Sexual Segregation in the Foraging Distribution, Behaviour, and Trophic Niche of the Endemic Boyd’s Shearwater (Puffinus lherminieri Boydi)

Sexual Segregation in the Foraging Distribution, Behaviour, and Trophic Niche of the Endemic Boyd’s Shearwater (Puffinus lherminieri Boydi) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sexual Segregation in the Foraging Distribution, Behaviour, and Trophic Niche of the Endemic Boyd’s Shearwater ( Puffinus lherminieri Boydi ) Ivo dos Santos, Jaime A. Ramos, Filipe R. Ceia, Isabel Rodrigues, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1428025/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Studies on sex-specific segregation in foraging and trophic niche have been focused on large and dimorphic seabirds, with less information on small monomorphic species. Here, we used mini-GPS loggers, habitat suitability models, and stable isotopes to assess the foraging movements, at-sea spatial distribution, and trophic ecology of male and female Boyd’s shearwaters Puffinus lherminieri boydi in Raso Islet (16°36’ N, 24°35’ W), Cabo Verde, during the breeding seasons of 2018–2019. The existence of sexual foraging segregation was tested in short and long foraging trips. Females engaged on longer foraging trips, travelling towards more distant and northward regions from the colony when compared to males, especially during long foraging excursions. Spatial overlap within and between sexes was generally low, indicating a sex-specific pattern in the foraging behaviour and spatial distribution of adult breeders. Habitat suitability models revealed a higher importance for chlorophyll a concentration in explaining females’ at-sea distribution during long foraging trips when compared to males, although the most important predictors in explaining adults’ distribution were sea surface temperature and height for short and long excursions, respectively. Stable isotope analysis revealed that both sexes occupied similar isotopic niches and stable isotope mixing models revealed no diet differences. This indicates that Boyd’s shearwaters segregate at the spatial level while foraging but rely on similar food resources. Our results suggest that female-biased nutritional requirements at the onset of chick-rearing may be driving sexual foraging segregation in this population, which depends upon resources from a rather oligotrophic environment. environmentally driven segregation little shearwater species distribution modelling stable isotope mixing models tropical seabirds Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Seabirds, as marine apex predators, are sensitive to changes at lower trophic levels, e.g. , oscillations in prey availability caused by shifts in oceanographic conditions (Becker et al. 2007 ; Chimienti et al. 2017 ), and many are seen as optimal sentinel organisms for monitoring environmental and trophic changes in marine ecosystems worldwide (Furness and Camphuysen 1997 ; Scopel et al. 2017 ). In their foraging choices, seabirds are driven by extrinsic factors ( i.e. , environmental conditions) and also by intrinsic traits, such as sex, breeding stage or age (Votier et al. 2017 ; Sztukowski et al. 2018 ; Cerveira et al. 2020 ). The combined effect of such factors may explain the inter- and intra-species partitioning of food resources (Schoener 1974 ; Paiva et al. 2017 ), which is particularly meaningful when resources are more unpredictable and patchily distributed, as happens in tropical regions (Weimerskirch 2007 ). Sexual segregation is one of the most studied subjects in seabird ecology, especially during the breeding season, when adult breeders adopt a ‘central-place’ foraging strategy becoming spatially constrained by their breeding duties (Weimerskirch et al. 1994 ; Magalhães et al. 2008 ), resulting in segregation of foraging patterns in some species. Sexual segregation can be explained by three main intrinsic/ ecological drivers. Firstly, the sexual size dimorphism (SSD), where one sex is anatomically larger than the other (González-Solís et al. 2000 ; Phillips et al. 2004 ; Weimerskirch et al. 2006 ), influencing flight performance, foraging range, or diving depth (Gilardi 1992 ; Lewis et al. 2005 ; Weimerskirch et al. 2009 ; Paiva et al. 2017 , 2018 ). Here, the smaller sex, or the less efficient forager could then be outcompeted by the larger sex, or by the more efficient forager, and forced to forage in less profitable waters or in remoter areas, as a way to avoid competition within foraging grounds, i.e. , ‘inter-sexual competition’ hypothesis (González-Solís et al. 2000 ; Paiva et al. 2018 ; Pereira et al. 2018 ; Almeida et al. 2021 ). Secondly, divergent parental roles, should shape nest attendance and/or provisioning rates. For instance, the sex contributing more to chick provisioning would be less engaged in brooding or nest-site defense, i.e. , ‘reproductive role specialization’ hypothesis (Paredes et al. 2006 ). Thirdly, sex-specific nutritional requirements. In long-lived seabirds, biparental care is usually recognized and denoted as crucial for successful rearing of offspring (Cockburn 2006 ). However, parental investment may differ among sexes, where one sex invests more in chick feeding (sex-biased provisioning) while the other invests more on self-maintenance (Gray and Hamer 2001 ; Welcker et al. 2009 ), as a consequence of sex-specific nutritional requirements, i.e. , ‘energetic constraint’ hypothesis. Furthermore, sexual segregation may also occur more sporadically when environmental conditions within adults’ foraging range are not particularly profitable, which denotes a lower prey availability (Gladbach et al. 2009 ; Paiva et al. 2017 ). Cory’s shearwaters ( Calonectris borealis ) evidenced a clear sexual segregation in years of great environmental stochasticity, which was not observed in years of good environmental conditions (Paiva et al. 2017 ). Female shearwaters undertook longer foraging trips, enlarged their isotopic niche, fed on prey of lower trophic level, and presented lower body condition than males (Paiva et al. 2017 ). This indicates that, in some circumstances, sexual segregation might be context-dependent and not linked straightforward to the sex factor. Sexual differences in foraging strategies (González-Solís et al. 2000 ; Stauss et al. 2012 ; Pereira et al. 2018 ; Zango et al. 2020 ), at-sea spatial distribution (Phillips et al. 2011 ; Stauss et al. 2012 ), parental roles (Austin et al. 2019 ), diet specialisation (Phillips et al. 2011 ), and niche partitioning (Paiva et al. 2017 , 2018 ; Almeida et al. 2021 ; Reyes-González et al. 2021 ) have been frequently described in sexually dimorphic seabirds, such as albatrosses, boobies, and shearwaters. Yet, some studies have also reported the occurrence of sex-specific differences in foraging patterns (Lewis et al. 2002 ; Welcker et al. 2009 ; Pinet et al. 2012 ), and isotopic niche in sexually monomorphic seabirds (Nisbet et al. 2002 ; Quillfeldt et al. 2008 ), indicating that body size would not be the explanation for sexual segregation in these species. For instance, in two sexually monomorphic gannet species, the northern gannet Morus bassanus and the Australasian gannet M. serrator , females occupied different trophic niches than males during the breeding stage (Cleasby et al. 2015 ; Ismar et al. 2017 ), and female northern gannets in particular were outcompeted by males and had to forage in farther and less profitable waters (Cleasby et al. 2015 ). In Barau’s petrel Pterodroma baraui , during the pre-laying exodus, males foraged in more chlorophyll-enriched waters farther from the colony, and systematically took the first incubation shift, in order to alleviate females’ energetic constraints increased by costs of oviposition (Pinet et al. 2012 ). Nevertheless, studies on sex-specific foraging patterns and niche partitioning have been mainly focused on large (González-Solís et al. 2000 ; Lewis et al. 2002 ; Phillips et al. 2004 ; Stauss et al. 2012 ; Almeida et al. 2021 ) or medium-sized seabirds (Gray and Hamer 2001 ; Peck and Congdon 2006 ; Pinet et al. 2012 ; Paiva et al. 2017 ; Zango et al. 2020 ), with few studies focused on small-sized (< 200 g) species (Gladbach et al. 2009 ; Welcker et al. 2009 ; Paiva et al. 2018 ; Carreiro et al. 2020 ). The ongoing miniaturisation of global positioning system (GPS) tags in the last few years, permitted the tracking of small-sized seabirds (Soanes et al. 2015 ; Surman et al. 2017 ; Zhang et al. 2019 ; Bolton 2020 ; Rotger et al. 2021 ), with much scarcer information on their detailed foraging movements, behaviour, and fine-scale spatial segregation, especially in tropical regions (Soanes et al. 2015 ; Surman et al. 2017 ). Boyd’s shearwater Puffinus lherminieri boydi is a small-sized monomorphic procellariiform (Flood and van der Vliet 2019 ), with a current lack of information about its foraging behaviour, spatial distribution, and trophic ecology during the breeding season. To the best of our knowledge only two studies tracked the movements and trophic ecology of Boyd’s shearwater using light-sensing geolocators (Zajková et al. 2017 ; Ramos et al. 2020 ). Additionally, the trophic ecology of Boyd’s shearwater during the breeding season is also less known when compared to its closest-related counterpart, the Macaronesian shearwater Puffinus baroli (Neves et al. 2012 ; Ramos et al. 2015 ), and other larger breeding seabirds of Cabo Verde (Cerveira et al. 2020 ; Almeida et al. 2021 ). Here, we tracked the foraging movements of Boyd’s shearwater using high-precision mini-GPS loggers and studied the trophic ecology during the breeding season at Raso islet, Cabo Verde. We aimed at assessing whether sex-specific foraging patterns explained differences on the at-sea foraging behaviour, spatial segregation, and foraging habitat choices during short and long foraging trips. During the breeding season, pelagic seabirds usually adopt a ‘dual foraging’ strategy, involving repeated alternation of several short foraging trips used mainly to search food for the offspring, with long foraging trips used for adult self-provisioning to replenish the nutritional reserves depleted during successive chick-provisioning trips (Weimerskirch et al. 1994 ; Congdon et al. 2005 ; Magalhães et al. 2008 ). For species breeding at low profitable areas, long foraging trips often extend to areas of enhanced productivity, associated with shelf slopes, continental shelves, or frontal zones (Magalhães et al. 2008 ; Pereira et al. 2022 ). Given the lack of SSD, and an apparent equal investment of both sexes in breeding duties, we do not expect a strong sex-specific segregation in the foraging behaviour or spatial distribution within short and long foraging trips. Simultaneously, we also aimed at assessing the isotopic niche occupied by each sex through stable isotope analysis (SIA) of carbon and nitrogen ratios ( δ 13 C and δ 15 N, respectively), and the diet composition through isotopic mixing models. We do not expect sex-specific segregation in isotopic niche nor in diet composition, as it was already reported for its close-related counterpart the Macaronesian shearwater (Neves et al. 2012 ; Ramos et al. 2015 ). Materials And Methods Study area and study species Our study was carried out at Raso Islet (16°36’ N, 24°35’ W), Cabo Verde, a flat and inhabit islet integrated in the Integral Natural Reserve of Santa Luzia (Vasconcelos et al. 2015). Boyd’s shearwater is a subspecies of little shearwater (c. 5,000 pairs), belonging to the lherminieri complex, breeding in the archipelago of Cabo Verde (BirdLife International 2020; Semedo et al. 2020). It is currently classified as “Least Concern” in the IUCN Red List; however there are some signs of decline owing to the impacts caused by introduced species (BirdLife International 2020). This small-sized pelagic seabird (~160 g) is an endemic subspecies of Cabo Verde, and it is the nearest counterpart of the Macaronesian shearwater, which breeds in Azores, Madeira, Selvagens and Canary Islands (BirdLife International 2020). It is a winter breeder, and like other Procellariiformes, lays a single egg each breeding season. Briefly, adults arrive at the colony in August-September to prospect and defend their breeding borrow, females lay the egg in January-February, which hatches about 50 days later (mid-March) and the chick is fed approximately for 60 days, leaving the nest between the last half of May and the first half of June (Zajková et al. 2017). GPS deployment and sample collection From March to April 2018 and 2019, mini-GPS loggers (nanoFixTMGeo & Geo+, PathTrack Ltd., UK) were attached to the four central tail feathers of breeding adults, using TESA© tape (Wilson et al. 1997). Each logger together with the tape did not exceed 4 g weight, representing less than 3% of adults’ body mass (~160 g), the standard and recommended threshold to not compromise individuals’ foraging abilities (Phillips et al. 2003). The body mass of tracked adults did not differ prior (162.0 ± 18.3 g) and after (170.0 ± 25.8 g) carrying the device (paired t-test, t 9 =1.591, p =0.146). GPS deployment did not last more than 5 minutes, and adults were returned to the respective nests. Each logger was programmed to record each geographical position every 10 minutes (~140 locations per day). During deployment sessions, some breast feathers were collected for molecular sexing (see Table S1 for more details), while during logger retrieval, a blood sample (~0.8 ml) was collected from the brachial vein, centrifuged to separate plasma from red blood cells (RBC), and both blood partitions were kept in ethanol (70%) until preparation for stable isotope analysis (SIA). Four tags were successfully retrieved in 2018 (3 males and 1 female) and 24 devices (12 males and 12 females) in 2019. Prey samples were collected along the breeding seasons of 2018 and 2019 for subsequent SIA. Main prey groups were created according to prey type (squid or fish), life-stage (larval or adult), and distribution in the water column (epipelagic or mesopelagic). Sample preparation and stable isotope analysis Plasma was selected for carbon and nitrogen isotopic analysis, because its turnover rate corresponds approximately to the tracking period duration, i.e., around 5-7 days (Inger and Bearhop 2008), while RBC would reflect a larger timeframe of about 3-4 weeks (Bearhop et al. 2002; Cherel et al. 2005b). Nitrogen ( δ 15 N; 15 N/ 14 N) isotopic values are commonly used as a proxy of predator’s trophic level, increasing about 2-5 ‰ at each trophic level (Minagawa and Wada 1986), while carbon ( δ 13 C; 13 C/ 12 C) values are used as a habitat indicator, because it only suffers a slight increase ( ca . 0-1 ‰) at each trophic level (Kelly 2000). Seabird plasma and prey muscle were dried during 24 and 48 hours, respectively, at 60 °C. Next, the samples were rinsed with a 2:1 chloroform: methanol solution to remove the overload of lipids that can deplete 13 C values (Cherel et al. 2005c; Post et al. 2007). All samples were ground to a powder, weighted (~0.35 mg) in tin capsules, and analysed through an elemental analyser/isotope ratio mass spectrometry (EA/IRMS). The results were expressed using the standard δ notation, following the equation: δX = [( R sample /R standard ) –1] × 1,000, where X is 13 C or 15 N, and R is the ratio 13 C: 12 C or 15 N: 14 N, respectively. R standard values correspond to the Vienna PeeDee Belemnite (V-PDB) and atmospheric N 2 , for 13 C and 15 N respectively (Bond and Jones 2009). Replicate measurements of internal laboratory standards (acetanilide) indicate a precision of ± 0.2 ‰ for both carbon and nitrogen isotopic ratios. The C/N ratio was examined to verify if lipid removal was effective in all plasma and muscle samples. Prey assemblage Four main prey groups were created: epipelagic fish, mesopelagic fish, squid, and fish larvae. Epipelagic fish was comprised by three fish species which inhabit the epipelagic layer of the ocean ( i.e., the upper 200 m of the water column): Tylosurus acus , Sardinella maderensis , Selar crumenophthalmus (mean ± SD: δ 13 C = –16.95 ± 0.48 ‰, δ 15 N = 9.72 ± 0.70 ‰, N = 16; C/N = 3.59 ± 0.06); mesopelagic fish was comprised by Myctophum affin e and Hygophum sp. (mean ± SD: δ 13 C = –18.66 ± 0.51 ‰, δ 15 N = 10.23 ± 0.43 ‰, N = 9; C/N = 3.19 ± 0.06); squid included individuals of two different species: Hyaloteuthis pelagica and Callimachus rancureli (mean ± SD: δ 13 C = –17.02 ± 1.56 ‰, δ 15 N = 11.66 ± 2.18 ‰, N = 6; C/N = 2.89 ± 0.11); finally, fish larvae included fingerlings captured near surface during pelagic tours, identified as Ophioblennius sp. and Synodus saurus (mean ± SD: δ 13 C = –18.47 ± 0.24 ‰, δ 15 N = 8.38 ± 0.43 ‰, N = 10; C/N = 3.04 ± 0.04). All potential prey were identified to the lowest possible taxonomic level, weighted, and measured the body-length (for fish) or mantle-length (for squid). Prey were initially identified using local guides or catalogues and, specifically, squid were identified using their beaks (Xavier and Cherel 2009). Also, a small piece of muscle tissue of each species was collected to create a DNA reference collection (see Table S1), either to confirm the previous identification or to achieve a lower taxonomic level. These species are among the most abundant of each group within Cabo Verde archipelago and are generally ingested by local breeding seabirds (Carreiro personal communication). GPS data analysis: behavioural classification and kernel estimation To estimate missing locations and standardize sampling effort, GPS tracks were resampled by linear interpolation to exactly 15 minutes interval. Individual foraging trips were divided in short (< 1 day) and long (≥ 1 day), after inspecting trip duration frequency using an histogram (Fig. S1). To avoid potential disturbance caused by social interaction and flying movements during landing at the colony, a distance to colony filter of 1 km radii was applied, to discard those locations. Maximum distance to colony, latitude and longitude at the distal point of each foray were computed using several functions within trip R package (Sumner et al. 2020). The classification of at-sea behaviours was carried out using a combination of instantaneous flight speed and path sinuosity (calculated as the ratio of instantaneous flight speed given the speed between every third positions). Histograms of the frequency of these two variables showed adults were drifting on the water, i.e., resting, when flying speed was below 2 km h -1 ; intensive search, i.e., foraging behaviour, was assigned when the flight speed was between 2-10 km h -1 and path sinuosity was above 7; extensive search, i.e. , relocating behaviour, was assigned when the flight speed was equal or above 10 km h -1 and path sinuosity was above 7; traveling behaviour was assigned when the flight speed was simultaneously above 2 km h -1 and below or equal to 7 (Fig. S2). After behaviour classification, the proportion of time spent on each behaviour was calculated for each foray within each individual. We computed the 50% kernel UD contours to represent adult foraging areas (FA), and the 95% UD contours to describe adult home ranges (HR). Following the methods and R scripts described by (Lascelles et al. 2016) we calculated mean ARS zones radii for short and long foraging trips, and used those values as smoothing parameters ( h ) in the computation of Kernel UDs. A smoothing parameter of 4 km was used for short foraging trips, and a smoothing parameter of 8 km for long foraging trips. Kernel UD contours (95% and 50%), and respective areas, were calculated using the ‘kernelUD’ and ‘kernel.area’ functions within the adehabitatHR R package (Calenge 2006). The overlap of UD contours was calculated between sexes within short and long foraging trips through the ‘kerneloverlap’ function, using the Bhattacharyya's affinity (BA), under the adehabitatHR R package (Calenge 2006). Environmental predictors and habitat suitability models Monthly values of ocean (1) bathymetry (BAT, blended ETOPO1 product, 0.01° spatial resolution, m), (2) chlorophyll a concentration (CHL, 0.04° spatial resolution, mg m –3 ), (3) ocean mixed layer thickness (OMLT, 0.08° spatial resolution, m), (4) sea surface height above geoid (SSH, 0.08° spatial resolution, cm), and (5) sea surface temperature (SST, 0.08° spatial resolution, °C) were extracted within the foraging range of Boyd’s shearwater for March 2018-2019 and April 2019, and the mean raster was calculated for each environmental predictor. Variable 1 was downloaded from https://www.ngdc.noaa while variables 2-5 were downloaded from http://marine.copernicus.eu. Spatial gradients of all environmental predictors were calculated using an estimating proportional change within a 3 x 3 cell grid following Louzao et al. (2009). Bathymetry gradient (BATG) identifies the presence of oceanic topographic features, such as seamounts or shelf-breaks ( i.e. , slope areas); the gradient of CHL (CHLG) and SST (SSTG) can be used as a proxy of oceanic fronts, while OMLT gradient (OMLTG) indicate the change level on the mixed layer thickness, and consequently, the depth of the thermocline which drives the abundance and distribution of marine prey; the gradient of SSH (SSHG) could help identify the occurrence of mesoscale eddies. All environmental predictors were rescaled to the coarsest spatial resolution ( i.e., 0.08°) and extracted for each GPS location, before running habitat suitability models. All computations were conducted under several functions within raster R package (Hijmans et al. 2020). Habitat suitability models were computed separately for males and females for long and short foraging trips, i.e. , four modelling exercises. Tracking data from 2018 and 2019 was jointly analysed given the low sample size of tags retrieved in 2018 and similar foraging range and distribution between years (Fig. S3). Prior to habitat modelling, all environmental predictors, and respective gradients, were inspected for multicollinearity. Multicollinearity was tested using the variation inflation factor (VIF) and Pearson correlation coefficients (r > 0.6) (Table S2), under the usdm R package (Naimi 2017). Testing for collinearity issues enables to account only with non-redundant variables, avoiding model overfitting and inflated errors (Zurell et al. 2020). Ensemble Species Distribution Models (ESDM; Marmion et al. 2009) were conducted using all GPS locations (presence data; Fig. S4) through the ‘ensemble_modelling’ function from the SSDM R package (Schmitt et al. 2017). We tested 7 modelling techniques: Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Generalized Additive Models (GAM), Generalized Linear Models (GLM), Multiple Adaptive Regression Splines (MARS), Random Forest (RF), and Support Vector Machine (SVM). Each model algorithm was computed ten times using a 10-fold cross-validation procedure, using 70% of all data set for model calibration and the remaining 30% grid squares as random test for model validation (Araujo et al. 2005; Marmion et al. 2009; Zurell et al. 2020). Model goodness of fit was examined using the area under the receiver operating characteristic (ROC) curve (AUC). Models were classified excellent when AUC > 0.90, good when 0.80 < AUC AUC < 0.80, and not acceptable when AUC < 0.70 (Araujo et al. 2005). The relative importance of environmental predictors to the probability of occurrence of each sex within short or long foraging trips, was given by the average contribution calculated from all models. Isotopic niche and mixing models Stable Isotope Bayesian Ellipses in R (SIBER) package was used to obtain the isotopic niches of females and males. The standard ellipse area corrected for small sample size (SEA C ) and considering 40% of all observations, was used to calculate the isotopic niche width of each sex, and to calculate niche overlap between sexes, using the ‘maxLikOverlap’ within the SIBER package (Jackson et al. 2011). Bayesian standard ellipse areas (SEA B ) were calculated using 10,000 iterations of Markov-chain Monte Carlo (MCMC) simulation to test for the probability of group 1 ( e.g. , females) being smaller than that of group 2 ( e.g., males), using the rjags R package (Plummer et al. 2019). The contribution of each prey group for Boyd’s shearwater diet was estimated using Bayesian mixing models within the simmr package (Parnell and Inger 2016). This package provides a wide range of new functions for a more accurate and realistic diet estimation, including the incorporation of prior diet information to the model (Parnell and Inger 2016); however, to the best of our knowledge there is no data about Boyd’s shearwater diet, so no prior information was added to the model. Trophic Discrimination Factors (TDFs) are needed to accurately run the isotopic mixing model and these are often tissue-specific, species-specific and diet-specific, meaning that they may vary according to consumer’s species and its diet, and the tissue analysed (Phillips et al. 2014; Jenkins et al. 2020). To our best knowledge there are no TDF available for Boyd’s shearwaters; hence, we opted to use the average values of fractionation between prey and plasma of 3 seabird species, from captive experiments (Barquete et al. 2013; Jenkins et al. 2020). So, we used a TDF of − 0.44 ‰ and + 2.10 ‰ enrichment for carbon and nitrogen, respectively. Although these species have different feeding ecologies than Boyd’s shearwaters, we believe that these average TDFs are the most adequate for our mixing model exercises, considering other values available in the literature for seabirds (Hobson and Clark 1992; Bearhop et al. 2002; Cherel et al. 2005a; Sears et al. 2009; Chiaradia et al. 2014; Ciancio et al. 2016). A standard deviation of ± 1.0 ‰ was used to account for possible differences on enrichment factors between species. Before running the models a simulation method proposed by Smith et al. (2013) was used to inspect the feasibility of the isotopic mixing polygons. The sensitivity analysis (using 1500 iterations) applied to mixing polygons indicated that adult isotopic signatures were within 95% of the simulated mixing regions (probability ranges: 0.21 to 0.61 for males, 0.43 to 0.63 for females), validating our models (Fig. S5). We ran a mixing model for each sex computed using the function ‘simmr_mcmc’ from the simmr R package (Parnell and Inger 2016). Statistical analysis Generalized linear mixed models (GLMMs) using the appropriate family or linear mixed models (LMMs) following a normal distribution, were used to test the effect of sex on adult trip parameters and at-sea foraging behaviour, separately for short and long foraging trips: (1) trip duration, (2) maximum distance to colony, (3) total distance travelled, (4) percentage of time spent foraging, (5) relocating, (6) resting, and (7) travelling, (8) latitude and (9) longitude coordinates at the maximum distance to colony. All models were run using sex as a fixed factor, while the bird identity ( i.e., individual) was included as a random factor to avoid pseudo-replications. Years were pooled together due to the lower sample size recorded in 2018 (3 males and 1 female). Mixed models were conducted with the lme4 (Bates et al. 2015) and lmerTest (Kuznetsova et al. 2017) R packages. Sexual differences on the size of FA and HR within short and long foraging trips were tested using t-tests for independent samples, when data followed a normal distribution, or Mann-Whitney tests, when data did not follow a normal distribution. All response variables were tested for normality, homoscedasticity, and log (total distance travelled), square root (maximum distance to colony and trip duration), or arcsine (time spent foraging, resting, and relocating) transformed whenever necessary. Throughout the results values are expressed as mean ± SD. All analyses and modelling computations were performed using the R software ver. 4.0.0 (R Core Team 2020) and the significance level was set at p ≤ 0.05. Results Foraging behaviour and foraging areas during short and long trips Over the two breeding seasons, each logger recorded an average of 5.1 ± 1.7 days in a total of 98 trips made by 28 adult Boyd’s shearwaters (M: N = 15; F: N = 13). Of these, 59 trips (60%) were classified as short foraging trips (M: N = 34; F: N = 25), while 39 trips (40%) were classified as long foraging trips (M: N = 20; F: N = 19). Overall, both sexes foraged close to the colony, travelling for short distances and for short periods of time (Table 1 ). However, significant differences were found for trip duration, maximum distance to colony, latitude of the distal point (at maximum distance), and percentage of time spent relocating, for both short and long foraging trips (Table 2 ). Specifically, females carried out longer trips, reached farther and northern distances when compared to males during both short and long foraging trips, while males exhibited more time spent on relocating than females during short and long foraging trips (Table 2 ). Also, there were sex differences for the total distance travelled during long foraging trips, as females travelled for longer distances than males, however, this did not occur during short foraging trips (Table 2 ). There were no sex differences for the longitude of the distal point (at maximum distance), nor for the percentage of time spent foraging, resting, or traveling for both short and long foraging trips (Table 2 ). Table 1 Trip characteristics, foraging behaviour, and spatial ecology parameters of male and female Boyd’s shearwaters during the 2018–2019 breeding seasons at Raso islet (joint data for both years), Cabo Verde. Individual trips were separated by its duration as short (< 1 day) or long (≥ 1 day) foraging excursions. The overlap of foraging areas (FA) was measured within each sex and trip type and between sexes (within trip type) using the Bhattacharyya’s affinity index (BA). Values are mean ± standard deviation. Values with the same superscript letter indicate no significant differences between sexes for short and long foraging trips (p > 0.05) based on t-tests for independent samples (normal data distribution) or Mann-Whitney tests (non-normal data distribution) Short trips Long trips Males Females Males Females Trip parameters Number of trips [N birds] 34 [13] 25 [12] 20 [13] 19 [11] Trip duration (days) 0.60 ± 0.05 0.63 ± 0.06 1.65 ± 0.80 2.59 ± 1.57 Maximum distance to colony (km) 52.17 ± 28.82 75.65 ± 20.68 107.93 ± 50.47 150.46 ± 57.34 Total distance travelled (km) 117.98 ± 51.87 143.11 ± 54.10 221.37 ± 97.49 309.51 ± 114.83 Latitude coordinates (at maximum distance, ºN) 16.77 ± 0.22 17.06 ± 0.27 17.01 ± 0.64 17.56 ± 0.56 Longitude coordinates (at maximum distance, ºW) -24.29 ± 0.38 -24.18 ± 0.27 -23.92 ± 0.44 -23.69 ± 0.38 Time spent foraging (%) 23.61 ± 11.11 20.74 ± 10.09 21.81 ± 8.51 19.89 ± 6.64 Time spent relocating (%) 5.47 ± 3.52 4.28 ± 2.92 5.61 ± 1.92 3.34 ± 1.61 Time spent resting (%) 23.33 ± 10.66 19.79 ± 11.49 32.33 ± 11.47 31.56 ± 11.44 Time spent travelling (%) 47.60 ± 19.54 55.18 ± 17.26 40.26 ± 13.82 45.20 ± 13.95 Proportion of short trips (%) 58.40 ± 30.70 48.30 ± 30.70 Spatial ecology parameters Foraging Areas (FA): 50% UD area (km 2 ) 143.87 ± 46.10 a 150.76 ± 56.30 a 652.38 ± 322.12 a 755.56 ± 502.10 a Home Range Areas (HR): 95% UD area (km 2 ) 626.45 ± 192.93 a 679.45 ± 251.08 a 3500.05 ± 2519.25 a 3647.71 ± 2395.50 a FA overlap within sex and trip type (BA index) 0.02 ± 0.02 0.01 ± 0.01 0.02 ± 0.04 0.01 ± 0.01 FA overlap among sex, within trip type (BA index) 0.01 ± 0.01 0.01 ± 0.01 Table 2 Summary of (generalized) linear mixed models used to test the effect of sex on trip parameters and foraging behaviour of adult Boyd’s shearwaters during short and long foraging trips. All models included bird identity (i.e., individual) as a random factor to avoid pseudo-replication issues Short trips Long trips Models : β ± SE t value p value Effect β ± SE t value p value Effect Trip duration -0.04 ± 0.02 -2.09 0.05 M < F -0.45 ± 0.22 -2.09 0.04 M < F Maximum distance to colony -24.44 ± 7.77 -3.15 < 0.01 M < F -2.03 ± 0.82 -2.49 0.02 M < F Total distance travelled -0.22 ± 0.13 -1.80 0.09 -0.34 ± 0.13 -2.49 0.01 M < F Latitude at maximum distance -0.33 ± 0.08 -4.16 < 0.001 M < F -0.54 ± 0.23 -2.35 0.03 M F 0.52 ± 0.16 3.28 F Time spent resting 5.15 ± 3.81 1.35 0.19 0.04 ± 0.12 0.31 0.76 Time spent travelling -8.96 ± 6.18 -1.45 0.16 -5.06 ± 5.07 -1.00 0.33 Parametric coefficients (β ± SE), t, and p values are also shown. Significant values (p ≤ 0.05) are shown in bold Overall, the size of foraging (FA, 50% UD) and home range areas (HR, 95% UD), calculated for each foraging trip, were similar between sexes for both short and long foraging trips (Fig. 1 , Table 1 ), though females showed slight larger foraging and home range areas than males (Table 1 ). Nonetheless, the overlap of FA within and between sexes and trip type was low during both short and long foraging trips, evidencing spatial segregation at the foraging trip and sex levels (Table 1 ). Habitat Modelling Multicollinearity examinations detected that only 8 of the 10 tested environmental variables (BAT, CHL, OMLT, SSH, SST, BATG, CHLG, OMLTG, SSHG, and SSTG) showed no collinearity issues ( i.e. , non-redundant variables). The ESDMs computed separately for short and long foraging trips for male and female adults (4 ensemble models in total), exhibited good to excellent predictive performance (0.88 < AUC < 0.94; Table S3), which indicates that models were quite efficient in separating the suitable from the unsuitable marine habitats for adult little shearwaters. Habitat suitability models suggested no apparent differences of habitat preferences between sexes during short foraging trips. SST was the variable that best explained the distribution of Boyd’s shearwaters during short foraging trips, followed by SSH and BATG (Fig. 2 , Table S3). However, there were differences on the habitat preferences of males and females during long foraging trips; specifically, CHL explained better the distribution of females (~ 15%) than that of males (~ 7%), while SSH (~ 35–41%) and SST (~ 16–22%) explained quite evenly the distribution of both sexes during long foraging trips (Fig. 2 , Table S3). Isotopic Niche And Diet Composition Overall, plasma isotopic signatures revealed no significant differences between male and female isotopic niches (MANOVA, Wilks’s λ, F 1,14 = 0.95, p = 0.41, N = 16; Table 3 , Fig. 3 ). A separate analysis for each stable isotope revealed that neither δ 13 C (F 1,14 = 0.46, p = 0.51) or δ 15 N (F 1,14 = 0.93, p = 0.35) values significantly differed between sexes. Males presented a broader isotopic niche than females, however, the Bayesian estimate of SEA (SEA B ) revealed no clear between-sex differences on isotopic niche size (probability that SEA B, females > SEA B, males = 0.19, Table 3 ). The overlap of isotopic niches, here represented by the overlap of standard ellipses (including 40% of data), indicated that approximately 32% of males’ isotopic niche overlapped with that of females (Fig. 3 ). The isotopic mixing models showed no apparent diet differences among sexes. Specifically, both sexes showed a higher reliance on fish larvae (M: 42.4 ± 11.4%; F: 42.7 ± 13.5%), followed by epipelagic fish (M: 21.0 ± 10.0%; F: 32.9 ± 13.5%) and mesopelagic fish (M: 26.4 ± 11.8%; F: 13.9 ± 0.09%); squid had a minor importance in Boyd’s shearwater diet (M: 10.2 ± 0.06%; F: 10.5 ± 0.07%) (Fig. 4 ). Table 3 Males and females isotopic niche measurements of Boyd's shearwaters, using plasma signatures collected during the 2019 breeding season. Carbon and nitrogen isotopic values (mean ± SD) are expressed in ‰. SEA C represents the area of the standard ellipse (explaining 40% of the total data) corrected for small samples sizes; SEA B (P value) represents the Bayesian estimates of standard ellipses and assess niche size probability differences; TA represents the convex hull area (= total area) of the isotopic niche; C/N represents the average ratio between carbon and nitrogen percentage values. Sex δ 13 C δ 15 N SEA C SEA B (P =) TA C/N Males (N = 8) -18.47 ± 0.55 11.11 ± 0.57 0.92 0.20 1.29 3.78 Females (N = 8) -18.31 ± 0.40 11.36 ± 0.44 0.63 0.95 3.79 Discussion Our study documented a sex-specific segregation in foraging by adult Boyd’s shearwaters during the breeding season. We found a sex-specific pattern on adult foraging behaviour, with females traveling for longer distances and foraging at farther and northern areas than males, although sex-specific differences were stronger during long foraging trips. Females’ foraging distribution during long foraging trips was clearly driven more by chlorophyll a concentration (CHL) than that of males, while during short foraging trips, the foraging distribution of both sexes was largely explained by sea surface temperature (SST). Nevertheless, sea surface height (SSH) was the most important environmental predictor in explaining the foraging distribution of both sexes during long foraging trips. As initially predicted, there was no difference in the isotopic niche between sexes, however, mixing models detected divergences on the importance of prey groups among sexes; specifically, females relied more on epipelagic fish while males relied more on mesopelagic fish, although the main prey consumed by both sexes was the lower δ 15 N-enriched fish larvae. Overall, Boyd’s shearwaters foraged mostly near the colony (up to 300 km), in the pelagic waters located northwards of the archipelago of Cabo Verde. This is in line with the prevalence of an oceanic foraging distribution in the colony surroundings, reported in previous studies using light-sensing geolocators (Zajková et al. 2017 ; Ramos et al. 2020 ). As expected, Boyd’s shearwaters exhibited a dual foraging strategy, a typical strategy adopted by Procellariiformes during the breeding season (Chaurand and Weimerskirch 1994 ; Weimerskirch et al. 1994 ; Magalhães et al. 2008 ), probably as an adaptation to the longer rearing periods undertaken by pelagic seabirds and buffer the constraints of central-place foraging behaviour. Pelagic seabirds often alternate between several short foraging trips to provision food to their offspring, in order to cope with the nutritional needs of their growing chick and ensure breeding success, with one or two long foraging trips for self-provisioning to replenish body reserves depleted during short trips (Weimerskirch et al. 1994 ; Congdon et al. 2005 ). Within the general dual foraging pattern observed in Boyd’s shearwaters, sex-specific patterns in foraging were evident. Females carried out longer foraging trips, foraged over more distant regions at higher latitudes and travelled for longer distances than males. This sex-specific foraging pattern was more evident during long foraging trips, albeit substantial divergences were also detected in short foraging trips. Our results are in line with previous studies on other monomorphic seabirds, such as the wedge-tailed shearwater Ardenna pacifica (Peck and Congdon 2006 ), the little auk Alle alle (Welcker et al. 2009 ), and the Manx shearwater Puffinus puffinus (Gray and Hamer 2001 ), where females took longer foraging trips, subsequently driving a male-biased provisioning with males delivering food to the chick at a higher rate, and showing a greater contribution to overall chick feeding. Peck and Congdon ( 2006 ) argued that the sex-specific foraging of wedge-tailed shearwaters were identical to the results obtained from SSD species, supporting the occurrence of inter-sexual competition at the FA, with the larger sex outcompeting the smaller one from the closest or more profitable regions (Paiva et al. 2017 ; Almeida et al. 2021 ). Despite the spatial segregation ( i.e. , low spatial overlap) and sex-specific differences in foraging, we do not have enough data to support the occurrence of inter-sexual competition at the foraging grounds. Instead, we believe that although Procellariiformes display biparental care during the rearing period, adult breeders may invest slight differently on chick provisioning duties, according to disparate self-energetic requirements. Indeed, once both parents share the incubation of the egg, we may argue that female Boyd’s shearwaters are energetically more depleted and in poorer body condition than males at the onset of the rearing period, due to carry-over costs incurred at the time of egg production and laying (Monaghan et al. 1998 ). Thus, the longer and more distant foraging trips carried out by female Boyd’s shearwaters may arise from their higher energetic costs during the initial breeding stages (the ‘energetic-constraint’ hypothesis), which drives females to allocate more time to self-feeding trips than males (Gray and Hamer 2001 ; Welcker et al. 2009 ). In addition, our results also revealed that females took slight longer short foraging trips than males. This may also strengthen the ‘energetic-constraint’ hypothesis, because females may be foraging to provisioning the chick, but may also forage at a faster rate to replenish their body reserves. Nevertheless, we acknowledge that to empirically ascertain the existence of a male-biased provisioning in Boyd’s shearwater, we would need to simultaneously monitor nest attendance and meal mass delivered to the chick. Habitat suitability models revealed that, regardless of sex, the distribution of breeding Boyd’s shearwaters was mostly driven by SST and SSH during short and long foraging trips, respectively. The importance of SST in explaining the at-sea distribution of shearwaters in tropical areas has already been reported (McDuie et al. 2018 ; Cerveira et al. 2020 ), while variations in SSH was reported to influence seabird foraging grounds, especially in oceanic areas (Pereira et al. 2020 ). Patterns in SST are closely linked to gradients of marine productivity (Catry et al. 2013 ; Cerveira et al. 2020 ; Pereira et al. 2020 ), which influence vertical and horizontal distribution of prey (Hsieh et al. 2009 ) and its abundance (Morato et al. 2008 ), and ultimately, seabirds’ breeding performance (Monticelli et al. 2014 ; Ramos et al. 2018 ). Patterns in SSH can be indicators of mesoscale eddies, which play an important role on the recycling of nutrients in oceanic areas, i.e. , oligotrophic regions (Stramma et al. 2013 ; Braun et al. 2019 ). Cyclonic mesoscale eddies pump the deeper and cooler waters (nutrient-enriched) to the euphotic zone, promoting ephemeral and localised events of enhanced productivity (Falkowski et al. 1991 ; Klein and Lapeyre 2009 ), a recurrent phenomenon inside and outside Cabo Verde (Meunier et al. 2012 ; Cardoso 2017 ). Despite the sex-specific differences in foraging behaviour and spatial distribution detected during short foraging trips, there was no apparent environmentally driven segregation between sexes, indicating that both males and females showed similar preferences during chick rearing. On the other hand, CHL was twice more important in explaining females’ foraging distribution than that of males during long foraging trips, suggesting that CHL would be driving the larger between-sex spatial and foraging behaviour segregation observed during self-feeding trips. In fact, this environmentally driven sexual segregation supports, once again, the ‘energetic-constraint’ hypothesis, highlighting the higher nutritional needs of females during the chick rearing period, forcing them to forage in association with CHL patterns which can indicate a higher reliance on fine-scale phenomena such as localised upwellings (Paiva et al. 2010 ; McDuie et al. 2018 ). Localised upwellings can promote the aggregations of planktivorous epipelagic fish and other predatory pelagic and mesopelagic species, e.g. , mesopelagic fish and squid (Ichii et al. 2004 ; López-Pérez et al. 2020 ), translating into higher foraging opportunities for the birds foraging within these regions (Jaquemet et al. 2005 ; Weimerskirch 2007 ). Within the archipelago of Cabo Verde, in the south of CVFZ, upwelling events only occur in winter, when the Intertropical Convergence Zone (ITCZ) migrates towards the south (Peña-Izquierdo et al. 2012 ). Together with the intense currents, and the subsequent formation of mesoscale eddies promoted by the convergence of currents generated by the inter-island channels (Meunier et al. 2012 ; Peña-Izquierdo et al. 2012 ; Cardoso 2017 ), the oligotrophic waters within the archipelago of Cabo Verde can become, albeit temporarily, nutrient-rich waters providing great foraging opportunities for breeding seabirds. Overall, we did not detect sexual segregation in the isotopic niche of Boyd’s shearwaters, which was further supported by similar diet composition between sexes obtained from stable isotopic mixing models. As expected, individuals showed lower δ 15 N values, feeding mainly on small epipelagic fish larvae, much less enriched in δ 15 N when compared to mesopelagic fish or squid. Despite the absence of sexual segregation in the isotopic niche, females exhibited a higher reliance on epipelagic fish, while males’ diet was more enriched on mesopelagic prey. Moreover, compared to the diet composition of its close-related counterpart the Macaronesian shearwater, breeding in Azores and Madeira archipelagos, our results indicate a minimal importance of squid on adults’ diet (Neves et al. 2012 ; Ramos et al. 2015 ), not exceeding the 11%. However, the method used to assess diet in those studies (stomach flushing) is prone to detect more squid beaks which accumulate in the stomach over time (Barrett et al. 2007 ). On the other hand, the small number of squid species included in our model and, more importantly, the higher trophic level of squid sampled for isotopic analysis might have caused model-biased estimates. Without previous knowledge of Boyd’s shearwater diet, it is difficult to ensure correct model-biased estimates. Only by studying the diet of this species with more detailed methods, such as DNA metabarcoding, it will be possible to better understand their trophic ecology (Xavier et al. 2018 ; Carreiro et al. 2020 ). Additionally, the high overlap in the isotopic niche suggests no resource partitioning between sexes and indicates a low intra-specific competition for food resources. The lack of sex-specific isotopic niche may be attributed to the absence of SSD in Boyd’s shearwaters, because this type of segregation has been more reported in SSD species, such as albatrosses (Awkerman et al. 2007 ; Phillips et al. 2011 ), boobies (Cherel et al. 2008 ; Almeida et al. 2021 ), giant petrels (Phillips et al. 2011 ), penguins (Forero et al. 2002 ; Bearhop et al. 2006 ), shags (Bearhop et al. 2006 ), and shearwaters (Navarro et al. 2009 ; Ramos et al. 2009 ) during the breeding season. Yet, there are some studies that reported sex-related differences in the isotopic niche of breeding monomorphic species, such as the common tern (Nisbet et al. 2002 ), thin-billed prion (Quillfeldt et al. 2008 ), and the northern (Cleasby et al. 2015 ) and Australasian gannets (Ismar et al. 2017 ). Nevertheless, the gannets were the only species that also exhibited different foraging patterns, with little spatial overlap of FA and exploiting areas with distinct oceanographic conditions (Cleasby et al. 2015 ; Ismar et al. 2017 ). Resource partitioning is expected to be greater when resources are scarcer or when larger individuals outcompete the smaller co-specifics from using the same resources (Young et al. 2010 ; Phillips et al. 2011 ; Almeida et al. 2021 ). In this study, Boyd’s shearwater males and females did segregate in the spatial distribution and foraging behaviour, but not in the isotopic niche, indicating that females extend the foraging range and the time of trip duration without changing isotopic niche or diet. Thus, we may argue that Boyd’s shearwaters can take advantage of the seasonal and localised upwelling felt during the winter within and around the archipelago of Cabo Verde, providing sufficient resources for both sexes when exploiting colony surroundings during short foraging trips and more pelagic and northerly waters during long foraging trips. In summary, this study provides the first detailed analysis on the foraging movements of the small and endemic Boyd’s shearwater, a winter breeder of Cabo Verde archipelago. This study benefited from the use of high-precision mini-GPS loggers that permitted a more detailed description of foraging behaviour and to identify core FA of adult breeders. Additional data are needed to evaluate the sex-related spatial and trophic consistency across several years, for a better comprehension of the effect of oceanographic conditions in driving the at-sea foraging distribution patterns and foraging behaviour. Despite the small number of years used in this study, we believe that our data are highly valuable for future marine spatial planning and further implementation of species’ conservation plans in the archipelago of Cabo Verde. Declarations Acknowledgements We are grateful to Biosfera I and its staff for the logistics, namely transport to the colony and all the provided conditions, supplies and companionship during fieldwork. Author contributions IS, JAR, VHP: conceptualization and methodology. IS, FRC, IR, NA, SA carried out the fieldwork and collected the samples. ARC, RJL undertook the molecular sexing of birds and prey identification. IS, DM carried out the stable isotope analysis. PG provided logistical and fieldwork support. IS, JAR, VHP: investigation, writing, and visualization. All authors have read, reviewed, and edited the manuscript and approved its submission. Funding This work received financial and logistic support (for fieldwork campaigns, GPS tracking devices and laboratory analysis) from the project Alcyon – Conservation of seabirds from Cabo Verde, coordinated by BirdLife International and funded by the MAVA foundation (MAVA17022; https://mava-foundation.org/oaps/promoting-the-conservation-of-sea-birds/), through its strategic plan for West Africa (2017–2022). IR and NA received PhD and MSc grants, respectively, from MAVA through the Alcyon project. AC were funded by PhD grants from the Portuguese Foundation for Science and Technology (FCT) (SFRH/BD/139019/2018). This study benefitted from funding by the strategic program of MARE, financed by FCT (UID/MAR/04292/ 2020), through national funds. Data availability Data will be available upon reasonable request to the authors. Compliance with Ethical Standards Conflict of interest/Competing interests: The authors declare that they have no conflicts of interest. 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Ecography (Cop) 43:1261–1277. doi: 10.1111/ecog.04960 Supplementary Files ESMdosSantosetal.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Mar, 2022 Reviewers invited by journal 11 Mar, 2022 Editor assigned by journal 08 Mar, 2022 First submitted to journal 07 Mar, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1428025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":90176452,"identity":"31bb7a08-fbb3-4bcd-99c9-394770b181c5","order_by":0,"name":"Ivo dos Santos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACNhDB2AAkDsCE2EE8AwuitRgwMPCAWAYS+K1C1SKRAGLg1sInkXzswccd9+T4jrc//vBzxx85+ZnPr274USDBwN/enYDVYRJp6YYzzxQbS545YybZe8bA2OB2TtnNHqDDJM6c3YBVC88ZM2netoTEDTdy2Bh42wwSN0jnpN3gAWoxkMjFq6V+w430xx//ArXMn3km7eYffFrYe8BaEgxuJBhIg2xpuMF+7DZeW9jb0iRnnkkA+gdonWybsbHBmRy22zIGEjy4/CLfzHxM4uOOBHlQiH182yYnJ99+/NnNN39s5Pjbe7FqwQZ4DMAkscpBgP0BKapHwSgYBaNg+AMAD8NjO2zOeMcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8415-1700","institution":"University of Coimbra Faculty of Sciences and Technology: Universidade de Coimbra Faculdade de Ciencias e Tecnologia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ivo","middleName":"dos","lastName":"Santos","suffix":""},{"id":90176453,"identity":"7e8d8662-0118-4abf-870f-46ee56ebe4af","order_by":1,"name":"Jaime A. 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Lopes","email":"","orcid":"","institution":"UP CIBIO: Universidade do Porto Centro de Investigacao em Biodiversidade e Recursos Geneticos","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"J.","lastName":"Lopes","suffix":""},{"id":90176461,"identity":"f3d6d648-8def-4deb-8ecb-08c1ea01b7cb","order_by":9,"name":"Pedro Geraldes","email":"","orcid":"","institution":"SPEA: Sociedade Portuguesa para o Estudo das Aves","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Geraldes","suffix":""},{"id":90176462,"identity":"1c3f37c3-9589-4839-a96f-0520e774b5de","order_by":10,"name":"Vítor H. Paiva","email":"","orcid":"","institution":"University of Coimbra: Universidade de Coimbra","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vítor","middleName":"H.","lastName":"Paiva","suffix":""}],"badges":[],"createdAt":"2022-03-07 16:13:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1428025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1428025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19254098,"identity":"2a866169-0978-4f9a-883b-6143e2ff178b","added_by":"auto","created_at":"2022-03-15 16:47:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105895,"visible":true,"origin":"","legend":"\u003cp\u003eMain core foraging areas (FA; 50% UD, filled polygons) and home range areas (HR; 95% Kernel UD, solid lines) used by male (blue) and female (pink) Boyd’s shearwaters during short (\u0026lt; 1 day trips; left panel) and long (≥ 1 day trips; right panel) foraging trips during the 2018-2019 breeding seasons. Bathymetric relief in the background\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1428025/v1/ee8a4fe57069250b7917191a.jpg"},{"id":19254325,"identity":"7eed38d5-4add-4690-be5f-52511f8649c8","added_by":"auto","created_at":"2022-03-15 16:53:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87161,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage contribution of gradient of bathymetry (BATG), chlorophyll a concentration (CHL) sea surface height (SSH), and sea surface temperature (SST) in explaining the at-sea distribution of adult male and female Boyd’s shearwater during short and long foraging trips, recorded during the 2018-2019 breeding seasons (more details in Table S3)\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1428025/v1/267d3b3ebaf1149c6125025f.jpg"},{"id":19254099,"identity":"6226b25d-ac15-4695-a0ce-b525ce09d84a","added_by":"auto","created_at":"2022-03-15 16:47:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59934,"visible":true,"origin":"","legend":"\u003cp\u003eIsotopic niches of male (blue) and female (pink) Boyd’s shearwaters plasma at Raso islet, Cabo Verde. The standard ellipse area corrected for small sample sizes (SEA\u003csub\u003eC\u003c/sub\u003e) is presented in solid bold lines and the convex hull area (TA) in dashed lines\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1428025/v1/17fd8399a2a061af90caf61e.jpg"},{"id":19254229,"identity":"50537ce4-912e-4eb8-8b17-7073a0162ac7","added_by":"auto","created_at":"2022-03-15 16:50:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53923,"visible":true,"origin":"","legend":"\u003cp\u003eStable isotope Bayesian mixing model graphical outputs exhibiting the prey contributions in the diet of Boyd’s shearwaters. Boxplots display the range between 25 and 75% quantiles, error bars extend to a maximum (97.5%) and minimal values (2.5%), and the mean is represented by the solid black line. The top panel corresponds to diet outputs obtained for males, and the bottom panel for females. For clarification of prey species belonging to each group, please consult “Sample preparation and isotopic analysis” in the Methods section\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1428025/v1/1788dbcb8328eb248a94ae75.jpg"},{"id":19254326,"identity":"0b6c14b0-7e5e-4932-a8c1-c93b35a29100","added_by":"auto","created_at":"2022-03-15 16:53:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":712649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1428025/v1/5e821ecc-7f06-467f-b45f-6ace5ddd9d73.pdf"},{"id":19254102,"identity":"66fc373c-816a-4c57-9a9d-777b2c23ff89","added_by":"auto","created_at":"2022-03-15 16:47:35","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4243751,"visible":true,"origin":"","legend":"","description":"","filename":"ESMdosSantosetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-1428025/v1/ecd7f455f62bba7a83ee9811.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSexual Segregation in the Foraging Distribution, Behaviour, and Trophic Niche of the Endemic Boyd’s Shearwater (\u003cem\u003ePuffinus lherminieri Boydi\u003c/em\u003e)\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSeabirds, as marine apex predators, are sensitive to changes at lower trophic levels, \u003cem\u003ee.g.\u003c/em\u003e, oscillations in prey availability caused by shifts in oceanographic conditions (Becker et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Chimienti et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and many are seen as optimal sentinel organisms for monitoring environmental and trophic changes in marine ecosystems worldwide (Furness and Camphuysen \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Scopel et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In their foraging choices, seabirds are driven by extrinsic factors (\u003cem\u003ei.e.\u003c/em\u003e, environmental conditions) and also by intrinsic traits, such as sex, breeding stage or age (Votier et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sztukowski et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cerveira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The combined effect of such factors may explain the inter- and intra-species partitioning of food resources (Schoener \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which is particularly meaningful when resources are more unpredictable and patchily distributed, as happens in tropical regions (Weimerskirch \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSexual segregation is one of the most studied subjects in seabird ecology, especially during the breeding season, when adult breeders adopt a \u0026lsquo;central-place\u0026rsquo; foraging strategy becoming spatially constrained by their breeding duties (Weimerskirch et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), resulting in segregation of foraging patterns in some species. Sexual segregation can be explained by three main intrinsic/ ecological drivers. Firstly, the sexual size dimorphism (SSD), where one sex is anatomically larger than the other (Gonz\u0026aacute;lez-Sol\u0026iacute;s et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Phillips et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Weimerskirch et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), influencing flight performance, foraging range, or diving depth (Gilardi \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Lewis et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Weimerskirch et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Here, the smaller sex, or the less efficient forager could then be outcompeted by the larger sex, or by the more efficient forager, and forced to forage in less profitable waters or in remoter areas, as a way to avoid competition within foraging grounds, \u003cem\u003ei.e.\u003c/em\u003e, \u0026lsquo;inter-sexual competition\u0026rsquo; hypothesis (Gonz\u0026aacute;lez-Sol\u0026iacute;s et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Paiva et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Secondly, divergent parental roles, should shape nest attendance and/or provisioning rates. For instance, the sex contributing more to chick provisioning would be less engaged in brooding or nest-site defense, \u003cem\u003ei.e.\u003c/em\u003e, \u0026lsquo;reproductive role specialization\u0026rsquo; hypothesis (Paredes et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Thirdly, sex-specific nutritional requirements. In long-lived seabirds, biparental care is usually recognized and denoted as crucial for successful rearing of offspring (Cockburn \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, parental investment may differ among sexes, where one sex invests more in chick feeding (sex-biased provisioning) while the other invests more on self-maintenance (Gray and Hamer \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Welcker et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), as a consequence of sex-specific nutritional requirements, \u003cem\u003ei.e.\u003c/em\u003e, \u0026lsquo;energetic constraint\u0026rsquo; hypothesis. Furthermore, sexual segregation may also occur more sporadically when environmental conditions within adults\u0026rsquo; foraging range are not particularly profitable, which denotes a lower prey availability (Gladbach et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cory\u0026rsquo;s shearwaters (\u003cem\u003eCalonectris borealis\u003c/em\u003e) evidenced a clear sexual segregation in years of great environmental stochasticity, which was not observed in years of good environmental conditions (Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Female shearwaters undertook longer foraging trips, enlarged their isotopic niche, fed on prey of lower trophic level, and presented lower body condition than males (Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This indicates that, in some circumstances, sexual segregation might be context-dependent and not linked straightforward to the sex factor.\u003c/p\u003e \u003cp\u003eSexual differences in foraging strategies (Gonz\u0026aacute;lez-Sol\u0026iacute;s et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Stauss et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zango et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), at-sea spatial distribution (Phillips et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Stauss et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), parental roles (Austin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), diet specialisation (Phillips et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and niche partitioning (Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reyes-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have been frequently described in sexually dimorphic seabirds, such as albatrosses, boobies, and shearwaters. Yet, some studies have also reported the occurrence of sex-specific differences in foraging patterns (Lewis et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Welcker et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pinet et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and isotopic niche in sexually monomorphic seabirds (Nisbet et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Quillfeldt et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), indicating that body size would not be the explanation for sexual segregation in these species. For instance, in two sexually monomorphic gannet species, the northern gannet \u003cem\u003eMorus bassanus\u003c/em\u003e and the Australasian gannet \u003cem\u003eM. serrator\u003c/em\u003e, females occupied different trophic niches than males during the breeding stage (Cleasby et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ismar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and female northern gannets in particular were outcompeted by males and had to forage in farther and less profitable waters (Cleasby et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In Barau\u0026rsquo;s petrel \u003cem\u003ePterodroma baraui\u003c/em\u003e, during the pre-laying exodus, males foraged in more chlorophyll-enriched waters farther from the colony, and systematically took the first incubation shift, in order to alleviate females\u0026rsquo; energetic constraints increased by costs of oviposition (Pinet et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Nevertheless, studies on sex-specific foraging patterns and niche partitioning have been mainly focused on large (Gonz\u0026aacute;lez-Sol\u0026iacute;s et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lewis et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Phillips et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Stauss et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or medium-sized seabirds (Gray and Hamer \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Peck and Congdon \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pinet et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zango et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with few studies focused on small-sized (\u0026lt;\u0026thinsp;200 g) species (Gladbach et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Welcker et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Paiva et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carreiro et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ongoing miniaturisation of global positioning system (GPS) tags in the last few years, permitted the tracking of small-sized seabirds (Soanes et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Surman et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bolton \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rotger et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with much scarcer information on their detailed foraging movements, behaviour, and fine-scale spatial segregation, especially in tropical regions (Soanes et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Surman et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Boyd\u0026rsquo;s shearwater \u003cem\u003ePuffinus lherminieri boydi\u003c/em\u003e is a small-sized monomorphic procellariiform (Flood and van der Vliet \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with a current lack of information about its foraging behaviour, spatial distribution, and trophic ecology during the breeding season. To the best of our knowledge only two studies tracked the movements and trophic ecology of Boyd\u0026rsquo;s shearwater using light-sensing geolocators (Zajkov\u0026aacute; et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the trophic ecology of Boyd\u0026rsquo;s shearwater during the breeding season is also less known when compared to its closest-related counterpart, the Macaronesian shearwater \u003cem\u003ePuffinus baroli\u003c/em\u003e (Neves et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and other larger breeding seabirds of Cabo Verde (Cerveira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Here, we tracked the foraging movements of Boyd\u0026rsquo;s shearwater using high-precision mini-GPS loggers and studied the trophic ecology during the breeding season at Raso islet, Cabo Verde. We aimed at assessing whether sex-specific foraging patterns explained differences on the at-sea foraging behaviour, spatial segregation, and foraging habitat choices during short and long foraging trips. During the breeding season, pelagic seabirds usually adopt a \u0026lsquo;dual foraging\u0026rsquo; strategy, involving repeated alternation of several short foraging trips used mainly to search food for the offspring, with long foraging trips used for adult self-provisioning to replenish the nutritional reserves depleted during successive chick-provisioning trips (Weimerskirch et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Congdon et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For species breeding at low profitable areas, long foraging trips often extend to areas of enhanced productivity, associated with shelf slopes, continental shelves, or frontal zones (Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the lack of SSD, and an apparent equal investment of both sexes in breeding duties, we do not expect a strong sex-specific segregation in the foraging behaviour or spatial distribution within short and long foraging trips. Simultaneously, we also aimed at assessing the isotopic niche occupied by each sex through stable isotope analysis (SIA) of carbon and nitrogen ratios (\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC and \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN, respectively), and the diet composition through isotopic mixing models. We do not expect sex-specific segregation in isotopic niche nor in diet composition, as it was already reported for its close-related counterpart the Macaronesian shearwater (Neves et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area and study species\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was carried out at Raso Islet (16\u0026deg;36\u0026rsquo; N, 24\u0026deg;35\u0026rsquo; W), Cabo Verde, a flat and inhabit islet integrated in the Integral Natural Reserve of Santa Luzia (Vasconcelos et al. 2015). Boyd\u0026rsquo;s shearwater is a subspecies of little shearwater (c. 5,000 pairs), belonging to the \u003cem\u003elherminieri\u003c/em\u003e complex, breeding in the archipelago of Cabo Verde (BirdLife International 2020; Semedo et al. 2020). It is currently classified as \u0026ldquo;Least Concern\u0026rdquo; in the IUCN Red List; however there are some signs of decline owing to the impacts caused by introduced species (BirdLife International 2020). This small-sized pelagic seabird (~160 g) is an endemic subspecies of Cabo Verde, and it is the nearest counterpart of the Macaronesian shearwater, which breeds in Azores, Madeira, Selvagens and Canary Islands (BirdLife International 2020). It is a winter breeder, and like other Procellariiformes, lays a single egg each breeding season. Briefly, adults arrive at the colony in August-September to prospect and defend their breeding borrow, females lay the egg in January-February, which hatches about 50 days later (mid-March) and the chick is fed approximately for 60 days, leaving the nest between the last half of May and the first half of June (Zajkov\u0026aacute; et al. 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGPS deployment and sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom March to April 2018 and 2019, mini-GPS loggers (nanoFixTMGeo \u0026amp; Geo+, PathTrack Ltd., UK) were attached to the four central tail feathers of breeding adults, using TESA\u0026copy; tape (Wilson et al. 1997). Each logger together with the tape did not exceed 4 g weight, representing less than 3% of adults\u0026rsquo; body mass (~160 g), the standard and recommended threshold to not compromise individuals\u0026rsquo; foraging abilities (Phillips et al. 2003). The body mass of tracked adults did not differ prior (162.0 \u0026plusmn; 18.3 g) and after (170.0 \u0026plusmn; 25.8 g) carrying the device (paired t-test, \u003cem\u003et\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e=1.591, \u003cem\u003ep\u003c/em\u003e=0.146). GPS deployment did not last more than 5 minutes, and adults were returned to the respective nests. Each logger was programmed to record each geographical position every 10 minutes (~140 locations per day). During deployment sessions, some breast feathers were collected for molecular sexing (see Table S1 for more details), while during logger retrieval, a blood sample (~0.8 ml) was collected from the brachial vein, centrifuged to separate plasma from red blood cells (RBC), and both blood partitions were kept in ethanol (70%) until preparation for stable isotope analysis (SIA). Four tags were successfully retrieved in 2018 (3 males and 1 female) and 24 devices (12 males and 12 females) in 2019. Prey samples were collected along the breeding seasons of 2018 and 2019 for subsequent SIA. Main prey groups were created according to prey type (squid or fish), life-stage (larval or adult), and distribution in the water column (epipelagic or mesopelagic).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample preparation and stable isotope analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma was selected for carbon and nitrogen isotopic analysis, because its turnover rate corresponds approximately to the tracking period duration, \u003cem\u003ei.e.,\u003c/em\u003e around 5-7 days (Inger and Bearhop 2008), while RBC would reflect a larger timeframe of about 3-4 weeks (Bearhop et al. 2002; Cherel et al. 2005b). Nitrogen (\u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN; \u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN) isotopic values are commonly used as a proxy of predator\u0026rsquo;s trophic level, increasing about 2-5 \u0026permil; at each trophic level\u0026nbsp;(Minagawa and Wada 1986), while carbon (\u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC; \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC) values are used as a habitat indicator, because it only suffers a slight increase (\u003cem\u003eca\u003c/em\u003e. 0-1 \u0026permil;) at each trophic level (Kelly 2000). Seabird plasma and prey muscle were dried during 24 and 48 hours, respectively, at 60 \u0026deg;C. Next, the samples were rinsed with a 2:1 chloroform: methanol solution to remove the overload of lipids that can deplete \u003csup\u003e13\u003c/sup\u003eC values (Cherel et al. 2005c; Post et al. 2007). All samples were ground to a powder, weighted (~0.35 mg) in tin capsules, and analysed through an elemental analyser/isotope ratio mass spectrometry (EA/IRMS). The results were expressed using the standard \u003cem\u003e\u0026delta;\u0026nbsp;\u003c/em\u003enotation, following the equation: \u0026delta;X = [(\u003cem\u003eR\u003c/em\u003e\u003csub\u003esample\u003c/sub\u003e\u003cem\u003e/R\u003c/em\u003e\u003csub\u003estandard\u003c/sub\u003e) \u0026ndash;1] \u0026times; 1,000, where X is \u003csup\u003e13\u003c/sup\u003eC or \u003csup\u003e15\u003c/sup\u003eN, and \u003cem\u003eR\u003c/em\u003e is the ratio \u003csup\u003e13\u003c/sup\u003eC:\u003csup\u003e12\u003c/sup\u003eC or \u003csup\u003e15\u003c/sup\u003eN:\u003csup\u003e14\u003c/sup\u003eN, respectively. \u003cem\u003eR\u003c/em\u003e\u003csub\u003estandard\u003c/sub\u003e values correspond to the Vienna PeeDee Belemnite (V-PDB) and atmospheric N\u003csub\u003e2\u003c/sub\u003e, for \u003csup\u003e13\u003c/sup\u003eC and \u003csup\u003e15\u003c/sup\u003eN respectively (Bond and Jones 2009). Replicate measurements of internal laboratory standards (acetanilide) indicate a precision of \u0026plusmn; 0.2 \u0026permil; for both carbon and nitrogen isotopic ratios. The C/N ratio was examined to verify if lipid removal was effective in all plasma and muscle samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrey assemblage\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour main prey groups were created: epipelagic fish, mesopelagic fish, squid, and fish larvae. Epipelagic fish was comprised by three fish species which inhabit the epipelagic layer of the ocean (\u003cem\u003ei.e.,\u003c/em\u003e the upper 200 m of the water column): \u003cem\u003eTylosurus acus\u003c/em\u003e, \u003cem\u003eSardinella\u003c/em\u003e \u003cem\u003emaderensis\u003c/em\u003e, \u003cem\u003eSelar\u003c/em\u003e \u003cem\u003ecrumenophthalmus\u003c/em\u003e (mean \u0026plusmn; SD: \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC = \u0026ndash;16.95 \u0026plusmn; 0.48 \u0026permil;, \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN = 9.72 \u0026plusmn; 0.70 \u0026permil;, N = 16; C/N = 3.59 \u0026plusmn; 0.06); mesopelagic fish was comprised by \u003cem\u003eMyctophum affin\u003c/em\u003ee and \u003cem\u003eHygophum\u003c/em\u003e sp. (mean \u0026plusmn; SD: \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC = \u0026ndash;18.66 \u0026plusmn; 0.51 \u0026permil;, \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN = 10.23 \u0026plusmn; 0.43 \u0026permil;, N = 9; C/N = 3.19 \u0026plusmn; 0.06); squid included individuals of two different species: \u003cem\u003eHyaloteuthis pelagica\u003c/em\u003e and \u003cem\u003eCallimachus rancureli\u003c/em\u003e (mean \u0026plusmn; SD: \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC = \u0026ndash;17.02 \u0026plusmn; 1.56 \u0026permil;, \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN = 11.66 \u0026plusmn; 2.18 \u0026permil;, N = 6; C/N = 2.89 \u0026plusmn; 0.11); finally, fish larvae included fingerlings captured near surface during pelagic tours, identified as \u003cem\u003eOphioblennius\u003c/em\u003e sp. and \u003cem\u003eSynodus saurus\u003c/em\u003e (mean \u0026plusmn; SD: \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC = \u0026ndash;18.47 \u0026plusmn; 0.24 \u0026permil;, \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN = 8.38 \u0026plusmn; 0.43 \u0026permil;, N = 10; C/N = 3.04 \u0026plusmn; 0.04). All potential prey were identified to the lowest possible taxonomic level, weighted, and measured the body-length (for fish) or mantle-length (for squid). Prey were initially identified using local guides or catalogues and, specifically, squid were identified using their beaks (Xavier and Cherel 2009). Also, a small piece of muscle tissue of each species was collected to create a DNA reference collection (see Table S1), either to confirm the previous identification or to achieve a lower taxonomic level. These species are among the most abundant of each group within Cabo Verde archipelago and are generally ingested by local breeding seabirds (Carreiro personal communication).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGPS data analysis: behavioural classification and kernel estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate missing locations and standardize sampling effort, GPS tracks were resampled by linear interpolation to exactly 15 minutes interval. Individual foraging trips were divided in short (\u0026lt; 1 day) and long (\u0026ge; 1 day), after inspecting trip duration frequency using an histogram (Fig. S1). To avoid potential disturbance caused by social interaction and flying movements during landing at the colony, a distance to colony filter of 1 km radii was applied, to discard those locations. Maximum distance to colony, latitude and longitude at the distal point of each foray were computed using several functions within \u003cem\u003etrip\u003c/em\u003e R package (Sumner et al. 2020). The classification of at-sea behaviours was carried out using a combination of instantaneous flight speed and path sinuosity (calculated as the ratio of instantaneous flight speed given the speed between every third positions). Histograms of the frequency of these two variables showed adults were drifting on the water, \u003cem\u003ei.e.,\u003c/em\u003e resting, when flying speed was below 2 km h\u003csup\u003e-1\u003c/sup\u003e; intensive search, \u003cem\u003ei.e.,\u003c/em\u003e foraging behaviour, was assigned when the flight speed was between 2-10 km h\u003csup\u003e-1\u003c/sup\u003e and path sinuosity was above 7; extensive search, \u003cem\u003ei.e.\u003c/em\u003e, relocating behaviour,\u0026nbsp;was assigned when the flight speed was equal or above\u0026nbsp;10 km h\u003csup\u003e-1\u003c/sup\u003e and path sinuosity was above 7; traveling behaviour was assigned when the flight speed was simultaneously above 2 km h\u003csup\u003e-1\u003c/sup\u003e and below or equal to 7 (Fig. S2). After behaviour classification, the proportion of time spent on each behaviour was calculated for each foray within each individual.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe computed the 50% kernel UD contours to represent adult foraging areas (FA), and the 95% UD contours to describe adult home ranges (HR). Following the methods and R scripts described by\u0026nbsp;(Lascelles et al. 2016)\u0026nbsp;we calculated mean ARS zones radii for short and long foraging trips, and used those values as smoothing parameters (\u003cem\u003eh\u003c/em\u003e) in the computation of Kernel UDs. A smoothing parameter of 4 km was used for short foraging trips, and a smoothing parameter of 8 km for long foraging trips. Kernel UD contours (95% and 50%), and respective areas, were calculated using the \u0026lsquo;kernelUD\u0026rsquo; and \u0026lsquo;kernel.area\u0026rsquo; functions within the \u003cem\u003eadehabitatHR\u003c/em\u003e R package (Calenge 2006). The overlap of UD contours was calculated between sexes within short and long foraging trips through the \u0026lsquo;kerneloverlap\u0026rsquo; function, using the Bhattacharyya\u0026apos;s affinity (BA), under the \u003cem\u003eadehabitatHR\u003c/em\u003e R package (Calenge 2006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental predictors and habitat suitability models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonthly values of ocean (1) bathymetry (BAT, blended ETOPO1 product, 0.01\u0026deg; spatial resolution, m), (2) chlorophyll \u003cem\u003ea\u003c/em\u003e concentration (CHL, 0.04\u0026deg; spatial resolution, mg m\u003csup\u003e\u0026ndash;3\u003c/sup\u003e), (3) ocean mixed layer thickness (OMLT, 0.08\u0026deg; spatial resolution, m), (4) sea surface height above geoid (SSH, 0.08\u0026deg; spatial resolution, cm), and (5) sea surface temperature (SST, 0.08\u0026deg; spatial resolution, \u0026deg;C) were extracted within the foraging range of Boyd\u0026rsquo;s shearwater for March 2018-2019 and April 2019, and the mean raster was calculated for each environmental predictor. Variable 1 was downloaded from https://www.ngdc.noaa while variables 2-5 were downloaded from http://marine.copernicus.eu. Spatial gradients of all environmental predictors were calculated using an estimating proportional change within a 3 x 3 cell grid following Louzao et al. (2009). Bathymetry gradient (BATG) identifies the presence of oceanic topographic features, such as seamounts or shelf-breaks (\u003cem\u003ei.e.\u003c/em\u003e, slope areas); the gradient of CHL (CHLG) and SST (SSTG) can be used as a proxy of oceanic fronts, while OMLT gradient (OMLTG) indicate the change level on the mixed layer thickness, and consequently, the depth of the thermocline which drives the abundance and distribution of marine prey; the gradient of SSH (SSHG) could help identify the occurrence of mesoscale eddies. All environmental predictors were\u0026nbsp;rescaled to the coarsest spatial resolution (\u003cem\u003ei.e.,\u003c/em\u003e 0.08\u0026deg;) and extracted for each GPS location, before running habitat suitability models. All computations were conducted under several functions within \u003cem\u003eraster\u003c/em\u003e R package (Hijmans et al. 2020).\u003c/p\u003e\n\u003cp\u003eHabitat suitability models were computed separately for males and females for long and short foraging trips, \u003cem\u003ei.e.\u003c/em\u003e, four modelling exercises. Tracking data from 2018 and 2019 was jointly analysed given the low sample size of tags retrieved in 2018 and similar foraging range and distribution between years (Fig. S3). Prior to habitat modelling, all environmental predictors, and respective gradients, were inspected for multicollinearity. Multicollinearity was tested using the variation inflation factor (VIF) and Pearson correlation coefficients (r \u0026gt; 0.6) (Table S2), under the \u003cem\u003eusdm\u003c/em\u003e R package (Naimi 2017). Testing for collinearity issues enables to account only with non-redundant variables, avoiding model overfitting and inflated errors (Zurell et al. 2020). Ensemble Species Distribution Models (ESDM; Marmion et al. 2009) were conducted using all GPS locations (presence data; Fig. S4) through the \u0026lsquo;ensemble_modelling\u0026rsquo; function from the \u003cem\u003eSSDM\u003c/em\u003e R package (Schmitt et al. 2017). We tested 7 modelling techniques: Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Generalized Additive Models (GAM), Generalized Linear Models (GLM), Multiple Adaptive Regression Splines (MARS), Random Forest (RF), and Support Vector Machine (SVM). Each model algorithm was computed ten times using a 10-fold cross-validation procedure, using 70% of all data set for model calibration and the remaining 30% grid squares as random test for model validation (Araujo et al. 2005; Marmion et al. 2009; Zurell et al. 2020). Model goodness of fit was examined using the area under the receiver operating characteristic (ROC) curve (AUC). Models were classified excellent when AUC \u0026gt; 0.90, good when 0.80 \u0026lt; AUC \u0026lt; 0.90, reasonable when 0.70 \u0026gt; AUC \u0026lt; 0.80, and not acceptable when AUC \u0026lt; 0.70 (Araujo et al. 2005). The relative importance of environmental predictors to the probability of occurrence of each sex within short or long foraging trips, was given by the average contribution calculated from all models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsotopic niche and mixing models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStable Isotope Bayesian Ellipses in R (SIBER) package was used to obtain the isotopic niches of females and males. The standard ellipse area corrected for small sample size (SEA\u003csub\u003eC\u003c/sub\u003e) and considering 40% of all observations, was used to calculate the isotopic niche width of each sex, and to calculate niche overlap between sexes, using the \u0026lsquo;maxLikOverlap\u0026rsquo; within the \u003cem\u003eSIBER\u003c/em\u003e package (Jackson et al. 2011). Bayesian standard ellipse areas (SEA\u003csub\u003eB\u003c/sub\u003e) were calculated using 10,000 iterations of Markov-chain Monte Carlo (MCMC) simulation to test for the probability of group 1 (\u003cem\u003ee.g.\u003c/em\u003e, females) being smaller than that of group 2 (\u003cem\u003ee.g.,\u003c/em\u003e males), using the \u003cem\u003erjags\u003c/em\u003e R package (Plummer et al. 2019). The contribution of each prey group for Boyd\u0026rsquo;s shearwater diet was estimated using Bayesian mixing models within the \u003cem\u003esimmr\u003c/em\u003e package (Parnell and Inger 2016). This package provides a wide range of new functions for a more accurate and realistic diet estimation, including the incorporation of prior diet information to the model (Parnell and Inger 2016); however, to the best of our knowledge there is no data about Boyd\u0026rsquo;s shearwater diet, so no prior information was added to the model. Trophic Discrimination Factors (TDFs) are needed to accurately run the isotopic mixing model and these are often tissue-specific, species-specific and diet-specific, meaning that they may vary according to consumer\u0026rsquo;s species and its diet, and the tissue analysed (Phillips et al. 2014; Jenkins et al. 2020). To our best knowledge there are no TDF available for Boyd\u0026rsquo;s shearwaters; hence, we opted to use the average values of fractionation between prey and plasma of 3 seabird species, from captive experiments (Barquete et al. 2013; Jenkins et al. 2020). So, we used a TDF of \u0026minus; 0.44 \u0026permil; and + 2.10 \u0026permil; enrichment for carbon and nitrogen, respectively. Although these species have different feeding ecologies than Boyd\u0026rsquo;s shearwaters, we believe that these average TDFs are the most adequate for our mixing model exercises, considering other values available in the literature for seabirds (Hobson and Clark 1992; Bearhop et al. 2002; Cherel et al. 2005a; Sears et al. 2009; Chiaradia et al. 2014; Ciancio et al. 2016). A standard deviation of \u0026plusmn; 1.0 \u0026permil; was used to account for possible differences on enrichment factors between species. Before running the models a simulation method proposed by Smith et al. (2013) was used to inspect the feasibility of the isotopic mixing polygons. The sensitivity analysis (using 1500 iterations) applied to mixing polygons indicated that adult isotopic signatures were within 95% of the simulated mixing regions (probability ranges: 0.21 to 0.61 for males, 0.43 to 0.63 for females), validating our models (Fig. S5). We ran a mixing model for each sex computed using the function \u0026lsquo;simmr_mcmc\u0026rsquo; from the simmr R package (Parnell and Inger 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralized linear mixed models (GLMMs) using the appropriate family or linear mixed models (LMMs) following a normal distribution, were used to test the effect of sex on adult trip parameters and at-sea foraging behaviour, separately for short and long foraging trips: (1) trip duration, (2) maximum distance to colony, (3) total distance travelled, (4) percentage of time spent foraging, (5) relocating, (6) resting, and (7) travelling, (8) latitude and (9) longitude coordinates at the maximum distance to colony. All models were run using sex as a fixed factor, while the bird identity (\u003cem\u003ei.e.,\u003c/em\u003e individual) was included as a random factor to avoid pseudo-replications. Years were pooled together due to the lower sample size recorded in 2018 (3 males and 1 female). Mixed models were conducted with the \u003cem\u003elme4\u003c/em\u003e (Bates et al. 2015)\u0026nbsp;and \u003cem\u003elmerTest\u003c/em\u003e (Kuznetsova et al. 2017)\u0026nbsp;R packages. Sexual differences on the size of FA and HR within short and long foraging trips were tested using t-tests for independent samples, when data followed a normal distribution, or Mann-Whitney tests, when data did not follow a normal distribution.\u0026nbsp;All response variables were tested for normality, homoscedasticity, and log (total distance travelled), square root (maximum distance to colony and trip duration), or arcsine (time spent foraging, resting, and relocating) transformed whenever necessary. Throughout the results values are expressed as mean \u0026plusmn; SD. All analyses and modelling computations were performed using the R software ver. 4.0.0\u0026nbsp;(R Core Team 2020)\u0026nbsp;and the significance level was set at \u003cem\u003ep\u003c/em\u003e \u0026le; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eForaging behaviour and foraging areas during short and long trips\u003c/h2\u003e \u003cp\u003eOver the two breeding seasons, each logger recorded an average of 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 days in a total of 98 trips made by 28 adult Boyd\u0026rsquo;s shearwaters (M: N\u0026thinsp;=\u0026thinsp;15; F: N\u0026thinsp;=\u0026thinsp;13). Of these, 59 trips (60%) were classified as short foraging trips (M: N\u0026thinsp;=\u0026thinsp;34; F: N\u0026thinsp;=\u0026thinsp;25), while 39 trips (40%) were classified as long foraging trips (M: N\u0026thinsp;=\u0026thinsp;20; F: N\u0026thinsp;=\u0026thinsp;19). Overall, both sexes foraged close to the colony, travelling for short distances and for short periods of time (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, significant differences were found for trip duration, maximum distance to colony, latitude of the distal point (at maximum distance), and percentage of time spent relocating, for both short and long foraging trips (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, females carried out longer trips, reached farther and northern distances when compared to males during both short and long foraging trips, while males exhibited more time spent on relocating than females during short and long foraging trips (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Also, there were sex differences for the total distance travelled during long foraging trips, as females travelled for longer distances than males, however, this did not occur during short foraging trips (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were no sex differences for the longitude of the distal point (at maximum distance), nor for the percentage of time spent foraging, resting, or traveling for both short and long foraging trips (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrip characteristics, foraging behaviour, and spatial ecology parameters of male and female Boyd\u0026rsquo;s shearwaters during the 2018\u0026ndash;2019 breeding seasons at Raso islet (joint data for both years), Cabo Verde. Individual trips were separated by its duration as short (\u0026lt;\u0026thinsp;1 day) or long (\u0026ge;\u0026thinsp;1 day) foraging excursions. The overlap of foraging areas (FA) was measured within each sex and trip type and between sexes (within trip type) using the Bhattacharyya\u0026rsquo;s affinity index (BA). Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Values with the same superscript letter indicate no significant differences between sexes for short and long foraging trips (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) based on t-tests for independent samples (normal data distribution) or Mann-Whitney tests (non-normal data distribution)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eShort trips\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c16\" namest=\"c10\"\u003e \u003cp\u003eLong trips\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrip parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of trips [N birds]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e34 [13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e25 [12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e20 [13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e19 [11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip duration (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum distance to colony (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e107.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e50.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e150.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e57.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal distance travelled (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e221.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e309.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e114.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude coordinates (at maximum distance, \u0026ordm;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e17.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude coordinates (at maximum distance, \u0026ordm;W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-23.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-23.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent foraging (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent relocating (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent resting (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e31.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e11.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent travelling (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e45.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of short trips (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpatial ecology parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForaging Areas (FA): 50% UD area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e652.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e322.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e755.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e502.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome Range Areas (HR): 95% UD area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e626.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e679.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e251.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3500.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2519.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3647.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2395.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA overlap within sex and trip type (BA index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA overlap among sex, within trip type (BA index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of (generalized) linear mixed models used to test the effect of sex on trip parameters and foraging behaviour of adult Boyd\u0026rsquo;s shearwaters during short and long foraging trips. All models included bird identity (i.e., individual) as a random factor to avoid pseudo-replication issues\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eShort trips\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003eLong trips\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModels\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003e β\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003e β\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrip duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum distance to colony\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal distance travelled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude at maximum distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eM\u0026thinsp;\u0026lt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude at maximum distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent foraging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent relocating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eM\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent resting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent travelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003eParametric coefficients (β\u0026thinsp;\u0026plusmn;\u0026thinsp;SE), t, and p values are also shown. Significant values (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) are shown in bold\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, the size of foraging (FA, 50% UD) and home range areas (HR, 95% UD), calculated for each foraging trip, were similar between sexes for both short and long foraging trips (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), though females showed slight larger foraging and home range areas than males (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nonetheless, the overlap of FA within and between sexes and trip type was low during both short and long foraging trips, evidencing spatial segregation at the foraging trip and sex levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \n\u003ch2\u003eHabitat Modelling\u003c/h2\u003e\n\u003cp\u003eMulticollinearity examinations detected that only 8 of the 10 tested environmental variables (BAT, CHL, OMLT, SSH, SST, BATG, CHLG, OMLTG, SSHG, and SSTG) showed no collinearity issues (\u003cem\u003ei.e.\u003c/em\u003e, non-redundant variables). The ESDMs computed separately for short and long foraging trips for male and female adults (4 ensemble models in total), exhibited good to excellent predictive performance (0.88\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.94; Table S3), which indicates that models were quite efficient in separating the suitable from the unsuitable marine habitats for adult little shearwaters. Habitat suitability models suggested no apparent differences of habitat preferences between sexes during short foraging trips. SST was the variable that best explained the distribution of Boyd\u0026rsquo;s shearwaters during short foraging trips, followed by SSH and BATG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S3). However, there were differences on the habitat preferences of males and females during long foraging trips; specifically, CHL explained better the distribution of females (~\u0026thinsp;15%) than that of males (~\u0026thinsp;7%), while SSH (~\u0026thinsp;35\u0026ndash;41%) and SST (~\u0026thinsp;16\u0026ndash;22%) explained quite evenly the distribution of both sexes during long foraging trips (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S3).\u003c/p\u003e\n\u003ch2\u003e Isotopic Niche And Diet Composition\u003c/h2\u003e\n\u003cp\u003eOverall, plasma isotopic signatures revealed no significant differences between male and female isotopic niches (MANOVA, Wilks\u0026rsquo;s λ, F\u003csub\u003e1,14\u003c/sub\u003e = 0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41, N\u0026thinsp;=\u0026thinsp;16; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A separate analysis for each stable isotope revealed that neither \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC (F\u003csub\u003e1,14\u003c/sub\u003e = 0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51) or \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN (F\u003csub\u003e1,14\u003c/sub\u003e = 0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35) values significantly differed between sexes. Males presented a broader isotopic niche than females, however, the Bayesian estimate of SEA (SEA\u003csub\u003eB\u003c/sub\u003e) revealed no clear between-sex differences on isotopic niche size (probability that SEA\u003csub\u003eB, females\u003c/sub\u003e \u0026gt; SEA\u003csub\u003eB, males\u003c/sub\u003e = 0.19, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overlap of isotopic niches, here represented by the overlap of standard ellipses (including 40% of data), indicated that approximately 32% of males\u0026rsquo; isotopic niche overlapped with that of females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The isotopic mixing models showed no apparent diet differences among sexes. Specifically, both sexes showed a higher reliance on fish larvae (M: 42.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4%; F: 42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5%), followed by epipelagic fish (M: 21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0%; F: 32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5%) and mesopelagic fish (M: 26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8%; F: 13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09%); squid had a minor importance in Boyd\u0026rsquo;s shearwater diet (M: 10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06%; F: 10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMales and females isotopic niche measurements of Boyd's shearwaters, using plasma signatures collected during the 2019 breeding season. Carbon and nitrogen isotopic values (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) are expressed in \u0026permil;. SEA\u003csub\u003eC\u003c/sub\u003e represents the area of the standard ellipse (explaining 40% of the total data) corrected for small samples sizes; SEA\u003csub\u003eB\u003c/sub\u003e (P value) represents the Bayesian estimates of standard ellipses and assess niche size probability differences; TA represents the convex hull area (=\u0026thinsp;total area) of the isotopic niche; C/N represents the average ratio between carbon and nitrogen percentage values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSEA\u003csub\u003eC\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSEA\u003csub\u003eB\u003c/sub\u003e (P =)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study documented a sex-specific segregation in foraging by adult Boyd\u0026rsquo;s shearwaters during the breeding season. We found a sex-specific pattern on adult foraging behaviour, with females traveling for longer distances and foraging at farther and northern areas than males, although sex-specific differences were stronger during long foraging trips. Females\u0026rsquo; foraging distribution during long foraging trips was clearly driven more by chlorophyll \u003cem\u003ea\u003c/em\u003e concentration (CHL) than that of males, while during short foraging trips, the foraging distribution of both sexes was largely explained by sea surface temperature (SST). Nevertheless, sea surface height (SSH) was the most important environmental predictor in explaining the foraging distribution of both sexes during long foraging trips. As initially predicted, there was no difference in the isotopic niche between sexes, however, mixing models detected divergences on the importance of prey groups among sexes; specifically, females relied more on epipelagic fish while males relied more on mesopelagic fish, although the main prey consumed by both sexes was the lower δ\u003csup\u003e15\u003c/sup\u003eN-enriched fish larvae.\u003c/p\u003e \u003cp\u003eOverall, Boyd\u0026rsquo;s shearwaters foraged mostly near the colony (up to 300 km), in the pelagic waters located northwards of the archipelago of Cabo Verde. This is in line with the prevalence of an oceanic foraging distribution in the colony surroundings, reported in previous studies using light-sensing geolocators (Zajkov\u0026aacute; et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As expected, Boyd\u0026rsquo;s shearwaters exhibited a dual foraging strategy, a typical strategy adopted by Procellariiformes during the breeding season (Chaurand and Weimerskirch \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Weimerskirch et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), probably as an adaptation to the longer rearing periods undertaken by pelagic seabirds and buffer the constraints of central-place foraging behaviour. Pelagic seabirds often alternate between several short foraging trips to provision food to their offspring, in order to cope with the nutritional needs of their growing chick and ensure breeding success, with one or two long foraging trips for self-provisioning to replenish body reserves depleted during short trips (Weimerskirch et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Congdon et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Within the general dual foraging pattern observed in Boyd\u0026rsquo;s shearwaters, sex-specific patterns in foraging were evident. Females carried out longer foraging trips, foraged over more distant regions at higher latitudes and travelled for longer distances than males. This sex-specific foraging pattern was more evident during long foraging trips, albeit substantial divergences were also detected in short foraging trips. Our results are in line with previous studies on other monomorphic seabirds, such as the wedge-tailed shearwater \u003cem\u003eArdenna pacifica\u003c/em\u003e (Peck and Congdon \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the little auk \u003cem\u003eAlle alle\u003c/em\u003e (Welcker et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the Manx shearwater \u003cem\u003ePuffinus puffinus\u003c/em\u003e (Gray and Hamer \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), where females took longer foraging trips, subsequently driving a male-biased provisioning with males delivering food to the chick at a higher rate, and showing a greater contribution to overall chick feeding. Peck and Congdon (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) argued that the sex-specific foraging of wedge-tailed shearwaters were identical to the results obtained from SSD species, supporting the occurrence of inter-sexual competition at the FA, with the larger sex outcompeting the smaller one from the closest or more profitable regions (Paiva et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite the spatial segregation (\u003cem\u003ei.e.\u003c/em\u003e, low spatial overlap) and sex-specific differences in foraging, we do not have enough data to support the occurrence of inter-sexual competition at the foraging grounds. Instead, we believe that although Procellariiformes display biparental care during the rearing period, adult breeders may invest slight differently on chick provisioning duties, according to disparate self-energetic requirements. Indeed, once both parents share the incubation of the egg, we may argue that female Boyd\u0026rsquo;s shearwaters are energetically more depleted and in poorer body condition than males at the onset of the rearing period, due to carry-over costs incurred at the time of egg production and laying (Monaghan et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Thus, the longer and more distant foraging trips carried out by female Boyd\u0026rsquo;s shearwaters may arise from their higher energetic costs during the initial breeding stages (the \u0026lsquo;energetic-constraint\u0026rsquo; hypothesis), which drives females to allocate more time to self-feeding trips than males (Gray and Hamer \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Welcker et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In addition, our results also revealed that females took slight longer short foraging trips than males. This may also strengthen the \u0026lsquo;energetic-constraint\u0026rsquo; hypothesis, because females may be foraging to provisioning the chick, but may also forage at a faster rate to replenish their body reserves. Nevertheless, we acknowledge that to empirically ascertain the existence of a male-biased provisioning in Boyd\u0026rsquo;s shearwater, we would need to simultaneously monitor nest attendance and meal mass delivered to the chick.\u003c/p\u003e \u003cp\u003eHabitat suitability models revealed that, regardless of sex, the distribution of breeding Boyd\u0026rsquo;s shearwaters was mostly driven by SST and SSH during short and long foraging trips, respectively. The importance of SST in explaining the at-sea distribution of shearwaters in tropical areas has already been reported (McDuie et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cerveira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while variations in SSH was reported to influence seabird foraging grounds, especially in oceanic areas (Pereira et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Patterns in SST are closely linked to gradients of marine productivity (Catry et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cerveira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which influence vertical and horizontal distribution of prey (Hsieh et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and its abundance (Morato et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and ultimately, seabirds\u0026rsquo; breeding performance (Monticelli et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Patterns in SSH can be indicators of mesoscale eddies, which play an important role on the recycling of nutrients in oceanic areas, \u003cem\u003ei.e.\u003c/em\u003e, oligotrophic regions (Stramma et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Braun et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cyclonic mesoscale eddies pump the deeper and cooler waters (nutrient-enriched) to the euphotic zone, promoting ephemeral and localised events of enhanced productivity (Falkowski et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Klein and Lapeyre \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), a recurrent phenomenon inside and outside Cabo Verde (Meunier et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cardoso \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the sex-specific differences in foraging behaviour and spatial distribution detected during short foraging trips, there was no apparent environmentally driven segregation between sexes, indicating that both males and females showed similar preferences during chick rearing. On the other hand, CHL was twice more important in explaining females\u0026rsquo; foraging distribution than that of males during long foraging trips, suggesting that CHL would be driving the larger between-sex spatial and foraging behaviour segregation observed during self-feeding trips. In fact, this environmentally driven sexual segregation supports, once again, the \u0026lsquo;energetic-constraint\u0026rsquo; hypothesis, highlighting the higher nutritional needs of females during the chick rearing period, forcing them to forage in association with CHL patterns which can indicate a higher reliance on fine-scale phenomena such as localised upwellings (Paiva et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McDuie et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Localised upwellings can promote the aggregations of planktivorous epipelagic fish and other predatory pelagic and mesopelagic species, \u003cem\u003ee.g.\u003c/em\u003e, mesopelagic fish and squid (Ichii et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; L\u0026oacute;pez-P\u0026eacute;rez et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), translating into higher foraging opportunities for the birds foraging within these regions (Jaquemet et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Weimerskirch \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Within the archipelago of Cabo Verde, in the south of CVFZ, upwelling events only occur in winter, when the Intertropical Convergence Zone (ITCZ) migrates towards the south (Pe\u0026ntilde;a-Izquierdo et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Together with the intense currents, and the subsequent formation of mesoscale eddies promoted by the convergence of currents generated by the inter-island channels (Meunier et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pe\u0026ntilde;a-Izquierdo et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cardoso \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the oligotrophic waters within the archipelago of Cabo Verde can become, albeit temporarily, nutrient-rich waters providing great foraging opportunities for breeding seabirds.\u003c/p\u003e \u003cp\u003eOverall, we did not detect sexual segregation in the isotopic niche of Boyd\u0026rsquo;s shearwaters, which was further supported by similar diet composition between sexes obtained from stable isotopic mixing models. As expected, individuals showed lower \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN values, feeding mainly on small epipelagic fish larvae, much less enriched in \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN when compared to mesopelagic fish or squid. Despite the absence of sexual segregation in the isotopic niche, females exhibited a higher reliance on epipelagic fish, while males\u0026rsquo; diet was more enriched on mesopelagic prey. Moreover, compared to the diet composition of its close-related counterpart the Macaronesian shearwater, breeding in Azores and Madeira archipelagos, our results indicate a minimal importance of squid on adults\u0026rsquo; diet (Neves et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), not exceeding the 11%. However, the method used to assess diet in those studies (stomach flushing) is prone to detect more squid beaks which accumulate in the stomach over time (Barrett et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). On the other hand, the small number of squid species included in our model and, more importantly, the higher trophic level of squid sampled for isotopic analysis might have caused model-biased estimates. Without previous knowledge of Boyd\u0026rsquo;s shearwater diet, it is difficult to ensure correct model-biased estimates. Only by studying the diet of this species with more detailed methods, such as DNA metabarcoding, it will be possible to better understand their trophic ecology (Xavier et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carreiro et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the high overlap in the isotopic niche suggests no resource partitioning between sexes and indicates a low intra-specific competition for food resources. The lack of sex-specific isotopic niche may be attributed to the absence of SSD in Boyd\u0026rsquo;s shearwaters, because this type of segregation has been more reported in SSD species, such as albatrosses (Awkerman et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Phillips et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), boobies (Cherel et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), giant petrels (Phillips et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), penguins (Forero et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bearhop et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), shags (Bearhop et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and shearwaters (Navarro et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) during the breeding season. Yet, there are some studies that reported sex-related differences in the isotopic niche of breeding monomorphic species, such as the common tern (Nisbet et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), thin-billed prion (Quillfeldt et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and the northern (Cleasby et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Australasian gannets (Ismar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, the gannets were the only species that also exhibited different foraging patterns, with little spatial overlap of FA and exploiting areas with distinct oceanographic conditions (Cleasby et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ismar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Resource partitioning is expected to be greater when resources are scarcer or when larger individuals outcompete the smaller co-specifics from using the same resources (Young et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Phillips et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, Boyd\u0026rsquo;s shearwater males and females did segregate in the spatial distribution and foraging behaviour, but not in the isotopic niche, indicating that females extend the foraging range and the time of trip duration without changing isotopic niche or diet. Thus, we may argue that Boyd\u0026rsquo;s shearwaters can take advantage of the seasonal and localised upwelling felt during the winter within and around the archipelago of Cabo Verde, providing sufficient resources for both sexes when exploiting colony surroundings during short foraging trips and more pelagic and northerly waters during long foraging trips.\u003c/p\u003e \u003cp\u003eIn summary, this study provides the first detailed analysis on the foraging movements of the small and endemic Boyd\u0026rsquo;s shearwater, a winter breeder of Cabo Verde archipelago. This study benefited from the use of high-precision mini-GPS loggers that permitted a more detailed description of foraging behaviour and to identify core FA of adult breeders. Additional data are needed to evaluate the sex-related spatial and trophic consistency across several years, for a better comprehension of the effect of oceanographic conditions in driving the at-sea foraging distribution patterns and foraging behaviour. Despite the small number of years used in this study, we believe that our data are highly valuable for future marine spatial planning and further implementation of species\u0026rsquo; conservation plans in the archipelago of Cabo Verde.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Biosfera I and its staff for the logistics, namely transport to the colony and all the provided conditions, supplies and companionship during fieldwork.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIS, JAR, VHP: conceptualization and methodology. IS, FRC, IR, NA, SA carried out the fieldwork and collected the samples. ARC, RJL undertook the molecular sexing of birds and prey identification. IS, DM carried out the stable isotope analysis. PG provided logistical and fieldwork support. IS, JAR, VHP: investigation, writing, and visualization. All authors have read, reviewed, and edited the manuscript and approved its submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received financial and logistic support (for fieldwork campaigns, GPS tracking devices and laboratory analysis) from the project Alcyon \u0026ndash; Conservation of seabirds from Cabo Verde, coordinated by BirdLife International and funded by the MAVA foundation (MAVA17022; https://mava-foundation.org/oaps/promoting-the-conservation-of-sea-birds/), through its strategic plan for West Africa (2017\u0026ndash;2022). IR and NA received PhD and MSc grants, respectively, from MAVA through the Alcyon project. AC were funded by PhD grants from the Portuguese Foundation for Science and Technology (FCT) (SFRH/BD/139019/2018). This study benefitted from funding by the strategic program of MARE, financed by FCT (UID/MAR/04292/ 2020), through national funds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available upon reasonable request to the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Competing interests:\u003c/strong\u003e The authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e All applicable institutional and/or national guidelines for the care and use of animals were followed. All animals were handled in strict accordance with good animal practice as defined by the current European legislation. All animal work was approved by the \u0026ldquo;National Directorate of the Environment\u0026rdquo; of Cabo Verde (DNA) through licences issued annually, authorising the work carried out at Raso Islet, Desertas Islands Natural Reserve. All sampling procedures and/or experimental manipulations have been reviewed and specifically approved as part of obtaining the field license.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmeida N, Ramos JA, Rodrigues I, dos Santos I, Pereira JM, Matos DM, Ara\u0026uacute;jo PM, Geraldes P, Melo T, Paiva VH (2021) Year-round at-sea distribution and trophic resources partitioning between two sympatric Sulids in the tropical Atlantic. 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Ecography (Cop) 43:1261\u0026ndash;1277. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ecog.04960\u003c/span\u003e\u003cspan address=\"10.1111/ecog.04960\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"environmentally driven segregation, little shearwater, species distribution modelling, stable isotope mixing models, tropical seabirds","lastPublishedDoi":"10.21203/rs.3.rs-1428025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1428025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudies on sex-specific segregation in foraging and trophic niche have been focused on large and dimorphic seabirds, with less information on small monomorphic species. Here, we used mini-GPS loggers, habitat suitability models, and stable isotopes to assess the foraging movements, at-sea spatial distribution, and trophic ecology of male and female Boyd\u0026rsquo;s shearwaters \u003cem\u003ePuffinus lherminieri boydi\u003c/em\u003e in Raso Islet (16\u0026deg;36\u0026rsquo; N, 24\u0026deg;35\u0026rsquo; W), Cabo Verde, during the breeding seasons of 2018\u0026ndash;2019. The existence of sexual foraging segregation was tested in short and long foraging trips. Females engaged on longer foraging trips, travelling towards more distant and northward regions from the colony when compared to males, especially during long foraging excursions. Spatial overlap within and between sexes was generally low, indicating a sex-specific pattern in the foraging behaviour and spatial distribution of adult breeders. Habitat suitability models revealed a higher importance for chlorophyll \u003cem\u003ea\u003c/em\u003e concentration in explaining females\u0026rsquo; at-sea distribution during long foraging trips when compared to males, although the most important predictors in explaining adults\u0026rsquo; distribution were sea surface temperature and height for short and long excursions, respectively. Stable isotope analysis revealed that both sexes occupied similar isotopic niches and stable isotope mixing models revealed no diet differences. This indicates that Boyd\u0026rsquo;s shearwaters segregate at the spatial level while foraging but rely on similar food resources. Our results suggest that female-biased nutritional requirements at the onset of chick-rearing may be driving sexual foraging segregation in this population, which depends upon resources from a rather oligotrophic environment.\u003c/p\u003e","manuscriptTitle":"Sexual Segregation in the Foraging Distribution, Behaviour, and Trophic Niche of the Endemic Boyd’s Shearwater (Puffinus lherminieri Boydi)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-15 16:47:32","doi":"10.21203/rs.3.rs-1428025/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-03-14T09:06:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-03-11T20:26:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-03-08T10:55:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Biology","date":"2022-03-07T11:08:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"32c219ba-9336-4ed4-baa6-8d301ecb13ca","owner":[],"postedDate":"March 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-10-05T10:04:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-15 16:47:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1428025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1428025","identity":"rs-1428025","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}