Short-term prey field lability constrains individual
specialisation in resource selection and foraging site
fidelity in a marine predator

Nicolas Courbin, Aurélien Besnard, Clara Péron, Claire Saraux, Jérôme Fort, Samuel Perret, Jérémy Tornos, David Grémillet

► To cite this version:

Nicolas Courbin, Aurélien Besnard, Clara Péron, Claire Saraux, Jérôme Fort, et al.. Short-term prey field lability constrains individual specialisation in resource selection and foraging site fidelity in a marine predator. Ecology Letters, 2018, 21 (7), pp.1043-1054. 10.1111/ele.12970. hal-01774958

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# Short-term prey field lability constrains individual specialisation in resource selection and foraging site fidelity in a marine predator

Courbin Nicolas<fnref n="1" />,*, Besnard Aurélien<fnref n="1" />, Péron Clara<fnref n="2" />, Saraux Claire<fnref n="3" />, Fort Jérôme<fnref n="4" />, Perret Samuel<fnref n="1" />, Tornos Jérémy<fnref n="1" />, Grémillet David<fnref n="1" />,<fnref n="5" />, Nathan Ran

## Abstract :

Spatio-temporally stable prey distributions coupled with individual foraging site fidelity are predicted to favour individual resource specialisation. Conversely, predators coping with dynamic prey distributions should diversify their individual diet and/or shift foraging areas to increase net intake. We studied individual specialisation in Scopoli's shearwaters (Calonectris diomedea) from the highly dynamic Western Mediterranean, using daily prey distributions together with resource selection, site fidelity and trophic-level analyses. As hypothesised, we found dietary diversification, low foraging site fidelity and almost no individual specialisation in resource selection. Crucially, shearwaters switched daily foraging tactics, selecting areas with contrasting prey of varying trophic levels. Overall, information use and plastic resource selection of individuals with reduced short-term foraging site fidelity allow predators to overcome prey field lability. Our study is an essential step towards a better understanding of individual responses to enhanced environmental stochasticity driven by global changes, and of pathways favouring population persistence.

**Keywords** : Behavioural consistency, Calonectris diomedea, foraging site fidelity, resource selection, Scopoli's shearwater, temporal resource dynamic, Western Mediterranean

<footnote n="1">¹ Centre d'Ecologie Fonctionnelle et Evolutive (CEFE); UMR 5175; Centre National de la Recherche Scientifique (CNRS); Université de Montpellier; Université Paul Valéry Montpellier; Ecole Pratiques des Hautes Etudes (EPHE); 1919 Route de Mende 34293 Montpellier Cedex 5, France</footnote>

<footnote n="2">² Marine Biodiversity Exploitation and Conservation (MARBEC); UMR 248; Institut de Recherche pour le Développement (IRD); Université de Montpellier; Place Eugène Bataillon - bât 24 - CC093 34095 Montpellier Cedex 5, France</footnote>

<footnote n="3">³ Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER); UMR 248 MARBEC; Avenue Jean Monnet CS 3017 34203 Sète, France</footnote>

<footnote n="4">⁴ Littoral, Environnement et Sociétés (LIENSs); UMR 7266; Centre National de la Recherche Scientifique (CNRS); Université La Rochelle; 2 rue Olympe de Gouges 17000 La Rochelle, France</footnote>

<footnote n="5">⁵ FitzPatrick Institute; DST/NRF Excellence Centre at the University of Cape Town; Rondebosch 7701, South Africa</footnote>

<footnote>* Corresponding author : Nicolas Courbin, email addresses : nicolas.courbin@cefe.cnrs.fr; ncourbin@gmail.com</footnote>
## 51 Introduction

Individual specialization (i.e. consistent trait variations among individuals [Sutherland 1987; Araújo et al. 2011]) shapes population-level behavioral diversity (Woo et al. 2008; Potier et al. 2015), and has strong implications for evolutionary processes and ecological interactions, affecting population and community dynamics (Araújo et al. 2011; Bolnick et al. 2011; Violle et al. 2012; Wolf & Weissing 2012; Spiegel et al. 2017). Individual specialization is related to differences in physiological, behavioral and ecological mechanisms (Sutherland 1987; Fortin et al. 2008; Araújo et al. 2011; Bolnick et al. 2011; Spiegel et al. 2017) and is found across terrestrial and marine taxa (Bell et al. 2009; Araújo et al. 2011; e.g. fish: Allgeier et al. 2017; mammals: Duchesne et al. 2010; Leclerc et al. 2016; birds: Phillips et al. 2017). Studying the mechanisms by which individual specialization arises is critical to test theoretical approaches (Bolnick et al. 2011), and develop relevant conservation strategies (Duchesne et al. 2010; Wakefield et al. 2011; Merrick & Koprowski 2017), especially in the context of rapid global change.

Individual specialization in resource selection (ISRS) is the within-individual consistency in selection of a subset within a range of available resources that differs from selection by other individuals. Mechanistic models suggested that individual resource specialization increases with intra-specific competition, to mitigate reduction in availability of the preferred prey, and decreases in dense populations to yield low resource levels and high fitness costs for specialists (Svanbäck & Bolnick 2005; Fortin et al. 2008). Empirical studies confirmed these predictions (Araújo et al. 2011; e.g. fish: Svanbäck & Bolnick 2007; sea otter: Tinker et al. 2012; seabirds: Navarro et al. 2017). For example, inter-individual variations in resource selection within a red deer (*Cervus elaphus*) population on the Isle of Rum (Scotland) reflected selective pressures for density- and frequency-dependent foraging tactics (Fortin et al. 2008). Individual specialization in foraging was also related to spatio-
temporal changes in resource availability and prey predictability (Woo et al. 2008; Phillips et al. 2017). Most empirical studies (e.g. reviewed for seabirds in Ceia & Ramos 2015) found reduced individual specialization in diet and habitat use in years with lower resource accessibility (Catry et al. 2014). In systems with rapid (day to week) changes in abiotic conditions, high prey patchiness and low prey predictability (e.g. Weimerskirch 2007; Fernandez et al. 2017), low ISRS is expected. Indeed, in such a situation, the costs of tracking one elusive resource could outweigh foraging benefits of specialization related to predator abilities and prey features.

ISRS offers a selective advantage if individuals increase foraging efficiency by reducing search times, and this is related to their abilities to locate prey (Spiegel & Crofoot 2016) and to prey predictability (Phillips et al. 2017). When prey distributions are predictable, predators can reduce search time using memory (Weimerskirch 2007; Wakefield et al. 2013) and/or public information (Weimerskirch et al. 2010; Wells et al. 2016). In contrast, if prey distributions are unpredictable, information cannot allow maintaining ISRS. Individuals may overcome low spatio-temporal prey predictability by diversifying their diet, either through generalist foraging tactics (no selection) or by alternating foraging tactics targeting specific prey items (narrow short-term resource selection). In colonial species, this is promoted by the breeding site functioning as an information centre (Harel et al. 2017). Previous studies showed predator specialization on resources that are temporally dynamic but predictable in space and/or time. For instance, Nile crocodiles (*Crocodylus niloticus*) prey on migrating wildebeest (*Connochaetes taurinus*) at predictable times (Subalusky et al. 2017). However, the relative effect of the two main contrasting drivers, intra-specific competition and prey predictability, acting upon ISRS remains largely unknown.

To tackle such question, we must consider behaviours that distort the interplay between resource selection, ISRS and prey predictability. Individual foraging site fidelity
101 (IFSF, i.e. consistent use of the same foraging areas by an individual, Wakefield et al. 2015)
102 is a widespread behaviour within colonial marine predator populations (Arthur et al. 2015;
103 Baylis et al. 2015; Wakefield et al. 2015), probably selected for the benefit of site familiarity
104 (Wakefield et al. 2015) or because predators go where their preferred prey usually occur.
105 IFSF increases with environmental stability (Switzer 1993) and should be positively
106 correlated with resource selection and ISRS when prey distribution is stable and not depleted
107 (Fig. 1). Accordingly, seabirds show both IFSF and individual specialization in habitat use
108 (reviewed in Ceia & Ramos 2015; e.g. Grémillet et al. 1999; Patrick et al. 2014; Wakefield et
109 al. 2015). The determinants of ISRS remain nevertheless unclear: Does individual
110 specialization on a given prey type occur because of IFSF, or do individuals track specific
111 prey types occurring systematically at the same place (ISRS as well as IFSF)? Theoretically,
112 prey predictability should enforce a trade-off between IFSF and ISRS. In environments with
113 rapidly changing spatio-temporal prey distribution, visits to the same foraging areas should
114 promote generalist foraging tactics (dietary diversification) and low ISRS, while low IFSF
115 allows specialized foraging tactics and high ISRS (Fig. 1). If specialization costs in prey type
116 (ISRS) or site (IFSF) are too high, individuals may use generalist or short-term specialized
117 foraging tactics and switch among foraging areas leading to low ISRS and low IFSF.
118 Understanding if and how ISRS arises in environments with low prey predictability thus
119 requires to simultaneously consider IFSF and ISRS in a dynamic habitat selection framework
120 including prey availability fluctuations (i.e. disproportionate use relative to resource
121 availability; Manly et al. 2002).

122 We studied the ecological drivers of ISRS in Scopoli's shearwaters (*Calonectris diomedea*) using an analytical framework combining modelling of prey field lability, spatial
123 and trophic ecology of shearwaters (see Appendix S1). We tracked birds with GPS loggers
124 during four chick-rearing seasons within the Gulf of Lions (Western Mediterranean, Fig. 2), a
125
marine system associated with daily to yearly environmental stochasticity (Saraux et al. 2014; Appendix S2, Fig. S2.1). We used hydro-acoustic estimations of biomass (summed individual masses) and net sampling estimations of abundance (number of individuals) of prey targeted by shearwaters, to develop dynamic prey habitat models and predict prey daily spatial distributions according to shifting oceanographic conditions. We then assessed resource selection for shearwaters, based on daily prey fields, to test the following predictions: 1) Shearwaters show low ISRS and low IFSF at the individual level, and switch between specialized foraging tactics (selection related to trophic level and/or prey type over the short-term). 2) IFSF is uncoupled from ISRS and resource selection at the individual level. 3) Information use for prey localization partly drives ISRS. Finally, we confronted our findings with seabird trophic analyses. Overall, our study aimed at determining how short-term predictability in prey distribution shapes ISRS and its mechanisms within foraging populations.

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## 140 Material and Methods

### 141 *Study system*

Our study was carried out during four chick-rearing seasons (in 2011, 2012, 2014 and 2015) at two Marseille islands (Frioul and Riou, 43°11'00"N, 05°23'00"E) located in the Western Mediterranean Sea (Fig. 2). Marseille's archipelago hosts one of the largest French breeding colonies of Scopoli's shearwater (Anselme & Durand 2012), with approximately 520 breeding pairs. During the chick-rearing period, Scopoli's shearwaters are known to feed on a wide range of prey, from zooplankton to small pelagic fish and squid (Sarà 1993; Ramos et al. 2009). The dynamic spatial distribution of these prey mainly relies on stochastic inputs of nutrients associated with the Rhône River discharge, on coastal upwelling due to northerly winds, and on the spatial extent of the Rhône freshwater plume induced by wind forcing.
Consequently, small pelagic fish show strong inter-annual heterogeneity in their summer
spatial distribution (Saraux *et al.* 2014). Additionally, we demonstrated high spatio-temporal
variations in fish and zooplankton distribution on a daily scale (Fig. S2.1).

154

155 GPS tracking and feather sampling

156 We tracked the foraging movements of 75 breeding Scopoli's shearwaters of both sexes
during the chick-rearing period using GPS loggers between the 29th July and the 18th August
157 in four years (n = 18, 27, 19 and 20 birds in 2011, 2012, 2014 and 2015, respectively; see
158 Appendix S3). Individuals were caught at the nest at night. We equipped birds with GPS
159 loggers (Perthold Engineering LCC) encapsulated in heat-shrink tubing and attached to back
160 feathers with Tesa tape. Tagged birds weighted 604g on average (range: 500 to 753g), and
161 tagging load (22.7g) represented ~3.8% of individual's body mass (range: 3.0 to 4.5%). GPSs
162 recorded a location every 1.5, 2 or 3 min depending on individuals and all GPS tracks were
163 interpolated to regular 2 min intervals. Birds were recaptured and loggers were retrieved after
164 1 to 5 days. At recapture, and before releasing birds, we collected the tip (1-2 cm) of the first
165 primary feather to assess their trophic position using stable isotopic analyses (see Appendix
166 S4). Through GPS-tracking, we thus had information on the foraging ecology of shearwaters
167 for periods of up to 5 days, and for approximately two-week periods (feather growth period)
168 through isotopic analyses. No effect of manipulation and instrumentation was found on the
169 breeding performance and divorce rate of shearwaters at this study site (Authier *et al.* 2013).

170 Also, device and exposure to stress potentially affecting short-term behavior was
171 standardized, as all birds were handled in exactly the same manner.

173

174 Identification of foraging locations
We considered a foraging trip as a round trip between colony/colony or colony/raft because sometimes shearwaters stopped at rafts localized within 4 km of the colony before starting a new trip. We identified the foraging locations of each trip using the residence in space and time method (Torres et al. 2017). This method discriminates between behavioural states by calculating the amount of space and time occupied by an individual in an area of constant surface (a circle of radius *r*): Movement across long distances and protracted residency time within an area reflect foraging behaviour; short movement distances and long residency time reflect resting, and short distance and time correspond to travelling. The radius *r* was determined with an automated dynamic scaling process for each foraging trip (Torres et al. 2017). Its average value was 0.52 ± 0.14 km (± SD). Foraging locations corresponding to surface pecking behavior may have been omitted, yet previous studies showed that this behavior is marginal in shearwaters (Camphuysen & Van Der Meer 2001).

187

188 *Prey field modelling*

189 To obtain daily shearwater prey distributions (zooplankton and pelagic fish; Sarà 1993; Ramos *et al.* 2009) across our entire study area, we modelled biomasses (tonnes) of European anchovies (*Engraulis encrasicolus*), sardines (*Sardina pilchardus*) and sprats (*Sprattus sprattus*) of both juvenile and adult stages. We also modelled the abundance (ind.m⁻³) of two zooplankton groups: ‘preferred zooplankton prey’ (copepods, euphausiids, fish eggs and fish larvae) and ‘others’, based on shearwater dietary preferences (Appendix S2). Empirical data for potential prey were collected each year in July during PELMED oceanographic campaigns (Appendix S2, Fig. S2.2). We used spatial generalized linear mixed models with a Horseshoe prior in a Bayesian framework (Authier *et al.* 2017) and models were fitted with daily dynamic oceanographic predictors (Fernandez *et al.* 2017, see Appendix S2). Such models make robust and accurate predictions for fish biomass in the Gulf of Lions, including
extrapolations (Authier *et al.* 2017). We assessed zooplankton model robustness using leave-one-out cross-validation (see Appendix S2). The ecological niches of small pelagic fish and zooplankton are sustained across the summer period in the Gulf of Lions (Plounevez & Champalbert 2000), and the slight temporal mismatch between prey field assessment and bird tracking was not an issue.

205

206 *Resource selection of shearwaters*

We assessed Scopoli's shearwater resource selection functions (RSF; Manly *et al.* 2002) based on daily prey distributions. Such analyses are rarely performed in marine ecology (but see Wakefield *et al.* 2009; Raymond *et al.* 2015) because direct information on prey availability is particularly difficult to obtain (Grémillet *et al.* 2004; Torres *et al.* 2008). RSF compared the daily predictions for the six fish biomasses, the two zooplankton abundances and their predictability (i.e. variance of biomasses or abundances calculated over the previous seven days) at foraging locations and at available locations where birds could potentially forage. Prey-related variables were standardized, and availability was determined in two steps. First, for each observed foraging trip, we simulated 10 tracks that started at the same location as the observed trip using a first-order vector autoregressive model (Raymond *et al.* 2015). Simulated tracks considered no habitat preference, while respecting constraints on trip structure (duration and travel speed; Raymond *et al.* 2015). We then identified available foraging locations for each simulated track using the residence in space and time method described previously (Torres *et al.* 2017). Foraging locations and their associated available locations occurred at the same spatio-temporal scale and we therefore controlled for short-term variability in prey spatial distribution. Additionally, with simulated tracks being spatially anchored to the colony, we used a non-uniform spatial distribution of availability adapted to

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study central-place foragers that use areas close to the colony more frequently than elsewhere
(Wakefield *et al.* 2009; Monsarrat *et al.* 2013).

We fitted one RSF for each of the 16 prey-related variables, and estimated RSF coefficients using mixed-effects logistic regressions (Gillies *et al.* 2006; Hebblewhite & Merrill 2008; Courbin *et al.* 2013). We used nested random intercepts to accommodate the three-level hierarchical structure of our data: foraging locations (n = 56945) within foraging trips (n = 182) within individuals (n = 75) (Hebblewhite & Merrill 2008). Random intercepts corrected for differences in sample size between foraging trips and individuals (Gillies *et al.* 2006). We added random coefficients (one slope per individual and per trip) to assess differences in resource selection between individuals and trips (conditional estimates) in addition to estimate population-level responses (marginal estimate; Gillies *et al.* 2006; Hebblewhite & Merrill 2008; Leclerc *et al.* 2016; Carneiro *et al.* 2017). We tested the added value of using random coefficients based on the Akaike Information Criterion (AIC). RSF modelled for foraging location *i*, trip *j* and shearwater *k* took the form:

$$w(\mathbf{x}_{ijk}) = \exp(\beta_0 + \mathbf{X}\boldsymbol{\beta} + \gamma_{0jk}^{(trip)} + \gamma_{0k}^{(bird)} + \gamma_{xjk}^{(trip)}x_{ijk} + \gamma_{xk}^{(bird)}x_{ijk}), \quad (1)$$

where $w(\mathbf{x}_{ijk})$ is the relative probability of selecting resources for shearwaters, $\mathbf{x}$ is a vector, $\beta_0$ is the mean intercept, $\mathbf{X}\boldsymbol{\beta}$ is the vector of the fixed-effect resource selection coefficients for the set of covariate (i.e. the standardized fish biomasses, standardized zooplankton abundances or their standardized variances, year [four levels] and islands [two levels]), $\gamma_{0jk}^{(trip)}$ and $\gamma_{0k}^{(bird)}$ are the random intercepts at the trip and individual levels, respectively, $\gamma_{xjk}^{(trip)}$ is the random coefficient of prey-related variable $x$ for trip $j$ and shearwater $k$, and $\gamma_{xk}^{(bird)}$ is the random coefficient of $x$ for shearwater $k$. No multicollinearity
248 between variables was detected (variance inflation factor being < 2; Dormann et al. 2013).

249 We accounted for multiple comparisons by adjusting the 95% confidence interval of RSF coefficients with Benjamini-Yekutieli correction (Benjamini & Yekutieli 2001). RSFs were performed with the lme4 package (Bates et al. 2015) of the R software (R Development Core Team 2016).

253

254 *Assessing individual specialization in resource selection (ISRS)*

255 We quantified ISRS, i.e. within-individual consistency in resource selection, by calculating an adjusted repeatability index $R_k$ for each shearwater $k$ between its different trips (47 birds were tracked for multiple trips) and each prey-related variable. We adapted the population-level repeatability index $R$ developed for logistic regression with random coefficients (Johnson 2014; Nakagawa et al. 2017) at the individual-level, by partitioning the global observation-level (GPS location level) variance $\sigma_e^2$ at the individual level, as usually done for linear models (Potier et al. 2015; Wakefield et al. 2015; see Appendix S5). In a mixed RSF for foraging location $i$, trip $j$ and shearwater $k$, $R_k$ took the form:

263

$$R_k = \frac{\sigma_{x,between}^2}{\sigma_{x,between}^2 + \sigma_{x,within}^2 + \sigma_{ek}^2} \quad (2)$$

265

266 where $\sigma_{x,between}^2$ and $\sigma_{x,within}^2$ are the between- and within-individual variances (bird and trip level, respectively) estimated from Eq. 1 with a random effect for the prey-related variable $x$, $\sigma_{ek}^2$ is the partitioned observation-level variance for the $k$th bird given a logit link.

269 $R_k$ values range between 0 and 1, with 1 meaning high consistency in resource selection for shearwater $k$ between its different trips. Low $R_k$ values arise from heterogeneous resource selection among foraging trips or locations of shearwater $k$ (i.e. high within-

271
individual and/or partitioned observation-level variance). Analyses were performed with the
`rptR` package (Stoffel *et al.* 2017) of the R software (R Development Core Team 2016).

Assessing individual foraging site fidelity (IFSF)

We quantified within-individual consistency in the use of space, an index of IFSF (Wakefield *et al.* 2015). For each shearwater *k* in each year (*n* = 47 individuals and 154 trips), we calculated the 95% utilization distributions (UDs) for each of its trips based on foraging locations only, and assessed the mean overlap between all pairwise combinations of consecutive UDs using Bhattacharyya's affinity (*BA*<sub>*k*</sub>, Fieberg & Kochanny 2005). *BA*<sub>*k*</sub> ranges between 0 (no spatial overlap) and 1 (perfect spatial overlap). All UDs were calculated with a fixed bandwidth (Wakefield *et al.* 2015) of 0.5 km that encompassed the extent of almost all bandwidth estimations (*href*) calculated for a random sample of foraging areas and individuals (*n* = 32, c.a. 93-quantile of *href* = 0.5 km), on a 0.3 km resolution grid.

We determined if overlap among foraging areas of different trips of a given individual
*k* (*BA*<sub>*k*</sub>) differed from a random expectation, *BA*<sub>*k*,random</sub>. *BA*<sub>*k*,random</sub> was the average overlap
between each UD for observed trips and each UD for the immediate previous and next
simulated tracks. We compared *BA*<sub>*k*</sub> and *BA*<sub>*k*,random</sub> with a paired t-test. We also tested if *BA*<sub>*k*</sub>
 differed from overlap among foraging areas of different individuals, *BA*<sub>*k*,between</sub>. *BA*<sub>*k*,between</sub> was
the average overlap for individual *k* between each of its UD and the UDs of other individuals
occurring within ± 1 day. We contrasted *BA*<sub>*k*</sub> and *BA*<sub>*k*,between</sub> with a paired t-test. Analyses were
performed with the *adehabitat* package (Calenge 2006) of the R software (R Development
Core Team 2016).

Finally, we used Spearman rank correlation to investigate the relationships between
IFSF (*BA*<sub>*k*</sub>) and ISRS (*R*<sub>*k*</sub>), and between IFSF (*BA*<sub>*k*</sub>) and individual selection coefficients
296 $(\gamma_{xk}^{(bird)})$ in Eq. 1) for each prey-related variable. P-values were adjusted with Benjamini-

297 Yekutieli correction (Benjamini & Yekutieli 2001).

298

299 *Identifying foraging trip tactics*

300 In order to identify the main foraging trip tactics within the shearwater population, reflecting a

301 potential gradient of specialization with respect to trophic level and/or prey type, we

302 discriminated between groups of trips (cluster) with similar resource selection. We conducted

303 the analysis at the trip level because of higher heterogeneity within, than between individuals

304 (Fig. 3). We ran a hierarchical cluster analysis with a Ward clustering method using selection

305 coefficients of 182 trips $(\gamma_{xjk}^{(trip)})$ in Eq. 1) for biomasses of adult anchovies, adult sprats and

306 abundances of both zooplankton groups. The selection coefficients for other variables of

307 biomass had low variations around 0 and were not considered in the cluster analysis (Fig. 3B).

308

309 **Results**

310 We collected 2.4 ± 1.8 trips per bird (mean ± SD, ranging from 1 to 11 trips), characterized by

311 an average trip range of 59 ± 46 km from the colony, a total travelled distance of 218 ± 167

312 km, and a duration of 22.2 ± 11.9 hours (Appendix S6, Fig. S6.1). Foraging areas mostly

313 occurred within the continental shelf of the Gulf of Lions and along the continental slope,

314 generally within a 40 km-radius from the colony and over the -100m isobath (Fig. 2, Fig.

315 S6.2).

316

317 *Rapid changes in resource selection feature low ISRS*

318 Random coefficients for prey-related variables improved fit of RSFs ($\Delta$AIC > 6652 for all

319 models). RSFs including the variance of biomasses of all juvenile fish and adult sardines

320 failed to converge. At the population-level, shearwaters selected foraging areas with higher
abundances of both zooplankton groups and generally lower fish biomass than expected
randomly (Appendix S6, Table S6.1). However, this apparent habitat selection pattern
resulted from heterogeneous selection among individuals, and to a greater extent, from
heterogeneous selection across trips of the same individuals (Table S6.1, Fig. 3). The
relatively high within-individual variability in selection featured a low ISRS for each prey-
related variable ($R_k < 0.3$, Fig. 4A, Table S6.2).

At the trip level, clustering revealed two foraging trip tactics (Fig. S6.3). Shearwaters
selected different prey and trophic levels among their trips, which entailed rapid changes in
their resource selection (Table S6.3). During most trips (54%), shearwaters selected areas
with higher abundances of both zooplankton groups, as well as areas with higher biomasses of
adult anchovies and adult sprats, than expected randomly (Fig. 5A). The biomass of pelagic
fish increased along the shelf edge and at the Cassis canyon head close to the colony, while
zooplankton-preferred group was associated with coastal areas, the plume of the Rhône river,
and canyons (Appendix S2, Table S2.1, S2.5, S2.7, Fig. S2.3). Selection of high zooplankton-
other abundance required cautious interpretation due to the poor prediction ability of its
model, while zooplankton-preferred abundance was correctly predicted (Fig. S2.4).
Shearwaters used zooplankton-only specialist trips to a lesser extent (46%), selecting areas
with a higher zooplankton-preferred abundance and lower fish biomasses than randomly
expected (Fig. 5B). Birds always avoided the most coastal foraging areas with high biomass
of sardines and juvenile sprats (Fig. 5, Table S2.3, S2.4, S2.6, Fig. S2.3). Isotopic analyses
revealed that the trophic position of shearwaters was 3.30 ± 0.25 (median ± IQR, Appendix
S4, Fig. S4.1), and confirmed that most birds used both foraging tactics and foraged on
different trophic levels including zooplankton (trophic position = 2) and small pelagic fish
(trophic position = 3, Fig. 6).
346 *Low individual foraging site fidelity*

347 Shearwaters showed low IFSF between consecutive trips, with values only slightly higher
348 than for a random process (mean BA<sub>k</sub> = 0.135, mean BA<sub>k,random</sub> = 0.084, t-test: t<sub>46</sub> = 5.43, P <
349 0.001, Fig. 4B). Different individuals shared some foraging areas, with similar BA index
350 found within and between birds (mean BA<sub>k,between</sub> = 0.131, t-test: t<sub>46</sub> = 0.39, P = 0.70, Fig. 4B).

351 IFSF was not related to the number of trips performed by single individuals (r² < 0.01, P =
352 0.51). IFSF was uncoupled from shearwater resource selection and its within-individual
353 consistency (ISRS).

354

355 *Prey predictability shapes search patterns in shearwaters*

356 Shearwaters used directional search to find fish as they selected foraging areas with high
357 predictability (i.e. lower variance) of biomasses of adult anchovies and sprats (Fig. 5A). In
358 contrast, they selected areas with high zooplankton abundance independently from their
359 predictability (Fig. 5). Interestingly, zooplankton abundances were less predictable than fish
360 biomasses and temporal autocorrelation in zooplankton abundances was low after only one
361 day, the mean trip duration (Fig. S2.1).

362

363 **Discussion**

364 As theoretically expected, our broad analytical framework allowed us to demonstrate that
365 short-term prey predictability induces low individual specialization in resource selection
366 (ISRS) and low individual foraging site fidelity (IFSF) within a predator population.

367 According to RSF and isotopic analyses, breeding Scopoli's shearwaters searched for
368 zooplankton and fish within the Gulf of Lions, where we showed daily changes in prey
369 distribution. To overcome prey field lability, we found that shearwaters searched for
370 zooplankton patches, which they detect remotely via olfactory cues (Dell'Ariccia et al. 2014).
371

Such tactics do not require previous information on prey distributions. Indeed, RSFs showed that shearwaters selected zooplankton patches independently from their predictability.

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However, environmental variation (e.g. wind conditions, low prey availability) and lack of luck (stochastic noise) may prevent shearwaters from detecting distant zooplankton patches.

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Consequently, we found that shearwaters frequently switched between alternative foraging tactics, and their selection of predictable fish patches may suggest memory/public information use. Crucially, we demonstrated how low prey predictability shaped specialized foraging tactics over the short-term and potentially information use, and resulted in the use of varied foraging sites and diet items.

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381

*Prey field lability constrains individual specialization in resource selection*

382

Costs associated with intra-specific competition and niche overlap favour individual specialization (Araújo *et al.* 2011; Bolnick *et al.* 2011; Wolf & Weissing 2012; Wakefield *et al.* 2013), and lead to the emergence of competing density-dependent foraging behaviours and diet (Fortin *et al.* 2008; Tinker *et al.* 2012). This is true in stable environments, but when prey distributions vary spatio-temporally and become temporarily inaccessible, fitness costs associated with narrow resource selection can increase (Fortin *et al.* 2008; Woo *et al.* 2008).

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To increase net intake when tracking highly mobile prey, predators can diversify their diet and use selection patterns allowing them to exploit different prey simultaneously, as demonstrated for wolves (*Canis lupus*; Courbin *et al.* 2013). Low ISRS is thus expected if behavioural plasticity is not dramatically constrained (Sih *et al.* 2004, Hendry 2016) and if higher encounter rates favoured by behavioural plasticity overcome the potential benefits of specialization (improved search image and handling technique). As hypothesized, shearwaters had low short-term ISRS, and isotopic analyses confirmed that most individuals consumed various prey at different trophic levels (Fig. 6; median individual trophic position was 3.30;

389

390

391

392

393

394

395
Fig. S4.1). Zooplankton-based diets are common in seabirds (Savoca & Nevitt 2014) and, in our study system, fish and zooplankton-preferred have close calorific contents (Harmelin-Vivien et al. 2012). Hence, diet diversification and low ISRS should not be promoted by a low zooplankton nutritional value. Overall, low spatio-temporal predictability in prey distributions constrained predators to diversify their diet at the individual level, thereby diminishing inter-individual differences. However, trophic position appeared more heterogeneous among individuals in some years (2010 and 2011; Fig. S4.1), revealing that competing foraging strategies may emerge (at low frequency) in dynamic systems.

Seabird populations can switch diets between years (Garthe et al. 2011; Tew Kai et al. 2013), ultimately limiting negative effects on population dynamics (Cury et al. 2011). Whether such dietary plasticity arises from variability in prey availability, or from predator behavioural changes is often unclear (Hunsicker et al. 2011). In our study system, we showed that inter-annual dietary switches can arise from changes in resource selection, rather than in prey availability (Table S6.5). Thereby, changes in prey distribution and availability occurring at multiple temporal scales (day to year) shaped individual specialization: Short-term changes prevented short- and long-term specialization, while long-term changes tended to favour individual specialization in the shorter-term.

Relaxing individual foraging site fidelity is not sufficient in dynamic prey fields

In systems with low prey predictability, low ISRS should arise when individuals use a generalist foraging tactic (no selection), or each individual uses different short-term specialized foraging tactics. IFSF is common in colonial central-place predators (Arthur et al. 2015; Baylis et al. 2015; Ceia & Ramos 2015; Wakefield et al. 2015). When prey distribution varies daily, as observed in our studied system, individuals have to balance ISRS and IFSF, and IFSF should imply generalising of diet and habitat use, while preventing specialization
421 (Fig. 1). As theoretically expected, by relaxing IFSF only, Scopoli's shearwaters were able to
422 track daily changes in prey distribution, selecting foraging areas with high biomasses of adult
423 anchovies and adult sprats, and high zooplankton abundance. Antarctic fur seals
424 (*Arctocephalus gazella*) tracking changes in prey distribution during the winter also showed
425 low IFSF (Arthur *et al.* 2015).

426 Our dynamic study area and associated low predictability in prey distributions implied,
427 however, that shearwaters switched foraging tactics on a daily basis. This resulted in rapid
428 changes in individual resource selection. Birds used 1-day trips, either focusing on nearby
429 areas with high zooplankton abundance, or also searching more distant waters harbouring
430 adult anchovies or sprats. Short-term resource selection by shearwaters highlighted their
431 foraging plasticity when exposed to changes in prey distributions. This strategy, which could
432 allow predators to maintain their foraging efficiency, requires some capacity to predict prey
433 distributions (Pettex *et al.* 2010), and/or to reduce search time with the prospect of exploring
434 alternative prey patches.
435

435

436 *Linking prey predictability, information use and ISRS*

437 Our study emphasizes how the coupling between the frequency of prey redistribution and the
438 frequency at which information on prey location can be updated and reused by the predator
439 shapes search patterns of predators and IRSR. Thereby, it sheds light on the mechanisms
440 linking prey predictability and narrow resource selection. Shearwaters searched for
441 zooplankton-rich areas independently of their predictability. Autocorrelation in zooplankton
442 abundance was low after only one day and birds were unlikely to use memory/public
443 information on zooplankton distribution between daily trips. In this context, sensory cues
444 available over long distances seem a far more efficient way to locate shifting resources than
445 public information (Savoca & Nevitt 2014; Spiegel & Crofoot 2016). Recent investigations
446 confirm that shearwaters use long to medium-scale olfactory cues to find zooplankton

447 (Dell'Ariccia et al. 2014). However, combination of unstable olfactory landscapes and

448 quickly outdated information on zooplankton distribution limits ISRS and requires alternative

449 foraging tactics. Further investigations should verify if the increase of individual

450 specialization in the capacity to use sensory cues according to age and experience (Harel et al.

451 2016) is balanced by prey unpredictability.

452 During trips specialized on zooplankton and fish, shearwaters selected areas with the

453 most predictable biomasses of adult anchovies and sprats. Fish biomasses being

454 autocorrelated for longer periods than shearwater daily trips (Fig. S2.1), shearwaters may gain

455 information on prey locations using short-term memory (Wakefield et al. 2013; Spiegel &

456 Crofoot 2016) or public information (Weimerskirch et al. 2010; Spiegel & Crofoot 2016;

457 Wells et al. 2016). Our results confirm that both mechanisms can occur in parallel, as

458 shearwaters shared a limited number of foraging areas across their consecutive trips, and

459 different individuals shared some foraging areas at similar time. Although information on

460 patch location can reduce search times and could favour foraging efficiency and ISRS, we

461 found almost no ISRS. Prey predictability being generally low, individuals rather used public

462 information to locate prey patches independently of the species, probably following their

463 peers (Harel et al. 2017). We found low overlap values among birds but the small proportion

464 of tracked individuals probably led to underestimating shared space use. Additional sources of

465 information may also be involved, such as the visual detection of tuna chasing smaller pelagic

466 fish, or knowledge of the most predictable fishing areas through long-term memory

467 (Weimerskirch 2007; Monsarrat et al. 2013). Interestingly, moderate ISRS for predictable

468 anchovy patches revealed individual differences in bird ability to use available information

469 (Fig. 4A; Spiegel & Crofoot 2016).

470
471

Conclusions

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Our study demonstrates that colonial predator populations preying on relatively unpredictable prey are subjected to ecological drivers leading to contrasting effects on ISRS. Thereby, the negative effect of prey unpredictability and patchiness on ISRS, overrides the positive effect of intra-specific competition (Fortin et al. 2008; Araújo et al. 2011). Prey unpredictability also seems to limit potential inter-sexual differences in individual specialization (both sexes were tracked here). Prey variability may further constrain the expected enhancement of individual specialization with age/experience due to longer sampling of environmental variation and development of specific skills (see also Woo et al. 2008). Future work explicitly considering age and sex of tagged birds should therefore quantify the opposite effects of low prey predictability and internal states on individual specialization. Moreover, individual specialization could vary among phases of the annual cycle, especially if birds migrate and/or encounter different foraging conditions.

Importantly, our study shows how individual responses to enhanced environmental stochasticity driven by global change could potentially impact population persistence. A population requires some degree of non-specialization to overcome novel conditions, e.g. generalist populations consisting of specialized individuals (Wolf & Weissing 2012; Merrick & Koprowski 2017). Here, we reinforce the idea that individual plasticity in foraging movements (low individual specialization and inter-annual variation in tactics used) could be a globally important pathway to cope with changing conditions and maintain population density, in the presence of limited behavioural plasticity (Dall et al. 2004, Sih et al. 2004, Hendry 2016). Finally, individual foraging plasticity may prevent the use of apex predators as ecological indicators of lower trophic levels (Grémillet & Charmantier 2010). This is particularly relevant as seabird populations are being flagged as indicators of food supplies within marine systems (Boyd et al. 2015, Brisson-Curadeau et al. 2017).
496

497 Acknowledgements

498 This study was funded by the French Agency for Biodiversity (AFB) within the program

499 PACOMM-Natura2000 en mer and INDEXPUF, and by the OSU OREME Montpellier.

500 Handling protocols for shearwaters were approved by the boards of the ‘Parc National des
501 Calanques’ (permit number: 2014-068, 2016-111), the French ‘Direction Départementale de

502 la Protection des Populations’ (permit number: 34-369, #A34-505), the French ‘Direction
503 Départementale des Territoires et de la Mer des Bouches-du-Rhône’ (permit number: 170-

504 0010, 184-0017) and the ‘Comité d’Ethique pour l’Expérimentation Animale Languedoc-
505 Rousillon’ (permit number: 1170). Thanks to O. Spiegel, R. Nathan and one anonymous
506 reviewer for their helpful comments. We are very grateful to M. Authier for his help with

507 Bayesian models for prey, S. Benhamou for our fruitful discussions and A. Kibler for revising
508 the manuscript. Thanks to all fieldworkers involved in this study: P. Giraudet, L. Martin, A.

509 Gaborit Loret, M. Kriloff, J.-B. Pons, C. de Franceschi. We also thank the agents from ‘Parc
510 National des Calanques’ of Marseille for providing support during the fieldwork (J.-P.

511 Durand, C. Pastorelli and A. Mante), N. El Ksabi for the preparation of feather samples,
512 Plateforme d'Analyses Chimiques en Ecologie at CEFE for logistic support, and Plateforme
513 'Spectrométrie isotopique' (University of La Rochelle – LIENSs) for isotopic analyses of
514 feathers samples. Thanks to the staff of PELagic MEDiterranean oceanographic campaigns to
515 share with us empirical data on fish and zooplankton (doi: http://dx.doi.org/10.18142/19) and
516 to Directive Cadre Stratégie pour le Milieu Marin for providing fish isotopic values in 2015.

517 We are grateful to B. Raymond from the Australian Antarctic Division for sharing his track
518 simulation R package. We finally are very grateful to NASA (Ocean Color project), CNES
519 (Aviso+ project), IEDA (Marine Geoscience Data System), IFREMER, SHOM and Meteo
520 France (PREVIMER project), and EUSeaMap European project for sharing oceanographic
data and satellite images.

522

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713 **Figure legends**

714 Figure 1. Theoretical outcomes for resource selection patterns (i.e. disproportionate use
715 relative to the availability of resources, from no selection [generalism] to positive selection) in
716 predators depending on individual foraging site fidelity (from low to high) and spatio-
717 temporal variability of prey distribution among days (from low to high). The intersection of
718 the three axes represents the lowest value along each axis.

719
720 Figure 2. Spatial distribution of foraging locations (black point) for Scopoli's shearwaters
721 breeding off Marseille (red triangle), Western Mediterranean during the chick-rearing period
722 in August 2011, 2012, 2014 and 2015. Foraging locations were determined using an
723 automatic process discriminating between different behavioural modes (travelling, resting and
724 foraging) along foraging trips (Torres et al. 2017).

725
726 Figure 3. RSF coefficients of Scopoli's shearwaters at the individual (A) and trip (B) levels
727 (solid circle), in the Gulf of Lions (Western Mediterranean) during the chick-rearing period
728 between 2011 and 2015, estimated from three-level mixed-effects logistic regression models
729 (i.e. 75 individuals, 182 trips, 56945 GPS locations). Grey square represent the population-
730 level inference. Values are jittered and * mean values are divided by 10 to ease visualization.

731
732 Figure 4. (A) Individual specialization in resource selection of Scopoli's shearwaters (n=47)
733 assessed with an index of repeatability *R*, varying between 0 and 1 (i.e. low to high selection
734 consistency). (B) Individual foraging site fidelity of Scopoli's shearwaters (n=47) among their
735 consecutive trips (*BA*), among trips of individuals foraging on the same day (*BA*<sub>between</sub>) and
736 expected under a random space use without selection (*BA*<sub>random</sub>), using a Bhattacharyya's
737 index ranging between 0 (no spatial overlap of UDs) and 1 (perfect spatial overlap of UDs).

738 Boxplot shows the median, 25th and 75th percentiles and solid circle represents the data.

739
740 Figure 5. RSF coefficients for (A) zooplankton-fish specialist trips and (B) zooplankton-only specialist trips (n = 83 and 99 trips, respectively) used by Scopoli's shearwaters in the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2011 and 2015.

743 Means (solid circle) are shown with their 95% confidence intervals. * mean values are divided by 10 to ease visualization.

745
746 Figure 6. Stable isotope ratios representing the diet of Scopoli's shearwaters in the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2010 and 2015. Due to the molting pattern of shearwaters, isotopic signature of birds sampled at year *n* are matched with isotopic signature of their prey at year *n*−1. Black points represent the stable isotope ratios of each shearwater. We show the mean expected range (solid lines) and its standard deviation (dotted lines) in prey isotopic values for shearwaters, based on shearwater tissue. We used a diet-tissue discrimination factor of δ¹⁵N: 3.7 +/- 1‰ [mean +/- SD] as recommended by Meier *et al.* (2017) for shearwaters. The mean stable isotope ratios with their standard deviations assessed independently for prey are also shown: zooplankton, copepods, euphausiids (empty squares), small pelagic fish (empty triangles) and squids (empty circles). Isotopic signatures of small pelagic fish are year-specific, while considered constant across years for the other prey (no data available). Inferences on diet of shearwaters from δ¹⁵N values were valuable because average δ¹⁵N isotopic signatures in zooplankton/copepods and fish species differed significantly and δ¹⁵N remained stable over space and time within the Gulf of Lions during our study (see details in Appendix S4).

761
<figure><img src="image_2.png" /></figure>

762

763 Figure 1.
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>Figure 4.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 5.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 6.</figcaption></figure>
1 Supporting Information for Ecology Letters

2

3 Short-term prey field lability constrains individual resource specialization and foraging

4 site fidelity in a marine predator

5

6 Nicolas Courbin¹, Aurélien Besnard¹, Clara Péron², Claire Saraux³, Jérôme Fort⁴, Samuel

7 Perret¹, Jérémy Tornos¹, David Grémillet¹,⁵

8

9 ¹ Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), UMR 5175, Centre National de la Recherche Scientifique (CNRS), Université de Montpellier, Université Paul Valéry Montpellier, Ecole Pratiques des Hautes Etudes (EPHE), 1919 Route de Mende, 34293 Montpellier Cedex 5, France

13 ² Marine Biodiversity Exploitation and Conservation (MARBEC), UMR 248, Institut de Recherche pour le Développement (IRD), Université de Montpellier, Place Eugène Bataillon - bât 24 - CC093, 34 095 Montpellier Cedex 5, France

16 ³ Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), UMR 248 MARBEC, Avenue Jean Monnet CS 3017, 34203 Sète, France

18 ⁴ Littoral, Environnement et Sociétés (LIENSs), UMR 7266, Centre National de la Recherche Scientifique (CNRS), Université La Rochelle, 2 rue Olympe de Gouges, 17000 La Rochelle, France

21 ⁵ FitzPatrick Institute, DST/NRF Excellence Centre at the University of Cape Town, Rondebosch 7701, South Africa
23

24

<figure><img src="image_12.png" /></figure>

25

26

27

28

Figure S1.1. Analytical framework to study the foraging behavior of breeding Scopoli's shearwaters in the Gulf of Lions (Western Mediterranean) showing input empirical datasets (blue boxes), the statistical models and analyses (orange boxes) and outcomes (green boxes).
29 Supporting Information S2. Modelling of daily spatial distribution of biomass and
30 abundance of prey of shearwaters

31
32 In order to determine the resource selection of shearwaters at foraging locations, we built
33 dynamic habitat models predicting the daily spatial distribution of biomass of European
34 anchovies (*Engraulis encrasicolus*), sardines (*Sardina pilchardus*) and sprats (*Sprattus
35 sprattus*) in our study area. We distinguished between juvenile and adult fish based on their
36 length-at-maturity (9 cm, 11 cm, and 9 cm for anchovies, sardines and sprats, respectively,
37 Authier *et al.* 2017) because juveniles and adults are spatially segregated during the summer
38 (see Fig. S2.3). We used biomass data per species and per stage, estimated from yearly
39 standardized hydro-acoustic and trawl surveys of small pelagic fish carried out every July
40 between 2010 and 2015 in the Gulf of Lions by IFREMER during the PELagic
41 MEDiterranean (PELMED) at-sea campaigns (doi: http://dx.doi.org/10.18142/19). Biomass
42 data were systematically collected each nautical mile along 9 parallel hydro-acoustic transects
43 perpendicular to the coastline, 12 nautical miles apart, covering the continental shelf of the
44 Gulf of Lions (see more details on sampling design and biomass data estimation in Authier *et
45 al.* 2017).

46
47 We modelled fish biomass (tonnes) using zero-inflated spatial generalized linear
48 mixed models with a Horseshoe prior in a Bayesian framework (except we used a non-spatial
49 model for juvenile anchovies due to convergence issue). Such models have already
50 demonstrated their robustness to predict fish biomass in the Gulf of Lions and allow accurate
51 predictions, including extrapolations (Authier *et al.* 2017). Random spatial effects were
52 modelled with a stationary Gaussian process of mean 0, an isotropic Matèrn covariance
53 function of order 3/2 and a year-specific sill parameter. The Horseshoe prior favours a
54 parsimonious variable selection by shrinking toward 0 small regression coefficient effects, yet
leaving larger regression coefficient effects unshrunk, while avoiding overfitting (see
statistical details in Authier *et al.* 2017). We used a set of dynamic predictors on a daily basis
and 1.2 km-resolution: sea surface salinity (g.kg⁻¹, PREVIMER-MARS3D model), sea surface
temperature (°C, MODIS satellite data) and its variance describing the spatio-temporal
dynamic of water mass features, and chlorophyll a concentration (mg.m⁻³, MODIS satellite
data) and its variance used as proxies of primary productivity dynamics. Variance was
calculated from the previous 15 days. We considered sea level anomaly (m) and Eddy kinetic
energy (cm².s⁻²) on a daily basis and 14km-resolution (AVISO satellite data) reflecting
hydrodynamic movements and turbulences occurring at the meso-scale within the Gulf of
Lions. Bathymetry with a 180 m-resolution (m, Marine Geoscience Data System), its gradient
and seabed substrate (four levels: sands, muddy sands, sandy muds and muds; EUSeaMap)
were used as static predictors in the models. Year was finally considered as a random effect to
control for the inter-annual variation in fish biomasses. Models were fitted by initializing four
chains with random starting values and running at least 3000 iterations to get the posterior
sample distributions. Convergence was assessed based on the Gelman-Rubin potential scale
reduction factor r̂. We used the *RStan* package (Stan Development Team 2016) of R software
(R Development Core Team 2016).

Similarly, we modelled mesozooplankton (thereafter ‘zooplankton’) abundance
(ind.m⁻³) using data collected with vertical WP2 plankton-net (200µm mesh size) during the
PELMED survey conducted in July 2015 (data were not available for previous years) at 37
stations (Fig. S2.2). Zooplankton samples (n = 37) were identified and classified in two
groups based on shearwater diet. The first group, ‘zooplankton-preferred’, included copepods,
euphausiids, fish eggs and fish larvae already found in stomach contents or regurgitates of
shearwaters (Beaubrun *et al.* 2013), and the rest of zooplankton formed the ‘zooplankton-
other’ group. We fitted a spatial GLM with a Horseshoe prior, and considered a bivariate
normal distribution for the dependent variable (both zooplankton groups) and the same set of environmental predictors as for fish biomass models. Model was fitted by initializing four chains with random starting values and running at least 3000 iterations to get the posterior sample distributions. Convergence was assessed based on the Gelman-Rubin potential scale reduction factor $\hat{r}$. We used the RStan package (Stan Development Team 2016) of R software (R Development Core Team 2016). We finally assessed the robustness and predictive power of the zooplankton model using leave-one-out cross-validation and compared the predicted abundance to the observed abundance within the range of abundance used by shearwaters.

All parameters converged (Gelman-Rubin potential scale reduction factor $\hat{r} < 1.2$) for fish and zooplankton models. The zooplankton model significantly predicted observed abundances of zooplankton-preferred group within the range of abundance used by shearwaters [0-800 ind.m$^{-3}$] ($r^2 = 0.24$, P < 0.005) (Fig. S2.4). The zooplankton model performed poorly to predict observed abundances of zooplankton-other group within the range of abundance used by shearwaters [0-600 ind.m$^{-3}$] ($r^2 = 0.02$, P = 0.44) (Fig. S2.4).

Sardines and juvenile sprats were coastal and their biomasses mainly decreased with bathymetry (Table S2.3, S2.4 and S2.6, Fig. S2.3). Juvenile anchovies were more widely distributed across the Gulf of Lions and were related to the plume of the Rhône river (Table S2.2, Fig. S2.3). Biomass of adult sprats was higher at the center of the Gulf than along the coast (Table S2.5, Fig. S2.3). Adult anchovies were the most pelagic prey category and their biomasses increased along the edge of the continental shelf, and also in some coastal areas (Table S2.1, Fig. S2.3). Both biomasses of adult anchovies and adult sprats increased near the Cassis canyon head close to the colony (Fig. S2.3). Abundance of zooplankton-preferred was related to coastal areas, the plume of the Rhône river and canyon areas (Table S2.7, Fig. S2.3). Zooplankton-other abundance was widely distributed across the Gulf of Lions (Table S2.7, Fig. S2.3).
We found high spatio-temporal variations in zooplankton abundances with a strong decrease of their temporal autocorrelation after only 1 day (Fig. S2.1). Autocorrelation of biomasses of adult anchovies and sprats strongly declined after 2-3 days, particularly in the eastern part of the Gulf of Lions where most foraging locations occurred (Fig. S2.1B).

Autocorrelation of other fish biomasses vanished in less than 5 days.

109

## References

111 Authier, M., Saraux, C. & Péron, C. (2017). Variable selection and accurate predictions in habitat modelling: a shrinkage approach. *Ecography*, 40, 549-560.

113 Beaubrun, P. et al. (2012). *Etat de l'art des connaissances sur les distributions spatiales des oiseaux marins et des petits poissons pélagiques dans le golfe du Lion*. Rapport final du Contrat DREAL-LR / IFREMER n°11/3211726/F, 1-580 pp.

116 Stan Development Team (2016). *RStan: the R interface to Stan*. R package version 2.14.1.

117 http://mc-stan.org/.
Table S2.1. Zero-inflated spatial generalized linear mixed model with Horseshoe priors predicting the spatial distribution of adult anchovies in the Gulf of Lions (Western Mediterranean) between 2010 and 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI) for both the absence (zero-inflated part modelling the probability of being absent) and biomass models of fish.

<table><thead><tr><th rowspan="2">Variable</th><th colspan="3">Absence (zero-inflated part)</th><th colspan="3">Biomass</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>-6.07</td><td>2.28</td><td>-11.87;-3.37</td><td>1.31</td><td>0.20</td><td>0.90;1.71</td></tr><tr><td>Bathymetry (m)</td><td>0.10</td><td>0.48</td><td>-0.54;1.35</td><td>-0.01</td><td>0.04</td><td>-0.10;0.07</td></tr><tr><td>G(bathymetry)</td><td>-0.18</td><td>0.61</td><td>-1.87;0.31</td><td>-0.04</td><td>0.04</td><td>-0.12;0.01</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>0.15</td><td>0.54</td><td>-0.42;1.63</td><td>-0.04</td><td>0.05</td><td>-0.16;0.03</td></tr><tr><td>Sea surface temperature (°C)</td><td>-0.002</td><td>0.33</td><td>-0.77;0.75</td><td>-0.01</td><td>0.04</td><td>-0.10;0.07</td></tr><tr><td>Var(sea surface temperature)</td><td>-0.05</td><td>0.42</td><td>-1.22;0.63</td><td>-0.01</td><td>0.03</td><td>-0.09;0.05</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>-0.13</td><td>0.39</td><td>-1.26;0.27</td><td>-0.02</td><td>0.03</td><td>-0.09;0.05</td></tr><tr><td>Var(chlorophyll a)</td><td>-0.20</td><td>0.62</td><td>-2.02;0.22</td><td>-0.01</td><td>0.02</td><td>-0.06;0.04</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>-0.06</td><td>0.43</td><td>-1.20;0.62</td><td>-0.02</td><td>0.04</td><td>-0.12;0.05</td></tr><tr><td>Sea level anomalies (m)</td><td>-0.03</td><td>0.41</td><td>-0.96;0.73</td><td>0.01</td><td>0.04</td><td>-0.06;0.12</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>-0.08</td><td>0.78</td><td>-1.87;1.02</td><td>-0.02</td><td>0.10</td><td>-0.28;0.15</td></tr><tr><td>Muddy sands</td><td>-0.09</td><td>0.88</td><td>-2.42;1.33</td><td>-0.17</td><td>0.18</td><td>-0.59;0.05</td></tr><tr><td>Sandy muds</td><td>-0.20</td><td>0.85</td><td>-2.51;0.75</td><td>0.05</td><td>0.12</td><td>-0.13;0.38</td></tr><tr><td>Sands</td><td>-0.16</td><td>0.79</td><td>-2.37;0.91</td><td>0.44</td><td>0.15</td><td>0.16;0.79</td></tr></tbody></table>

*Random effect*
<table><tbody><tr><td>2010</td><td>-6.14</td><td>2.37</td><td>-12.33;-3.27</td><td>1.28</td><td>0.17</td><td>0.92;1.60</td></tr><tr><td>2011</td><td>-6.17</td><td>2.38</td><td>-12.13;-3.33</td><td>1.27</td><td>0.18</td><td>0.91;1.62</td></tr><tr><td>2012</td><td>-6.11</td><td>2.42</td><td>-12.33;-3.22</td><td>1.41</td><td>0.18</td><td>1.05;1.76</td></tr><tr><td>2013</td><td>-6.14</td><td>2.41</td><td>-12.15;-3.14</td><td>1.01</td><td>0.23</td><td>0.58;1.42</td></tr><tr><td>2014</td><td>-6.13</td><td>2.36</td><td>-12.10;-3.29</td><td>1.38</td><td>0.18</td><td>0.99;1.72</td></tr><tr><td>2015</td><td>-6.17</td><td>2.49</td><td>-12.71;-3.15</td><td>1.56</td><td>0.20</td><td>1.16;1.96</td></tr><tr><td colspan="7"><em>Spatial effect</em></td></tr><tr><td>Rho</td><td></td><td></td><td></td><td>6.73</td><td>0.87</td><td>5.24;8.61</td></tr><tr><td>Sill in 2010</td><td></td><td></td><td></td><td>0.78</td><td>0.10</td><td>0.61;0.98</td></tr><tr><td>Sill in 2011</td><td></td><td></td><td></td><td>0.79</td><td>0.10</td><td>0.63;0.99</td></tr><tr><td>Sill in 2012</td><td></td><td></td><td></td><td>0.89</td><td>0.10</td><td>0.73;1.08</td></tr><tr><td>Sill in 2013</td><td></td><td></td><td></td><td>0.95</td><td>0.11</td><td>0.76;1.18</td></tr><tr><td>Sill in 2014</td><td></td><td></td><td></td><td>0.90</td><td>0.12</td><td>0.69;1.14</td></tr><tr><td>Sill in 2015</td><td></td><td></td><td></td><td>0.95</td><td>0.12</td><td>0.73;1.19</td></tr><tr><td><em>Horseshoe (global)</em></td><td>0.26</td><td>0.26</td><td>0.03;0.96</td><td>0.07</td><td>0.07</td><td>0.01;0.27</td></tr></tbody></table>
<table><caption>Table S2.2. Zero-inflated generalized linear mixed model with Horseshoe priors predicting the spatial distribution of juvenile anchovies in the Gulf of Lions (Western Mediterranean) between 2010 and 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI) for both the absence (zero-inflated part modelling the probability of being absent) and biomass models of fish.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Absence (zero-inflated part)</th><th colspan="3">Biomass</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>-3.91</td><td>0.71</td><td>-5.55;-2.95</td><td>0.01</td><td>0.01</td><td>-0.002;0.03</td></tr><tr><td>Bathymetry (m)</td><td>-0.04</td><td>0.14</td><td>-0.35;0.26</td><td>0.015</td><td>0.002</td><td>0.010;0.018</td></tr><tr><td>G(bathymetry)</td><td>-0.21</td><td>0.38</td><td>-1.27;0.16</td><td>0.007</td><td>0.002</td><td>0.003;0.010</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>-0.62</td><td>0.11</td><td>-0.85;-0.42</td><td>-0.05</td><td>0.003</td><td>-0.06;-0.05</td></tr><tr><td>Sea surface temperature (°C)</td><td>0.39</td><td>0.37</td><td>-0.05;1.26</td><td>0.005</td><td>0.002</td><td>0.001;0.009</td></tr><tr><td>Var(sea surface temperature)</td><td>0.17</td><td>0.19</td><td>-0.14;0.54</td><td>-0.008</td><td>0.002</td><td>-0.011;-0.004</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>-0.06</td><td>0.09</td><td>-0.28;0.06</td><td>-0.007</td><td>0.002</td><td>-0.011;-0.002</td></tr><tr><td>Var(chlorophyll a)</td><td>-0.07</td><td>0.19</td><td>-0.60;0.18</td><td>-0.001</td><td>0.001</td><td>-0.003;0.002</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>-0.76</td><td>0.47</td><td>-1.77;-0.003</td><td>0.002</td><td>0.002</td><td>-0.001;0.006</td></tr><tr><td>Sea level anomalies (m)</td><td>-0.06</td><td>0.24</td><td>-0.68;0.36</td><td>-0.001</td><td>0.002</td><td>-0.005;0.002</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>0.07</td><td>0.37</td><td>-0.47;1.01</td><td>-0.001</td><td>0.004</td><td>-0.009;0.009</td></tr><tr><td>Muddy sands</td><td>-0.38</td><td>1.50</td><td>-4.37;0.61</td><td>-0.01</td><td>0.01</td><td>-0.03;0.002</td></tr><tr><td>Sandy muds</td><td>-0.01</td><td>0.37</td><td>-0.63;0.83</td><td>0.002</td><td>0.004</td><td>-0.006;0.011</td></tr><tr><td>Sands</td><td>-0.94</td><td>3.21</td><td>-15.01;0.70</td><td>0.003</td><td>0.007</td><td>-0.007;0.021</td></tr></tbody></table>

*Random effect*
<table><tr><td>2010</td><td>-4.12</td><td>0.84</td><td>-6.10;-2.97</td><td>0.01</td><td>0.005</td><td>-0.002;0.02</td></tr><tr><td>2011</td><td>-3.99</td><td>0.81</td><td>-5.84;-2.87</td><td>0.01</td><td>0.005</td><td>-0.002;0.02</td></tr><tr><td>2012</td><td>-4.03</td><td>0.82</td><td>-5.90;-2.91</td><td>0.01</td><td>0.005</td><td>0.001;0.02</td></tr><tr><td>2013</td><td>-3.71</td><td>0.71</td><td>-5.40;-2.59</td><td>0.02</td><td>0.005</td><td>0.01;0.03</td></tr><tr><td>2014</td><td>-3.91</td><td>0.68</td><td>-5.49;-2.90</td><td>0.03</td><td>0.005</td><td>0.02;0.04</td></tr><tr><td>2015</td><td>-3.87</td><td>0.74</td><td>-5.57;-2.79</td><td>0.01</td><td>0.005</td><td>-0.002;0.02</td></tr><tr><td>Horseshoe (global)</td><td>0.32</td><td>0.22</td><td>0.08;0.87</td><td>0.009</td><td>0.005</td><td>0.003;0.240</td></tr></table>
Table S2.3. Zero-inflated spatial generalized linear mixed model with Horseshoe priors predicting the spatial distribution of adult sardines in the Gulf of Lions (Western Mediterranean) between 2010 and 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI) for both the absence (zero-inflated part modelling the probability of being absent) and biomass models of fish.

<table><thead><tr><th rowspan="2">Variable</th><th colspan="3">Absence (zero-inflated part)</th><th colspan="3">Biomass</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>-5.61</td><td>1.99</td><td>-9.73;-1.41</td><td>1.32</td><td>0.22</td><td>0.89;1.78</td></tr><tr><td>Bathymetry (m)</td><td>2.09</td><td>0.78</td><td>0.96;3.91</td><td>0.63</td><td>0.07</td><td>0.50;0.76</td></tr><tr><td>G(bathymetry)</td><td>-0.07</td><td>0.74</td><td>-1.90;1.10</td><td>0.16</td><td>0.05</td><td>0.07;0.26</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>0.22</td><td>0.71</td><td>-0.27;1.81</td><td>-0.01</td><td>0.04</td><td>-0.09;0.07</td></tr><tr><td>Sea surface temperature (°C)</td><td>0.16</td><td>0.26</td><td>-0.18;0.80</td><td>-0.04</td><td>0.05</td><td>-0.17;0.04</td></tr><tr><td>Var(sea surface temperature)</td><td>-0.03</td><td>0.19</td><td>-0.44;0.34</td><td>0.02</td><td>0.04</td><td>-0.06;0.13</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>0.02</td><td>0.16</td><td>-0.37;0.32</td><td>-0.05</td><td>0.06</td><td>-0.18;0.03</td></tr><tr><td>Var(chlorophyll a)</td><td>0.05</td><td>0.08</td><td>-0.07;0.22</td><td>-0.06</td><td>0.06</td><td>-0.19;0.04</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>0.04</td><td>0.26</td><td>-0.52;0.61</td><td>0.01</td><td>0.04</td><td>-0.06;0.11</td></tr><tr><td>Sea level anomalies (m)</td><td>0.01</td><td>0.18</td><td>-0.38;0.40</td><td>-0.01</td><td>0.05</td><td>-0.12;0.07</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>-0.49</td><td>1.51</td><td>-4.59;0.60</td><td>-0.02</td><td>0.10</td><td>-0.29;0.18</td></tr><tr><td>Muddy sands</td><td>-0.03</td><td>0.36</td><td>-0.83;0.89</td><td>-0.01</td><td>0.10</td><td>-0.27;0.19</td></tr><tr><td>Sandy muds</td><td>0.31</td><td>0.47</td><td>-0.27;1.60</td><td>-0.03</td><td>0.07</td><td>-0.20;0.10</td></tr><tr><td>Sands</td><td>-1.22</td><td>3.63</td><td>-8.00;0.28</td><td>0.07</td><td>0.09</td><td>-0.06;0.28</td></tr></tbody></table>

*Random effect*
<table><tbody><tr><td>2010</td><td>-4.56</td><td>2.06</td><td>-8.54;-2.37</td><td>1.10</td><td>0.14</td><td>0.81;1.38</td></tr><tr><td>2011</td><td>-8.49</td><td>5.69</td><td>-19.18;-4.23</td><td>1.03</td><td>0.14</td><td>0.75;1.30</td></tr><tr><td>2012</td><td>-8.27</td><td>5.43</td><td>-19.44;-4.10</td><td>1.26</td><td>0.15</td><td>0.95;1.55</td></tr><tr><td>2013</td><td>-4.51</td><td>1.44</td><td>-7.97;-2.41</td><td>1.71</td><td>0.20</td><td>1.31;2.10</td></tr><tr><td>2014</td><td>-8.04</td><td>3.82</td><td>-18.76;-4.20</td><td>1.47</td><td>0.16</td><td>1.17;1.78</td></tr><tr><td>2015</td><td>-8.16</td><td>5.01</td><td>-18.55;-4.05</td><td>1.35</td><td>0.16</td><td>1.02;1.68</td></tr><tr><td colspan="7"><em>Spatial effect</em></td></tr><tr><td>Rho</td><td></td><td></td><td></td><td>4.92</td><td>0.73</td><td>3.71;6.53</td></tr><tr><td>Sill in 2010</td><td></td><td></td><td></td><td>0.71</td><td>0.10</td><td>0.52;0.91</td></tr><tr><td>Sill in 2011</td><td></td><td></td><td></td><td>0.70</td><td>0.11</td><td>0.49;0.92</td></tr><tr><td>Sill in 2012</td><td></td><td></td><td></td><td>0.79</td><td>0.10</td><td>0.61;1.00</td></tr><tr><td>Sill in 2013</td><td></td><td></td><td></td><td>1.00</td><td>0.13</td><td>0.78;1.27</td></tr><tr><td>Sill in 2014</td><td></td><td></td><td></td><td>0.89</td><td>0.11</td><td>0.69;1.11</td></tr><tr><td>Sill in 2015</td><td></td><td></td><td></td><td>0.92</td><td>0.11</td><td>0.71;1.16</td></tr><tr><td><em>Horseshoe (global)</em></td><td>0.33</td><td>0.25</td><td>0.06;0.97</td><td>0.09</td><td>0.07</td><td>0.02;0.26</td></tr></tbody></table>
<table><caption>Table S2.4. Zero-inflated spatial generalized linear mixed model with Horseshoe priors predicting the spatial distribution of juvenile sardines in the Gulf of Lions (Western Mediterranean) between 2010 and 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI) for both the absence (zero-inflated part modelling the probability of being absent) and biomass models of fish.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Absence (zero-inflated part)</th><th colspan="3">Biomass</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>-0.76</td><td>0.62</td><td>-2.08;0.44</td><td>0.81</td><td>0.17</td><td>0.47;1.16</td></tr><tr><td>Bathymetry (m)</td><td>-1.80</td><td>0.18</td><td>-2.16;-1.47</td><td>0.92</td><td>0.08</td><td>0.77;1.06</td></tr><tr><td>G(bathymetry)</td><td>-0.62</td><td>0.18</td><td>-1.04;-0.32</td><td>0.19</td><td>0.08</td><td>0.03;0.33</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>0.11</td><td>0.11</td><td>-0.08;0.34</td><td>-0.005</td><td>0.03</td><td>-0.07;0.06</td></tr><tr><td>Sea surface temperature (°C)</td><td>0.01</td><td>0.07</td><td>-0.14;0.16</td><td>-0.03</td><td>0.05</td><td>-0.17;0.05</td></tr><tr><td>Var(sea surface temperature)</td><td>-0.002</td><td>0.08</td><td>-0.19;0.15</td><td>0.01</td><td>0.04</td><td>-0.06;0.11</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>-0.08</td><td>0.13</td><td>-0.40;0.13</td><td>0.002</td><td>0.03</td><td>-0.06;0.07</td></tr><tr><td>Var(chlorophyll a)</td><td>0.21</td><td>0.10</td><td>-0.001;0.40</td><td>0.01</td><td>0.03</td><td>-0.04;0.07</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>0.03</td><td>0.07</td><td>-0.10;0.18</td><td>-0.0001</td><td>0.04</td><td>-0.08;0.09</td></tr><tr><td>Sea level anomalies (m)</td><td>-0.03</td><td>0.09</td><td>-0.23;0.13</td><td>-0.03</td><td>0.06</td><td>-0.20;0.05</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>0.10</td><td>0.24</td><td>-0.28;0.79</td><td>-0.004</td><td>0.10</td><td>-0.24;0.22</td></tr><tr><td>Muddy sands</td><td>-0.14</td><td>1.29</td><td>-1.12;0.78</td><td>-0.002</td><td>0.08</td><td>-0.19;0.17</td></tr><tr><td>Sandy muds</td><td>0.05</td><td>0.17</td><td>-0.30;0.42</td><td>0.003</td><td>0.06</td><td>-0.13;0.14</td></tr><tr><td>Sands</td><td>-0.19</td><td>0.21</td><td>-0.67;0.09</td><td>-0.005</td><td>0.06</td><td>-0.15;0.12</td></tr></tbody></table>

*Random effect*
<table><tbody><tr><td>2010</td><td>-0.66</td><td>0.24</td><td>-1.13;-0.17</td><td>0.92</td><td>0.15</td><td>0.64;1.25</td></tr><tr><td>2011</td><td>0.63</td><td>0.24</td><td>0.19;1.14</td><td>0.61</td><td>0.23</td><td>0.16;1.07</td></tr><tr><td>2012</td><td>-2.12</td><td>0.35</td><td>-2.87;-1.44</td><td>1.12</td><td>0.17</td><td>0.81;1.42</td></tr><tr><td>2013</td><td>-2.67</td><td>1.27</td><td>-6.54;-1.35</td><td>0.78</td><td>0.15</td><td>0.49;1.05</td></tr><tr><td>2014</td><td>0.26</td><td>0.24</td><td>-0.18;0.76</td><td>0.63</td><td>0.14</td><td>0.36;0.92</td></tr><tr><td>2015</td><td>-0.81</td><td>0.27</td><td>-1.33;-0.28</td><td>0.87</td><td>0.15</td><td>0.56;1.17</td></tr><tr><td colspan="7"><em>Spatial effect</em></td></tr><tr><td>Rho</td><td></td><td></td><td></td><td>4.02</td><td>0.83</td><td>2.69;5.93</td></tr><tr><td>Sill in 2010</td><td></td><td></td><td></td><td>0.97</td><td>0.13</td><td>0.71;1.23</td></tr><tr><td>Sill in 2011</td><td></td><td></td><td></td><td>1.28</td><td>0.17</td><td>0.99;1.64</td></tr><tr><td>Sill in 2012</td><td></td><td></td><td></td><td>0.96</td><td>0.11</td><td>0.77;1.18</td></tr><tr><td>Sill in 2013</td><td></td><td></td><td></td><td>0.74</td><td>0.09</td><td>0.57;0.93</td></tr><tr><td>Sill in 2014</td><td></td><td></td><td></td><td>0.56</td><td>0.14</td><td>0.31;0.84</td></tr><tr><td>Sill in 2015</td><td></td><td></td><td></td><td>0.74</td><td>0.12</td><td>0.52;0.98</td></tr><tr><td><em>Horseshoe (global)</em></td><td>0.24</td><td>0.15</td><td>0.07;0.62</td><td>0.07</td><td>0.05</td><td>0.01;0.21</td></tr></tbody></table>
<table><caption>Table S2.5. Zero-inflated spatial generalized linear mixed model with Horseshoe priors predicting the spatial distribution of adult sprats in the Gulf of Lions (Western Mediterranean) between 2010 and 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI) for both the absence (zero-inflated part modelling the probability of being absent) and biomass models of fish.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Absence (zero-inflated part)</th><th colspan="3">Biomass</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>-4.41</td><td>1.82</td><td>-7.92;-0.30</td><td>1.17</td><td>0.28</td><td>0.60;1.78</td></tr><tr><td>Bathymetry (m)</td><td>2.39</td><td>0.41</td><td>1.76;3.31</td><td>0.11</td><td>0.06</td><td>-0.01;0.23</td></tr><tr><td>G(bathymetry)</td><td>0.45</td><td>0.72</td><td>-0.83;1.65</td><td>-0.03</td><td>0.04</td><td>-0.11;0.03</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>0.08</td><td>0.12</td><td>-0.11;0.38</td><td>0.02</td><td>0.04</td><td>-0.04;0.11</td></tr><tr><td>Sea surface temperature (°C)</td><td>0.29</td><td>0.25</td><td>-0.04;0.84</td><td>-0.02</td><td>0.04</td><td>-0.14;0.05</td></tr><tr><td>Var(sea surface temperature)</td><td>-0.03</td><td>0.13</td><td>-0.33;0.19</td><td>-0.03</td><td>0.04</td><td>-0.14;0.05</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>0.03</td><td>0.10</td><td>-0.16;0.26</td><td>-0.001</td><td>0.04</td><td>-0.09;0.09</td></tr><tr><td>Var(chlorophyll a)</td><td>0.03</td><td>0.08</td><td>-0.12;0.19</td><td>0.04</td><td>0.06</td><td>-0.06;0.18</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>0.14</td><td>0.17</td><td>-0.13;0.50</td><td>-0.002</td><td>0.03</td><td>-0.08;0.07</td></tr><tr><td>Sea level anomalies (m)</td><td>0.05</td><td>0.16</td><td>-0.27;0.40</td><td>-0.05</td><td>0.06</td><td>-0.21;0.04</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>-0.04</td><td>0.38</td><td>-0.87;0.66</td><td>0.02</td><td>0.08</td><td>-0.14;0.23</td></tr><tr><td>Muddy sands</td><td>-0.04</td><td>0.28</td><td>-0.69;0.49</td><td>-0.12</td><td>0.18</td><td>-0.61;0.07</td></tr><tr><td>Sandy muds</td><td>-0.08</td><td>0.22</td><td>-0.68;0.26</td><td>-0.03</td><td>0.10</td><td>-0.26;0.15</td></tr><tr><td>Sands</td><td>0.07</td><td>0.21</td><td>-0.28;0.65</td><td>0.22</td><td>0.14</td><td>-0.01;0.49</td></tr></tbody></table>

*Random effect*
<table><tbody><tr><td>2010</td><td>-3.48</td><td>0.65</td><td>-5.02;-2.42</td><td>0.72</td><td>0.17</td><td>0.40;1.08</td></tr><tr><td>2011</td><td>-8.40</td><td>3.66</td><td>-19.01;-4.91</td><td>1.11</td><td>0.21</td><td>0.72;1.55</td></tr><tr><td>2012</td><td>-6.84</td><td>2.70</td><td>-14.71;-4.01</td><td>1.34</td><td>0.21</td><td>0.92;1.76</td></tr><tr><td>2013</td><td>-1.36</td><td>0.44</td><td>-2.33;-0.67</td><td>1.78</td><td>0.31</td><td>1.15;2.33</td></tr><tr><td>2014</td><td>-8.47</td><td>3.07</td><td>-16.93;-5.27</td><td>1.06</td><td>0.19</td><td>0.69;1.44</td></tr><tr><td>2015</td><td>-7.65</td><td>3.72</td><td>-18.21;-4.16</td><td>0.81</td><td>0.15</td><td>0.50;1.13</td></tr><tr><td colspan="7"><em>Spatial effect</em></td></tr><tr><td>Rho</td><td></td><td></td><td></td><td>6.92</td><td>1.31</td><td>4.63;9.66</td></tr><tr><td>Sill in 2010</td><td></td><td></td><td></td><td>0.65</td><td>0.08</td><td>0.50;0.81</td></tr><tr><td>Sill in 2011</td><td></td><td></td><td></td><td>1.05</td><td>0.11</td><td>0.85;1.28</td></tr><tr><td>Sill in 2012</td><td></td><td></td><td></td><td>1.00</td><td>0.10</td><td>0.81;1.20</td></tr><tr><td>Sill in 2013</td><td></td><td></td><td></td><td>1.16</td><td>0.15</td><td>0.90;1.48</td></tr><tr><td>Sill in 2014</td><td></td><td></td><td></td><td>1.00</td><td>0.14</td><td>0.76;1.31</td></tr><tr><td>Sill in 2015</td><td></td><td></td><td></td><td>0.57</td><td>0.07</td><td>0.43;0.72</td></tr><tr><td><em>Horseshoe (global)</em></td><td>0.26</td><td>0.18</td><td>0.05;0.72</td><td>0.07</td><td>0.05</td><td>0.01;0.21</td></tr></tbody></table>
Table S2.6. Zero-inflated spatial generalized linear mixed model with Horseshoe priors predicting the spatial distribution of juvenile sprats in the Gulf of Lions (Western Mediterranean) between 2010 and 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI) for both the absence (zero-inflated part) modelling the probability of being absent) and biomass models of fish.

<table><thead><tr><th rowspan="2">Variable</th><th colspan="3">Absence (zero-inflated part)</th><th colspan="3">Biomass</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>-2.43</td><td>1.98</td><td>-6.11;2.68</td><td>0.73</td><td>0.25</td><td>0.31;1.25</td></tr><tr><td>Bathymetry (m)</td><td>-1.31</td><td>0.13</td><td>-1.62;-1.07</td><td>0.19</td><td>0.08</td><td>0.03;0.34</td></tr><tr><td>G(bathymetry)</td><td>-0.18</td><td>0.17</td><td>-0.58;0.07</td><td>0.03</td><td>0.04</td><td>-0.02;0.12</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>0.08</td><td>0.18</td><td>-0.16;0.61</td><td>0.02</td><td>0.03</td><td>-0.02;0.08</td></tr><tr><td>Sea surface temperature (°C)</td><td>0.15</td><td>0.20</td><td>-0.09;0.65</td><td>-0.04</td><td>0.05</td><td>-0.15;0.03</td></tr><tr><td>Var(sea surface temperature)</td><td>-0.17</td><td>0.14</td><td>-0.50;0.02</td><td>-0.003</td><td>0.05</td><td>-0.13;0.08</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>0.02</td><td>0.10</td><td>-0.17;0.24</td><td>-0.02</td><td>0.03</td><td>-0.09;0.02</td></tr><tr><td>Var(chlorophyll a)</td><td>-0.02</td><td>0.06</td><td>-0.18;0.08</td><td>-0.01</td><td>0.02</td><td>-0.06;0.04</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>-0.02</td><td>0.06</td><td>-0.17;0.10</td><td>-0.03</td><td>0.04</td><td>-0.12;0.04</td></tr><tr><td>Sea level anomalies (m)</td><td>0.01</td><td>0.08</td><td>-0.16;0.24</td><td>-0.04</td><td>0.06</td><td>-0.18;0.04</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>0.07</td><td>0.45</td><td>-0.87;1.04</td><td>-0.02</td><td>0.08</td><td>-0.26;0.11</td></tr><tr><td>Muddy sands</td><td>1.33</td><td>0.58</td><td>0.01;2.24</td><td>-0.10</td><td>0.13</td><td>-0.42;0.05</td></tr><tr><td>Sandy muds</td><td>-0.01</td><td>0.16</td><td>-0.39;0.32</td><td>0.02</td><td>0.07</td><td>-0.10;0.18</td></tr><tr><td>Sands</td><td>-0.11</td><td>0.21</td><td>-0.70;0.15</td><td>0.14</td><td>0.10</td><td>-0.01;0.35</td></tr></tbody></table>

*Random effect*
<table><tbody><tr><td>2010</td><td>-7.84</td><td>3.37</td><td>-18.38;-4.24</td><td>0.48</td><td>0.18</td><td>0.13;0.86</td></tr><tr><td>2011</td><td>-0.01</td><td>0.25</td><td>-0.46;0.52</td><td>0.65</td><td>0.26</td><td>0.17;1.15</td></tr><tr><td>2012</td><td>-6.41</td><td>3.33</td><td>-13.18;-2.96</td><td>0.77</td><td>0.28</td><td>0.15;1.26</td></tr><tr><td>2013</td><td>0.82</td><td>0.24</td><td>0.43;1.42</td><td>1.11</td><td>0.42</td><td>0.40;1.96</td></tr><tr><td>2014</td><td>-5.74</td><td>3.06</td><td>-13.20;-2.21</td><td>0.38</td><td>0.15</td><td>0.09;0.66</td></tr><tr><td>2015</td><td>-5.06</td><td>2.18</td><td>-11.13;-2.78</td><td>0.67</td><td>0.20</td><td>0.31;1.07</td></tr><tr><td colspan="7"><em>Spatial effect</em></td></tr><tr><td>Rho</td><td></td><td></td><td></td><td>11.97</td><td>1.49</td><td>9.45;15.62</td></tr><tr><td>Sill in 2010</td><td></td><td></td><td></td><td>0.72</td><td>0.10</td><td>0.53;0.93</td></tr><tr><td>Sill in 2011</td><td></td><td></td><td></td><td>0.96</td><td>0.16</td><td>0.68;1.33</td></tr><tr><td>Sill in 2012</td><td></td><td></td><td></td><td>1.13</td><td>0.13</td><td>0.90;1.46</td></tr><tr><td>Sill in 2013</td><td></td><td></td><td></td><td>1.51</td><td>0.27</td><td>1.04;2.09</td></tr><tr><td>Sill in 2014</td><td></td><td></td><td></td><td>0.49</td><td>0.07</td><td>0.36;0.65</td></tr><tr><td>Sill in 2015</td><td></td><td></td><td></td><td>0.81</td><td>0.11</td><td>0.61;1.04</td></tr><tr><td><em>Horseshoe (global)</em></td><td>0.25</td><td>0.21</td><td>0.04;0.81</td><td>0.07</td><td>0.06</td><td>0.01;0.19</td></tr></tbody></table>
<table><caption>Table S2.7. Spatial generalized linear mixed model with Horseshoe priors predicting the spatial distribution of zooplankton abundance in the Gulf of Lions (Western Mediterranean) in 2015. Model parameters (β) are shown with their standard deviation (SD) and 95% confidence intervals (CI).</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Zooplankton-preferred</th><th colspan="3">Zooplankton-other</th></tr><tr><th>β</th><th>SD</th><th>95% CI</th><th>β</th><th>SD</th><th>95% CI</th></tr></thead><tbody><tr><td colspan="7"><em>Fixed effects</em></td></tr><tr><td>Intercept</td><td>5.46</td><td>0.80</td><td>3.32;6.57</td><td>5.53</td><td>0.62</td><td>3.98;6.54</td></tr><tr><td>Bathymetry (m)</td><td>0.001</td><td>0.06</td><td>-0.14;0.15</td><td>0.03</td><td>0.09</td><td>-0.12;0.26</td></tr><tr><td>G(bathymetry)</td><td>0.02</td><td>0.07</td><td>-0.08;0.21</td><td>-0.07</td><td>0.11</td><td>-0.35;0.08</td></tr><tr><td>Sea surface salinity (g.kg<sup>-1</sup>)</td><td>-0.01</td><td>0.06</td><td>-0.17;0.11</td><td>0.16</td><td>0.15</td><td>-0.03;0.48</td></tr><tr><td>Sea surface temperature (°C)</td><td>0.005</td><td>0.06</td><td>-0.11;0.15</td><td>-0.01</td><td>0.08</td><td>-0.19;0.17</td></tr><tr><td>Var(sea surface temperature)</td><td>-0.04</td><td>0.09</td><td>-0.28;0.08</td><td>-0.02</td><td>0.08</td><td>-0.23;0.12</td></tr><tr><td>Chlorophyll a (mg.m<sup>-3</sup>)</td><td>0.03</td><td>0.07</td><td>-0.07;0.21</td><td>-0.003</td><td>0.07</td><td>-0.16;0.14</td></tr><tr><td>Var(chlorophyll a)</td><td>-0.08</td><td>0.11</td><td>-0.36;0.04</td><td>-0.04</td><td>0.10</td><td>-0.30;0.09</td></tr><tr><td>Eddy kinetic energy (cm<sup>2</sup>.s<sup>-2</sup>)</td><td>-0.01</td><td>0.06</td><td>-0.18;0.10</td><td>-0.01</td><td>0.08</td><td>-0.20;0.14</td></tr><tr><td>Sea level anomalies (m)</td><td>-0.01</td><td>0.06</td><td>-0.18;0.10</td><td>0.05</td><td>0.10</td><td>-0.10;0.31</td></tr><tr><td colspan="7"><em>Seabed substrate</em></td></tr><tr><td>Muds</td><td>0.03</td><td>0.11</td><td>-0.12;0.35</td><td>-0.09</td><td>0.18</td><td>-0.61;0.12</td></tr><tr><td>Muddy sands</td><td>-0.01</td><td>0.09</td><td>-0.25;0.16</td><td>0.11</td><td>0.20</td><td>-0.12;0.68</td></tr><tr><td>Sandy muds</td><td>-0.02</td><td>0.14</td><td>-0.39;0.19</td><td>0.02</td><td>0.18</td><td>-0.31;0.48</td></tr><tr><td>Sands</td><td>-0.03</td><td>0.18</td><td>-0.55;0.22</td><td>-0.001</td><td>0.22</td><td>-0.44;0.44</td></tr><tr><td><em>Spatial effect</em> (Rho)</td><td>112.71</td><td>57.42</td><td>3.87;197.20</td><td>89.73</td><td>59.84</td><td>4.35;194.25</td></tr><tr><td><em>Horseshoe</em> (global)</td><td>0.06</td><td>0.05</td><td>0.003;0.20</td><td>0.09</td><td>0.08</td><td>0.01;0.30</td></tr></tbody></table>
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

164

165

166

167

168

169

170

171

172

173

174

175

Figure S2.1. Temporal autocorrelation in the zooplankton abundances (ind.m⁻³) and small pelagic fish biomasses (tonnes) (A) in the entire Gulf of Lions (Western Mediterranean) and (B) in its eastern part where most foraging locations occurred (longitude > 4.5°), during the chick-rearing period of Scopoli's shearwater (August). We considered three fish species (anchovy, sardine and sprat) at two stages (adult or juvenile) and zooplankton in two distinct categories: zooplankton-preferred that includes mesozooplankton already found in the diet of shearwater (copepods, euphausiid, fish eggs and fish larvae) and zooplankton-other that includes the rest of holoplankton and meroplankton. We assessed an autocorrelogram for each prey abundance/biomass daily predicted between the 20 July and 20 August 2015 at 900 locations randomly distributed in the Gulf of Lions. For each prey type, values were averaged among the 900 autocorrelograms at each lag and the mean values are presented. Abundances
176 and biomasses data are predicted based on habitat models estimated from data collected on

177 July 2015 using plankton-net and hydro-acoustic survey (PELagic MEDITerranean campaigns,

178 IFREMER), respectively.
<figure><img src="image_15.png" /></figure>

179

180

181

182

Figure S2.2. Spatial distribution of the 37 stations where zooplankton abundance was sampled during the PELMED campaign in July 2015, within the Gulf of Lions (Western Mediterranean).
183

<figure><img src="image_16.png" /></figure>

184

<figure><img src="image_17.png" /></figure>

185

<figure><img src="image_18.png" /></figure>

186

<figure><img src="image_19.png" /></figure>

187

Figure S2.3. Example of predictions of fish biomasses and zooplankton abundances in our study area (Gulf of Lions, Western Mediterranean) for the August 17th 2014.

188
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

Figure S2.4. Relationship between observed and predicted zooplankton-preferred and zooplankton-other abundances within the range of abundances used by shearwaters, between 0 and 800 ind.m⁻³ for zooplankton-preferred group and between 0 and 600 ind.m⁻³ for zooplankton-other group. A significant positive correlated relationship represents the ability of the model to correctly predict zooplankton abundance. Predicted abundances were estimated using a leave-one-out cross-validation. Note that an exceptional low salinity or deep bathymetry or type of seabed substrate found at three samples stations produced unrealistic predictions (>3000 ind.m⁻³) and we removed these outliers before determining the correlation between observed and predicted abundances.
<table><caption>Supporting Information S3. Summary of the features of foraging trips of the GPS-tracked Scopoli's shearwaters in the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2011 and 2015.</caption><thead><tr><th>ID</th><th>Island</th><th>Year</th><th>Trip ID</th><th>Number of foraging locations</th><th>Duration (h)</th><th>Total travelled distance (km)</th><th>Trip range (km)</th></tr></thead><tbody><tr><td>EA536869</td><td>Riou</td><td>2015</td><td>1</td><td>301</td><td>17.26667</td><td>130.3072</td><td>37.24427</td></tr><tr><td>EA537115</td><td>Frioul</td><td>2012</td><td>1</td><td>169</td><td>17.83333</td><td>95.32814</td><td>33.77627</td></tr><tr><td>EA537115</td><td>Frioul</td><td>2012</td><td>2</td><td>527</td><td>23.86667</td><td>150.7962</td><td>44.54079</td></tr><tr><td>EA537115</td><td>Frioul</td><td>2012</td><td>3</td><td>262</td><td>22.9</td><td>235.3361</td><td>81.88775</td></tr><tr><td>EA537119</td><td>Frioul</td><td>2012</td><td>1</td><td>352</td><td>17.86667</td><td>77.92335</td><td>21.20606</td></tr><tr><td>EA537119</td><td>Frioul</td><td>2012</td><td>2</td><td>386</td><td>21.16667</td><td>161.5663</td><td>44.26024</td></tr><tr><td>EA537119</td><td>Frioul</td><td>2012</td><td>3</td><td>402</td><td>22.66667</td><td>112.9971</td><td>41.98626</td></tr><tr><td>EA537314</td><td>Riou</td><td>2014</td><td>1</td><td>362</td><td>17.96667</td><td>197.3995</td><td>66.18423</td></tr><tr><td>EA537342</td><td>Riou</td><td>2015</td><td>1</td><td>431</td><td>21.23333</td><td>202.8944</td><td>46.61669</td></tr><tr><td>EA574518</td><td>Frioul</td><td>2012</td><td>1</td><td>392</td><td>18.63333</td><td>81.73911</td><td>10.45051</td></tr><tr><td>EA574518</td><td>Frioul</td><td>2012</td><td>2</td><td>406</td><td>17.03333</td><td>67.91646</td><td>12.91926</td></tr><tr><td>EA575316</td><td>Frioul</td><td>2012</td><td>1</td><td>134</td><td>17.8</td><td>158.9419</td><td>19.47904</td></tr><tr><td>EA575316</td><td>Frioul</td><td>2012</td><td>2</td><td>309</td><td>17.6</td><td>174.6852</td><td>32.09053</td></tr><tr><td>EA575316</td><td>Frioul</td><td>2012</td><td>3</td><td>279</td><td>17.43333</td><td>129.372</td><td>21.23269</td></tr><tr><td>EA583455</td><td>Riou</td><td>2014</td><td>1</td><td>202</td><td>20.13333</td><td>272.5217</td><td>107.4334</td></tr><tr><td>EA583462</td><td>Riou</td><td>2011</td><td>1</td><td>1077</td><td>90.2</td><td>1041.618</td><td>187.9398</td></tr><tr><td>EA583462</td><td>Riou</td><td>2012</td><td>1</td><td>400</td><td>22.36667</td><td>102.2403</td><td>26.37054</td></tr><tr><td>EA583462</td><td>Riou</td><td>2014</td><td>1</td><td>123</td><td>22.36667</td><td>195.9221</td><td>57.64454</td></tr><tr><td>EA583471</td><td>Riou</td><td>2011</td><td>1</td><td>185</td><td>17.96667</td><td>112.6837</td><td>22.71272</td></tr><tr><td>EA583471</td><td>Riou</td><td>2011</td><td>2</td><td>163</td><td>18.6</td><td>151.9209</td><td>48.85248</td></tr><tr><td>EA583471</td><td>Riou</td><td>2011</td><td>3</td><td>267</td><td>17.66667</td><td>255.0441</td><td>81.3694</td></tr><tr><td>EA583471</td><td>Riou</td><td>2012</td><td>1</td><td>171</td><td>16.93333</td><td>56.63242</td><td>10.74711</td></tr><tr><td>EA583471</td><td>Riou</td><td>2012</td><td>2</td><td>297</td><td>16.86667</td><td>119.7053</td><td>35.72179</td></tr><tr><td>EA583471</td><td>Riou</td><td>2012</td><td>3</td><td>399</td><td>21.9</td><td>91.69251</td><td>20.61444</td></tr><tr><td>EA583471</td><td>Riou</td><td>2012</td><td>4</td><td>288</td><td>16.66667</td><td>62.78475</td><td>15.29072</td></tr><tr><td>EA583473</td><td>Riou</td><td>2011</td><td>1</td><td>267</td><td>20.03333</td><td>217.588</td><td>59.64424</td></tr><tr><td>EA583473</td><td>Riou</td><td>2011</td><td>2</td><td>422</td><td>23.3</td><td>204.8924</td><td>23.58706</td></tr><tr><td>EA583473</td><td>Riou</td><td>2011</td><td>3</td><td>380</td><td>22.53333</td><td>247.112</td><td>101.1507</td></tr><tr><td>EA583473</td><td>Riou</td><td>2011</td><td>4</td><td>777</td><td>47.1</td><td>478.3253</td><td>117.6529</td></tr><tr><td>EA583473</td><td>Riou</td><td>2011</td><td>5</td><td>221</td><td>25.6</td><td>227.249</td><td>59.64342</td></tr><tr><td>EA583473</td><td>Riou</td><td>2014</td><td>1</td><td>510</td><td>26.1</td><td>251.4774</td><td>52.51986</td></tr><tr><td>EA583473</td><td>Riou</td><td>2014</td><td>2</td><td>330</td><td>20.2</td><td>144.4306</td><td>28.60502</td></tr><tr><td>EA583515</td><td>Riou</td><td>2014</td><td>1</td><td>224</td><td>25</td><td>369.067</td><td>61.83389</td></tr><tr><td>EA583515</td><td>Riou</td><td>2014</td><td>2</td><td>299</td><td>21.03333</td><td>170.5801</td><td>38.05091</td></tr><tr><td>EA583521</td><td>Riou</td><td>2011</td><td>1</td><td>735</td><td>47.9</td><td>654.1349</td><td>166.7566</td></tr><tr><td>EA583521</td><td>Riou</td><td>2011</td><td>2</td><td>310</td><td>18.96667</td><td>177.8097</td><td>52.61471</td></tr><tr><td>EA583521</td><td>Riou</td><td>2011</td><td>3</td><td>836</td><td>52.76667</td><td>674.7173</td><td>231.5562</td></tr><tr><td>EA584082</td><td>Frioul</td><td>2012</td><td>1</td><td>278</td><td>17.2</td><td>140.4598</td><td>52.02414</td></tr><tr><td>EA584082</td><td>Frioul</td><td>2012</td><td>2</td><td>437</td><td>22.2</td><td>153.8898</td><td>44.59631</td></tr><tr><td>EA584082</td><td>Frioul</td><td>2012</td><td>3</td><td>283</td><td>17.13333</td><td>135.7145</td><td>47.14116</td></tr><tr><td>EA587492</td><td>Riou</td><td>2012</td><td>1</td><td>197</td><td>19.76667</td><td>163.0775</td><td>37.56865</td></tr><tr><td>EA587492</td><td>Riou</td><td>2012</td><td>2</td><td>204</td><td>22.2</td><td>116.4792</td><td>29.6461</td></tr><tr><td>EA587494</td><td>Riou</td><td>2015</td><td>1</td><td>119</td><td>18.6</td><td>302.4084</td><td>117.0496</td></tr><tr><td>EA587495</td><td>Riou</td><td>2012</td><td>1</td><td>215</td><td>21.66667</td><td>322.8619</td><td>132.7221</td></tr><tr><td>EA587495</td><td>Riou</td><td>2012</td><td>2</td><td>420</td><td>19.4</td><td>89.86135</td><td>29.49951</td></tr><tr><td>EA588706</td><td>Frioul</td><td>2012</td><td>1</td><td>312</td><td>16.96667</td><td>109.8093</td><td>24.28834</td></tr><tr><td>EA588706</td><td>Frioul</td><td>2012</td><td>2</td><td>328</td><td>18.06667</td><td>176.1783</td><td>34.26667</td></tr><tr><td>EA588706</td><td>Frioul</td><td>2012</td><td>3</td><td>453</td><td>23.4</td><td>240.9952</td><td>62.65067</td></tr><tr><td>EA591287</td><td>Frioul</td><td>2012</td><td>1</td><td>225</td><td>18.1</td><td>147.6267</td><td>34.95949</td></tr><tr><td>EA591287</td><td>Frioul</td><td>2012</td><td>2</td><td>192</td><td>17.33333</td><td>211.9506</td><td>83.03631</td></tr><tr><td>EA591287</td><td>Frioul</td><td>2012</td><td>3</td><td>318</td><td>20.3</td><td>174.3363</td><td>52.14879</td></tr><tr><td>EA591293</td><td>Frioul</td><td>2011</td><td>1</td><td>214</td><td>17.9</td><td>301.16</td><td>90.29346</td></tr><tr><td>EA591293</td><td>Frioul</td><td>2011</td><td>2</td><td>371</td><td>18.63333</td><td>341.9844</td><td>110.5827</td></tr><tr><td>EA591293</td><td>Frioul</td><td>2011</td><td>3</td><td>140</td><td>19.93333</td><td>439.2692</td><td>201.86</td></tr><tr><td>EA591293</td><td>Frioul</td><td>2011</td><td>4</td><td>358</td><td>24.03333</td><td>344.7705</td><td>130.0115</td></tr><tr><td>EA591293</td><td>Frioul</td><td>2012</td><td>1</td><td>220</td><td>19.76667</td><td>106.7811</td><td>22.414</td></tr><tr><td>EA591293</td><td>Frioul</td><td>2012</td><td>2</td><td>239</td><td>17.53333</td><td>146.2526</td><td>56.44107</td></tr></tbody></table>
<table>
  <tr>
    <td>EA591293</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>3</td>
    <td>365</td>
    <td>17.33333</td>
    <td>68.91761</td>
    <td>11.27812</td>
  </tr>
  <tr>
    <td>EA591317</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>117</td>
    <td>17.8</td>
    <td>274.1575</td>
    <td>112.9897</td>
  </tr>
  <tr>
    <td>EA591317</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>135</td>
    <td>19.1</td>
    <td>257.104</td>
    <td>85.67987</td>
  </tr>
  <tr>
    <td>EA591317</td>
    <td>Riou</td>
    <td>2011</td>
    <td>3</td>
    <td>114</td>
    <td>17.16667</td>
    <td>213.4884</td>
    <td>89.32922</td>
  </tr>
  <tr>
    <td>EA591396</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>253</td>
    <td>19.33333</td>
    <td>254.674</td>
    <td>106.3596</td>
  </tr>
  <tr>
    <td>EA591396</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>534</td>
    <td>41.93333</td>
    <td>558.7664</td>
    <td>178.8871</td>
  </tr>
  <tr>
    <td>EA591396</td>
    <td>Riou</td>
    <td>2011</td>
    <td>3</td>
    <td>443</td>
    <td>22.1</td>
    <td>205.4152</td>
    <td>30.0629</td>
  </tr>
  <tr>
    <td>EA591396</td>
    <td>Riou</td>
    <td>2011</td>
    <td>4</td>
    <td>238</td>
    <td>22.6</td>
    <td>105.0809</td>
    <td>17.01076</td>
  </tr>
  <tr>
    <td>EA591603</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>232</td>
    <td>17.26667</td>
    <td>85.05565</td>
    <td>21.20548</td>
  </tr>
  <tr>
    <td>EA591603</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>242</td>
    <td>17.1</td>
    <td>246.9954</td>
    <td>109.9167</td>
  </tr>
  <tr>
    <td>EA591604</td>
    <td>Frioul</td>
    <td>2011</td>
    <td>1</td>
    <td>242</td>
    <td>19.5</td>
    <td>312.323</td>
    <td>117.5258</td>
  </tr>
  <tr>
    <td>EA591643</td>
    <td>Frioul</td>
    <td>2011</td>
    <td>1</td>
    <td>496</td>
    <td>25.16667</td>
    <td>170.1195</td>
    <td>22.71646</td>
  </tr>
  <tr>
    <td>EA591643</td>
    <td>Frioul</td>
    <td>2011</td>
    <td>2</td>
    <td>564</td>
    <td>46</td>
    <td>737.3911</td>
    <td>216.5993</td>
  </tr>
  <tr>
    <td>EA591643</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>177</td>
    <td>21.93333</td>
    <td>158.3072</td>
    <td>46.2028</td>
  </tr>
  <tr>
    <td>EA591643</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>498</td>
    <td>23.43333</td>
    <td>134.9211</td>
    <td>34.51857</td>
  </tr>
  <tr>
    <td>EA593790</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>290</td>
    <td>22.26667</td>
    <td>323.6014</td>
    <td>58.25641</td>
  </tr>
  <tr>
    <td>EA593790</td>
    <td>Riou</td>
    <td>2014</td>
    <td>2</td>
    <td>224</td>
    <td>19.7</td>
    <td>88.61498</td>
    <td>17.1535</td>
  </tr>
  <tr>
    <td>EA593791</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>960</td>
    <td>45.6</td>
    <td>442.3402</td>
    <td>125.0831</td>
  </tr>
  <tr>
    <td>EA593791</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>311</td>
    <td>17.8</td>
    <td>156.4401</td>
    <td>38.93911</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>281</td>
    <td>19.73333</td>
    <td>345.7348</td>
    <td>144.5087</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>527</td>
    <td>41.63333</td>
    <td>595.6626</td>
    <td>185.988</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2011</td>
    <td>3</td>
    <td>59</td>
    <td>5.866667</td>
    <td>34.50492</td>
    <td>6.127479</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2011</td>
    <td>4</td>
    <td>403</td>
    <td>22.93333</td>
    <td>181.4883</td>
    <td>14.81019</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2011</td>
    <td>5</td>
    <td>41</td>
    <td>5.3</td>
    <td>53.00642</td>
    <td>22.24902</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2011</td>
    <td>6</td>
    <td>311</td>
    <td>21.63333</td>
    <td>110.9618</td>
    <td>17.80161</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>238</td>
    <td>17.16667</td>
    <td>125.5197</td>
    <td>37.19858</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>177</td>
    <td>17.66667</td>
    <td>323.8028</td>
    <td>133.3971</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2012</td>
    <td>3</td>
    <td>270</td>
    <td>23.23333</td>
    <td>148.8923</td>
    <td>39.52289</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>130</td>
    <td>13.6</td>
    <td>159.055</td>
    <td>23.17425</td>
  </tr>
  <tr>
    <td>EA593793</td>
    <td>Riou</td>
    <td>2014</td>
    <td>2</td>
    <td>33</td>
    <td>3.533333</td>
    <td>19.12922</td>
    <td>5.73451</td>
  </tr>
  <tr>
    <td>EA593798</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>270</td>
    <td>19.66667</td>
    <td>79.77859</td>
    <td>13.90533</td>
  </tr>
  <tr>
    <td>EA593798</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>193</td>
    <td>17.13333</td>
    <td>86.4203</td>
    <td>16.60192</td>
  </tr>
  <tr>
    <td>EA593798</td>
    <td>Riou</td>
    <td>2012</td>
    <td>3</td>
    <td>420</td>
    <td>25.83333</td>
    <td>275.3548</td>
    <td>48.85784</td>
  </tr>
  <tr>
    <td>EA593798</td>
    <td>Riou</td>
    <td>2012</td>
    <td>4</td>
    <td>164</td>
    <td>16.33333</td>
    <td>135.0189</td>
    <td>52.13851</td>
  </tr>
  <tr>
    <td>EA595979</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>336</td>
    <td>17.66667</td>
    <td>173.7901</td>
    <td>71.66334</td>
  </tr>
  <tr>
    <td>EA621014</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>770</td>
    <td>69.26667</td>
    <td>682.8429</td>
    <td>146.2361</td>
  </tr>
  <tr>
    <td>EA621014</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>587</td>
    <td>47.36667</td>
    <td>755.6292</td>
    <td>259.5073</td>
  </tr>
  <tr>
    <td>EA621029</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>61</td>
    <td>19.9</td>
    <td>185.4475</td>
    <td>35.27625</td>
  </tr>
  <tr>
    <td>EA621030</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>230</td>
    <td>16.33333</td>
    <td>210.5041</td>
    <td>73.7311</td>
  </tr>
  <tr>
    <td>EA621030</td>
    <td>Riou</td>
    <td>2014</td>
    <td>2</td>
    <td>455</td>
    <td>29</td>
    <td>248.8434</td>
    <td>44.4035</td>
  </tr>
  <tr>
    <td>EA621030</td>
    <td>Riou</td>
    <td>2014</td>
    <td>3</td>
    <td>305</td>
    <td>21.06667</td>
    <td>212.2364</td>
    <td>36.69691</td>
  </tr>
  <tr>
    <td>EA621030</td>
    <td>Riou</td>
    <td>2014</td>
    <td>4</td>
    <td>460</td>
    <td>22.83333</td>
    <td>130.2794</td>
    <td>33.13248</td>
  </tr>
  <tr>
    <td>EA621030</td>
    <td>Riou</td>
    <td>2014</td>
    <td>5</td>
    <td>322</td>
    <td>17.16667</td>
    <td>175.1493</td>
    <td>35.69736</td>
  </tr>
  <tr>
    <td>EA621030</td>
    <td>Riou</td>
    <td>2014</td>
    <td>6</td>
    <td>216</td>
    <td>19.16667</td>
    <td>191.0858</td>
    <td>53.17421</td>
  </tr>
  <tr>
    <td>EA621053</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>1252</td>
    <td>112.7333</td>
    <td>1290.359</td>
    <td>131.7114</td>
  </tr>
  <tr>
    <td>EA621081</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>71</td>
    <td>17.8</td>
    <td>172.1036</td>
    <td>47.827</td>
  </tr>
  <tr>
    <td>EA621083</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>246</td>
    <td>18.33333</td>
    <td>136.6396</td>
    <td>32.34254</td>
  </tr>
  <tr>
    <td>EA621083</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>183</td>
    <td>16.66667</td>
    <td>273.245</td>
    <td>108.3758</td>
  </tr>
  <tr>
    <td>EA621085</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>186</td>
    <td>21.43333</td>
    <td>222.1787</td>
    <td>19.29173</td>
  </tr>
  <tr>
    <td>EA621085</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>814</td>
    <td>48.86667</td>
    <td>452.8178</td>
    <td>69.15876</td>
  </tr>
  <tr>
    <td>EA621124</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>229</td>
    <td>19.96667</td>
    <td>278.0153</td>
    <td>118.5711</td>
  </tr>
  <tr>
    <td>EA621124</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>186</td>
    <td>21.13333</td>
    <td>155.2585</td>
    <td>33.80452</td>
  </tr>
  <tr>
    <td>EA621124</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>3</td>
    <td>574</td>
    <td>26.33333</td>
    <td>182.9879</td>
    <td>31.47239</td>
  </tr>
  <tr>
    <td>EA621252</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>381</td>
    <td>21.2</td>
    <td>220.8059</td>
    <td>23.32588</td>
  </tr>
  <tr>
    <td>EA621252</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>246</td>
    <td>16.66667</td>
    <td>120.7745</td>
    <td>32.78243</td>
  </tr>
  <tr>
    <td>EA621254</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>227</td>
    <td>19.1</td>
    <td>31.38855</td>
    <td>9.478269</td>
  </tr>
  <tr>
    <td>EA621282</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>359</td>
    <td>19.66667</td>
    <td>131.3543</td>
    <td>19.99216</td>
  </tr>
  <tr>
    <td>EA621282</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>269</td>
    <td>20.8</td>
    <td>185.2309</td>
    <td>42.75986</td>
  </tr>
  <tr>
    <td>EA621282</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>3</td>
    <td>219</td>
    <td>17.26667</td>
    <td>137.758</td>
    <td>34.19235</td>
  </tr>
  <tr>
    <td>EA621347</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>96</td>
    <td>16.66667</td>
    <td>133.4084</td>
    <td>25.11375</td>
  </tr>
  <tr>
    <td>EA621347</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>258</td>
    <td>16.73333</td>
    <td>159.0478</td>
    <td>18.94242</td>
  </tr>
  <tr>
    <td>EA621379</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>455</td>
    <td>25.53333</td>
    <td>282.486</td>
    <td>48.67823</td>
  </tr>
  <tr>
    <td>EA621379</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>355</td>
    <td>24.3</td>
    <td>229.622</td>
    <td>85.31952</td>
  </tr>
  <tr>
    <td>EA621379</td>
    <td>Riou</td>
    <td>2011</td>
    <td>3</td>
    <td>267</td>
    <td>20.6</td>
    <td>240.5288</td>
    <td>78.12428</td>
  </tr>
  <tr>
    <td>EA621379</td>
    <td>Riou</td>
    <td>2011</td>
    <td>4</td>
    <td>368</td>
    <td>21.03333</td>
    <td>99.82077</td>
    <td>27.36082</td>
  </tr>
  <tr>
    <td>EA621673</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>167</td>
    <td>17.43333</td>
    <td>237.5918</td>
    <td>61.14044</td>
  </tr>
  <tr>
    <td>EA621673</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>266</td>
    <td>18.4</td>
    <td>309.4056</td>
    <td>112.24</td>
  </tr>
  <tr>
    <td>EA621673</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>3</td>
    <td>181</td>
    <td>17.56667</td>
    <td>251.4703</td>
    <td>100.8403</td>
  </tr>
  <tr>
    <td>EA621673</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>4</td>
    <td>304</td>
    <td>22.76667</td>
    <td>362.6366</td>
    <td>141.9427</td>
  </tr>
  <tr>
    <td>EA621683</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>392</td>
    <td>17.83333</td>
    <td>120.8243</td>
    <td>17.21523</td>
  </tr>
</table>
<table>
  <tr>
    <td>EA621683</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>217</td>
    <td>19.3</td>
    <td>143.1574</td>
    <td>33.86665</td>
  </tr>
  <tr>
    <td>EA621683</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>3</td>
    <td>437</td>
    <td>20.8</td>
    <td>138.6181</td>
    <td>24.19683</td>
  </tr>
  <tr>
    <td>EC100001</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>185</td>
    <td>15.36667</td>
    <td>109.4745</td>
    <td>25.37992</td>
  </tr>
  <tr>
    <td>EC100001</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>134</td>
    <td>11.43333</td>
    <td>172.7164</td>
    <td>58.56319</td>
  </tr>
  <tr>
    <td>EC100002</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>228</td>
    <td>17.83333</td>
    <td>169.9823</td>
    <td>57.03959</td>
  </tr>
  <tr>
    <td>EC100002</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>307</td>
    <td>22.76667</td>
    <td>259.0814</td>
    <td>95.54314</td>
  </tr>
  <tr>
    <td>EC100002</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>544</td>
    <td>42.36667</td>
    <td>560.7254</td>
    <td>115.1856</td>
  </tr>
  <tr>
    <td>EC100054</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>1</td>
    <td>317</td>
    <td>22.46667</td>
    <td>335.7444</td>
    <td>118.0449</td>
  </tr>
  <tr>
    <td>EC100054</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>2</td>
    <td>223</td>
    <td>23.33333</td>
    <td>102.0563</td>
    <td>17.26958</td>
  </tr>
  <tr>
    <td>EC100054</td>
    <td>Frioul</td>
    <td>2012</td>
    <td>3</td>
    <td>379</td>
    <td>21.83333</td>
    <td>123.5627</td>
    <td>43.27236</td>
  </tr>
  <tr>
    <td>EC100104</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>347</td>
    <td>22.7</td>
    <td>344.462</td>
    <td>90.98925</td>
  </tr>
  <tr>
    <td>EC100104</td>
    <td>Riou</td>
    <td>2015</td>
    <td>2</td>
    <td>279</td>
    <td>18.93333</td>
    <td>237.5836</td>
    <td>76.64678</td>
  </tr>
  <tr>
    <td>EC100156</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>107</td>
    <td>18.16667</td>
    <td>74.70529</td>
    <td>26.8197</td>
  </tr>
  <tr>
    <td>EC100156</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>218</td>
    <td>19.16667</td>
    <td>116.7197</td>
    <td>31.65386</td>
  </tr>
  <tr>
    <td>EC100156</td>
    <td>Riou</td>
    <td>2012</td>
    <td>3</td>
    <td>336</td>
    <td>21</td>
    <td>138.7979</td>
    <td>50.91555</td>
  </tr>
  <tr>
    <td>EC100160</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>423</td>
    <td>22.4</td>
    <td>99.63473</td>
    <td>26.54514</td>
  </tr>
  <tr>
    <td>EC100160</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>258</td>
    <td>17.1</td>
    <td>168.0411</td>
    <td>34.38288</td>
  </tr>
  <tr>
    <td>EC100183</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>310</td>
    <td>16.73333</td>
    <td>150.7264</td>
    <td>60.51673</td>
  </tr>
  <tr>
    <td>EC100253</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>264</td>
    <td>17.03333</td>
    <td>174.5967</td>
    <td>68.18407</td>
  </tr>
  <tr>
    <td>EC102691</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>433</td>
    <td>21.86667</td>
    <td>158.9545</td>
    <td>34.90184</td>
  </tr>
  <tr>
    <td>EC102692</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>636</td>
    <td>46.96667</td>
    <td>495.5599</td>
    <td>84.38068</td>
  </tr>
  <tr>
    <td>EC102698</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>259</td>
    <td>18.73333</td>
    <td>152.3522</td>
    <td>49.09335</td>
  </tr>
  <tr>
    <td>NB02</td>
    <td>Riou</td>
    <td>2011</td>
    <td>1</td>
    <td>211</td>
    <td>19.76667</td>
    <td>145.5024</td>
    <td>23.07429</td>
  </tr>
  <tr>
    <td>NB02</td>
    <td>Riou</td>
    <td>2011</td>
    <td>2</td>
    <td>22</td>
    <td>6.766667</td>
    <td>13.02388</td>
    <td>2.097616</td>
  </tr>
  <tr>
    <td>NB02</td>
    <td>Riou</td>
    <td>2011</td>
    <td>3</td>
    <td>269</td>
    <td>16.93333</td>
    <td>138.1201</td>
    <td>42.86953</td>
  </tr>
  <tr>
    <td>NB06</td>
    <td>Riou</td>
    <td>2012</td>
    <td>1</td>
    <td>230</td>
    <td>18.2</td>
    <td>162.8009</td>
    <td>37.6337</td>
  </tr>
  <tr>
    <td>NB06</td>
    <td>Riou</td>
    <td>2012</td>
    <td>2</td>
    <td>319</td>
    <td>21.13333</td>
    <td>126.9835</td>
    <td>28.01322</td>
  </tr>
  <tr>
    <td>NB06</td>
    <td>Riou</td>
    <td>2012</td>
    <td>3</td>
    <td>418</td>
    <td>22.4</td>
    <td>88.22749</td>
    <td>18.16903</td>
  </tr>
  <tr>
    <td>NB07</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>471</td>
    <td>18.2</td>
    <td>83.71739</td>
    <td>21.39259</td>
  </tr>
  <tr>
    <td>NB08</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>108</td>
    <td>26.46667</td>
    <td>294.8945</td>
    <td>47.0458</td>
  </tr>
  <tr>
    <td>NB08</td>
    <td>Riou</td>
    <td>2014</td>
    <td>2</td>
    <td>362</td>
    <td>20.33333</td>
    <td>101.7575</td>
    <td>23.6823</td>
  </tr>
  <tr>
    <td>NB09</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>326</td>
    <td>20.13333</td>
    <td>140.1232</td>
    <td>49.31408</td>
  </tr>
  <tr>
    <td>NB10</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>279</td>
    <td>20.66667</td>
    <td>159.4168</td>
    <td>41.84232</td>
  </tr>
  <tr>
    <td>NB11</td>
    <td>Riou</td>
    <td>2014</td>
    <td>1</td>
    <td>243</td>
    <td>25.23333</td>
    <td>298.2127</td>
    <td>96.93353</td>
  </tr>
  <tr>
    <td>NB11</td>
    <td>Riou</td>
    <td>2014</td>
    <td>2</td>
    <td>94</td>
    <td>17.86667</td>
    <td>102.0556</td>
    <td>23.6134</td>
  </tr>
  <tr>
    <td>NB12</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>329</td>
    <td>18.66667</td>
    <td>280.2737</td>
    <td>124.3417</td>
  </tr>
  <tr>
    <td>NB13</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>171</td>
    <td>19</td>
    <td>162.264</td>
    <td>60.2429</td>
  </tr>
  <tr>
    <td>NB13</td>
    <td>Riou</td>
    <td>2015</td>
    <td>2</td>
    <td>102</td>
    <td>4.166667</td>
    <td>9.352807</td>
    <td>3.394749</td>
  </tr>
  <tr>
    <td>NB13</td>
    <td>Riou</td>
    <td>2015</td>
    <td>3</td>
    <td>335</td>
    <td>22.4</td>
    <td>98.93079</td>
    <td>19.69591</td>
  </tr>
  <tr>
    <td>NB13</td>
    <td>Riou</td>
    <td>2015</td>
    <td>4</td>
    <td>277</td>
    <td>17.33333</td>
    <td>330.5388</td>
    <td>94.25448</td>
  </tr>
  <tr>
    <td>NB14</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>277</td>
    <td>17.8</td>
    <td>176.8317</td>
    <td>53.54744</td>
  </tr>
  <tr>
    <td>NB15</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>80</td>
    <td>17</td>
    <td>255.3445</td>
    <td>102.2782</td>
  </tr>
  <tr>
    <td>NB16</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>405</td>
    <td>21.7</td>
    <td>203.2518</td>
    <td>29.24086</td>
  </tr>
  <tr>
    <td>NB16</td>
    <td>Riou</td>
    <td>2015</td>
    <td>2</td>
    <td>423</td>
    <td>24.1</td>
    <td>272.6973</td>
    <td>29.55321</td>
  </tr>
  <tr>
    <td>NB17</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>349</td>
    <td>21.7</td>
    <td>320.6357</td>
    <td>73.82406</td>
  </tr>
  <tr>
    <td>NB17</td>
    <td>Riou</td>
    <td>2015</td>
    <td>2</td>
    <td>269</td>
    <td>20.06667</td>
    <td>230.7422</td>
    <td>82.62435</td>
  </tr>
  <tr>
    <td>NB17</td>
    <td>Riou</td>
    <td>2015</td>
    <td>3</td>
    <td>171</td>
    <td>23.46667</td>
    <td>204.3194</td>
    <td>59.18303</td>
  </tr>
  <tr>
    <td>NB18</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>313</td>
    <td>23.5</td>
    <td>270.2717</td>
    <td>58.78955</td>
  </tr>
  <tr>
    <td>NB18</td>
    <td>Riou</td>
    <td>2015</td>
    <td>2</td>
    <td>25</td>
    <td>3.2</td>
    <td>8.57647</td>
    <td>1.871632</td>
  </tr>
  <tr>
    <td>NB19</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>359</td>
    <td>21.8</td>
    <td>215.4816</td>
    <td>46.68042</td>
  </tr>
  <tr>
    <td>NB20</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>292</td>
    <td>17.06667</td>
    <td>55.43941</td>
    <td>13.44915</td>
  </tr>
  <tr>
    <td>NB21</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>237</td>
    <td>17.16667</td>
    <td>230.0234</td>
    <td>49.71648</td>
  </tr>
  <tr>
    <td>NB22</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>284</td>
    <td>21.53333</td>
    <td>305.2421</td>
    <td>59.42231</td>
  </tr>
  <tr>
    <td>NB23</td>
    <td>Riou</td>
    <td>2015</td>
    <td>1</td>
    <td>266</td>
    <td>16.96667</td>
    <td>157.3242</td>
    <td>38.13538</td>
  </tr>
  <tr>
    <td>NB23</td>
    <td>Riou</td>
    <td>2015</td>
    <td>2</td>
    <td>426</td>
    <td>24.26667</td>
    <td>253.7867</td>
    <td>53.44138</td>
  </tr>
</table>
203 Supporting Information S4. Stable isotope analyses and trophic position of shearwaters

204

To corroborate the degree of trophic specialization found in the resource selection patterns and their inter-annual variations, we assessed the individual trophic position of shearwaters using stable isotope analyses of the first primary feather (P1) of each individual. Considering the moult pattern of Scopoli's shearwaters, P1 feathers collected in August reflect diet during the previous chick-rearing period (Ramos et al. 2009). Therefore, feathers collected on GPS-tracked shearwaters during chick-rearing at year *n* described their diet during the chick-rearing period of year *n*-1. A total of 134 P1 feathers were collected during the biotelemetry fieldwork (*n* = 31 in 2011, *n* = 33 in 2012, *n* = 20 in 2014, *n* = 20 in 2015 and *n* = 30 in 2016).

We collected both GPS data and P1 samples in 2016, but did not consider GPS data collected in 2016 in our analyses. Relative abundance of stable isotopes of nitrogen (¹⁵N:¹⁴N, thereafter 'δ¹⁵N') and carbon (¹³C:¹²C, thereafter 'δ¹³C') were determined on about 0.4 mg subsamples of P1 feathers by continuous-flow isotope-ratio spectrometry (see Péron et al. [2013] for more details on sample preparation and calculation of isotopic ratios). δ¹⁵N values measured in predators reflect their trophic status and δ¹³C values reflect the characteristics of their foraging location (Ramos et al. 2009; Cardona et al. 2012; Péron et al. 2013). We estimated and compared the trophic position (TP) between individuals using the following equation as described by Hobson and Bond (2012) for seabirds:

222

$$223 TP_i = 3 + (\delta^{15}N_i - \Delta d_{feather} - \delta^{15}N_{copepods})/3.4 \qquad (1)$$

224

where δ¹⁵Nᵢ is the stable nitrogen isotope ratio for individual *i* in its P1 feather, Δd<sub>feather</sub> is the mean diet-tissue discrimination factor between feather and prey muscle protein for comparable species because no data is available for adult Procellariforms (3.7‰ for δ¹⁵N,
Meier *et al.* 2017), δ¹⁵N<sub>copepods</sub> (4.7‰) is the average value measured on copepods in the Gulf of Lions based on samples collected during the PELMED campaign in 2014, with an assumed trophic level of 2. We found a similar value for the δ¹⁵N<sub>copepods</sub> than in 2008 in the Gulf of Lions (4.5‰; Costalago *et al.* 2012). We considered a constant trophic enrichment factor of 3.4‰ (Post 2002).

To gain additional insights into diet specialization of Scopoli's shearwaters, we related the isotopic signature of individuals to those of their potential prey. The isotopic signatures of European anchovy, sardine and sprat were determined yearly between 2010 and 2015 based on sample collected during PELMED surveys covering our study area (n ranged between 18 and 61 for anchovies, n ranged between 36 and 51 for sardines, n ranged between 32 and 57 for sprats; Brosset *et al.* 2016; Mialet *et al.* 2017). No data was available for sprat in 2015 and we used the isotopic values found in 2014 for this species in 2015. We also considered isotopic values estimated for zooplankton in 2014 (n = 174 samples, Brosset *et al.* 2016) and copepods only (calanoids) in 2014 (δ¹⁵N = 4.74 +/- 0.77 [mean +/- SD], δ¹³C = -22.59 +/- 0.63, n = 134 samples, unpublished data) in the Gulf of Lions by IFREMER. Finally, isotopic signatures of European common squids (*Loligo vulgaris*), European flying squids (*Todarodes sagittatus*) and euphausiids (*Meganyctiphanes norvegica*) were taken from oceanographic surveys carried out in 2006-2007 in the Western Mediterranean (Cardona *et al.* 2012).

Importantly, inference on diet of shearwaters from δ¹⁵N values were valuable because 1) the average δ¹⁵N isotopic signatures in zooplankton/copepods (4.75‰/4.74‰) were clearly different (at least ~3.3‰, i.e. about one trophic level) from δ¹⁵N in the three fish species (anchovy: 8.03‰, sardine: 8.31‰ and sprat: 8.34‰) in 2014 and standard deviation was small for each prey type, 2) the isotopic landscape is relatively homogeneous in space and time (Fig. S4.2). We found a low spatial variability among δ¹⁵N values within the Gulf of Lion for fish species and zooplankton in 2014 and for fish species in other years (Fig. S4.2).
We fitted a linear mixed model assessing the relationship between δ¹⁵N values for each prey type (response variable) and a set of independent variables: longitude, latitude, fish length or zooplankton size class, and added a nested random intercept for fish (year/station) or a random intercept for zooplankton (station) to control for non-independence among multiple samples from the same year and/or the same station. Models indicated a slight decrease in isotopic values from West to East for anchovy (beta_longitude = -0.36, 95%CI = -0.54;-0.17) and sardine (beta_longitude = -0.21, 95%CI = -0.36;-0.05), but δ¹⁵N values remained very different between fish and zooplankton throughout the longitudinal gradient. No longitudinal trend was found for sprat and zooplankton and no latitudinal trend occurred for any species.
These results were supported by the low standard deviation in δ¹⁵N for any species at each year. Similarly, we also found a very low inter-annual variability among the δ¹⁵N values for any fish species in our study, with average values ranging between 7.15‰ and 8.03‰ for anchovy, 7.81‰ and 8.5‰ for sardines, and 7.65‰ and 8.34‰ for sprats (see also Brosset et al. 2016). We have no multi-year data for zooplankton/copepod but we expect that if δ¹⁵N values for zooplankton/copepod changed significantly among years, small pelagic fish (anchovy, sardine and sprat) preying on zooplankton/copepod should have also changed in their δ¹⁵N values among years, which is not the case. We thus are confident with the fact that δ¹⁵N values for zooplankton/copepod should be relatively stable during our study period.
This is reinforced by the fact that δ¹⁵N values remained stable over time (2004, 2005 and 2014) in the Gulf of Lion (Brosset et al. 2016).

We found heterogeneity in the trophic position of individuals within and across years, while most birds had low trophic position, with a mean trophic position of 3.39 +/- 0.03 (mean +/- SE, Fig. S4.1). In 2010 and 2011, shearwaters showed individual dietary specialization with their trophic position ranging from 2.9 to 4.7, corresponding to a diet exclusively based on zooplankton, euphausiids and copepods, or on small pelagic fish and
squid (Fig. S4.1, Fig. 6 in the main text). The following years had lower individual heterogeneity. The trophic position and contribution of fish in the diet in 2013 seemed to increase related to other years (P < 0.02), but we have to be cautious with this interpretation because $\delta^{15}N_{copepods}$ was considered constant among years based on an estimation in 2014 (i.e. constant baseline). An increase of the $\delta^{15}N_{copepods}$ value in 2013 could imply higher trophic position values for shearwaters in 2013. However, $\delta^{15}N_{copepods}$ seemed to be relatively stable over years with similar values in 2008 and 2014, and isotopic signature of shearwaters well matched isotopic signature of small pelagic fish in 2013 (Fig. 6 in the main text). Shearwaters became more specialized on prey of lower trophic levels in 2014 with few variations among individuals (Fig. S4.1, Fig. 6 in the main text). Mean trophic position of individuals was not related to sex and age ($F_{6,93} = 2.24, r^2 = 0.07, P < 0.05$).

## References

291 Brosset, P. et al. (2016). Linking small pelagic dietary shifts with ecosystem changes in the Gulf of Lions. *Mar. Ecol. Prog. Ser.*, 554, 157-171.

293 Cardona, L., Álvarez de Quevedo, I., Borrell, A. & Aguilar, A. (2012). Massive Consumption of Gelatinous Plankton by Mediterranean Apex Predators. *PLoS ONE*, 7, e31329.

295 Costalago, D., Navarro, J., Álvarez-Calleja, I. & Palomera, I. (2012). Ontogenic and seasonal changes in the feeding habits and trophic levels of two small pelagic fish species. *Mar. Ecol. Prog. Ser.*, 460, 169-181.

298 Hobson, K.A. & Bond, A.L. (2012). Extending an indicator: year-round information on seabird trophic ecology from multiple-tissue stable-isotope analyses. *Mar. Ecol. Prog. Ser.*, 461, 233-243.

301 Meier, R.E. et al. (2017). Tracking, feather moult and stable isotopes reveal foraging behaviour of a critically endangered seabird during the non-breeding season. *Divers. Distrib.*,
303 23, 130-145.

304 Mialet, B. (2017). Bilan des essais et optimisation du suivi mutualisé « réseaux trophiques et

305 contaminants » sur les campagnes halieutiques DCF 2014-2015. Rapport pour le projet

306 DCSMM. http://archimer.ifremer.fr/doc/00373/48447/

307 Péron, C. et al. (2013). Importance of coastal Marine Protected Areas for the conservation of

308 pelagic seabirds: the case of vulnerable yelkouan shearwaters in the Mediterranean Sea. *Biol.*

309 *Conserv.*, 168, 210-221.

310 Post, D.M. (2002) Using stable isotopes to estimate trophic position: models, methods, and

311 assumptions. *Ecology*, 83,703-718.

312 Ramos, R., González-Solís, J. & Ruiz, X. (2009). Linking isotopic and migratory patterns in a

313 pelagic seabird. *Oecologia*, 160, 97-105.
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /><figcaption>Figure S4.1. Annual distributions of inter-individual heterogeneity of trophic position of Scopoli's shearwaters in the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2010 and 2015. Dotted lines represent median values within distributions.</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>Figure S4.2. Spatial trends in the δ¹⁵N isotopic values along longitudinal and latitudinal gradients for three pelagic fish species (black circles) in July 2010 to 2015 and for zooplankton in July 2014 (red circles) within the Gulf of Lion (Western Mediterranean). δ¹⁵N values for zooplankton higher than 7‰ correspond to anchovy eggs.</figcaption></figure>

318

319

320

321

322
325
326 We quantified individual specialization in resource selection (i.e. within-individual consistency in resource selection) with an adjusted repeatability index $R_k$ for each shearwater 327
328 k. We adapted the population-level repeatability index $R$ developed for logistic regression 329
330 with random coefficients (Johnson 2014; Nakagawa et al. 2017) that took the form:

$$331 \qquad R = \frac{\sigma_{x,between}^2}{\sigma_{x,between}^2 + \sigma_{x,within}^2 + \sigma_e^2}, \qquad (1)$$

$$332 \qquad \sigma_{x,between}^2 = (\sum_k \sum_i \text{var}(\gamma_{0k}^{(\text{bird})} + \gamma_{xk}^{(\text{bird})} x_{ijk})) / n, \qquad (2)$$

$$333 \qquad \sigma_{x,within}^2 = (\sum_{jk} \sum_i \text{var}(\gamma_{0jk}^{(\text{trip})} + \gamma_{xjk}^{(\text{trip})} x_{ijk})) / n, \qquad (3)$$

$$334 \qquad \sigma_e^2 = \frac{1}{p(1-p)}, \qquad (4)$$

$$335 \qquad p \sim \text{logit}^{-1} \left( \beta_0 \sqrt{1 + \left(\frac{16\sqrt{3}}{15\pi}\right)^2 \left(\sigma_{x,between}^2 + \sigma_{x,within}^2 + \sigma_e^2\right)} \right)^{-1}, \qquad (5)$$

336
337 where $\sigma_{x,between}^2$ and $\sigma_{x,within}^2$ are the between- and within-individual variances (bird and trip
338 level, respectively) estimated within resource selection function with a random effect for the
339 prey-related variable $x$, $\sigma_e^2$ is global observation-level (GPS location level) variance at the
340 individual level given a logit link, $\gamma_{0k}^{(\text{bird})}$ and $\gamma_{xk}^{(\text{bird})}$ are the random intercepts and the
341 random coefficients of $x$, respectively, for shearwater $k$ at the individual level, $\gamma_{0jk}^{(\text{trip})}$ and
342 $\gamma_{xjk}^{(\text{trip})}$ are the random intercepts and the random coefficients of $x$, respectively, for trip $j$ and
343 shearwater $k$ at the trip level, $n$ the total number of observations, $p$ is the expected proportion
344 of successes on the observed scale, and $\sigma_e^2$ is the fixed-effect variance.
We adapted the population-level index R with an individual-level index $R_k$ by partitioning $\sigma_e^2$ at the individual level, as usually done for linear models (Potier et al. 2015; Wakefield et al. 2015). In a mixed RSF for foraging location i, trip j and shearwater k, $R_k$ took the form:

349

$$R_k = \frac{\sigma_{x,between}^2}{\sigma_{x,between}^2 + \sigma_{x,within}^2 + \sigma_{ek}^2}, \qquad (6)$$

$$\sigma_{ek}^2 = \frac{1}{p_k(1-p_k)}, \qquad (7)$$

$$p_k \sim \text{logit}^{-1} \left( \beta_0 \sqrt{1 + \left(\frac{16\sqrt{3}}{15\pi}\right)^2 \left(\sigma_{xk,between}^2 + \sigma_{xk,within}^2 + \sigma_{ek}^2\right)} \right)^{-1}, \qquad (8)$$

353

where $\sigma_{ek}^2$ is the partitioned observation-level variance for the k^th^ bird, $p_k$ is the expected proportion of successes on the observed scale for the k^th^ bird, $\sigma_{xk,between}^2$ and $\sigma_{xk,within}^2$ are the between- and within-individual variance attributable to the k^th^ bird, respectively, and $\sigma_{ek}^2$ is the fixed-effect variance for the k^th^ bird.

$R_k$ values range between 0 and 1. $R_k$ values close to 1 imply high specialization (i.e. consistency) in resource selection for shearwater k. Low $R_k$ values can arise from a relatively high heterogeneity in resource selection among foraging trips or locations of shearwater k (i.e. high within-individual or partitioned observation-level variance). Analyses were performed with the rptR package (Stoffel et al. 2017) of the R software (R Development Core Team 2016).

364

## References

366 Johnson, P.C.D. (2014) Extension of Nakagawa & Schielzeth's $R^2_{GLMM}$ to random slopes models. *Methods Ecol. Evol.*, 5, 944-946.
368 Nakagawa, S., Johnson, P.C.D. & Schielzeth, H. (2017). The coefficient of determination R2
369 and intra-class correlation coefficient from generalized linear mixed-effects models revisited
370 and expanded. *J. R. Soc. Interface*, 14, 20170213.

371 Potier, S., Carpentier, A., Grémillet, D., Leroy, B. & Lescroël, A. (2015). Individual
372 repeatability of foraging behaviour in a marine predator, the great cormorant, *Phalacrocorax
373 carbo*. *Anim. Behav.*, 103, 83-90.

374 R Development Core Team 2016. R: A Language and Environment for Statistical Computing.
375 R Foundation for Statistical Computing, Vienna.

376 Stoffel, M.A., Nakagawa, S. & Schielzeth, H. (2017). rptR: Repeatability estimation and
377 variance decomposition by generalized linear mixed-effects models. *Methods Ecol. Evol.*, 8,
378 1639-1644.

379 Wakefield, E.D. et al. (2015). Long-term individual foraging site fidelity – why some gannets
don’t change their spots. *Ecology*, 96, 3058-3074.
381 Supporting Information S6. Supplementary figures and tables.

Table S6.1. Mixed-effects logistic regression models for the resource selection of Scopoli's shearwater in the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2010 and 2015, estimated with GPS foraging locations (n = 56,945 locations) with random intercepts and coefficients for trip (n = 182 trips) and bird levels (n = 75 shearwaters). We showed the marginal selection coefficients (β) with their standard errors (SE) and 95% confidence intervals (CI), the variance of random effects at the bird (VAR<sub>bird</sub>) and trip levels (VAR<sub>trip</sub>). Models were assessed independently for each prey-related variable (12 models have converged) and summarized in the table. We accounted for multiple comparisons by adjusting our 95% CI with a Benjamini-Yekutieli correction at alpha = 0.0014 and z-value = 3.19.

<table><thead><tr><th>Variable</th><th>B</th><th>SE</th><th>Adjusted 95% CI</th><th>VAR<sub>bird</sub></th><th>VAR<sub>trip</sub></th></tr></thead><tbody><tr><td colspan="6"><em>Anchovy adult biomass</em></td></tr><tr><td>Intercept</td><td>-2.45*</td><td>0.11</td><td>-2.80;-2.10</td><td>0.02</td><td>0.20</td></tr><tr><td>Biomass</td><td>-0.26</td><td>0.10</td><td>-0.58;0.06</td><td>0.22</td><td>1.02</td></tr><tr><td colspan="6"><em>Anchovy juvenile biomass</em></td></tr><tr><td>Intercept</td><td>2.34*</td><td>0.09</td><td>2.05;2.63</td><td>0.14</td><td>0.25</td></tr><tr><td>Biomass</td><td>-1.07*</td><td>0.10</td><td>-1.39;-0.75</td><td>1.31</td><td>0.96</td></tr><tr><td colspan="6"><em>Sardine adult biomass</em></td></tr><tr><td>Intercept</td><td>-2.34*</td><td>0.10</td><td>-2.66;-2.02</td><td>0.12</td><td>0.29</td></tr><tr><td>Biomass</td><td>-2.04*</td><td>0.09</td><td>-2.33;-1.75</td><td>0.67</td><td>5.65</td></tr><tr><td colspan="6"><em>Sardine juvenile biomass</em></td></tr><tr><td>Intercept</td><td>-3.03*</td><td>0.08</td><td>-3.29;-2.77</td><td>0.32</td><td>0.81</td></tr><tr><td>Biomass</td><td>-14.70*</td><td>0.09</td><td>-14.98;-14.41</td><td>34.41</td><td>210.43</td></tr></tbody></table>
Sprat adult biomass

<table><tr><td>Intercept</td><td>-2.59*</td><td>0.09</td><td>-2.88;-2.30</td><td>0.06</td><td>0.22</td></tr><tr><td>Biomass</td><td>-0.01</td><td>0.07</td><td>-0.23;0.21</td><td>0.12</td><td>0.94</td></tr></table>

Sprat juvenile biomass

<table><tr><td>Intercept</td><td>-2.22*</td><td>0.08</td><td>-2.48;-1.96</td><td>0.08</td><td>0.27</td></tr><tr><td>Biomass</td><td>-0.77*</td><td>0.07</td><td>-0.99;-0.55</td><td>0.06</td><td>1.06</td></tr></table>

Zooplankton-preferred abundance

<table><tr><td>Intercept</td><td>-3.02*</td><td>0.17</td><td>-3.56;-2.48</td><td>0.06</td><td>0.42</td></tr><tr><td>Abundance</td><td>0.30*</td><td>0.09</td><td>0.01;0.59</td><td>0.16</td><td>0.82</td></tr></table>

Zooplankton-other abundance

<table><tr><td>Intercept</td><td>-2.31*</td><td>0.15</td><td>-2.79;-1.83</td><td>0.15</td><td>0.34</td></tr><tr><td>Abundance</td><td>0.29*</td><td>0.09</td><td>0.003;0.58</td><td>0.22</td><td>0.79</td></tr></table>

Anchovy adult predictability

<table><tr><td>Intercept</td><td>-8.29*</td><td>0.30</td><td>-9.25;-7.33</td><td>0.63</td><td>3.40</td></tr><tr><td>Var(biomass)</td><td>-11.36*</td><td>0.69</td><td>-13.56;-9.15</td><td>171.90</td><td>430.57</td></tr></table>

Sprat adult predictability

<table><tr><td>Intercept</td><td>-7.80*</td><td>0.15</td><td>-8.28;-7.32</td><td>2.35</td><td>1.16</td></tr><tr><td>Var(biomass)</td><td>-2.70*</td><td>0.12</td><td>-3.08;-2.32</td><td>0.90</td><td>24.89</td></tr></table>

Zooplankton-preferred predictability

<table><tr><td>Intercept</td><td>-2.15*</td><td>0.11</td><td>-2.50;-1.80</td><td>0.02</td><td>0.20</td></tr><tr><td>Var(abundance)</td><td>0.29</td><td>0.18</td><td>-0.28;0.86</td><td>0.85</td><td>3.97</td></tr></table>

Zooplankton-other predictability

<table><tr><td>Intercept</td><td>-2.21*</td><td>0.12</td><td>-2.59;-1.83</td><td>0.01</td><td>0.34</td></tr><tr><td>Var(abundance)</td><td>-0.06</td><td>0.16</td><td>-0.57;0.45</td><td>0.32</td><td>4.04</td></tr></table>

<footnote>* Benjamini-Yekutieli corrected 95% CI exclude 0.</footnote>
Table S6.2. Variance components used to estimate the within-individual consistency in resource selection of shearwaters within the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2011 and 2015. We show the between-individual variance ($\sigma_{x,between}^2$), the within-individual variance ($\sigma_{x,within}^2$) and the distribution (mean ± SE) of all the partitioned observation-level variance of each bird $k$ ($\sigma_{\varepsilon k}^2$) used in the analyses.

<table><thead><tr><th>Model</th><th>σ<sup>2</sup><sub>x,between</sub></th><th>σ<sup>2</sup><sub>x,within</sub></th><th>σ<sup>2</sup><sub>εk</sub></th></tr></thead><tbody><tr><td>Anchovy adult biomass</td><td>0.234</td><td>1.224</td><td>9.589 ± 0.102</td></tr><tr><td>Anchovy juvenile biomass</td><td>1.449</td><td>1.217</td><td>7.200 ± 0.110</td></tr><tr><td>Sardine adult biomass</td><td>0.784</td><td>5.936</td><td>3.139 ± 0.145</td></tr><tr><td>Sardine juvenile biomass</td><td>34.726</td><td>211.235</td><td>4.671 ± 0.127</td></tr><tr><td>Sprat adult biomass</td><td>0.175</td><td>1.163</td><td>11.245 ± 0.136</td></tr><tr><td>Sprat juvenile biomass</td><td>0.139</td><td>1.331</td><td>7.980 ± 0.079</td></tr><tr><td>Zooplankton-preferred abundance</td><td>0.223</td><td>1.239</td><td>13.930 ± 0.148</td></tr><tr><td>Zooplankton-other abundance</td><td>0.369</td><td>1.133</td><td>8.735 ± 0.072</td></tr><tr><td>Anchovy adult predictability</td><td>172.535</td><td>433.963</td><td>36.178 ± 5.766</td></tr><tr><td>Sprat adult predictability</td><td>3.249</td><td>26.049</td><td>49.715 ± 4.830</td></tr><tr><td>Zooplankton-preferred predictability</td><td>0.868</td><td>4.173</td><td>7.557 ± 0.197</td></tr><tr><td>Zooplankton-other predictability</td><td>0.319</td><td>4.378</td><td>7.114 ± 0.192</td></tr></tbody></table>
Table S6.3. Resource selection for the two foraging trip tactics of shearwaters within the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2011 and 2015.
Mean selection coefficient (β̄) are shown with their 95% confidence interval (CI).

404

<table><thead><tr><th>Variable</th><th>Zooplankton-fish trips</th><th>Zooplankton-only trips</th></tr></thead><tbody><tr><td>Anchovy adult biomass</td><td>0.31 [0.20;0.42]*</td><td>-0.95 [-1.13;-0.76]*</td></tr><tr><td>Anchovy juvenile biomass</td><td>-0.85 [-0.99;-0.71]*</td><td>-1.32 [-1.49;-1.15]*</td></tr><tr><td>Sardine adult biomass</td><td>-1.03 [-1.36;-0.70]*</td><td>-3.21 [-3.71;-2.70]*</td></tr><tr><td>Sardine juvenile biomass</td><td>-9.08 [-10.91;-7.25]*</td><td>-19.85 [-23.16;-16.54]*</td></tr><tr><td>Sprat adult biomass</td><td>0.62 [0.49;0.75]*</td><td>-0.75 [-0.89;-0.62]*</td></tr><tr><td>Sprat juvenile biomass</td><td>-0.26 [-0.40;-0.12]*</td><td>-1.37 [-1.58;-1.16]*</td></tr><tr><td>Zooplankton-preferred abundance</td><td>0.24 [0.08;0.41]*</td><td>0.37 [0.19;0.54]*</td></tr><tr><td>Zooplankton-other abundance</td><td>0.50 [0.35;0.65]*</td><td>0.05 [-0.13;0.23]</td></tr><tr><td>Anchovy adult predictability</td><td>-5.17 [-8.19;-2.14]*</td><td>-14.20 [-19.67;-8.74]*</td></tr><tr><td>Sprat adult predictability</td><td>-0.87 [-1.45;-0.29]*</td><td>-4.42 [-5.49;-3.36]*</td></tr><tr><td>Zooplankton-preferred predictability</td><td>0.16 [-0.22;0.53]</td><td>0.47 [0.11;0.84]*</td></tr><tr><td>Zooplankton-other predictability</td><td>0.24 [-0.10;0.57]</td><td>-0.38 [-0.83;0.06]</td></tr></tbody></table>

405

*95% CI exclude 0.
Table S6.4. Spearman rank correlations (r_s) and their significance (P) in parentheses between individual foraging site fidelity (IFSF) and individual specialization in resource selection (ISRS), and between IFSF and resource selection for Scopoli's shearwater (n = 47 individuals), in the Gulf of Lions (Western Mediterranean) during four chick-rearing periods between 2011 and 2015 and each prey-related variable. P-values are adjusted with Benjamini-Yekutieli correction to account for the 24 multiple comparisons.

<table><thead><tr><th>Model</th><th>IFSF versus ISRS</th><th>IFSF versus resource selection</th></tr></thead><tbody><tr><td>Anchovy adult biomass</td><td>-0.04 (1.00)</td><td>-0.08 (1.00)</td></tr><tr><td>Anchovy juvenile biomass</td><td>0.17 (1.00)</td><td>-0.10 (1.00)</td></tr><tr><td>Sardine adult biomass</td><td>0.13 (1.00)</td><td>0.26 (1.00)</td></tr><tr><td>Sardine juvenile biomass</td><td>-0.03 (1.00)</td><td>0.27 (1.00)</td></tr><tr><td>Sprat adult biomass</td><td>-0.11 (1.00)</td><td>0.07 (1.00)</td></tr><tr><td>Sprat juvenile biomass</td><td>0.02 (1.00)</td><td>0.33 (1.00)</td></tr><tr><td>Zooplankton-preferred abundance</td><td>-0.27 (1.00)</td><td>-0.17 (1.00)</td></tr><tr><td>Zooplankton-other abundance</td><td>-0.43 (0.90)</td><td>-0.16 (1.00)</td></tr><tr><td>Anchovy adult predictability</td><td>-0.13 (1.00)</td><td>0.14 (1.00)</td></tr><tr><td>Sprat adult predictability</td><td>0.28 (1.00)</td><td>0.24 (1.00)</td></tr><tr><td>Zooplankton-preferred predictability</td><td>-0.17 (1.00)</td><td>0.13 (1.00)</td></tr><tr><td>Zooplankton-other predictability</td><td>-0.14 (1.00)</td><td>-0.002 (1.00)</td></tr></tbody></table>
Table S6.5. Proportions of the different foraging trip tactics of GPS-tracked shearwaters each year within the Gulf of Lions (Western Mediterranean) during the chick-rearing period. We tested for annual variations in the relative importance of selection tactics (n = 182 trips) using mixed logistic regression with individuals (n = 75 shearwaters) as random intercept and year as predictor. We found inter-annual variability in the probability to use zooplankton-fish over zooplankton-only trips. In 2011, shearwaters mainly (66%) used zooplankton-fish specialist trips, while zooplankton-only specialist trips became predominant (63%) in 2014. The probability to use a zooplankton-fish trip over a zooplankton-only trip was significantly higher in 2011 than in 2014 (z-test = -2.508, P = 0.01). Interestingly, isotopic analyses confirmed that shearwaters had low individual trophic position in 2014, relatively homogeneous among birds, and that their individual dietary specialization were poorly based on small pelagic fish this year (Supporting Information S4, Fig. S4.1, S4.2). Contrary to other years, selection patterns were more balanced among trips in 2012 and 2015 and the probability to use a zooplankton-fish trip over a zooplankton-only trip did not differ statistically from other years.

<table><thead><tr><th>Year</th><th>Zooplankton-fish trip</th><th>Zooplankton-only trip</th><th>Nb trip</th></tr></thead><tbody><tr><td>2011</td><td>0.66</td><td>0.34</td><td>50</td></tr><tr><td>2012</td><td>0.55</td><td>0.45</td><td>73</td></tr><tr><td>2014</td><td>0.37</td><td>0.63</td><td>30</td></tr><tr><td>2015</td><td>0.52</td><td>0.48</td><td>29</td></tr></tbody></table>
<figure><img src="image_28.png" /><figcaption>Figure S6.1. Foraging trip features of the different foraging trip tactics (zooplankton-only specialist trip and zooplankton-fish specialist trip) of Scopoli's shearwaters within the Gulf of Lions (Western Mediterranean) during the chick-rearing period between 2011 and 2015. We characterized differences between features of trip tactics considering tortuosity with a straightness index (i.e. [2 x trip range] / total travelled distance), trip duration, mean distance to the colony, mean distance to the colony for foraging, and proportion of time spent in foraging, resting or travelling. We fitted linear or logistic mixed models depending on the dependent variable with the trip tactics (i.e. groups of trips from the cluster analysis) and year as predictors. We used individuals as random intercept to accommodate for multiple trips. We calculated 95% confidence intervals with a bootstrap procedure. Relative to zooplankton-only</figcaption></figure>
specialist trips, zooplankton-fish specialist trips had higher trip duration (24.2h versus 19.8h, t-test = 2.106, P < 0.03), total travelled distance (247km versus 183km, t-test = 2.237, P < 0.03), trip range (67km versus 49km, t-test = 2.221, P < 0.03), mean distance to the colony (36km versus 25km, t-test = 2.654, P < 0.01), mean distance to the colony for foraging locations (38km versus 26km, t-test = 2.401, P < 0.02), but tortuosity (mean straightness index = 0.55 versus 0.52, z-test = -0.031, P = 0.98) and proportion of time spent in resting (0.30 versus 0.32, z-test = -0.685, P = 0.49), foraging (0.48 versus 0.46, z-test = 1.556, P = 0.12) or travelling (0.21 versus 0.21, z-test = -0.303, P = 0.76) were similar between trip tactics.
<figure><img src="image_29.png" /><figcaption>Figure S6.2. Distribution of foraging locations as a function of distance to the colony (A) and bathymetry (B). The distributions are truncated at 130 km (95% of data are shown, the tail of the distribution reach 260 km) and -350 m (84% of data are shown, the tail of the distribution reach -2380 m). Mean (solid line) and median (dashed line) values of the distributions are showed.</figcaption></figure>
<figure><img src="image_30.png" /></figure>

456

457

Figure S6.3. Dendrogram for the cluster analysis based on selection coefficients for 182

458

foraging trips of shearwaters in the Gulf of Lions (Western Mediterranean) occurring during

459

the chick-rearing period between 2011 and 2015. We considered two different clusters

460

corresponding to two foraging trip tactics.