Promontorio San Martín, 39080 29 14 Santander, Spain 30 31 15 6Instituto Español de Oceanografía, Centro Oceanográfico de Málaga, Puerto Pesquero, 29640 Fuengirola, 32 33 16 Spain 34 35 17 8 Marine and Freshwater Research Institute, Skúlagata 4, PO Box 1390, 121 Reykjavík, Iceland 36 37 18 Email: Laurene Pecuchet [email protected] , Martin Lindegren [email protected] , Manuel Hidalgo 38 39 19 [email protected] , Marina Delgado [email protected] , Antonio Esteban 40 41 20 [email protected] , Heino Fock [email protected] , Luis Gil de Sola [email protected] , 42 21 Antonio Punzón [email protected] , Jón Sólmundsson [email protected] and Mark Payne 43 44 22 [email protected] 45 46 23 Keywords: community composition; marine fish; life history strategies; trait; trade-off; fecundity; size; 47 24 offspring survival; temperature; depth 48 49 50 25 Running title: Life history strategies of European fish communities 51 26 *corresponding author: Laurene Pecuchet, [email protected] 52 53 27 Words in abstract: 297 54 55 28 Words in main body of the paper: 5,200 56 57 29 Number of references: 59 58 30 59 60 Global Ecology and Biogeography Page 2 of 47
1 2 3 4 31 Abstract 5 6 32 Aim 7 33 Characterizing communities based on biological traits is challenging since traits are often correlated and 8 9 34 share similar information. Here we investigate whether the composition of marine fish communities can be 10 11 35 understood in terms of a set of life history strategies which result from combination of traits and their 12 36 trades-offs and whether the prevalence of the strategies in the fish communities follow specific spatial 13 14 37 patterns that can be related to the environment. 15 16 38 Location 17 39 North-Eastern Atlantic shelf and associated semi enclosed seas (Mediterranean and the Baltic Sea) 18 For Peer Review 19 40 Methods 20 41 Using an extensive set of scientific bottom trawl surveys we obtained the species composition of fish 21 22 42 communities across European Seas. We complemented this data with species-specific information 23 24 43 regarding six life history traits, reflecting reproductive, growth and feeding modes. We then calculated the 25 44 optimal number of strategies needed to summarize the information contained in the traits. We calculated 26 27 45 the proportion of each strategy in the communities, and explained their spatial patterns as a function of 28 29 46 abiotic variables describing the average and variability of the physical environment. 30 47 Results 31 32 48 Three strategies, resulting from trades-offs between reproduction and survival, sufficiently summarized the 33 49 traits information. The strategies demonstrate marked spatial patterns, with long-lived and low-fecundity 34 35 50 species dominating at high latitude while short-lived species dominate at low latitudes. Sea surface 36 37 51 temperature and its seasonality together with depth explained up to 77% of the strategies spatial 38 52 variability. 39 40 53 Main conclusions 41 42 54 Based on their traits attributes, fish species can be categorized into three strategies that reflect the 43 55 evolutionary and environmental constraint of the species. The prevalence of the strategies in the 44 45 56 communities was linked to temperature and depth. Due to their tight coupling to the environment life 46 57 history strategies can be a suitable tool to monitor and understand community changes in response to 47 48 58 natural and anthropogenic stressors, including climate change. 49 50 51 52 53 54 55 56 57 58 59 60 Page 3 of 47 Global Ecology and Biogeography
1 2 3 4 59 Introduction 5 6 7 60 Trait-based studies of biodiversity has been an expanding field in recent decades (Petchey & Gaston 2002, 8 9 61 Schleuter et al. 2010, Cadotte et al. 2013). Traits are often referred to as any characteristics of an 10 11 62 individual, or a group of organisms, which encompass morphological, demographic or physiological 12 13 63 attributes (Violle et al. 2007). Using traits instead of taxonomic information has several advantages when 14 15 64 studying biodiversity since they provide a more fundamental and mechanistic approach to understanding 16 17 18 65 community compositionFor (Shipley etPeer al. 2006, McGill Review et al. 2006, Pecuchet et al. 2016). Trait-based 19 20 66 approaches also permit to reduce the complexity of community diversity analysis (Litchman et al. 2013) and 21 22 67 has also been successfully related to ecosystem functions and services in the terrestrial (Diaz & Cabido 23 24 68 2001, Flynn et al. 2011) and marine (Bellwood et al. 2003) realms. 25 26 27 69 Selecting a relevant set of key traits to characterize the species and ultimately the communities can, 28 29 70 however, be difficult (Petchey and Gaston, 2006), especially when the aim is to explain ecosystem functions 30 31 71 (McIntyre et al. 1999, Lavorel & Garnier 2002). In particular, deciding on how many traits to include is 32 33 72 controversial since using too many traits increase complexity and may introduce redundancy due to 34 35 73 correlation among closely related traits. Trait information can be reduced into combinations of particular 36 37 38 74 trait values, so-called functional groups. Functional groups are defined as a unit of species sharing similar 39 40 75 trait-attributes, for example body size and trophic guild (e.g Halpern & Floeter 2008). However, adding new 41 42 76 traits to this analysis can substantially increase the number of functional groups. Hence, one of the 43 44 77 drawbacks of functional groups is that they are defined as a combination of a discrete and analysis-specific 45 46 78 set of traits. 47 48 49 79 One solution to avoid problems caused by correlations between traits is to study life history strategies. Life 50 51 80 history strategies are defined as a combination of trait-attributes and are the result of inter-correlations 52 53 81 and trades-offs among key traits (Winemiller et al. 2015). They are often based on a theoretical framework 54 55 82 and are used to shed light on the evolution of the life history of the organisms, as well as the environment 56 57 58 59 60 Global Ecology and Biogeography Page 4 of 47
1 2 3 4 83 wherein the species occur (Charnov et al. 2013). Some well-known examples of life history strategies are 5 6 84 the r and K-selection (Pianka 1970) or the fast-slow life history continuum (Franco & Silvertown 1996). Due 7 8 85 to their simplicity, these linear models of life history strategies have been used in numerous studies ranging 9 10 86 from taxonomic groups strategies (e.g. Oli 2004) to communities spatial pattern (e.g. Wiedmann et al. 11 12 13 87 2014). However, this approach has also been criticized for oversimplifying reality (Nichols et al. 1976, Bielby 14 15 88 et al. 2007). 16 17 89 For fish communities, Winemiller and Rose (1992) developed the Equilibrium-Periodic-Opportunistic (EPO) 18 For Peer Review 19 20 90 model. This theoretical model links three strategies characterized by trades-off between fecundity, juvenile 21 22 91 survival and generation time to environmental stability and predictability: “Equilibrium species” with high 23 24 92 juvenile survivorship; “Opportunistic species” with low generation time; and “Periodic species” with high 25 26 93 fecundity. The Equilibrium strategy is thought to prevail in stable and predictable biotic and abiotic 27 28 94 environment while the Opportunistic strategy prevails in opposite environmental conditions. The Periodic 29 30 95 strategy is thought to occur in seasonal and periodically suitable environments. A number of studies have 31 32 33 96 used this model to investigate temporal changes and spatial distribution of the composition of freshwater 34 35 97 fish communities (Olden & Kennard 2010, Mims & Olden 2012). 36 37 98 In this study, we investigate whether marine fish species present in European Seas can be categorized into 38 39 40 99 similar life-history strategies based on their trait attributes. Furthermore, we investigate whether there are 41 42 100 any consistent geographical patterns in the prevalence of life history strategies within fish communities 43 44 101 across Europe and whether the patterns can be explained by external (environmental) factors. For this 45 46 102 analysis, the European Seas provide an excellent study area since they contain pronounced natural 47 48 103 gradients in terms of e.g. temperature, productivity and biodiversity, and are subjected to various 49 50 104 anthropogenic pressures including fishing and eutrophication (Grizzetti et al. 2012). The North-Eastern 51 52 53 105 Atlantic Sea Shelf is also one of the most studied areas in the world, and therefore an ideal area to test the 54 55 106 life history strategies hypothesis on the fish communities using standardized datasets with a high 56 57 107 taxonomic and spatial resolution. 58 59 60 Page 5 of 47 Global Ecology and Biogeography
1 2 3 4 108 5 6 7 109 Materials and Methods 8 9 110 Bottom trawl surveys 10 11 12 111 Fifteen scientific bottom trawl surveys covering ecosystems from the Mediterranean Sea to Greenland and 13 14 112 spanning on average ten recent years (2002-2012) were collected (Appendix S1 in Supporting Information). 15 16 113 As our focus was on offshore fish communities, and due to different survey sampling schemes, hauls 17 18 For Peer Review 19 114 sampled at a depth shallower than 20 meters were excluded to avoid the inclusion of coastal fish species. 20 21 115 The refined data set contained approximately 20.000 individual hauls (i.e. stations) that lasted on average 22 23 116 30 minutes and covered 3 nautical miles. Not all surveys used the same taxonomic recording, therefore 24 25 117 species scientific names were checked using World Register of Marine Species (worms) and updated by the 26 27 118 ‘accepted’ scientific name when appropriate. For some species that are difficult to identify, taxonomic 28 29 119 recording was specified to the genus level. For each ¼ degree cell covered by the surveys, we derived all the 30 31 32 120 species present and their relative abundances. 33 34 121 Trait information 35 36 37 122 Six traits were selected to cover the fundamental Darwinian objectives of an organism: to feed, survive and 38 39 123 reproduce. These traits were also selected as they were previously used to describe the theoretical life 40 41 124 history strategies of fish species, notably freshwater species (Winemiller 2005, Mims et al. 2010). These 42 43 125 traits are: maximum length, lifespan, trophic level, fecundity, offspring size and parental care. Maximum 44 45 46 126 length is described as the longest total length recorded for a given species. Lifespan is the maximum 47 48 127 expected age for a species. Trophic level represents the position of a species in the food chain, ranging 49 50 128 from a value of 2 where the diet is based on plant or detritus to 4.5 for top predators. These three traits 51 52 129 were extracted from FishBase (Froese & Pauly 2012). Fecundity is the average total number of offspring 53 54 130 produced in a year per mature female. The offspring size corresponds to the average size of the offspring 55 56 131 released in the water, i.e. eggs for oviparous or larvae/juveniles for ovoviviparous. Parental care relates to 57 58 59 60 Global Ecology and Biogeography Page 6 of 47
1 2 3 4 132 the investment of the parents in the survival of their offspring. Parental care was transformed from 5 6 133 categorical to continuous values, using a similar approach to Winemiller (1989), as follow: (1) pelagic egg, 7 8 134 (2) benthic egg, (3) hidden brood, (4) guarded brood and (5) bearer. These three traits values were 9 10 135 primarily derived from FishBase and from literature. A list of the data sources is found in Appendix 1 . There were 11 12 13 136 approximately 600 unique species or genus recorded in the survey. Out of these, 285 species and 32 genus 14 15 137 (50%) had complete information for all six traits and were therefore used in the analysis. Although 16 17 138 representing only half of the entire species pool, these species were the most frequently occurring, with 18 For Peer Review 19 139 more than 97% of the entire occurrence and 99% of the abundance in the surveys. As such, a majority of 20 21 140 the grid cells had trait information for all the species recorded therein, and on average 96% of the species 22 23 141 in the grid cells had the full trait information (Appendix S2). 24 25 26 142 Life history strategies 27 28 29 143 We used the unsupervised learning method Archetypal analysis (AA) to obtain the optimal number of life- 30 31 144 history strategies needed to characterize European marine fish species. AA is similar to a cluster analysis, 32 33 145 but focuses on extreme values instead of means or medians (Cutler & Breiman 1994) and can therefore be 34 35 146 regarded as the optimization of the number of points needed to encompass the convex hull volume, 36 37 147 defined as the trait space. This method has several advantages, notably it permits characterization of the 38 39 40 148 species as a percentage of each strategy. This is an important feature, as species might not belong strictly 41 42 149 to one life history strategy, but instead represent a combination of several strategies. To normalize the 43 44 150 data, we log 10 transformed fecundity, offspring size, body size and lifespan. To obtain equal weights in the 45 46 151 AA, the six traits were then standardized (i.e. to a mean of 0 and a variance of 1). The AA was performed for 47 48 152 k ε [1;10] fixed number of strategies and the sum of square errors (SSE) of 10 iterations was calculated for 49 50 153 each k using the package ‘archetypes’ in R (Eugster & Leisch 2009). We used the ‘elbow criterion’ to select 51 52 53 154 the optimal number k of strategies that permitted to minimize the SSE while minimizing the number of 54 55 155 strategies. This is done visually by assessing the number k which permits the SSE to drops dramatically. 56 57 58 59 60 Page 7 of 47 Global Ecology and Biogeography
1 2 3 4 156 The prevalence of the optimal strategies in the European Seas fish communities was then mapped as a 5 6 157 proportion weighted by the species relative abundances or based on species presence only. The 7 8 158 proportions were calculated as the mean of each life history strategy from the species composition in each 9 10 159 grid cell. Hence, each grid cell has: p + p + p = 1, where p is the proportion of the corresponding 11 LHS1 LHS2 LHS3 12 13 160 life history strategy. 14 15 161 Environmental predictors 16 17 18 162 In order to explain the observedFor spatial Peer patterns of the Review life history strategies, abiotic variables were used as 19 20 163 environmental predictors. Sea temperature and salinity at surface and bottom were obtained on a ¼ 21 22 164 degree grid resolution from the World Ocean Atlas (www.nodc.noaa.gov/OC5/woa13). Both winter 23 24 165 (January-March) and summer (July-September) means were obtained for the period 2005-2012: surface 25 26 27 166 temperature seasonality was calculated as the difference between the summer and winter temperature. 28 29 167 Chlorophyll data, used as a proxy for primary production, were obtained from the GlobColour database 30 31 168 (hermes.acri.fr) as monthly averages for the years 2002-2012 on a ¼ degree grid resolution. Several metrics 32 33 169 were derived from the dataset: mean year chlorophyll concentration, mean chlorophyll concentration in 34 35 170 spring, i.e. March to May, chlorophyll variability during the year (proxy for seasonality and resource 36 37 171 stability) and the variability of the chlorophyll concentration in spring bloom between the years (proxy for 38 39 40 172 resource predictability). Minimum, maximum and variability of depth were obtained for each ¼ degree cell 41 42 173 from ETOPO1 (www.ngdc.noaa.gov/mgg/global ) and mean depth was calculated directly from the depth of 43 44 174 the hauls performed. Vessel Monitoring System (VMS) intensity was used as a proxy for bottom fishing 45 46 175 intensity and was extracted from the ICES Working group WGFSD (ICES 2015). Sediment type and 47 48 176 dominance were used to characterize the seabed habitat and were extracted and calculated from 49 50 177 EMODnet ( http://www.emodnet-seabedhabitats.eu ). Sediment type was divided into 5 categories (mud to 51 52 53 178 sandy mud, sand to muddy sand, mixed sediment, coarse sediment, and rock or other hard substrates) and 54 55 179 was defined as the category with highest spatial coverage in each grid cell. Sediment dominance was 56 57 180 calculated as the standard deviation of the spatial coverage of the sediment categories in each grid cell (i.e. 58 59 60 Global Ecology and Biogeography Page 8 of 47
1 2 3 4 181 the higher, the less diverse is the seabed). Both fishing intensity and habitat were only available for a 5 6 182 subset of the study area. 7 8 9 183 Modelling of the life history strategies 10 11 184 The different species strategies emerge from life history evolution and as such their prevalence has been 12 13 14 185 hypothesized to be non-randomly distributed and intimately linked to the environment (Grime, 1977; 15 16 186 Winemiller and Rose, 1992). We hence tested the hypothesis that the prevalence of the life history 17 18 187 strategies could be explainedFor by environmental Peer variables Review using generalized additive models (gam), allowing 19 20 188 for potential nonlinearities between the response and explanatory variables (Wood 2006), e.g. in case of an 21 22 189 environmental optimum or saturation. In a prior analysis, we reduced the number of environmental 23 24 190 variables to avoid problems with correlation among predictors (Dormann et al. 2013): we retained only 25 26 27 191 unclustered or not highly correlated (r < 0.8) variables (Appendix S3). Nine abiotic variables were retained 28 29 192 for the analysis: mean, maximum and variability of depth; mean and seasonality of sea surface 30 31 193 temperature; mean and variability of chlorophyll concentration and sea surface salinity. 32 33 34 194 We modelled the life history strategies as a function of the abiotic variables. As life history strategies are 35 36 195 proportional data between 0 and 1, we used a beta regression model with logit link function. The 37 38 196 smoothing spline function (s) was constrained to four degrees of freedom (k=4), which allows for third 39 40 197 degree relationships, but restricts flexibility during model fitting. The strategies proportions were corrected 41 42 198 for sampling effort by including the number of hauls performed in each grid cell as an explanatory variable 43 44 199 in each model and only grid cells with more than two hauls were included (n=1797). All the possible models 45 46 47 200 containing from zero to a maximum of four environmental variables were fitted and evaluated using the 48 49 201 package ‘MuMIn’ in R (Barton 2016) and the best models, i.e. with the lowest Akaike´s Information 50 51 202 Criterion (AIC), were retained. For each strategy the predictors of the best model were plotted against the 52 53 203 response variable. Standard model checking diagnostics were applied. We calculated the relative variable 54 55 204 importance (RVI) to assess the contribution of each variable to the performance of the final multivariate 56 57 58 59 60 Page 9 of 47 Global Ecology and Biogeography
1 2 3 4 205 gam. RVI was quantified for each variable of the final models by randomly permuting the values of the 5 6 206 variable of interest and measuring the difference between the deviance explained of the newly fitted 7 8 207 model in comparison to the original model, i.e. measuring the drop in model performance. Thus, a variable 9 10 208 that caused a large decrease in model performance when randomized contributed greatly to the quality of 11 12 13 209 the model. Due to limited spatial coverage, fishing intensity and sediment type and dominance were not 14 15 210 used in the main model. Instead, fishing intensity and sediment were added as explanatory variables of the 16 17 211 strategies prevalence in a sub-area for which the data were available. The same modelling method was 18 For Peer Review 19 212 used for this subset. 20 21 22 213 23 24 214 25 Results 26 27 215 Summarizing traits variability into life history strategies 28 29 30 216 The optimal number of extreme points (k) needed to encompass the spatial volume of the traits space was 31 32 217 found to be three (Appendix S4). The largest drop in the residuals sum of squares (RSS) occurred when 33 34 218 passing from two to three extreme points and adding a fourth point did not significantly reduce the RSS. 35 36 219 The three corresponding life history strategies were represented in a biplot where the first two axes of the 37 38 39 220 underlying principal component analysis (PCA) explained most of the trait variability (78%) (Fig. 1). The first 40 41 221 axis (PC1) explained almost half of the total variability (46%) and was driven by offspring size, maximum 42 43 222 length, lifespan and trophic level. The second axis was driven by fecundity and parental care. Several traits 44 45 223 are clustered (maximum length, lifespan and trophic level), while others are negatively correlated 46 47 224 (Fecundity against parental care and offspring size). The three life history strategies could be visualized as a 48 49 225 triangle in trait space with each extreme point representing a unique combination of traits characteristics 50 51 52 226 (Fig. 2). The first strategy was characterized by small size, low trophic level and short lifespan but with 53 54 227 relatively high fecundity and parental care. The second strategy was characterized by medium to high 55 56 228 lifespan, length and trophic level, high fecundity but low parental care and offspring size. The third strategy 57 58 59 60 Global Ecology and Biogeography Page 10 of 47
1 2 3 4 229 was characterized by high length, lifespan and trophic level, low fecundity but large offspring size and high 5 6 230 parental care. These strategies and their relations with traits corresponded closely with the theoretical 7 8 231 model of Winemiller and Rose (1992), and are henceforth referred to as Opportunistic, Periodic and 9 10 232 Equilibrium strategies, respectively. 11 12 13 233 The species were not clustered around the three strategies end-points but instead demonstrated a 14 15 234 continuum between the strategies (Fig. 1). A species could therefore follow several strategies at the same 16 17 235 time, and were thus characterized by the proportions expressed in each strategy. Both the Opportunistic 18 For Peer Review 19 20 236 and the Periodic strategies were prominent in the species pool, while the Equilibrium strategy was the least 21 22 237 common. 23 24 238 Patterns of life history strategies 25 26 27 239 The distribution of life history strategies exhibited clear spatial patterns (Fig. 3). The Equilibrium strategy 28 29 240 occurred primarily in high latitudes, in Iceland and Greenland, and in the Balearic and Irish Seas (Fig. 3a). 30 31 241 The Opportunistic strategy displayed a South-East to North-West gradient, prevailing notably in the Baltic 32 33 34 242 Sea, the southern North Sea and the Mediterranean (Fig. 3b). The Periodic strategy prevailed south of 35 36 243 Iceland, in the northern offshore of the North Sea and in the Celtic Sea (Fig. 3c). The proportion of the 37 38 244 Equilibrium strategy in the communities was generally much lower than the Periodic and Opportunistic 39 40 245 strategies with values ranging between 0 to 33%. On the contrary, the proportions of the Periodic and 41 42 246 Opportunistic strategies were never lower than 27% and 18%, and reached a maximum of 82% and 70%, 43 44 247 respectively. We found the same overall spatial pattern when using presence data (Appendix S5) and the 45 46 47 248 strategies prevalence in the communities calculated using abundance and presence were highly correlated. 48 49 249 A notable change was a shift to a lower prevalence of the Equilibrium strategies when using abundance 50 51 250 compared to presence (Appendix S6). The spatial patterns were robust to different seasons, as seen for 52 53 251 example in the North Sea (Appendix S7). 54 55 56 252 57 58 59 60 Page 11 of 47 Global Ecology and Biogeography
1 2 3 4 253 Environmental predictors of life history strategies 5 6 7 254 The best models with maximum three abiotic predictors explained 50.8%, 77% and 77.6% of the variability 8 9 255 of the Periodic-, Opportunistic- and Equilibrium strategy, respectively (Table 1). Depth, Sea Surface 10 11 256 Temperature (SST) and Sea surface temperature seasonality (SST season) were retained in all the final 12 13 257 models. Sea Surface temperature (SST) was the variable explaining most of the variability in the 14 15 258 Opportunistic and Equilibrium strategies, with a positive and negative relationship, respectively (Fig. 4). 16 17 259 Temperature seasonality was the second most important variable explaining the variability of the 18 For Peer Review 19 20 260 Opportunistic strategy, and followed a positive and saturating relationship. On the other hand, the 21 22 261 Equilibrium strategy had a negative relationship with seasonality. Depth was also an important predictor of 23 24 262 the Opportunistic and Equilibrium strategies, with a negative and positive relationship, respectively, while it 25 26 263 explained the most variability of the Periodic strategy with higher prevalence at intermediate depth. The 27 28 264 same explanatory variables were found across different spatial aggregation and when using presence only 29 30 265 (Appendix S8 & S9). For the subarea for which fishing intensity and sediment data were available, the best 31 32 33 266 predictors of the Opportunistic and Periodic strategies were depth, temperature seasonality and 34 35 267 chlorophyll concentration, explaining 77% and 69.4% of the variability respectively (Appendix S10). For the 36 37 268 Equilibrium strategy, fishing intensity, depth and chlorophyll concentration were the most important 38 39 269 variable but explained only 36.7% of the strategy variability. The Equilibrium prevalence had a negative 40 41 270 relationship with fishing intensity (Appendix S11). The sediment type was not retained in the final models. 42 43 44 271 45 46 47 272 Discussion 48 49 50 273 Characterizing ecological communities by a set of biological traits is challenging since the selected traits are 51 52 274 often correlated and therefore contain similar information. In this study, we show that several key traits of 53 54 275 marine fish were correlated and that the variability of these traits could be reduced into fewer 55 56 276 components. The first two axes of a PCA could explain almost 80% of the six traits variability emphasizing a 57 58 59 60 Global Ecology and Biogeography Page 12 of 47
1 2 3 4 277 strong collinearity between the traits. In accordance with the theoretical model of Winemiller and Rose 5 6 278 (1992), the trait-space is encompassed by a triangular shape where each endpoint corresponded to a life 7 8 279 history strategy - Opportunistic, Equilibrium and Periodic. At the community level the proportion of the 9 10 280 strategy expressed followed a strong spatial pattern in the Europeans seas. These spatial patterns can be 11 12 13 281 explained by the abiotic variables where each strategy prevails under different environmental conditions, 14 15 282 largely depending on sea surface temperature and its seasonality, as well as depth. 16 17 283 Summarizing traits space 18 For Peer Review 19 20 284 For large species, two distinctive strategies were apparent: either producing many small offspring at the 21 22 285 cost of offspring survival or few large offspring with high survival. The first strategy is composed mainly of 23 24 286 bonyfish species, with e.g. large flatfish and gadoids species, while the second one is mainly composed of 25 26 27 287 elasmobranchs, e.g. sharks and rays. These strategies reflect the evolutionary and environmental constraint 28 29 288 and trade-offs shaping variability in life history strategies (Neuheimer et al. 2015). High fecundity balances 30 31 289 high pre-adult mortality while longevity balances unfavourable periods for recruitment in typical poor 32 33 290 environmental conditions (Gunderson 1997, Longhurst 2002). The small and short lived species, such as 34 35 291 gobies and clupeids, were typical r-strategists producing many small offspring. The lifetime reproductive 36 37 292 value of these short lived species is often compensated by early maturation and indeterminate spawning 38 39 40 293 (Tsoukali et al. 2016). 41 42 294 The simpler, and often used, slow-fast continuum model was not appropriate here and would have failed to 43 44 295 explain some of the trait-variability. However, in smaller and extreme ecosystems with fewer species, e.g. 45 46 47 296 the Barents Sea, the slow-fast continuum model can explain well the life history variation (Wiedmann et al. 48 49 297 2014). In this study, we kept the raw trait data and did not correct for body size as is done when 50 51 298 investigating the slow-fast continuum (Jeschke & Kokko 2009, Wiedmann et al. 2014). Instead, we kept 52 53 299 body size as a covariate trait in the analysis as it is a central trait which is used in the theoretical triangular 54 55 56 57 58 59 60 Page 13 of 47 Global Ecology and Biogeography
1 2 3 4 300 model of Winemiller and Rose (1992), and is used to define the different theoretical life history strategies 5 6 301 of marine fish (e.g. King and McFarlane, 2003). 7 8 9 302 Links between life history strategies and the environment 10 11 303 The strategies prevalence in the communities was linked to the environment and its variability. Notably, we 12 13 14 304 found that the Opportunistic strategy was prevailing in environments with high temperature and strong 15 16 305 temperature seasonality while the Equilibrium strategy prevailed in environment with lower and more 17 18 306 stable temperature. TheFor Opportunistic Peer reproductive Reviewstrategy corresponding to the production of many 19 20 307 small eggs is favoured in seasonal environment to compensate for the short availability of resources (Boyce 21 22 308 1979). This reproductive strategy is also favoured in warm environment to balance the higher egg mortality 23 24 309 resulting from higher temperature (Pepin 1991). The strong relationship between the Opportunistic 25 26 27 310 strategy and temperature is verified by the recent distributional shift of mostly small and short-lived 28 29 311 species in response to the recent temperature increase (Perry et al. 2005, Magurran et al. 2015). However, 30 31 312 the Opportunistic strategy is also tightly coupled to shallow areas where depth acts as a physical boundary 32 33 313 that will likely constrain the future distribution of these species under climate change (Rutterford et al. 34 35 314 2015). Abiotic variables failed to explain well the Periodic strategies prevalence and demonstrated hump- 36 37 315 shaped relationships with temperature and its seasonality. Here, we assessed the strategies prevalence in 38 39 40 316 the communities as a proportion, i.e. the strategies dominance in the communities, thus the three 41 42 317 strategies proportions are dependent on each other: as one strategy proportion goes up, the other two go 43 44 318 down. Therefore, the Periodic strategy may behave as an intermediate strategy that instead of responding 45 46 319 directly to the environment, it rises and falls as a result of changes in the two antagonistic Opportunistic 47 48 320 and Equilibrium strategies. 49 50 51 321 Potential effects of fishing on communities’ composition 52 53 322 Fishing can impact community composition by affecting the relative abundances of both commercial and 54 55 56 323 non-commercial species, as well as by physical impacts on the seabed (Myers & Worm 2003, Hiddink et al. 57 58 59 60 Global Ecology and Biogeography Page 14 of 47
1 2 3 4 324 2006). Fishing impacts on individual species is dependent of their traits and life history strategies (Jennings 5 6 325 et al. 1999, Winemiller 2005). As fishing increases disturbance, it is most likely going to benefit 7 8 326 opportunistic species while it will disadvantage the Equilibrium species (Jennings et al. 1999, Stevens et al. 9 10 327 2000). In our study, we found a negative effect of fishing intensity on the prevalence of the Equilibrium 11 12 13 328 strategy. This negative relationship is expected, as Equilibrium species, e.g. sharks and rays, are particularly 14 15 329 vulnerable to fishing and habitat disturbance due to their low productivity arising from their life history 16 17 330 characteristics, e.g. low fecundity and slow growth (Dulvy et al. 2008, Sguotti et al. 2016). Though, the 18 For Peer Review 19 331 variability explained by the final model of the Equilibrium strategy in the subset area was low, notably due 20 21 332 to a lack of contrast in the prevalence of the strategy in this area. The fishing intensity was neither available 22 23 333 for the Icelandic waters where there is a high proportion of Equilibrium strategists, nor for the heavily 24 25 26 334 fished Mediterranean Sea. The period of the study (2002-2012) corresponds to a period with relatively low 27 28 335 fishing effort in the available areas, which resulted in some biological improvement, e.g. an increase in the 29 30 336 number of large fish in the communities (Engelhard et al. 2015). Hence, the relatively lower fishing effort 31 32 337 and the lack of contrast in the fishing intensity and strategies prevalence throughout the studied area may 33 34 338 underlie the weak response. Finally, historical fishing might be more appropriate than present fishing when 35 36 339 analysing the current communities composition (Engelhard et al. 2011, Sguotti et al. 2016). 37 38 39 340 The advantages of using life history strategies 40 41 42 341 Life history strategies permit traits information to be reduced into fewer, ecologically meaningful 43 44 342 components. They can be explained by a combination of traits and their trade-offs reflecting the 45 46 343 evolutionary and environmental constraint of the species. Life history strategies can be implemented in 47 48 344 various type of studies, e.g. to investigate population dynamics (Mims & Olden 2012), colonization (Olden 49 50 345 et al. 2006), fisheries management (King & McFarlane 2003), or biological succession (Silvertown & Franco 51 52 53 346 1993). Life history strategies can hence be a suitable management tool to deconstruct and characterize 54 55 347 communities’ composition and monitor changes in the communities in response to exploitation and climate 56 57 348 change. 58 59 60 Page 15 of 47 Global Ecology and Biogeography
1 2 3 4 349 5 6 7 350 Acknowledgments 8 9 351 This work was funded by the Centre for Ocean Life, a VKR center of excellence supported by the Villum 10 11 12 352 foundation, as well as a VILLUM research grant (13159). This work has received funding from the European 13 14 353 Community’s Seventh Frame- work Programme (FP7 2007-2013) under grant agreement no. 308299 15 16 354 (NACLIM). We acknowledge the ICES Working Group on Comparative Analyses between European Atlantic 17 18 355 and Mediterranean marineFor ecosystems Peer to move towardsReview an Ecosystem-based Approach to Fisheries 19 20 356 (WGCOMEDA) during which the study was initiated and are thankful to all the WGCOMEDA participants for 21 22 357 their valuable comments. The WGCOMEDA meeting was supported by a European Consortium 23 24 25 358 EUROMARINE grant. We are also grateful to all the scientific and crew members engaged in the 26 27 359 International Scientific Trawl Surveys. 28 29 360 30 Supporting Information 31 32 361 Appendix S1 Information on the Bottom trawl surveys. 33 34 35 362 Appendix S2 Spatial coverage of traits information. 36 37 363 Appendix S3 Explanatory variables selected in the study. 38 39 40 364 Appendix S4 Residuals sum of squares in function of the number of strategies. 41 42 365 Appendix S5 Spatial pattern of the strategy based on species presence. 43 44 45 366 Appendix S6. Strategies proportion calculated based on abundance versus presence only. 46 47 367 Appendix S7 Seasonality and life history strategies pattern in the North Sea. 48 49 368 Appendix S8 Relative variable importance and scale of the observations. 50 51 52 369 Appendix S9 Variables and parameters of the best final generalized additive models based on presence. 53 54 370 Appendix S10 Fishing intensity and sediment impact on strategies. 55 56 57 371 Appendix S11 Best final gam models in the subarea where fishing intensity is available. 58 59 60 Global Ecology and Biogeography Page 16 of 47
1 2 3 4 372 Biosketches 5 6 7 373 Laurene Pecuchet is a PhD student at the centre for Ocean life at the Technical University of Denmark, with 8 9 374 interests on trait based study of community assemblage and the interplay between traits and the 10 11 375 environment. Further information on the research group can be found at www.ocean-lifecentre.dk 12 13 14 376 The work presented here was made possible thanks to collaboration between different European institute 15 16 377 through the ‘Working Group on Comparative Analyses between European Atlantic and Mediterranean 17 18 For Peer Review 19 378 marine ecosystems to move towards an Ecosystem-based Approach to Fisheries’ (WGCOMEDA). Further 20 21 379 information on the expert group activities can be found at 22 23 380 www.ices.dk/community/groups/Pages/WGCOMEDA.aspx 24 25 26 381 27 28 382 References 29 30 31 383 Barton, K. 2012. Package ‘MuMIn’. Available at: http://cran.r-project.org/web/ 32 384 packages/MuMIn/MuMIn.pdf. (accessed June 2016). 33 34 385 Barton A.D., Pershing A.J., Litchman E., Record N.R., Edwards K.F., Finkel Z.V., Kiørboe T., Ward B.A. (2013) 35 386 The biogeography of marine plankton traits. Ecology letters 16:522–34 36 37 387 Bellwood D.R., Hoey A.S., Choat J.H. (2003) Limited functional redundancy in high diversity systems: 38 388 resilience and ecosystem function on coral reefs. Ecology Letters 6:281–285 39 389 Bielby J., Mace G.M., Bininda-Emonds O.R.P., Cardillo M., Gittleman J.L., Jones K.E., Orme C.D.L., Purvis A. 40 41 390 (2007) The fast-slow continuum in mammalian life history: an empirical reevaluation. The American 42 391 naturalist 169:748–57 43 44 392 Boyce M.S. (1979) Seasonality and Patterns of Natural Selection for Life Histories. The American Naturalist 45 393 114:569–583 46 47 394 Cadotte M., Albert C.H., Walker S.C. (2013) The ecology of differences: assessing community assembly with 48 395 trait and evolutionary distances. Ecology Letters 16:1234–1244 49 50 396 Charnov E.L., Gislason H., Pope J.G. (2013) Evolutionary assembly rules for fish life histories. Fish and 51 397 Fisheries 14:213–224 52 53 398 Cutler A., Breiman L. (1994) Archetypal Analysis. Technometrics. 36(4):338–348. 54 399 Diaz S., Cabido M. (2001) Vive la différence: plant functional diversity matters to ecosystem processes. 55 56 400 Trends in ecology & evolution 16:646–655 57 58 59 60 Page 17 of 47 Global Ecology and Biogeography
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1 2 3 4 438 Evolutionary Ecology 23:867–878 5 6 439 King J.R., McFarlane G.A. (2003) Marine fish life history strategies: applications to fishery management. 7 440 Fisheries Management and Ecology 10:249–264 8 9 441 Lavorel S., Garnier E. (2002) Predicting changes in community composition and ecosystem functioning from 10 442 plant traits: revisiting the Holy Grail. Functional Ecology 16:545–556 11 12 443 Litchman E., Ohman M.D., Kiorboe T. (2013) Trait-based approaches to zooplankton communities. Journal 13 444 of Plankton Research 35:473–484 14 15 445 Longhurst A. (2002) Murphy’s law revisited: longevity as a factor in recruitment to fish populations. 16 446 Fisheries Research 56:125–131 17 18 447 Magurran A.E., DornelasFor M., Moyes F.,Peer Gotelli N.J., McGill Review B. (2015) Rapid biotic homogenization of marine 19 448 fish assemblages. Nature communications 6:8405 20 21 449 McGill B.J., Enquist B.J., Weiher E., Westoby M. (2006) Rebuilding community ecology from functional 22 450 traits. Trends in ecology & evolution 21:178–85 23 24 451 McIntyre S., Lavorel S., Landsberg J., Forbes T.D.A. (1999) Disturbance response in vegetation towards a 25 452 global perspective on functional traits. Journal of Vegetation Science 10:621–630 26 453 Mims M.C., Olden J.D. (2012) Life history theory predicts fish assemblage response to hydrologic regimes. 27 28 454 Ecology 93:35–45 29 455 Myers R.A., Worm B. (2003) Rapid worldwide depletion of predatory fish communities. Nature 423:280– 30 31 456 283 32 457 Neuheimer A.B., Hartvig M., Heuschele J., Hylander S., Kiørboe T., Olsson K., Sainmont J., Andersen K.H. 33 34 458 (2015) Adult and offspring size in the ocean over 17 orders of magnitude follows two life-history 35 459 strategies. Ecology 96:3303–3311 36 37 460 Nichols J.D., Conley W., Batt B., Tipton A.R. (1976) Temporally Dynamic Reproductive Strategies and the 38 461 Concept of R- and K-Selection. The American naturalist 110:995–1005 39 40 462 Olden J.D., Kennard M.J. (2010) Intercontinental comparison of fish life history strategies along a gradient 41 463 of hydrologic variability. Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques 42 464 73:83–107 43 44 465 Olden J.D., Leroy Poff N., Bestgen K.R. (2006) Life-history strategies predict fish invasions and extirpations in 45 466 the Colorado River Basin. Ecological Monographs 76:25–40 46 47 467 Oli M.K. (2004) The fast-slow continuum and mammalian life-history patterns: An empirical evaluation. 48 468 Basic and Applied Ecology 5:449–463 49 50 469 Pecuchet L., Törnroos A., Lindegren M. (2016) Patterns and drivers of fish community assembly in a large 51 470 marine ecosystem. Marine Ecology Progress Series 546:239–248 52 53 471 Pepin P. (1991) Effect of temperature and size on development, mortality and survival rates of the pelagic 54 472 early life history satges of marine fish. Canadian Journal of Fisheries and Aquatic Sciences 48:503–518 55 56 473 Perry A.L., Low P.J., Ellis J.R., Reynolds J.D. (2005) Climate Change and Distribution Shifts in Marine Fishes. 57 474 Science 308:1912–1915 58 59 60 Page 19 of 47 Global Ecology and Biogeography
1 2 3 4 475 Petchey O.L., Gaston K.J. (2002) Functional diversity (FD), species richness and community composition. 5 476 Ecology Letters 5:402–411 6 7 477 Petchey O.L., Gaston K.J. (2006) Functional diversity: back to basics and looking forward. Ecology letters 8 478 9:741–58 9 10 479 Pianka E.R. (1970) On r- and K-selection. American Naturalist 104:592–597 11 12 480 Rutterford L.A., Simpson S.D., Jennings S., Johnson M.P., Blanchard J.L., Schön P-J., Sims D.W., Tinker J., 13 481 Genner M.J. (2015) Future fish distributions constrained by depth in warming seas. Nature Climate 14 482 Change:1–6 15 16 483 Schleuter A.D., Daufresne M., Massol F., Argillier C., Monographs SE, August N (2010) A user ’s guide to 17 484 functional diversity indices. Ecological Monographs 80:469–484 18 For Peer Review 19 485 Sguotti C., Lynam C.P., García-Carreras B., Ellis J.R., Engelhard G.H. (2016) Distribution of skates and sharks 20 486 in the North Sea: 112 years of change. Global Change Biology 21 22 487 Shipley B., Vile D., Garnier E. (2006) From Plant Traits to Plant Communities: A Statistical Mechanistic 23 488 Approach to Biodiversity. Science 314:812–814 24 25 489 Silvertown J., Franco M. (1993) Plant Demography and Habitat: A Comparative Approach. Plant Species 26 490 Biology 8:67–73 27 28 491 Stevens J., Bonfil R., Dulvy N.K., Walker P.A. (2000) The effects of fishing on sharks, rays, and chimaeras 29 492 (chondrichthyans), and the implications for marine ecosystems. ICES Journal of Marine Science 30 493 57:476–494 31 32 494 Tsoukali S., Olsson K.H., Visser A.W., Mackenzie B.R. (2016) Adult lifetime reproductive value in fish 33 495 depends on size and fecundity type. 8:1–8 34 35 496 Violle C., Navas M-L., Vile D., Kazakou E., Fortunel C., Hummel I., Garnier E. (2007) Let the concept of trait 36 497 be functional! Oikos 116:882–892 37 38 498 Wiedmann M.A., Primicerio R., Dolgov A., Ottesen C.A.M., Aschan M. (2014) Life history variation in Barents 39 499 Sea fish: implications for sensitivity to fishing in a changing environment. Ecology and Evolution 40 500 4:3596–3611 41 42 501 Winemiller K.O. (1989) Patterns of variation in life history among South American fishes in seasonal 43 502 environments. Oecologia:225–241 44 45 503 Winemiller K.O. (2005) Life history strategies , population regulation , and implications for fisheries 46 504 management 1. Canadian Journal of Fisheries and Aquatic Sciences 62:872–885 47 48 505 Winemiller K.O., Fitzgerald D.B., Bower L.M., Pianka E.R. (2015) Functional traits, convergent evolution, and 49 506 periodic tables of niches. Ecology letters 18:737–751 50 51 507 Winemiller K.O., Rose K.A. (1992) Patterns of Life-History Diversification in North American Fishes: 52 508 implications for Population Regulation. Canadian Journal of Fisheries and Aquatic Sciences 49:2196– 53 54 509 2218 55 510 Wood S.N. (2006) Generalized additive models: an introduction with R. CRC press 56 57 58 59 60 Global Ecology and Biogeography Page 20 of 47
1 2 3 4 511 Table 5 6 7 512 Table 1. Variables and parameters of the best final generalized additive models (GAMs), with a 8 513 maximum of three abiotic variables and corrected for the sampling effort. DEV is the total 9 10 514 deviance explained by the model and RVI is the estimated relative variable importance of each 11 12 515 variable present in the final model, it corresponds to the percentage drop in the deviance 13 14 516 explained by the model when the variable is randomized. All variables in the final model are 15 517 significant (p<0.001). 16 17 18 Life history strategies ForBest model PeerDEV Review RVI (%) 19 20 SST + SST season + Depth 77 % 21 SST 12.4 22 Opportunistic 23 SST Seasonality 6.0 24 25 Depth 4.8 26 Depth + S BT + SST season 50.8 % 27 28 Depth 17.6 29 Periodic SST 10.5 30 31 SST Seasonality 4.5 32 33 SST + Depth + SST season 77.6 % 34 SST 18.3 35 Equilibrium 36 Depth 7.7 37 38 SST Seasonality 7.1 39 518 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 21 of 47 Global Ecology and Biogeography
1 2 3 4 519 Figures legends 5 6 7 520 Figure 1. Biplot of a principal component analysis (PCA) of the six selected traits. Each individual 8 521 species used in the study is represented as a dot. The three extreme points that envelope the trait- 9 10 522 space are represented as triangle and correspond to the three endpoints life history strategies, the 11 12 523 white, grey and black fill represents the Equilibrium, Opportunistic and Periodic strategies, 13 14 524 respectively. The two first principals components represent 77% of the traits variability. 15 16 525 Figure 2. Boxplot representing the median trait values and interquartile range for each life history 17 18 526 strategies, calculated fromFor their relativePeer species composition.Review 19 20 21 527 Figure 3. Proportion of the life history strategies (a) Equilibrium, (b) Opportunistic and (c) Periodic 22 528 in each ¼ degree cell based on the species abundance in the surveys. At each grid cell, the sum of 23 24 529 the three life history strategies proportion adds to one. The colour scheme represents the deciles 25 26 530 of the proportion distribution. 27 28 29 531 Figure 4. Relationships between the life history strategies proportion in the communities and their 30 532 environmental predictors retained in the best gam model. 31 32 33 533 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Global Ecology and Biogeography Page 22 of 47
1 2 3 4 534 Figures 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 535 38 39 536 Figure 1. 40 41 42 537 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 23 of 47 Global Ecology and Biogeography
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 538 29 30 539 Figure 2. 31 32 33 540 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Global Ecology and Biogeography Page 24 of 47
1 2 3 4 541 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 542 27 28 29 543 Figure 3. 30 31 544 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 25 of 47 Global Ecology and Biogeography
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 545 36 37 546 Figure 4. 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Global Ecology and Biogeography Page 26 of 47
1 2 3 Appendix S1: Information on the Bottom trawl surveys 4 5 Survey Area Years Month Number Gear type Codend Depth 6 of hauls (stretched mesh Range 7 size mm) (m)* 8 MEDITS Spanish 2002-2012 5-7 1460 GOC 73 20 25-799 9 Mediteranean Sea 76-128 10 MEDITS Balearic Islands 2002-2012 5-7 307 GOC73 20 38-755 11 74-252 12 SP-ARSA Gulf of Cadiz 2002-2012 11 447 Baka trawl 10 20-770 13 44/60 56-258 14 PT-IBTS Portugal Shelf Sea 2002-2011 9-11 505 NCT 20 21-196 15 No 2008 76-135 16 SP- North of Spain -Atl 2002-2012 2 1404 Baka trawl 10 33-808 17 NORTH 44/60 130-219 18 EVHOE Bay of Biscay For 2002-2012 Peer 10-12 Review 1312 GOV 36/47 20 21-198 19 103-145 20 NS-IBTS North Sea 2002-2012 1-2 3968 GOV 36/47 20 21-195 21 40-93 22 BITS Baltic Sea 2002-2012 10-11 1945 TV-3 20 21-116 23 28-53 24 IE-IGFS Ireland Shelf Sea 2003-2008 10-12 716 GOV 36/47 20 21-187 25 76-128 26 BTS-VIIa Irish Sea 2002-2012 9-10 913 GOV 36/47 20 21-120 27 30 -59 28 NI-GFS Irish Sea - Ireland 2008-2012 3 62 Rock 20 22-120 29 Hopper 36-83 30 SWC- Scotland Shelf Sea 2002-2012 11-12 380 GOV 36/47 20 30-196 31 IBTS No 2010 91-145 32 ROCKALL Rockall plateau 2002-2012 9 280 GOV 36/47 20 130-199 33 No 2004;2010 160-185 34 IGS Iceland 2002-2012 3 ~ 3500 Granton 40 25-504 35 trawl 135-246 36 G-BTS Greenland 2002-2012 10-11 933 140' bottom 20 49-449 37 trawl with 174-336 38 steel bobbins 39 * minimum-maximum Depth and interquantiles (25%-75%) 40 References 41 42 Bertrand, J.A., Gil de Sola, L., Papaconstantinou, C., Relini, G. and Souplet, A.. 2002. The general specifications of the MEDITS surveys. 43 Sci.Mar. vol 66 (Suppl.2): 9-17 p. 44 45 Bertrand JA, Leonori I, Dremiere PY, Cosimi G. 2002. Depth trajectory and performance of a trawl used for an international bottom 46 trawl survey in the Mediterranean. Sci Mar 66 Suppl 2):169–182 p. 47 48 ICES. 2010. Manual for the International Bottom Trawl Surveys in the Western and Southern Areas Revision III Agreed during the 49 meeting of the International Bottom Trawl Survey Working Group 22–26 March 2010, Lisbon 50 51 ICES. 2014. Manual for the Baltic International Trawl Surveys (BITS). Series of ICES Survey Protocols SISP 7 - BITS. 71 pp. 52 53 ICES. 2012. Manual for the International Bottom Trawl Surveys. Series of ICES Survey Protocols. SISP 1-IBTS VIII. 68 pp. 54 55 Fiorentini, L., P.-Y. Dremière, I. Leonori, A. Sala and V. Palumbo. 1999. Efficiency of the bottom trawl used for the Mediterranean 56 international trawl survey (MEDITS). Aquat. Living Resour.,12 (3): 187-205. 57 58 59 60 Page 27 of 47 Global Ecology and Biogeography
1 2 3 Fock, H.O., 2008. Driving-forces for Greenland Offshore Groundfish Assemblages: Interplay of Climate, Ocean Productivity and 4 Fisheries. J. Northw. Atl. Fish. Sci. 49, 103–118. 5 6 Pálsson O.K., Jonsson E., Schopka S., Stefansson G., Steinarsson B.AE. 1989. Icelandic groundfish survey data used to improve 7 precision in stock assessments. J. Northw. Atl. Fish. Sci. 9: 53-72. 8 Sólmundsson J., Steinarsson B.Æ., Jónsson E., Karlsson H., Björnsson H., Pálsson J., Bogason V.. 2010. Manuals for the Icelandic 9 bottom trawl surveys in spring and autumn. Marine Research in Iceland: 125 pp. 10 11
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1 2 3 Appendix S2: Spatial coverage of traits information. Percentages of species present in the ¼ degree 4 grid cells, for which all the traits were collected, and thus used in the analysis. The ¼ degree cells 5 6 mean is 96% and the median is 100% of the species present in the communities. 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 29 of 47 Global Ecology and Biogeography
1 2 3 Appendix S3: Explanatory variables selected in the study. Pairwise correlation plot of the 4 environmental variables kept in the study. All variables are correlated to at least r<0.8. 5 6 7 8 -600 -200 -1 1 2 5 15 30 0 400
9 Depth.sd *** *** *** *** *** *** *** *** 10 0.20 0. 086 0.36 0.21 0 . 1 7 0 . 1 3 0.47 0 . 1 4
11 0 800
12 Depth.max . *** *** *** *** *** 13 0. 0052 0.43 0.24 0.24 0.45 0.79 0. 041 14 -600 0 15 . CHLsd ***
16 0. 04 3 0. 0011 0. 0025 0.18 0. 012 0. 037 17 0.4 1.0 18 For PeerCHL Review 19 *** *** *** *** *** 0.27 0.60 0.53 0.56 0. 085
20 -1 1 21 . 22 SST *** *** 0 . 1 7 0.22 0. 043 0. 016
23 5 15 24 25 SSS *** *** ** 26 0.71 0.31 0. 063 27 5 20 35 28 SST.sea *** *** 29 0.42 0. 096 30 2 8 14 31 Depth *** 32 0.18
33 0 400 34 nbhauls 35
36 0 40 37 0 600 1400 0.4 0.8 5 15 2 6 12 0 40 80 38 39 40
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1 2 3 Appendix S4. Residuals sum of squares in function of the number of strategies. Mean and standard deviation 4 of the residuals sum of squares (RSS), i.e. quality of fit, of 10 archetypal analysis iterations in function of the 5 numbers of extreme points (k) used to encompass the traits space and representing the life history 6 7 strategies end-point. 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 31 of 47 Global Ecology and Biogeography
1 2 3 4 5 Appendix S4 Spatial pattern of the strategy based on species presence. Proportion of the life history 6 strategies (a) Equilibrium, (b) Opportunistic and (c) Periodic in each ¼ degree cell based on species presence 7 in the surveys. At each grid cell, the sum of the three life history strategies adds to one. The colour scheme 8 9 represents the deciles of the proportion distribution. 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Global Ecology and Biogeography Page 32 of 47
1 2 3 Appendix S5. Strategies proportion calculated based on abundance versus presence only. The grey line 4 correspond to the 1:1 relationship and the red line the fitted relationship 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 33 of 47 Global Ecology and Biogeography
1 2 3 Appendix S6. Seasonality and life history strategies pattern in the North Sea. Seasonality pattern of 4 the Opportunistic, Equilibrium and Periodic strategy in the North Sea from the NS-IBTS survey from 5 6 1991-1997 from Quarter 1 (Jan-Mar) to Quarter 4 (Oct-Dec). 7 8 Opportunistic 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Equilibrium 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 Periodic 57 58 59 60 Global Ecology and Biogeography Page 34 of 47
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 35 of 47 Global Ecology and Biogeography
1 2 3 Appendix S7 Relative variable importance and scale of the observations. Best model explaining the 4 variability of the strategies prevalence across different spatial scales. SST (sea surface temperature 5 6 in °C), SSTsea (SST seasonality in °C), CHL (Chlorophyll a concentration in mg/m3), CHL (CHL 7 variability throughout the year), Depth (mean depth of the samples in meters), Depth.max 8 (maximum depth or the studied grid in meters), Depth sd (depth variability in the grid), SSS (sea 9 10 surface salinity) Tot Dev: total deviance explained by the best final model (%) n: total number of 11 observations, x grid: number of original grid cell encompassed in the new scale. 12 13 Periodic 14 Scale (lat,lon) SST SST.sea CHL CHL.sd DEPTH DEPTH.max DEPTH.sd SSS Tot Dev. n x grid 15 (0.25,0.25) 10.50% 4.50% 17.60% 1797 1 16 (0.5,0.25) 10.60% 4.20% 17.80% 50.3 1386 2 17 (0.5,0.5) 11.40% 4.52% 17.63% 52.2 915 4 18 (1,1) 14.56% 4.25%For Peer 19.96% Review 57.6 368 16 19 (1.5,1.5) 18.00% 37.20% 7.60% 65.2 202 36 20 (2,2) 7.80% 15.10% 9.49% 64.3 150 64 21 (3,3) 15.43% 43.70% 7.81% 72.2 83 144 22 (4,4) 25.60% 31.89% 7.30% 81.6 62 256 23 (5,5) 22.56% 36.70% 72.7 43 400 24 (6.5,6.5) 53.60% 23.32% 64.4 29 676 25 (8.5,8.5) 22.76% 32.30% 9.90% 92.9 20 1156 26 27 Equilibrium 28 Scale (lat,lon) SST SST.sea CHL CHL.sd DEPTH DEPTH.max DEPTH.sd SSS Tot Dev. n x grid 29 (0.25,0.25) 18.30% 7.10% 7.70% 1797 1 30 (0.5,0.25) 17.80% 5.90% 8.60% 77.1 1386 2 17.80% 5.60% 9.20% 31 (0.5,0.5) 78.2 915 4 18.62% 5.75% 10.48% 32 (1,1) 82.9 368 16 16.30% 4.20% 8.60% 33 (1.5,1.5) 85.1 202 36 (2,2) 14.39% 12.43% 3.37% 85 150 64 34 (3,3) 11.57% 10.86% 1.37% 88.2 83 144 35 (4,4) 22.20% 8.77% 5.75% 91 62 256 36 (5,5) 3.46% 2.84% 9.21% 93.7 43 400 37 (6.5,6.5) 14.40% 9.78% 0.03% 95.9 29 676 38 (8.5,8.5) 22.90% 18.40% 95.9 20 1156 39 40 Opportunistic 41 Scale (lat,lon) SST SST.sea CHL CHL.sd DEPTH DEPTH.max DEPTH.sd SSS Tot Dev. n x grid 42 (0.25,0.25) 12.40% 6% 4.80% 1797 1 43 (0.5,0.25) 10.80% 6.80% 4.30% 76 1386 2 44 (0.5,0.5) 10.62% 7.24% 4.46% 76.8 915 4 45 (1,1) 12.90% 6.38% 5.18% 81.1 368 16 46 (1.5,1.5) 12.68% 3.71% 5.40% 83.9 202 36 47 (2,2) 16.59% 11.56% 4.80% 85.2 150 64 48 (3,3) 18.90% 12.70% 4.00% 88.4 83 144 49 (4,4) 9.69% 2.78% 3.12% 92.3 62 256 50 (5,5) 39.89% 7.20% 8.21% 93.1 43 400 51 (6.5,6.5) 26.30% 2.77% 7.69% 95.9 29 676 52 (8.5,8.5) 5.02% 4.24% 4.80% 98.1 20 1156 53 54 55 56 57 58 59 60 Global Ecology and Biogeography Page 36 of 47
1 2 3 Appendix S8 Variables and parameters of the best final generalized additive models based on 4 presence. DEV: degree of explained variance; RVI is the estimated variable importance of each 5 6 variables in the model. All variables in the final model are significant at a level p<0.001. 7 8 Life history strategies Best model DEV RVI (%) 9 SST season + SST + Depth 68.1 % 10 SST Seasonality 9.4 Opportunistic 11 SST 7.4 12 Depth 4.8 13 SST + Depth + SST season 45.2 % 14 SST 16.7 15 Periodic Depth 11.1 16 17 SST Seasonality 5.4 18 SST season + SST + Depth 69.2 % SSTFor seasonality Peer Review17.7 19 Equilibrium 20 SST 15.8 21 Depth 3.9 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 37 of 47 Global Ecology and Biogeography
1 2 3 Appendix S9 Fishing intensity and sediment impact on strategies. Best model and relative variable 4 importance of the strategies proportions in function of a spatial subset of the environmental 5 6 variable for which fishing intensity and sediment type and dominance are available (n=936) 7 8 Life history strategies Best model DE V RVI (%) 9 SST season + Depth + CHL 77 % 10 SST Seasonality 5.7 Opportunistic 11 Depth 3.3 12 CHL 2.4 13 CHL + Depth + SST season 69.4 % 14 CHL 6.9 15 Periodic Depth 3.7 16 17 SST Seasonality 1.4 18 SST + Depth + Fishing intensity 36.7 % SSTFor Peer Review16.6 19 Equilibrium 20 Depth 6.0 21 Fishing intensity 8.4 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Global Ecology and Biogeography Page 38 of 47
1 2 3 Appendix S10 Best final gam models in the subarea where fishing intensity is available. 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 For Peer Review 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 39 of 47 Global Ecology and Biogeography
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