How fishing intensity affects the spatial and trophic ecology of two gull species breeding in sympatry?

How fishing intensity affects the spatial and trophic ecology of two gull species breeding in... Abstract Fisheries produce large quantities of discards, an important resource for scavenging seabirds. However, a policy reform banning discards, which is soon to be implemented within the EU, will impose a food shortage upon scavengers, and it is still largely unknown how scavengers will behave. We studied the diet (hard remains), trophic (stable isotope analysis), and foraging (individual tracking) ecology of two gull species breeding in sympatry: Audouin’s gull Larus audouinii (AG) and yellow-legged gull Larus michahellis (YLG), in South Portugal, under normal fishery activity (NFA; work days) and low fishery activity (LFA; weekends), over two consecutive years. We established a pattern of dietary, spatial, and temporal segregation between the two gull species. Under LFA, yellow-legged gulls reduced their time spent at-sea, thus foraging more in alternative habitats (e.g. refuse dumps) and widening their isotopic niche (i.e. generalist behaviour). Contrastingly, Audouin’s gull had a narrower trophic niche (i.e. specialist behaviour), foraging exclusively at-sea, reducing the amount of demersal fish and increasing the amount of pelagic fish in their diet. Under NFA, both species foraged mostly at-sea, feeding almost exclusively on fish, with increased consumption of demersal species (i.e. fishery discards). In general, yellow-legged gull had a broader trophic niche (i.e. generalist behaviour) when compared with the narrower isotopic niche of Audouin’s gull (i.e. specialist behaviour). Overall, both gull species relied heavily on fishery discards. However, there was visible dietary, spatial, and temporal segregation between the two species, associated with their dietary and habitat preferences that could be attributed to the availability of anthropogenic resources, such as fishery discards. Introduction Ecological segregation seeks to explain the extent to which species and populations overlap in their use of limited resources. The main processes proposed for species coexistence are trophic and resource partitioning between species, including habitat and dietary segregation (temporal or spatial; Navarro et al., 2009,, 2013 ), as well as species-specific differences in foraging ecology, for example foraging abilities, and habitat and dietary preferences (Quillfeldt et al., 2013; Mancini and Bugoni, 2014). Therefore, when food resources are highly abundant and predictable as a result of anthropogenic actions, this should decrease competition between species (Oro et al., 2013), and allow coexistence. However, the lack of food partitioning may contribute to increase the niche overlap between species (Afán et al., 2014), and enhance the impact of opportunistic species on vulnerable species (Oro et al., 2013); especially during periods when anthropogenic food resources decline. Anthropogenic activities such as fisheries affect marine species mainly as a result of overfishing, discards, and bycatch (Pauly et al., 2002; Lewison et al., 2004). Commercial fisheries generate large quantities of discards (Kelleher, 2005; Bellido et al., 2011), provide vast amounts of food for marine top predators such as seabirds, and may impact their breeding biology (Oro et al., 1996; Bicknell et al., 2013), distribution, and population dynamics (Votier et al., 2010; Cama et al., 2012). Discards usually provide large amounts of lipid-poor demersal and benthic species (Furness, 2003; Oro et al., 2013), which seabirds would not access naturally (Tasker et al., 2000; Grémillet et al., 2008). Fishing intensity, and thus the amount of discarded fish, can show large temporal fluctuations attributed to variations in the types of fishing gear operating during daily and weekly periods, as well weather events affecting the type of gears that can operate (Furness, 2003; Bécares et al., 2015). It is therefore important to understand how fishing intensity influences the feeding and foraging behaviour of opportunistic seabird species, such as gulls, that make strong use of discards (Camphuysen, 1995; Garthe et al., 1996; Garthe and Scherp, 2003; González-Solís, 2003; Navarro et al., 2010; Cama et al., 2012; Bicknell et al., 2013; Bécares et al., 2015). Diet composition reconstructed through the identification of hard prey remains (e.g. pellets) provides quantitative information about prey consumption and possible overlap in the feeding niche among seabird species (Duffy and Jackson, 1986), as well as the importance of fisheries discards in their diet (Calado et al., 2018). However, this method shows some limitations such as the overestimation of prey items with more resistant hard parts compared with prey items composed mostly of soft parts (which are fully digested; Duffy and Jackson, 1986; Bearhop et al., 2001; Votier et al. 2003). The stable isotope analysis (SIA) of carbon (13C/12C, expressed as δ13C) and nitrogen (15N/14N, expressed as δ15N) in seabird tissues provides information on their trophic niche and habitat use (Cherel et al., 2005; Newsome et al., 2007). These are useful to investigate (1) variations in niche width in relation to food resource partitioning (e.g. Ceia et al., 2014), and (2) the consumption of fishery discards by seabirds, because demersal fish, only available through discards, often exhibit distinguishable isotopic signatures which will be retained by the consumers’ tissues (Calado et al., 2018). The use of tracking devices, such as Global Positioning System loggers (GPS loggers), in seabirds with known trophic ecology (from SIA) and information on diet (e.g. from pellets), provides a better view of their foraging behaviours. Furthermore, GPS tracking allows the quantification of overlap between seabird distribution and anthropogenic activities such as fisheries, which may determine their foraging ecology (Votier et al., 2010; Cama et al., 2012). Also, information on fishing vessels such as fishing gears (e.g. purse seine, trawlers, and multi-gear vessels), fishing activity patterns (week/daily patterns), and main target species (fish landings) allow to understand the relationship between seabird diet, foraging patterns, and fishing activities (Arcos et al., 2001; Bécares et al., 2015; Tyson et al., 2015). Combining all these approaches and methods can provide us with a broader view of species’ feeding and foraging behaviour. Here we assessed the influence of Normal and Low Fishery Activity (LFA) on the foraging, feeding, and trophic ecology of Audouin’s (Larus audouinii, AG) and yellow-legged (Larus michahellis, YLG) gulls. Audouin’s gull is a medium-sized (body mass 500–700 g) food specialist, foraging mainly at-sea, and feeding mostly on pelagic fish species (>50% of the diet; Oro et al., 1996, 1997; González-Solís, 2003). This species is endemic to the Mediterranean region (Oro et al., 1996, 2011), and has undergone a recent population expansion, at least in part due to the exploitation of fishery discards (Oro, 2003; BirdLifeInternational, 2016). The yellow-legged gull is a large (body mass 800–1200 g) food generalist, foraging only during the day in marine and terrestrial habitats (Sanz-Aguilar et al., 2009). Their abundance has increased in multiple colonies (e.g. Vidal et al., 1998; Meirinho et al., 2014), due to their scavenging capacity and feeding plasticity; they are able to forage on several anthropogenic resources including fishery discards and refuse tips (Ramos et al., 2009; Pedro et al., 2013). Audouin’s gull is able to forage both during the day and at night, feeding on epipelagic prey caught naturally or in association with fisheries (purse-seine vessels; Arcos and Oro, 2002; Mañosa et al., 2004). Similarly to the yellow-legged gull, Audouin’s gull may also feed on anthropogenic food sources, including fishery discards (Calado et al., 2018), as well as other aquatic prey such as the invasive American crayfish Procambarus clarkii, in some locations (Navarro et al., 2010). Previous studies have revealed that both Audouin’s and yellow-legged gulls change their distribution and daily patterns in response to fishery activity, in order to take advantage of discards (Arcos et al., 2001; Cama et al., 2012, 2013; Bécares et al., 2015). Audouin’s gull are also known to change their distribution to avoid possible competition with yellow-legged gull (Arcos et al., 2001; Christel et al., 2012). We studied the diet, trophic ecology, weekly/daily, and hourly variation in foraging habitat use of Audouin’s and yellow-legged gull breeding in sympatry at Deserta Island, Ria Formosa Natural Park, Algarve, Southern Portugal. Data was collected in 2015 and 2016 to address specifically: (1) how periods of (A) Normal Fishery Activity (NFA; workdays in 2015 and 2016) and (B) LFA (weekends in 2015 and 2016, and a storm period without fishery activity in 2016) influenced their foraging and feeding ecology, and (2) how the isotopic niche varied between a period with fishing (in 2015) and a period with virtually no fishing (unfavourable sea conditions for fishing in 2016). During NFA, we expect Audouin’s gulls to feed mainly on pelagic prey, caught naturally or in association with fisheries, and to supplement their diet with demersal prey from fishery discards. This species should forage mainly at-sea, nocturnally, and in association with fishing vessels (purse seiners). On the other hand, yellow-legged gulls should exhibit a diurnal foraging pattern, over marine areas, taking advantage of fishery discards (from trawlers and multi-gears); their diet should contain a high abundance of marine prey (i.e. fish), of both pelagic and demersal species, as well as alternative diet items from terrestrial habitats. During LFA, Audouin’s gull should increase their time spent foraging at-sea, in deeper, pelagic, and more productive areas, feeding mostly on naturally caught pelagic prey, and feeding less on fishery-discarded demersal prey. Therefore, they should exhibit a narrower isotopic niche. As for yellow-legged gull, they should decrease their time spent foraging at-sea and increase foraging in alternative habitats, such as refuse dumps and fishing ports, thus increasing their consumption of alternative food resources (e.g. refuse). During periods of fishery activity, both species should exhibit a relatively narrow isotopic niche, related with a more specialized, fish-based diet. On the other hand, during a period with virtually no fishery activity the yellow-legged gull should broaden their isotopic niche, given their opportunistic foraging behaviour, whereas Audouin’s gull should maintain its narrow isotopic niche, given its exclusively marine habits, by shifting their diet from demersal to pelagic prey. Our results should contribute toward a better understanding of how resource partitioning and habitat segregation occurs between these two sympatric gull species, under a scenario where predictable and abundant food resource decrease, such as a decline in fishery discards during LFA periods. Furthermore, we provide insights into how such niche partitioning and dependence on trawling activity occurs at relatively low population numbers of gulls (lower than 2000 breeding pairs for each species; unpublished data) from recently founded colonies (since 2008). Methods This study was conducted on Deserta (Barreta) Island (36°57′40″N 7°53′20″W), which is one of five barrier islands (and two peninsulas) within the Ria Formosa National Park, Algarve, Southern Portugal (Ceia et al., 2010). Data was collected in the breeding season, during the incubation periods of 2015 and 2016 (May). The island hosts an estimate of 1800 and 1200 breeding pairs of Audouin’s (AG) and yellow-legged (YLG) gulls, respectively. This area is characterized by high fishing activity (INE, 2016), with the main fishing port (Olhão) close to the breeding colonies (ca. 8 km). The majority (90%) of fishing vessels in Portugal are smaller than 12 m and only operate up to 6 miles (ca. 9 km) from shore (DGRM, 2018; https://bluehub.jrc.ec.europa.eu/mspPublic/). The Portuguese fishing fleet comprises purse seines, trawlers, and multi-gear vessels (Borges et al., 2001). The main targeted species are European pilchard (Sardina pilchardus; Stratoudakis and Marçalo, 2002), mackerel (Scomber scombrus; Marçalo et al., 2010), horse mackerel (Trachurus trachurus; Almeida et al., 2014), sea-breams (e.g. Diplodus spp., Pagellus spp., Sparus aurata), European sea-bass (Dicentrarchus labrax), and European hake (Merluccius merluccius; Borges et al., 2001; Campos and Fonseca, 2004; Costa et al., 2008; Gonçalves et al., 2008). The highest bycatch and discards rate are attributed to trawler vessels, in which more than 50% of their catch can be discarded (Borges et al., 2001; Erzini et al., 2002; Costa et al., 2008). Commercial species with low commercial value are the main discarded species (e.g. Chub mackerel; Scomber janponicus, Blue whiting; Micromesistius poutassou; and blue jack mackerel; Trachurus picturatus (Borges et al., 2001; Costa et al., 2008; Fernandes et al., 2015), and several juveniles of other species are also discarded at sea (e.g. sea-breams; Gonçalves et al., 2008). Sample collection Two different periods were established, both in 2015 and 2016, related with the trawlers activity, which generate the highest amount of discards (Borges et al., 2001; Costa et al., 2008a, b). (A) NFA during workdays, when all types of fishing boats operated (purse seiners, multi-gear and trawlers) and, (B) LFA on weekends when trawlers do not operate and overall fishery activity decreases (unpublished data; Docapesca 2015), and between 9th–13th May 2016 when virtually all fisheries did not operate due to unfavourable weather conditions (unpublished data; Docapesca 2016). We established three transects along the colonies of both Audouin’s and yellow-legged gulls, and removed pellet remains before starting the collection of samples. After that, during the two data collection periods, transects were repeated to ensure consistency in collection of pellets. These transects were followed during the month of May (i.e. incubation), two days each week, based on fishing intensity: we collected pellets around nests on each Friday, to assess the gull’s diet composition during NFA, and each Monday to assess the diet composition during LFA. A total of 604 pellets were collected during the breeding season of 2015 (AG: NFA n = 77, LFA n = 12; YLG: NFA n = 55, LFA n = 18) and 2016 (AG: NFA n = 176, LFA n = 103; YLG: NFA n = 96, LFA n = 67). The samples were placed in plastic bags and stored in the refrigerator until laboratory analysis. It should be noted that most pellets are produced between 6 and 24 h after a meal (Votier et al., 2001), thus some pellets from Friday meals (NFA) could have been collected on Monday (LFA). Additionally, we established two periods of differing fishing intensity between 2015 and 2016 to compare the isotopic niche between these two periods: (C) 6–15 May 2015 representing a period with regular fishery activities and (D) 9–13 May 2016 representing a period virtually without fishery activities due to unfavourable weather conditions (unpublished data; Docapesca, 2016). We compared the isotopic niche between these two periods using plasma samples of birds captured on the 15–16 May 2015 and 13–14 May 2016. Breeding adults were caught during incubation, using nest traps (2015: AG = 15, YLG = 12; 2016: AG = 12, YLG = 11). Blood samples (ca. 0.5 ml) were collected from the tarsal vein using 1 ml syringes and centrifuged at 12000 rpm for 5 min within 3–5 h of collection to separate red blood cells (RBC) from plasma for SIA (in each year). Stable isotope values obtained from plasma represent the dietary composition during the incubation period, i.e. approximately 5–7 days prior to sample collection; Hobson et al., 1994; Cherel et al., 2005). Samples were frozen until preparation for SIA. Diet and stable isotope analysis Pellet samples were examined under a stereomicroscope and separated by prey type: fish (further separated into pelagic and demersal species), refuse, Mollusca, Cephalopoda, Brachyura, Insecta, birds, Rattus rattus, egg shells, and vegetable matter. The fish prey items were identified to species-level taxonomic discrimination, using vertebrae and otoliths from our own collection, complemented with the collection from the National Museum of Natural History and Science (Lisbon) and published identification guides (Assis, 2004; Tuset et al., 2008). Cephalopod beaks were identified using beak collections at the Marine and Environmental Science Centre, University of Coimbra, Portugal. Inorganic material from refuse was represented by a range of items, including plastic, glass, paper, bones, and organs (e.g. gastrointestinal tract remains) from unknown species, and wood pieces. Some of these items were probably ingested accidentally; nevertheless, they provide indication of the foraging areas (FA) used by the species, and therefore were not excluded from the dietary analysis. Plasma samples were treated with successive rinses in a 2:1 chloroform/methanol solution to extract external lipids (Ceia et al., 2012). The relative abundance of stable isotopes of carbon and nitrogen were determined by a continuous-flow isotope ratio mass spectrometer using a CF-IRMS (Isoprime, Micromass, UK). Approximately 0.35 mg of each sample was combusted in a tin cup for determination of nitrogen and carbon isotope ratios. Results are presented in the common delta (δ) notation expressed in parts per mil (‰) according to the equation δX = [(Rsample/Rstandard) − 1], where the X is 13 C or 15 N, and Rsample is the corresponding ratio: 13C/12C or 15N/14N, and Rstandard is the ratio for the international references Vienna-PeeDee Belemnite (V-PDB) for carbon and atmospheric N2 (AIR) for nitrogen. Replicate measurements of internal laboratory standards (acetanilide) indicate measurements errors < 0.1‰ for both carbon and nitrogen. At-sea distribution and environmental variables In 2015 (early May), Audouin’s (n = 8) and yellow-legged gull (n = 8) breeding adults were equipped with GPS loggers (CatTraq GT-120, Perthold Engineering LLC), which weigh 15 g; always representing <3% of the adult’s body mass, which was set to be an upper threshold to avoid deleterious effects on seabirds (Phillips et al., 2003). Furthermore, there was no significant decrease in body mass of tagged individuals (n = 5 for both species) between capture (body mass AG = 659 ± 34 g; YLG = 952 ± 167 g) and recapture (AG = 623 ± 40 g; YLG = 955 ± 168 g; t18= 1.13, p = 0.86). The devices were deployed on birds when they were incubating, with clutches of three eggs at a similar stage of incubation period. The GPS loggers have an accuracy of 4 m and store the date, time, longitude, latitude and speed, every 2 min with logger batteries lasting about 10 days. Devices were attached to feathers in the mantle region with Tesa® tape. The process took less than 10 min, minimizing the overall stress to the animal. Of the 16 loggers deployed, 12 were recovered (AG: n = 6; YLG: n = 6) after 5–7 days. Birds’ foraging locations were selected by calculating path sinuosity for all the locations, defined as the ratio of the actual flight speed given by the GPS receiver to the velocity between every third fix (geographical location). Birds that are circling an area will display a lower calculated speed than the actual GPS speed, and therefore have a higher sinuosity index (Grémillet et al., 2004). A histogram of the sinuosity distribution was used to determine the break-off value, thus all positions with a sinuosity index ≥ 2.7 were considered foraging locations (see Supplementary Figure S1). Foraging locations were examined under the adehabitatHR R package (Calenge, 2006) generating Kernel Utilization Distribution (Kernel UD) estimates within the R environment (R Core Team 2015). The most appropriate smoothing parameter (h) was chosen via least squares cross-validation for the unsmoothed GPS data (h = 0.09°), and then applied as standard for the other datasets, and grid size was set at 0.04° (to match the grid of environmental predictors). We considered the 50% and 95% kernel UD contours to represent the core FA and the home range (HR), respectively. The foraging trips were defined from the time birds departed from the colony until their return, thereby, GPS positions at the colony were excluded from the analysis. The overlap between kernel FAs of different (1) species (AG or YLG) and (2) fishery activity period (NFA or LFA) were computed to study the spatial segregation within and among groups with the kernel overlap function and VI method of the adehabitatHR library (Calenge, 2006). To characterize the oceanographic conditions in areas used by the tracked individuals, we extracted: (1) Bathymetry (BAT, blended ETOPO1 product, 0.03° spatial resolution, m), (2) Sea Surface Temperature (SST, Aqua MODIS NPP, 0.04°, °C), and (3) sea surface chlorophyll a concentration (CHL, Aqua MODIS NPP, 0.04°, mgm−3); BAT give us the underwater depth at-sea, SST and CHL are proxies for marine productivity (Grémillet et al., 2004) in the species FA. BAT was downloaded from http://ngdc.noaa.gov/mgg/global/global.html, while SST and CHL were extracted from http://oceancolor.gsfc.nasa.gov. Weekly averages were used for the dynamic variables (variables 2–3), matching the overall tracking period for both gull species (i.e. 06/05/2015–17/05/2015). To characterize the fishing activity in the areas used by the tracked individuals we extracted the map of fishing intensity (available for all EU waters) from https://bluehub.jrc.ec.europa.eu/mspPublic/. Data analysis We calculated the frequency of occurrence (FO; %), as the percentage of pellets where a prey type occurred (Alonso et al., 2013), for the pellets of each gull species, in each year, and each fishery activity period (NFA and LFA). We divided the fish into two groups, pelagic and demersal fish (using data available on Fishbase, http://www.fishbase.org). Additionally, fish species were classified into three groups on the basis of their frequency of rejection (occasional, frequent, and systematically discarded, according to Borges et al., 2001; Monteiro et al., 2001; Erzini et al., 2002, see Table 1); this classification also takes into account commercial value and the amount of fish landed (Gonçalves et al., 2013; unpublished data; Docapesca 2015 and 2016). Table 1. Comparison of the frequency of occurrence (FO; %) between NFA and LFA of prey items in pellets of Audouin’s (AG) and yellow-legged (YLG) gulls. Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Classification of fish species, caught by fisheries in the study site and their frequency of rejection: * Occasional; **frequent; ***systematic discarded. a Includes: Microchirus azevia and Pegusa lascaris. b Includes: Class Bivalvia and Gastropoda. c Includes: Order Sepiida (Sepia officinalis) and Order Teuthida. d Includes: Polybius henslowii. e Includes: Order Hymenoptera, Coleoptera, Diptera, Lepidoptera. f Includes: Order Passeriformes and Order Charadriiformes. Table 1. Comparison of the frequency of occurrence (FO; %) between NFA and LFA of prey items in pellets of Audouin’s (AG) and yellow-legged (YLG) gulls. Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Classification of fish species, caught by fisheries in the study site and their frequency of rejection: * Occasional; **frequent; ***systematic discarded. a Includes: Microchirus azevia and Pegusa lascaris. b Includes: Class Bivalvia and Gastropoda. c Includes: Order Sepiida (Sepia officinalis) and Order Teuthida. d Includes: Polybius henslowii. e Includes: Order Hymenoptera, Coleoptera, Diptera, Lepidoptera. f Includes: Order Passeriformes and Order Charadriiformes. To assess the effect of fishery activity period on gulls’ diet, we assembled data for 2015 and 2016 because the sample size for the 2015 LFA was low for both species (AG n = 12; YLG n = 18) We assessed differences in the occurrence of main prey types with FO > 10%: pelagic fish, demersal fish, refuse, Brachyura, Insecta, and Atlantic saury Scomberesox saurus plus garfish Belone belone (these two species were grouped because they are also grouped in fishery landings), Blue whiting Micromesistius poutassou, horse/blue jack mackerel Trachurus sp., European pilchard Sardina pilchardus, and sea-breams Diplodus sp. We used non-metric multidimensional scaling (NMDS) with a stress-value associated, to obtain a graphical distribution of the parameters (gull species and fishery activity period) influenced by diet variables (prey types with a FO > 10%). Stress values represent the extent to which the 2-dimensional map is accurate in summarizing the separation of observations, with values lower than 0.2 allowing a good NMDS analysis. The influence of species, fishery activity period, and interaction species*fishery activity period in the assessment of diet composition (FO > 10%) were tested with Generalized Linear Models (GLMs), with a binomial distribution. Using δ13C and δ15N signatures were used to compare the isotopic niche between two gull species (Audouni’s and yellow-legged gulls) during two study years. First, a MANOVA, followed by factorial ANOVAs and post-hoc Tukey test allowed to disentangle differences on the mean δ13C and δ15N isotopic values. Second, the position and dimension of the isotopic niche was compared using the metrics available within SIBER (Stable Isotope Bayesian Ellipses in R) for plasma isotopic values. The area of the standard ellipse (SEAc), was calculated after small sample size correction, between each species and year (which represents their isotopic niche width): the niche overlap. A Bayesian estimate of the standard ellipse (SEAB) was also calculated to test for differences in niche widths between each species and year (see Jackson et al., 2011 for more details). The differences in trip characteristics (e.g. trip duration, maximum distance from the colony, minimum distance to fishing ports, minimum distance to very high fishing intensity areas), spatial ecology parameters (species interactions and fishery periods within FA), and the habitat of FA (BAT, SST, CHL) were tested on breeding adults with Generalized Linear Mixed Models (GLMMs). We tested the effect of species, fishery activity period, and the interaction between species*fishery activity period on the foraging trip characteristics, spatial ecology parameters, and habitat of FA. For this last category, we used time spent foraging on the main habitats surrounding the colony: beach, lagoon, sea, water treatment station, refuse dump, and fishing port (arcsine transformed percentage data). Because all individual birds made multiple trips, we used bird identity as a random term to avoid potential pseudo-replication problems in all GLMMs. GPS locations were assigned to either foraging trips or colony locations (Supplementary Figure S1). The foraging trip was defined as the locations visited from when a bird leaves the colony until it returns. The main foraging habitats (beach, lagoon, sea, water treatment station, refuse dump, fishing ports, and colony) were assigned to each GPS location. Because the main habitat used by both gull species was the colony (Supplementary Figure S1), to have more information on differences in habitat distribution and segregation between the two gull species in relation to daily and fishery activity periods we established two representative time intervals (i.e. daily periods), related with different fishing activities and the foraging ecology of gulls: (1) Night Time (between 20 and 08 h, when purse seiners operate) and; (2) Day Time (between 08 and 20 h, when trawlers and multi-gear operate). During data analysis, fishery activity was compared between NFA (workdays—full fishery activity) and LFA (weekends—very LFA). To assess differences in the use of foraging habitats by Audouin’s and yellow-legged gulls in relation to Daily and Fishery Activity Periods, we used GLMs, with a Poisson distribution, to test for the effect of species (AG vs. YLG), fishery activity period (NFA vs. LFA), daily period (night vs. day) and the second-term interactions species * fishery activity period, species * daily period, fishery activity period * daily period on the percentage of time spent per daily period and between fishery activity periods in the main habitats (i.e. beach, lagoon, sea, and water treatment station). Because Audouin’s gull did not use refuse dumps or fishing ports, these two variables were excluded from the analysis. Results are presented as mean ± SD, unless otherwise stated. All statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Response variables were tested for normality (Q-Q plots) and homogeneity (Cleveland dot plots) before each statistical test and transformed when needed (Zuur et al., 2010). Percentages (1) of time spent in FA and (2) overlap between FA were arcsin transformed to meet normality. All analyses were performed assuming a significance level of p ≤ 0.05. Results Diet composition We identified a total of 2482 prey items in gull pellets (Supplementary Table S1). Audouin’s gull fed almost exclusively on fish (>95% FO during both NFA and LFA periods), whilst yellow-legged gull also fed on alternative prey items, such as refuse, Mollusca, Brachyura, and Insecta; although fish was also their main prey (>65% FO in both fishery activity periods; Table 1). Pelagic fish had the highest percentage of occurrence in pellets for both gull species (Table 1). The stress value of NMDS analysis was 0.16. The NMDS 1 segregated the Audouin’s from yellow-legged gulls, while NMDS 2 segregated the NFA period from the LFA period (Figure 1). Sardina pilchardus was closely associated with NFA periods (Figure 1). Furthermore, Scomberesox saurus/Belone belone were very important in the Audouin’s gull diet for both NFA and LFA periods. NMDS also revealed an association between the main prey targeted by fishery activities in the Algarve, Trachurus sp., and Sardina pilchardus with yellow-legged gull diet during both fishery activity periods, and also with refuse items, the last one closely linked with LFA periods (Figure 1). Items such as Insecta, Brackyura, Diplodus sp., and Micromessitius poutassou were important for both Audouin’s gull and yellow-legged gull diets during both fishery activity periods (Figure 1 and Table 1). Figure 1. View largeDownload slide Two-dimensional NMDS ordination plot of Audouin’s (AG) and yellow legged (YLG) gulls diet items (F0 > 10%) between two different fishery activity periods (Normal Fishery Activity—NFA, and Low Fishery Activity—LFA). Stress value = 0.16. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 1. View largeDownload slide Two-dimensional NMDS ordination plot of Audouin’s (AG) and yellow legged (YLG) gulls diet items (F0 > 10%) between two different fishery activity periods (Normal Fishery Activity—NFA, and Low Fishery Activity—LFA). Stress value = 0.16. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). GLMs revealed a significantly higher occurrence of pelagic fish in Audouin’s gull diet (p = 0.0001, Table 2). Two pelagic fish species, Scomberesox saurus/Belone belone, were the main prey for Audouin’s gull (p = 0.0001, Table 2; Figures 1). Compared with Audouin’s gull, yellow-legged gull showed a higher consumption of Sardina pilchardus (p = 0.01), Trachurus sp. (p = 0.001), and refuse (p = 0.0001, Table 2; Figure 1). In relation to fishery activity periods, Scomberesox saurus/Belone belone (p = 0.001) occurred significantly more in the diet of gulls during LFA than during NFA, contrarily to Sardina pilchardus (p = 0.0001) that occurred significantly more in the diet of the gulls during the NFA period than during the LFA period (Table 2; Figure 1). No significant results were obtained for the interaction species * fishery activity period (Table 2). Table 2. GLMs, testing the effect of species (FO% > 10%; yellow-legged—YLG and Audouin—AG gulls), and fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on the occurrence of gulls’ main prey (see Table 1). Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Significant effects are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Table 2. GLMs, testing the effect of species (FO% > 10%; yellow-legged—YLG and Audouin—AG gulls), and fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on the occurrence of gulls’ main prey (see Table 1). Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Significant effects are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 2. View largeDownload slide Isotopic niche space based on carbon and nitrogen isotope ratios (δ13C and δ15N) of Audouin’s (AG; Larus audouinii) and yellow-legged (YLG; L. michahellis) gulls sampled in the breeding season of 2015 (AG n = 15, YLG n = 12) and 2016 (AG n = 12, YLG n = 11), during incubation (data from Plasma). Solid lines represent the standard ellipses areas corrected for small sample size (SEAc) calculated in SIBER (stable isotope Bayesian ellipses in R; Jackson et al. 2011). We used the computational code to calculate the metrics from SIBER implemented in the package SIAR (Parnell et al., 2010). All the metrics were calculated using standard.ellipse and convexhull functions. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 2. View largeDownload slide Isotopic niche space based on carbon and nitrogen isotope ratios (δ13C and δ15N) of Audouin’s (AG; Larus audouinii) and yellow-legged (YLG; L. michahellis) gulls sampled in the breeding season of 2015 (AG n = 15, YLG n = 12) and 2016 (AG n = 12, YLG n = 11), during incubation (data from Plasma). Solid lines represent the standard ellipses areas corrected for small sample size (SEAc) calculated in SIBER (stable isotope Bayesian ellipses in R; Jackson et al. 2011). We used the computational code to calculate the metrics from SIBER implemented in the package SIAR (Parnell et al., 2010). All the metrics were calculated using standard.ellipse and convexhull functions. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Stable isotopes SIA suggested important differences between the two gull species and years (Table 3; in 2015 AG n = 15, YLG n = 12/in 2016 AG n = 12, YLG n = 11). The plasma values of birds during the breeding period differed significantly between Audouin’s and yellow-legged gull (MANOVA, Wilk’s lambda, F2,45 = 12.27, p < 0.001), between the two years (MANOVA, Wilk’s lambda, F2,45 = 12.27, p = 0.02), but the interaction species*year was not significant (MANOVA, Wilk’s lambda, F2,45 = 1, 39, p = 0.260). A factorial ANOVA for each stable isotope revealed that Audouin’s gull had significantly lower values for both carbon and nitrogen (Table 3). Overall, 2015 revealed a significant lower value for nitrogen (Table 3). Table 3. Stable isotope ratios and factorial ANOVA results of carbon (δ13C) and nitrogen (δ15N) in plasma of Audouin’s (AG) and yellow-legged (YLG) gulls, for 2015 (AG n = 15; YLG n = 12) and 2016 (AG n = 12; YLG n = 11). Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Values are mean ± SD, with sample size in parenthesis. Significant effects are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Tukey corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Table 3. Stable isotope ratios and factorial ANOVA results of carbon (δ13C) and nitrogen (δ15N) in plasma of Audouin’s (AG) and yellow-legged (YLG) gulls, for 2015 (AG n = 15; YLG n = 12) and 2016 (AG n = 12; YLG n = 11). Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Values are mean ± SD, with sample size in parenthesis. Significant effects are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Tukey corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). SIBER analysis revealed a significantly lower isotopic niche in 2016, when fisheries almost completely ceased for one week, Audouin’s gull exhibited a significantly narrower isotopic niche when compared with yellow-legged gulls (SEAB: p = 0.03), against the regular fishing intensity period of 2015. Furthermore, in 2016 yellow-legged gull showed an important isotopic niche segregation from Audouin’s gull for both 2015 and 2016 (Figure 2): the overlap was lower between yellow-legged gull in 2016 with Audouin’s gull in 2015 (4.3%), but also with Audouin’s gull in 2016 (5.6%), and higher within Audouin’s gull between both years (27.5%). At-sea distribution and environmental variables During LFA, Audouin’s gulls spent more time at-sea, foraged farther from the colony, closer to areas with high fishery activity, that were deeper and had a higher chl a concentration, and overall were more consistent in the use of habitat (i.e. higher % overlap with congeners) when compared with yellow-legged gulls during both LFA and NFA. Also during LFA, yellow-legged gulls foraged closer to fishing ports when compared with, to Audouin's gulls during both LFA and NFA (Figure 3a;Tables 4 and 5). Table 4. Mean (±SD) foraging trip characteristics of yellow-legged (YLG) and Audouin’s (AG) gulls in May (incubation period) of 2015 between the two fishery activity period (NFA and LFA). Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 FA—core Foraging Area; 50% Kernel Utilization Distribution. Environmental predictors for 06/05/2015–17/05/2015. a http://www.worldportsource.com/ports/MAR.php. b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPublic/). Table 4. Mean (±SD) foraging trip characteristics of yellow-legged (YLG) and Audouin’s (AG) gulls in May (incubation period) of 2015 between the two fishery activity period (NFA and LFA). Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 FA—core Foraging Area; 50% Kernel Utilization Distribution. Environmental predictors for 06/05/2015–17/05/2015. a http://www.worldportsource.com/ports/MAR.php. b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPublic/). Table 5. Generalized Linear Mixed Models (GLMMs) testing the effect of gull species (yellow-legged gulls—YLG and Audouin gulls—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on foraging trip characteristics, spatial ecology parameters, and habitat characteristics of FA. Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – FA—core Foraging Area; 50% Kernel Utilization Distribution (50 Kernel UD). Environmental predictors for May 2015. The individual was used as a random effect to avoid pseudo-replication issues. Significant results are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Bonferroni corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). a http://www.worldportsource.com/ports/MAR.php b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPubl. Table 5. Generalized Linear Mixed Models (GLMMs) testing the effect of gull species (yellow-legged gulls—YLG and Audouin gulls—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on foraging trip characteristics, spatial ecology parameters, and habitat characteristics of FA. Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – FA—core Foraging Area; 50% Kernel Utilization Distribution (50 Kernel UD). Environmental predictors for May 2015. The individual was used as a random effect to avoid pseudo-replication issues. Significant results are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Bonferroni corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). a http://www.worldportsource.com/ports/MAR.php b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPubl. Figure 3. View largeDownload slide (a) GPS locations of yellow-legged (YLG; black; n = 6 birds) and Audouin’s (AG; grey (red in the online version); n = 6 birds) gulls foraging movements during NFA (n = 102 and 69 foraging trips, respectively) and LFA period (n = 35 and 16 foraging trips, respectively) of May 2015, overlaid with fishing intensity (https://bluehub.jrc.ec.europa.eu/mspPublic/). 1, very low; 2, low; 3, medium; 4, high; 5, very high. (b) Time spend per day (%) during the two fishery activity periods by each gull species in the six main foraging habitats. Treat. Station = Water treatment station. (c) Detail view (from panel A) of YLG (black) and AG (grey (red in the online version)); using (1) refuse dump, (2) water treatment station, and (3) fishing port habitats. Figure 3. View largeDownload slide (a) GPS locations of yellow-legged (YLG; black; n = 6 birds) and Audouin’s (AG; grey (red in the online version); n = 6 birds) gulls foraging movements during NFA (n = 102 and 69 foraging trips, respectively) and LFA period (n = 35 and 16 foraging trips, respectively) of May 2015, overlaid with fishing intensity (https://bluehub.jrc.ec.europa.eu/mspPublic/). 1, very low; 2, low; 3, medium; 4, high; 5, very high. (b) Time spend per day (%) during the two fishery activity periods by each gull species in the six main foraging habitats. Treat. Station = Water treatment station. (c) Detail view (from panel A) of YLG (black) and AG (grey (red in the online version)); using (1) refuse dump, (2) water treatment station, and (3) fishing port habitats. In relation to foraging patterns, the GLM analysis revealed that Audouin’s gull presented mainly a marine foraging behaviour during both fishery activity periods (NFA and LFA); as did the yellow-legged gull (Figures 3b and 4; Table 6). The Sea was used significantly more at Night Time than during Day Time (Table 6), and significantly more by Audouin’s gull, which showed nocturnal behaviour (night time period, n = 6; Figure 4a), in contrast to the yellow-legged gull (Figure 4b;Table 6). During the NFA period, yellow-legged gull foraged significantly more at sea (Figures 3b and 4b;Table 6). The lagoon was used as a resting site, significantly more during the NFA period (Figure 4; Table 6). Audouin’s gull rested significantly more during the Day Time period (10–18 h), whereas the yellow-legged gull rested significantly more during the Night Time period (Figure 4; Table 6). The water treatment station was used significantly more during the LFA period (Figures 3b and 4; Table 6). The beach was used significantly more during the NFA at Night Time (Figure 4; Table 6). In relation to alternative foraging habitats, such as refuse dumps and fishing ports, these were only used by yellow-legged gull (Figures 3b, 3c, and 4). Table 6. GLMs testing the effect of gull species (yellow-legged gull—YLG and Audouin’s gull—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA), daily period (night time vs. day time) on the use of the different habitats (Beach, Sea, Lagoon, and Water treatment station). Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Significant results are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Table 6. GLMs testing the effect of gull species (yellow-legged gull—YLG and Audouin’s gull—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA), daily period (night time vs. day time) on the use of the different habitats (Beach, Sea, Lagoon, and Water treatment station). Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Significant results are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 4. View largeDownload slide Percentage of time spent (within 2 h slots) in different foraging habitats by: a—Audouin gulls (AG) and b—yellow-legged gulls (YLG) during normal and LFA period. Treat.station = water treatment station. Figure 4. View largeDownload slide Percentage of time spent (within 2 h slots) in different foraging habitats by: a—Audouin gulls (AG) and b—yellow-legged gulls (YLG) during normal and LFA period. Treat.station = water treatment station. Discussion This study assessed differences in feeding, trophic, and foraging ecology of two gull species, Audouin’s and yellow-legged gull, between two periods of differing fishery activity (NFA vs. LFA). We established dietary, spatial, and temporal segregation between the species. Audouin’s gull showed strict marine foraging behaviour, contrasting with the generalist behaviour of the yellow-legged gull, which foraged in several habitats and on a wide range of prey. The two gull species fed mostly on highly commercial species, such as Sardina pilchardus, more frequently during the NFA period than the LFA period. On the other hand, they fed more frequently on species with low commercial value, such as Scomberesox saurus/Belone belone during the LFA period than the NFA period. Additionally, when fisheries almost completely ceased for one week (LFA—2016), according to species-specific differences Audouin’s gull exhibited a narrower isotopic niche (food/foraging-specialist) and yellow-legged gull a wider isotopic niche (food/foraging-generalist). However, no significant differences were found in diet and trophic ecology between species*fishery activity period. Nevertheless, as a result of LFA, gull species revealed foraging activity differences between the two fishery activity periods. Overall, there was visible dietary, spatial and temporal segregation between the two species; mostly attributed to their diet and habitat preferences. These differences can be related to the availability of anthropogenic resources, such as fishery discards. Dietary segregation: differences between week periods The two gull species consumed a diverse range of fish species, including demersal prey which is only available through fishery discards (Furness et al., 2007; Votier et al., 2010). However, the specialized feeding behaviour of Audouin’s gull was evident from the pellet analysis. During the two consecutive years, and the two fishery activity periods, their main prey species were Scomberesox saurus/Belone belone, which are epipelagic fish that could be “naturally” caught (Arcos and Oro, 2002) or provided by nocturnal fisheries (purse-seiner discards; Borges et al., 2001; Gonçalves et al., 2008). Similar to Audouin’s gull, yellow-legged gull fed mainly on fish, during both fishery activity periods (NFA vs. LFA), taking Sardina pilchardus and Trachurus sp., which were also the most landed fish species in the Algarve (Supplementary Table S2) and the most frequently discarded by Portuguese fisheries (Borges et al., 2001; Erzini et al., 2002; Fernandes et al., 2015). Terrestrial items, such as refuse and insects were mostly taken by yellow-legged gull, in line with the findings of González-Solís et al., (1997a). During NFA, the two gull species fed mostly on commercial fish species (Supplementary Table S2), for example Sardina pilchardus; which could be caught in association with fisheries activities, and as fishery discards (Oro et al., 1996; Ruiz et al., 1996) by yellow-legged gull (Ramos et al., 2009; Foster et al., 2017) and also naturally caught in the case of Audouin’s gull (Pedrocchi et al., 2002). During LFA, the two gulls’ species fed more frequently on epipelagic prey with low commercial value (Table 1), such as Scomberesox saurus/Belone Belone; both species could be taken from fisheries discards (Borges et al., 2001; Erzini et al., 2002; Stratoudakis and Marçalo, 2002; Gonçalves et al., 2008; Vázquez-Rowe et al., 2012), and also caught naturally by Audouin’s gull (Arcos and Oro, 2002). Contrary to our expectations, during the LFA period, we did not detect a significant increase in the consumption of refuse or terrestrial prey by yellow-legged gulls as reported in previous studies (Oro et al., 1995; González-Solís et al., 1997a; Ceia et al., 2014). This result could be explained through a combination of three factors: (1) the low accessibility to refuse dumps (Duhem et al., 2005) and travelling distance to refuse dumps, as shown by Ramos et al., (2009) in different Mediterranean islands, (2) low population numbers of both species in our study area, which even under LFA could allow high dependence on fishery discards, or (3) the fact that refuse items do not present hard remains and thus are not detectable in pellets. The plasma δ13C and δ15N values for the incubation period suggest trophic segregation between the two gull species. Using SIBER, specialized feeding and foraging behaviour was evident for Audouin’s gull (Figure 2), which exhibited a narrow isotopic niche during the two periods of differing fishery activity (2015 vs. 2016), and individuals exhibited higher intra-specific niche overlap. However, Audouin’s gull niche width decreased during the LFA period (2016), which can be attributed to its specialized fish-based diet (i.e. feeding mainly on fish and foraging at-sea). The yellow-legged gull, similarly to the Audouin’s gull, demonstrated a narrow isotopic niche width during the NFA period (2015). Between the two fishery activity periods (2015 vs. 2016), there was a shift in yellow-legged gull isotopic niche and therefore in its foraging ecology; though we were unable to detect a strong switch in diet with the pellet analysis (see above). However, consistent with a generalist feeding strategy, in the LFA period (2016), yellow-legged gulls broadened their isotopic niche, suggesting a possible decrease in their main fish food resources (Ceia et al., 2014). During the LFA Period (2016), Audouin’s gull exhibited a narrow niche and yellow-legged gull a broader niche, in concordance with the species-specific difference in foraging ecology, such as foraging ability, dietary, and habitat preferences (Wilson, 2010; Navarro et al., 2013). We found significant differences in δ13C stable isotopic values between gull species; higher for yellow-legged than Audouin’s gull, which suggests a spatial segregation between species (see below). δ15N values revealed differences between species, with higher values for 2016 than 2015, which could be attributed to the consumption of alternative prey (e.g. refuse) and high trophic level prey, mostly by yellow-legged gull (Figure 2; Forero and Hobson, 2003,;Navarro et al., 2009; Ceia et al., 2014), which strongly suggests dietary segregation. We are aware that other possible variables, such as poor weather conditions at-sea and strong winds, could influence the behavioural foraging ecology of the birds. However, if we assume an effect of strong winds it should have affected mostly yellow-legged gulls, because Audouin’s gulls only foraged at-sea and restricted their isotopic niche, contrary to yellow-legged gulls that changed their niche during the period without fishery discards. Therefore, we believe that variation in fishing intensity and consequently the strong reduction in the availability of fishery discards had a much stronger influence on the diet and foraging patterns of gulls. Spatial and temporal distribution of gulls between periods of different fishery intensities Using the tracking devices, we confirmed the specialist foraging behaviour of Audouin’s gull in both fishery activity periods Audouin’s gull is characterized as a specialist species (Pedrocchi et al., 2002), and foraging exclusively within marine areas, mostly during Night Time, for both fishery activity periods. However, these patterns fully coincide with the departure of purse-seine vessels, suggesting that such fishery activities could define the daily foraging patterns of this species (González-Solís, 2003; Bécares et al., 2015). Furthermore, the association with purse seines could facilitate their natural fishing behaviour (exploiting vertical migration of epipelagic and pelagic species under the vessels’ lights) or the exploitation of discards (Arcos and Oro, 2002; Mañosa et al., 2004). Moreover, during the LFA period Audouin’s gull foraged over significantly deeper and more productive waters (higher Chl a concentration); such pelagic and productive habitats are also targeted by commercial fisheries (Ramos et al., 2013). Similarly to Audouin’s gull, yellow-legged gull individuals foraged mainly at-sea, contrasting with other studies (e.g. Christel et al., 2012). They foraged mostly during NFA and in the Day Time periods, which fit with fishing activity schedules, in contrast to Audouin’s gull daily patterns, suggesting the existence of spatial and temporal segregation between the two species (González-Solís, 2003). As expected, and in agreement with δ13C values, yellow-legged gulls foraged inland with some individuals foraging at fishing ports and refuse dumps, in agreement with what has been reported for other populations of this species (Ceia et al., 2014; Navarro et al., 2016). Similarly to Mediterranean colonies, when fishery activity decreases, foraging in alternative habitats, for example refuse dumps, increases (González-Solís et al., 1997a). However, the lower availability of refuse dumps in the vicinity of our study area may explain the low number of individuals exploiting those areas (Bertellotti et al., 2001). Conservation implications Former studies report that under LFA, both Audouin’s and yellow-legged gulls usually shift their distribution to forage on alternative habitats and prey (González-Solís et al., 1997a, b; Ceia et al., 2014; Alonso et al., 2015; Bécares et al., 2015). However, in our study, both species kept foraging mostly in marine habitats. These results may be explained by the relatively small population numbers of both species in our study area (fewer than 2000 breeding pairs for each species; unpublished data) when compared with highly populated Mediterranean colonies, where competition for resources should be much higher, thus forcing birds to use alternative foraging habitats and prey (Oro et al., 1997; Duhem et al., 2008; Catry et al., 2010; Meirinho et al., 2014; Bécares et al., 2015). The populations of both Audouin’s gull and yellow-legged gull in our study area have increased about 300 breeding pairs annually for the last 5 years (unpublished data); the high fishing activities close to the breeding colonies should help to explain this, given their high dependence on commercial fish species and the fact that both species foraging movements and daily patterns synchronized with the fisheries activities in the area. Under the coming scenario of the EU discard ban policy, to be applied onward and thus banning the fisheries discards at-sea (https://ec.europa.eu/fisheries/cfp/fishing_rules/discards/); these Audouin’s gull and yellow-legged gull populations may suffer a decline due to their high dependence on fisheries resources, as revealed by Foster et al. (2017) for other gull population. However, competition for food resources should increase and lead to (the larger) yellow-legged gull predating on (the smaller) Audouin’s gull, a previously documented behaviour (Martínez-Abraín et al., 2003; Catry et al., 2004; Alonso et al., 2015). A discard ban policy should lead to an increase in the use of land resources by yellow-legged gull, which will potentiate more conflicts with wildlife and humans, e.g. individuals turning into urban dwellers, breeding in roofs and terraces, and potential spread of diseases through the contamination of water reservoirs with faecal matter (Belant, 1997; Bertellotti et al., 2001; Rock, 2005; Charles and Linklater, 2013; Alm et al., 2018). This new fishery policy should be implemented gradually, and closely monitored in order to facilitate species adaptation and to minimize possible negative effects of opportunistic yellow-legged gulls on more specialized gull and tern species. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Acknowledgements VHP, FRC, and JGC acknowledge their fellowships (SFRH/BPD/85024/2012, SFRH/BPD/95372/2013, and PD/BD/127991/2016, respectively) attributed by the ‘Fundação para a Ciência e Tecnologia’ (FCT; Portugal) and the European Social Fund (POPH, EU). This project benefited from the strategic project UID/MAR/04292/2013 granted by FCT to MARE. We are thankful for the fishery landings data supplied by Dados Estatísticos 2015/2016, Docapesca—Portos e Lotas, S.A. Logistic support was also provided by the Ria Formosa Natural Park (ICNF). References Afán I. , Navarro J. , Cardador L. , Ramírez F. , Kato A. , Rodríguez B. , Ropert-Coudert Y. , et al. 2014 . Foraging movements and habitat niche of two closely related seabirds breeding in sympatry . Marine Biology , 161 : 657 – 668 . Google Scholar CrossRef Search ADS Alm E. W. , Daniels-Witt Q. R. , Learman D. R. , Ryu H. , Jordan D. W. , Gehring T. M. , Santo Domingo J. 2018 . 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For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ICES Journal of Marine Science Oxford University Press

How fishing intensity affects the spatial and trophic ecology of two gull species breeding in sympatry?

ICES Journal of Marine Science , Volume Advance Article – Jul 30, 2018

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Abstract

Abstract Fisheries produce large quantities of discards, an important resource for scavenging seabirds. However, a policy reform banning discards, which is soon to be implemented within the EU, will impose a food shortage upon scavengers, and it is still largely unknown how scavengers will behave. We studied the diet (hard remains), trophic (stable isotope analysis), and foraging (individual tracking) ecology of two gull species breeding in sympatry: Audouin’s gull Larus audouinii (AG) and yellow-legged gull Larus michahellis (YLG), in South Portugal, under normal fishery activity (NFA; work days) and low fishery activity (LFA; weekends), over two consecutive years. We established a pattern of dietary, spatial, and temporal segregation between the two gull species. Under LFA, yellow-legged gulls reduced their time spent at-sea, thus foraging more in alternative habitats (e.g. refuse dumps) and widening their isotopic niche (i.e. generalist behaviour). Contrastingly, Audouin’s gull had a narrower trophic niche (i.e. specialist behaviour), foraging exclusively at-sea, reducing the amount of demersal fish and increasing the amount of pelagic fish in their diet. Under NFA, both species foraged mostly at-sea, feeding almost exclusively on fish, with increased consumption of demersal species (i.e. fishery discards). In general, yellow-legged gull had a broader trophic niche (i.e. generalist behaviour) when compared with the narrower isotopic niche of Audouin’s gull (i.e. specialist behaviour). Overall, both gull species relied heavily on fishery discards. However, there was visible dietary, spatial, and temporal segregation between the two species, associated with their dietary and habitat preferences that could be attributed to the availability of anthropogenic resources, such as fishery discards. Introduction Ecological segregation seeks to explain the extent to which species and populations overlap in their use of limited resources. The main processes proposed for species coexistence are trophic and resource partitioning between species, including habitat and dietary segregation (temporal or spatial; Navarro et al., 2009,, 2013 ), as well as species-specific differences in foraging ecology, for example foraging abilities, and habitat and dietary preferences (Quillfeldt et al., 2013; Mancini and Bugoni, 2014). Therefore, when food resources are highly abundant and predictable as a result of anthropogenic actions, this should decrease competition between species (Oro et al., 2013), and allow coexistence. However, the lack of food partitioning may contribute to increase the niche overlap between species (Afán et al., 2014), and enhance the impact of opportunistic species on vulnerable species (Oro et al., 2013); especially during periods when anthropogenic food resources decline. Anthropogenic activities such as fisheries affect marine species mainly as a result of overfishing, discards, and bycatch (Pauly et al., 2002; Lewison et al., 2004). Commercial fisheries generate large quantities of discards (Kelleher, 2005; Bellido et al., 2011), provide vast amounts of food for marine top predators such as seabirds, and may impact their breeding biology (Oro et al., 1996; Bicknell et al., 2013), distribution, and population dynamics (Votier et al., 2010; Cama et al., 2012). Discards usually provide large amounts of lipid-poor demersal and benthic species (Furness, 2003; Oro et al., 2013), which seabirds would not access naturally (Tasker et al., 2000; Grémillet et al., 2008). Fishing intensity, and thus the amount of discarded fish, can show large temporal fluctuations attributed to variations in the types of fishing gear operating during daily and weekly periods, as well weather events affecting the type of gears that can operate (Furness, 2003; Bécares et al., 2015). It is therefore important to understand how fishing intensity influences the feeding and foraging behaviour of opportunistic seabird species, such as gulls, that make strong use of discards (Camphuysen, 1995; Garthe et al., 1996; Garthe and Scherp, 2003; González-Solís, 2003; Navarro et al., 2010; Cama et al., 2012; Bicknell et al., 2013; Bécares et al., 2015). Diet composition reconstructed through the identification of hard prey remains (e.g. pellets) provides quantitative information about prey consumption and possible overlap in the feeding niche among seabird species (Duffy and Jackson, 1986), as well as the importance of fisheries discards in their diet (Calado et al., 2018). However, this method shows some limitations such as the overestimation of prey items with more resistant hard parts compared with prey items composed mostly of soft parts (which are fully digested; Duffy and Jackson, 1986; Bearhop et al., 2001; Votier et al. 2003). The stable isotope analysis (SIA) of carbon (13C/12C, expressed as δ13C) and nitrogen (15N/14N, expressed as δ15N) in seabird tissues provides information on their trophic niche and habitat use (Cherel et al., 2005; Newsome et al., 2007). These are useful to investigate (1) variations in niche width in relation to food resource partitioning (e.g. Ceia et al., 2014), and (2) the consumption of fishery discards by seabirds, because demersal fish, only available through discards, often exhibit distinguishable isotopic signatures which will be retained by the consumers’ tissues (Calado et al., 2018). The use of tracking devices, such as Global Positioning System loggers (GPS loggers), in seabirds with known trophic ecology (from SIA) and information on diet (e.g. from pellets), provides a better view of their foraging behaviours. Furthermore, GPS tracking allows the quantification of overlap between seabird distribution and anthropogenic activities such as fisheries, which may determine their foraging ecology (Votier et al., 2010; Cama et al., 2012). Also, information on fishing vessels such as fishing gears (e.g. purse seine, trawlers, and multi-gear vessels), fishing activity patterns (week/daily patterns), and main target species (fish landings) allow to understand the relationship between seabird diet, foraging patterns, and fishing activities (Arcos et al., 2001; Bécares et al., 2015; Tyson et al., 2015). Combining all these approaches and methods can provide us with a broader view of species’ feeding and foraging behaviour. Here we assessed the influence of Normal and Low Fishery Activity (LFA) on the foraging, feeding, and trophic ecology of Audouin’s (Larus audouinii, AG) and yellow-legged (Larus michahellis, YLG) gulls. Audouin’s gull is a medium-sized (body mass 500–700 g) food specialist, foraging mainly at-sea, and feeding mostly on pelagic fish species (>50% of the diet; Oro et al., 1996, 1997; González-Solís, 2003). This species is endemic to the Mediterranean region (Oro et al., 1996, 2011), and has undergone a recent population expansion, at least in part due to the exploitation of fishery discards (Oro, 2003; BirdLifeInternational, 2016). The yellow-legged gull is a large (body mass 800–1200 g) food generalist, foraging only during the day in marine and terrestrial habitats (Sanz-Aguilar et al., 2009). Their abundance has increased in multiple colonies (e.g. Vidal et al., 1998; Meirinho et al., 2014), due to their scavenging capacity and feeding plasticity; they are able to forage on several anthropogenic resources including fishery discards and refuse tips (Ramos et al., 2009; Pedro et al., 2013). Audouin’s gull is able to forage both during the day and at night, feeding on epipelagic prey caught naturally or in association with fisheries (purse-seine vessels; Arcos and Oro, 2002; Mañosa et al., 2004). Similarly to the yellow-legged gull, Audouin’s gull may also feed on anthropogenic food sources, including fishery discards (Calado et al., 2018), as well as other aquatic prey such as the invasive American crayfish Procambarus clarkii, in some locations (Navarro et al., 2010). Previous studies have revealed that both Audouin’s and yellow-legged gulls change their distribution and daily patterns in response to fishery activity, in order to take advantage of discards (Arcos et al., 2001; Cama et al., 2012, 2013; Bécares et al., 2015). Audouin’s gull are also known to change their distribution to avoid possible competition with yellow-legged gull (Arcos et al., 2001; Christel et al., 2012). We studied the diet, trophic ecology, weekly/daily, and hourly variation in foraging habitat use of Audouin’s and yellow-legged gull breeding in sympatry at Deserta Island, Ria Formosa Natural Park, Algarve, Southern Portugal. Data was collected in 2015 and 2016 to address specifically: (1) how periods of (A) Normal Fishery Activity (NFA; workdays in 2015 and 2016) and (B) LFA (weekends in 2015 and 2016, and a storm period without fishery activity in 2016) influenced their foraging and feeding ecology, and (2) how the isotopic niche varied between a period with fishing (in 2015) and a period with virtually no fishing (unfavourable sea conditions for fishing in 2016). During NFA, we expect Audouin’s gulls to feed mainly on pelagic prey, caught naturally or in association with fisheries, and to supplement their diet with demersal prey from fishery discards. This species should forage mainly at-sea, nocturnally, and in association with fishing vessels (purse seiners). On the other hand, yellow-legged gulls should exhibit a diurnal foraging pattern, over marine areas, taking advantage of fishery discards (from trawlers and multi-gears); their diet should contain a high abundance of marine prey (i.e. fish), of both pelagic and demersal species, as well as alternative diet items from terrestrial habitats. During LFA, Audouin’s gull should increase their time spent foraging at-sea, in deeper, pelagic, and more productive areas, feeding mostly on naturally caught pelagic prey, and feeding less on fishery-discarded demersal prey. Therefore, they should exhibit a narrower isotopic niche. As for yellow-legged gull, they should decrease their time spent foraging at-sea and increase foraging in alternative habitats, such as refuse dumps and fishing ports, thus increasing their consumption of alternative food resources (e.g. refuse). During periods of fishery activity, both species should exhibit a relatively narrow isotopic niche, related with a more specialized, fish-based diet. On the other hand, during a period with virtually no fishery activity the yellow-legged gull should broaden their isotopic niche, given their opportunistic foraging behaviour, whereas Audouin’s gull should maintain its narrow isotopic niche, given its exclusively marine habits, by shifting their diet from demersal to pelagic prey. Our results should contribute toward a better understanding of how resource partitioning and habitat segregation occurs between these two sympatric gull species, under a scenario where predictable and abundant food resource decrease, such as a decline in fishery discards during LFA periods. Furthermore, we provide insights into how such niche partitioning and dependence on trawling activity occurs at relatively low population numbers of gulls (lower than 2000 breeding pairs for each species; unpublished data) from recently founded colonies (since 2008). Methods This study was conducted on Deserta (Barreta) Island (36°57′40″N 7°53′20″W), which is one of five barrier islands (and two peninsulas) within the Ria Formosa National Park, Algarve, Southern Portugal (Ceia et al., 2010). Data was collected in the breeding season, during the incubation periods of 2015 and 2016 (May). The island hosts an estimate of 1800 and 1200 breeding pairs of Audouin’s (AG) and yellow-legged (YLG) gulls, respectively. This area is characterized by high fishing activity (INE, 2016), with the main fishing port (Olhão) close to the breeding colonies (ca. 8 km). The majority (90%) of fishing vessels in Portugal are smaller than 12 m and only operate up to 6 miles (ca. 9 km) from shore (DGRM, 2018; https://bluehub.jrc.ec.europa.eu/mspPublic/). The Portuguese fishing fleet comprises purse seines, trawlers, and multi-gear vessels (Borges et al., 2001). The main targeted species are European pilchard (Sardina pilchardus; Stratoudakis and Marçalo, 2002), mackerel (Scomber scombrus; Marçalo et al., 2010), horse mackerel (Trachurus trachurus; Almeida et al., 2014), sea-breams (e.g. Diplodus spp., Pagellus spp., Sparus aurata), European sea-bass (Dicentrarchus labrax), and European hake (Merluccius merluccius; Borges et al., 2001; Campos and Fonseca, 2004; Costa et al., 2008; Gonçalves et al., 2008). The highest bycatch and discards rate are attributed to trawler vessels, in which more than 50% of their catch can be discarded (Borges et al., 2001; Erzini et al., 2002; Costa et al., 2008). Commercial species with low commercial value are the main discarded species (e.g. Chub mackerel; Scomber janponicus, Blue whiting; Micromesistius poutassou; and blue jack mackerel; Trachurus picturatus (Borges et al., 2001; Costa et al., 2008; Fernandes et al., 2015), and several juveniles of other species are also discarded at sea (e.g. sea-breams; Gonçalves et al., 2008). Sample collection Two different periods were established, both in 2015 and 2016, related with the trawlers activity, which generate the highest amount of discards (Borges et al., 2001; Costa et al., 2008a, b). (A) NFA during workdays, when all types of fishing boats operated (purse seiners, multi-gear and trawlers) and, (B) LFA on weekends when trawlers do not operate and overall fishery activity decreases (unpublished data; Docapesca 2015), and between 9th–13th May 2016 when virtually all fisheries did not operate due to unfavourable weather conditions (unpublished data; Docapesca 2016). We established three transects along the colonies of both Audouin’s and yellow-legged gulls, and removed pellet remains before starting the collection of samples. After that, during the two data collection periods, transects were repeated to ensure consistency in collection of pellets. These transects were followed during the month of May (i.e. incubation), two days each week, based on fishing intensity: we collected pellets around nests on each Friday, to assess the gull’s diet composition during NFA, and each Monday to assess the diet composition during LFA. A total of 604 pellets were collected during the breeding season of 2015 (AG: NFA n = 77, LFA n = 12; YLG: NFA n = 55, LFA n = 18) and 2016 (AG: NFA n = 176, LFA n = 103; YLG: NFA n = 96, LFA n = 67). The samples were placed in plastic bags and stored in the refrigerator until laboratory analysis. It should be noted that most pellets are produced between 6 and 24 h after a meal (Votier et al., 2001), thus some pellets from Friday meals (NFA) could have been collected on Monday (LFA). Additionally, we established two periods of differing fishing intensity between 2015 and 2016 to compare the isotopic niche between these two periods: (C) 6–15 May 2015 representing a period with regular fishery activities and (D) 9–13 May 2016 representing a period virtually without fishery activities due to unfavourable weather conditions (unpublished data; Docapesca, 2016). We compared the isotopic niche between these two periods using plasma samples of birds captured on the 15–16 May 2015 and 13–14 May 2016. Breeding adults were caught during incubation, using nest traps (2015: AG = 15, YLG = 12; 2016: AG = 12, YLG = 11). Blood samples (ca. 0.5 ml) were collected from the tarsal vein using 1 ml syringes and centrifuged at 12000 rpm for 5 min within 3–5 h of collection to separate red blood cells (RBC) from plasma for SIA (in each year). Stable isotope values obtained from plasma represent the dietary composition during the incubation period, i.e. approximately 5–7 days prior to sample collection; Hobson et al., 1994; Cherel et al., 2005). Samples were frozen until preparation for SIA. Diet and stable isotope analysis Pellet samples were examined under a stereomicroscope and separated by prey type: fish (further separated into pelagic and demersal species), refuse, Mollusca, Cephalopoda, Brachyura, Insecta, birds, Rattus rattus, egg shells, and vegetable matter. The fish prey items were identified to species-level taxonomic discrimination, using vertebrae and otoliths from our own collection, complemented with the collection from the National Museum of Natural History and Science (Lisbon) and published identification guides (Assis, 2004; Tuset et al., 2008). Cephalopod beaks were identified using beak collections at the Marine and Environmental Science Centre, University of Coimbra, Portugal. Inorganic material from refuse was represented by a range of items, including plastic, glass, paper, bones, and organs (e.g. gastrointestinal tract remains) from unknown species, and wood pieces. Some of these items were probably ingested accidentally; nevertheless, they provide indication of the foraging areas (FA) used by the species, and therefore were not excluded from the dietary analysis. Plasma samples were treated with successive rinses in a 2:1 chloroform/methanol solution to extract external lipids (Ceia et al., 2012). The relative abundance of stable isotopes of carbon and nitrogen were determined by a continuous-flow isotope ratio mass spectrometer using a CF-IRMS (Isoprime, Micromass, UK). Approximately 0.35 mg of each sample was combusted in a tin cup for determination of nitrogen and carbon isotope ratios. Results are presented in the common delta (δ) notation expressed in parts per mil (‰) according to the equation δX = [(Rsample/Rstandard) − 1], where the X is 13 C or 15 N, and Rsample is the corresponding ratio: 13C/12C or 15N/14N, and Rstandard is the ratio for the international references Vienna-PeeDee Belemnite (V-PDB) for carbon and atmospheric N2 (AIR) for nitrogen. Replicate measurements of internal laboratory standards (acetanilide) indicate measurements errors < 0.1‰ for both carbon and nitrogen. At-sea distribution and environmental variables In 2015 (early May), Audouin’s (n = 8) and yellow-legged gull (n = 8) breeding adults were equipped with GPS loggers (CatTraq GT-120, Perthold Engineering LLC), which weigh 15 g; always representing <3% of the adult’s body mass, which was set to be an upper threshold to avoid deleterious effects on seabirds (Phillips et al., 2003). Furthermore, there was no significant decrease in body mass of tagged individuals (n = 5 for both species) between capture (body mass AG = 659 ± 34 g; YLG = 952 ± 167 g) and recapture (AG = 623 ± 40 g; YLG = 955 ± 168 g; t18= 1.13, p = 0.86). The devices were deployed on birds when they were incubating, with clutches of three eggs at a similar stage of incubation period. The GPS loggers have an accuracy of 4 m and store the date, time, longitude, latitude and speed, every 2 min with logger batteries lasting about 10 days. Devices were attached to feathers in the mantle region with Tesa® tape. The process took less than 10 min, minimizing the overall stress to the animal. Of the 16 loggers deployed, 12 were recovered (AG: n = 6; YLG: n = 6) after 5–7 days. Birds’ foraging locations were selected by calculating path sinuosity for all the locations, defined as the ratio of the actual flight speed given by the GPS receiver to the velocity between every third fix (geographical location). Birds that are circling an area will display a lower calculated speed than the actual GPS speed, and therefore have a higher sinuosity index (Grémillet et al., 2004). A histogram of the sinuosity distribution was used to determine the break-off value, thus all positions with a sinuosity index ≥ 2.7 were considered foraging locations (see Supplementary Figure S1). Foraging locations were examined under the adehabitatHR R package (Calenge, 2006) generating Kernel Utilization Distribution (Kernel UD) estimates within the R environment (R Core Team 2015). The most appropriate smoothing parameter (h) was chosen via least squares cross-validation for the unsmoothed GPS data (h = 0.09°), and then applied as standard for the other datasets, and grid size was set at 0.04° (to match the grid of environmental predictors). We considered the 50% and 95% kernel UD contours to represent the core FA and the home range (HR), respectively. The foraging trips were defined from the time birds departed from the colony until their return, thereby, GPS positions at the colony were excluded from the analysis. The overlap between kernel FAs of different (1) species (AG or YLG) and (2) fishery activity period (NFA or LFA) were computed to study the spatial segregation within and among groups with the kernel overlap function and VI method of the adehabitatHR library (Calenge, 2006). To characterize the oceanographic conditions in areas used by the tracked individuals, we extracted: (1) Bathymetry (BAT, blended ETOPO1 product, 0.03° spatial resolution, m), (2) Sea Surface Temperature (SST, Aqua MODIS NPP, 0.04°, °C), and (3) sea surface chlorophyll a concentration (CHL, Aqua MODIS NPP, 0.04°, mgm−3); BAT give us the underwater depth at-sea, SST and CHL are proxies for marine productivity (Grémillet et al., 2004) in the species FA. BAT was downloaded from http://ngdc.noaa.gov/mgg/global/global.html, while SST and CHL were extracted from http://oceancolor.gsfc.nasa.gov. Weekly averages were used for the dynamic variables (variables 2–3), matching the overall tracking period for both gull species (i.e. 06/05/2015–17/05/2015). To characterize the fishing activity in the areas used by the tracked individuals we extracted the map of fishing intensity (available for all EU waters) from https://bluehub.jrc.ec.europa.eu/mspPublic/. Data analysis We calculated the frequency of occurrence (FO; %), as the percentage of pellets where a prey type occurred (Alonso et al., 2013), for the pellets of each gull species, in each year, and each fishery activity period (NFA and LFA). We divided the fish into two groups, pelagic and demersal fish (using data available on Fishbase, http://www.fishbase.org). Additionally, fish species were classified into three groups on the basis of their frequency of rejection (occasional, frequent, and systematically discarded, according to Borges et al., 2001; Monteiro et al., 2001; Erzini et al., 2002, see Table 1); this classification also takes into account commercial value and the amount of fish landed (Gonçalves et al., 2013; unpublished data; Docapesca 2015 and 2016). Table 1. Comparison of the frequency of occurrence (FO; %) between NFA and LFA of prey items in pellets of Audouin’s (AG) and yellow-legged (YLG) gulls. Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Classification of fish species, caught by fisheries in the study site and their frequency of rejection: * Occasional; **frequent; ***systematic discarded. a Includes: Microchirus azevia and Pegusa lascaris. b Includes: Class Bivalvia and Gastropoda. c Includes: Order Sepiida (Sepia officinalis) and Order Teuthida. d Includes: Polybius henslowii. e Includes: Order Hymenoptera, Coleoptera, Diptera, Lepidoptera. f Includes: Order Passeriformes and Order Charadriiformes. Table 1. Comparison of the frequency of occurrence (FO; %) between NFA and LFA of prey items in pellets of Audouin’s (AG) and yellow-legged (YLG) gulls. Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Larus michahellis Larus audouinii 2015 2016 2015 2016 Prey items NFA (n = 55) LFA (n = 18) NFA (n = 96) LFA (n = 67) NFA (n = 77) LFA (n = 12) NFA (n = 103) LFA (n = 176) Pelagic fish 54.5 55.6 67.7 49.3 88.3 58 92.2 94.3 Atheriniformes Atherina sp.** – – – – – – – 1.1 Beloniformes Scomberesox saurus/ Belone belone*** – 5.6 14.6 16.4 41.6 33.3 81.6 85.8 Clupeiformes Sardina pilchardus** 18.2 16.7 15.6 6.0 29.9 – 1.0 2.3 Engraulis encrasiculos** – – 1.0 – 2.6 – 3.9 2.8 Gadiformes Micromesistius poutassou** 21.8 11.1 22.9 20.9 23.4 16.7 17.5 20.5 Gadiculus argenteus*** – – 2.1 4.5 9.1 8.3 4.9 3.4 Mugiliformes Liza sp.** – – 2.1 1.5 – – – – Myctophiformes Myctophum sp.*** – – – – – – 1.9 2.3 Perciformes Ammodytes tubianos** – – 2.1 1.5 – – – 0.6 Scomber sp.** – 5.6 10.4 9.0 16.9 8.3 1.9 4.0 Trachurus sp.** 10.9 16.7 31.3 11.9 9.1 8.3 1.9 2.8 Demersal fish 39.4 27.8 39.6 32.8 49.4 66.7 20.4 18.2 Anguilliformes Anguilla anguilla* – – 1.0 – – – – – Conger conger** – – 2.1 3.0 6.5 – 6.8 2.3 Batrachoidiformes Halobatrachus spp.** – – – 1.5 – 8.3 – – Carcharhiniformes Galeus melastomus*** – – 1.0 – – – – – Gadiformes Coelorinchus caelorhincus*** 3.6 – – – 3.9 8.3 – 0.6 Malacocephalus laevis*** – – 1.0 1.5 – – 2.9 0.6 Merluccius merluccius** 5.5 – 1.0 3.0 2.6 – 1.9 3.4 Phycis sp.** 1.8 5.6 1.0 – – – – – Trisopterus sp.** – – 1.0 1.5 – – 1.0 0.6 Mugiliformes Chelon labrosus** – 5.6 4.2 3.0 – – – – Ophidiiformes Echiodon sp.*** – – – 3.0 – – 2.9 0.6 Perciformes Boops boops** 1.8 – 4.2 3.0 6.5 16.7 1.0 1.7 Capros aper*** – – – – 2.6 – – – Cepola macrophthalma** – – – – 1.3 – 1.0 – Dicentrarchus labrax* 1.8 – 1.0 – – – – – Diplodus sp.** 14.5 16.7 9.4 11.9 20.8 33.3 3.9 6.3 Echiichitys vipera*** 3.6 – 5.2 – – – – – Gobius sp.*** 1.8 5.6 1.0 – 3.9 8.3 – 1.1 Lithognathus mormyrus** 3.6 – 1.0 – 2.6 – – – Mullus surmuletos** 1.8 – – – – – – – Pagrus sp.* 3.6 – 1.0 – – – – 0.6 Sarpa salpa** – – 1.0 1.5 – – – – Serranus sp.*** 5.5 5.6 7.3 3.0 14.3 25.0 4.9 4.0 Spondyliosoma cantharus** – – 1.0 – – – – – Sparus aurata** – – 1.0 – – – – – Pleuronectiformes Arnoglosus laterna** 3.6 – – – – – – – Cithaurus linguatula** 3.6 – 1.0 – – – – – Family solidaea** – – 3.1 – – – – – Scorpaeniformes Trigla lyra** – – 2.1 3.0 – – 1.0 – Syngnathiformes Macroramphosus scolopax*** – – – – – – – – Zeiformes Zeus Faber* 1.8 – – – – – – 0.6 Unidentified fish 25.5 27.8 29.2 23.9 31.2 5.0 23.3 24.4 Total fish 72.7 66.7 85.4 73.1 100 100 97.1 96.6 Others Refuse 20.0 44.4 16.7 17.9 – 8.0 1.9 – Molluscab 16.4 5.6 – – – 8.0 – – Cephalopodac 1.8 – 3.1 4.5 1.3 – 3.8 2.3 Brachyurad 3.6 – 16.7 14.9 7.8 16.6 13.6 10.8 Insectae 27.3 5.0 13.5 10.4 23.0 8.0 9.7 17.0 Birdsf 5.5 5.6 3.1 7.5 – – – – Rattus rattus – – – 1.5 – – – – Egg Shell – – – 1.5 – – – 0.6 Vegetal matter – 5.6 17.7 6.0 – – – – Unidentified 3.6 – 1.0 1.5 1.3 – – 1.1 Classification of fish species, caught by fisheries in the study site and their frequency of rejection: * Occasional; **frequent; ***systematic discarded. a Includes: Microchirus azevia and Pegusa lascaris. b Includes: Class Bivalvia and Gastropoda. c Includes: Order Sepiida (Sepia officinalis) and Order Teuthida. d Includes: Polybius henslowii. e Includes: Order Hymenoptera, Coleoptera, Diptera, Lepidoptera. f Includes: Order Passeriformes and Order Charadriiformes. To assess the effect of fishery activity period on gulls’ diet, we assembled data for 2015 and 2016 because the sample size for the 2015 LFA was low for both species (AG n = 12; YLG n = 18) We assessed differences in the occurrence of main prey types with FO > 10%: pelagic fish, demersal fish, refuse, Brachyura, Insecta, and Atlantic saury Scomberesox saurus plus garfish Belone belone (these two species were grouped because they are also grouped in fishery landings), Blue whiting Micromesistius poutassou, horse/blue jack mackerel Trachurus sp., European pilchard Sardina pilchardus, and sea-breams Diplodus sp. We used non-metric multidimensional scaling (NMDS) with a stress-value associated, to obtain a graphical distribution of the parameters (gull species and fishery activity period) influenced by diet variables (prey types with a FO > 10%). Stress values represent the extent to which the 2-dimensional map is accurate in summarizing the separation of observations, with values lower than 0.2 allowing a good NMDS analysis. The influence of species, fishery activity period, and interaction species*fishery activity period in the assessment of diet composition (FO > 10%) were tested with Generalized Linear Models (GLMs), with a binomial distribution. Using δ13C and δ15N signatures were used to compare the isotopic niche between two gull species (Audouni’s and yellow-legged gulls) during two study years. First, a MANOVA, followed by factorial ANOVAs and post-hoc Tukey test allowed to disentangle differences on the mean δ13C and δ15N isotopic values. Second, the position and dimension of the isotopic niche was compared using the metrics available within SIBER (Stable Isotope Bayesian Ellipses in R) for plasma isotopic values. The area of the standard ellipse (SEAc), was calculated after small sample size correction, between each species and year (which represents their isotopic niche width): the niche overlap. A Bayesian estimate of the standard ellipse (SEAB) was also calculated to test for differences in niche widths between each species and year (see Jackson et al., 2011 for more details). The differences in trip characteristics (e.g. trip duration, maximum distance from the colony, minimum distance to fishing ports, minimum distance to very high fishing intensity areas), spatial ecology parameters (species interactions and fishery periods within FA), and the habitat of FA (BAT, SST, CHL) were tested on breeding adults with Generalized Linear Mixed Models (GLMMs). We tested the effect of species, fishery activity period, and the interaction between species*fishery activity period on the foraging trip characteristics, spatial ecology parameters, and habitat of FA. For this last category, we used time spent foraging on the main habitats surrounding the colony: beach, lagoon, sea, water treatment station, refuse dump, and fishing port (arcsine transformed percentage data). Because all individual birds made multiple trips, we used bird identity as a random term to avoid potential pseudo-replication problems in all GLMMs. GPS locations were assigned to either foraging trips or colony locations (Supplementary Figure S1). The foraging trip was defined as the locations visited from when a bird leaves the colony until it returns. The main foraging habitats (beach, lagoon, sea, water treatment station, refuse dump, fishing ports, and colony) were assigned to each GPS location. Because the main habitat used by both gull species was the colony (Supplementary Figure S1), to have more information on differences in habitat distribution and segregation between the two gull species in relation to daily and fishery activity periods we established two representative time intervals (i.e. daily periods), related with different fishing activities and the foraging ecology of gulls: (1) Night Time (between 20 and 08 h, when purse seiners operate) and; (2) Day Time (between 08 and 20 h, when trawlers and multi-gear operate). During data analysis, fishery activity was compared between NFA (workdays—full fishery activity) and LFA (weekends—very LFA). To assess differences in the use of foraging habitats by Audouin’s and yellow-legged gulls in relation to Daily and Fishery Activity Periods, we used GLMs, with a Poisson distribution, to test for the effect of species (AG vs. YLG), fishery activity period (NFA vs. LFA), daily period (night vs. day) and the second-term interactions species * fishery activity period, species * daily period, fishery activity period * daily period on the percentage of time spent per daily period and between fishery activity periods in the main habitats (i.e. beach, lagoon, sea, and water treatment station). Because Audouin’s gull did not use refuse dumps or fishing ports, these two variables were excluded from the analysis. Results are presented as mean ± SD, unless otherwise stated. All statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Response variables were tested for normality (Q-Q plots) and homogeneity (Cleveland dot plots) before each statistical test and transformed when needed (Zuur et al., 2010). Percentages (1) of time spent in FA and (2) overlap between FA were arcsin transformed to meet normality. All analyses were performed assuming a significance level of p ≤ 0.05. Results Diet composition We identified a total of 2482 prey items in gull pellets (Supplementary Table S1). Audouin’s gull fed almost exclusively on fish (>95% FO during both NFA and LFA periods), whilst yellow-legged gull also fed on alternative prey items, such as refuse, Mollusca, Brachyura, and Insecta; although fish was also their main prey (>65% FO in both fishery activity periods; Table 1). Pelagic fish had the highest percentage of occurrence in pellets for both gull species (Table 1). The stress value of NMDS analysis was 0.16. The NMDS 1 segregated the Audouin’s from yellow-legged gulls, while NMDS 2 segregated the NFA period from the LFA period (Figure 1). Sardina pilchardus was closely associated with NFA periods (Figure 1). Furthermore, Scomberesox saurus/Belone belone were very important in the Audouin’s gull diet for both NFA and LFA periods. NMDS also revealed an association between the main prey targeted by fishery activities in the Algarve, Trachurus sp., and Sardina pilchardus with yellow-legged gull diet during both fishery activity periods, and also with refuse items, the last one closely linked with LFA periods (Figure 1). Items such as Insecta, Brackyura, Diplodus sp., and Micromessitius poutassou were important for both Audouin’s gull and yellow-legged gull diets during both fishery activity periods (Figure 1 and Table 1). Figure 1. View largeDownload slide Two-dimensional NMDS ordination plot of Audouin’s (AG) and yellow legged (YLG) gulls diet items (F0 > 10%) between two different fishery activity periods (Normal Fishery Activity—NFA, and Low Fishery Activity—LFA). Stress value = 0.16. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 1. View largeDownload slide Two-dimensional NMDS ordination plot of Audouin’s (AG) and yellow legged (YLG) gulls diet items (F0 > 10%) between two different fishery activity periods (Normal Fishery Activity—NFA, and Low Fishery Activity—LFA). Stress value = 0.16. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). GLMs revealed a significantly higher occurrence of pelagic fish in Audouin’s gull diet (p = 0.0001, Table 2). Two pelagic fish species, Scomberesox saurus/Belone belone, were the main prey for Audouin’s gull (p = 0.0001, Table 2; Figures 1). Compared with Audouin’s gull, yellow-legged gull showed a higher consumption of Sardina pilchardus (p = 0.01), Trachurus sp. (p = 0.001), and refuse (p = 0.0001, Table 2; Figure 1). In relation to fishery activity periods, Scomberesox saurus/Belone belone (p = 0.001) occurred significantly more in the diet of gulls during LFA than during NFA, contrarily to Sardina pilchardus (p = 0.0001) that occurred significantly more in the diet of the gulls during the NFA period than during the LFA period (Table 2; Figure 1). No significant results were obtained for the interaction species * fishery activity period (Table 2). Table 2. GLMs, testing the effect of species (FO% > 10%; yellow-legged—YLG and Audouin—AG gulls), and fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on the occurrence of gulls’ main prey (see Table 1). Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Significant effects are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Table 2. GLMs, testing the effect of species (FO% > 10%; yellow-legged—YLG and Audouin—AG gulls), and fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on the occurrence of gulls’ main prey (see Table 1). Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Species Period Species * Period β±SE |Z| p Main effect β±SE |Z| p Main effect β±SE |Z| p Main effect Pelagic fish −2.05 ± 0.32 −6.46 0.0001 YLG < AG 0.01 ± 0.33 0.02 0.97 – 0.50 ± 0.43 1.16 0.25 – Demersal fish −0.20 ± 0.29 −0.71 0.48 – 0.19 ± 0.22 0.87 0.39 – 0.11 ± 0.36 0.30 0.77 – Scomberesox saurus / Belone belone −3.45 ± 0.38 −9.19 0.0001 YLG < AG −0.80 ± 0.25 −3.22 0.001 NFA < LFA 0.43 ± 0.50 0.87 0.39 – Diplodus sp. 0.54 ± 0.42 1.28 0.20 – 0.37 ± 0.36 1.02 0.31 – −0.52 ± 0.55 −0.96 0.34 – Sardina pilchardus 1.42 ± 0.64 2.21 0.01 YLG > AG 1.96 ± 0.55 3.55 0.0001 NFA > LFA −1.16 ± 0.71 −1.63 0.10 – Trachurus sp. 1.51 ± 0.53 2.86 0.001 YLG > AG 0.47 ± 0.54 0.87 0.38 – 0.28 ± 0.66 0.42 0.67 – Micromesistius poutassou −0.09 ± 0.33 −0.27 0.79 – −0.01 ± 0.26 −0.05 0.96 – 0.24 ± 0.43 0.56 0.58 – Refuse 4.05 ± 1.03 3.92 0.0001 YLG > AG 0.74 ± 1.23 0.60 0.55 – −1.08 ± 1.27 −0.86 0.39 – Insects 0.16 ± 0.34 0.47 0.64 – −0.20 ± 0.29 −0.69 0.49 – 0.18 ± 0.45 0.41 0.69 – Brachyura 0.17 ± 0.40 0.42 0.67 – −0.01 ± 0.33 −0.02 0.99 – −0.09 ± 0.53 −0.17 0.89 – Significant effects are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 2. View largeDownload slide Isotopic niche space based on carbon and nitrogen isotope ratios (δ13C and δ15N) of Audouin’s (AG; Larus audouinii) and yellow-legged (YLG; L. michahellis) gulls sampled in the breeding season of 2015 (AG n = 15, YLG n = 12) and 2016 (AG n = 12, YLG n = 11), during incubation (data from Plasma). Solid lines represent the standard ellipses areas corrected for small sample size (SEAc) calculated in SIBER (stable isotope Bayesian ellipses in R; Jackson et al. 2011). We used the computational code to calculate the metrics from SIBER implemented in the package SIAR (Parnell et al., 2010). All the metrics were calculated using standard.ellipse and convexhull functions. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 2. View largeDownload slide Isotopic niche space based on carbon and nitrogen isotope ratios (δ13C and δ15N) of Audouin’s (AG; Larus audouinii) and yellow-legged (YLG; L. michahellis) gulls sampled in the breeding season of 2015 (AG n = 15, YLG n = 12) and 2016 (AG n = 12, YLG n = 11), during incubation (data from Plasma). Solid lines represent the standard ellipses areas corrected for small sample size (SEAc) calculated in SIBER (stable isotope Bayesian ellipses in R; Jackson et al. 2011). We used the computational code to calculate the metrics from SIBER implemented in the package SIAR (Parnell et al., 2010). All the metrics were calculated using standard.ellipse and convexhull functions. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Stable isotopes SIA suggested important differences between the two gull species and years (Table 3; in 2015 AG n = 15, YLG n = 12/in 2016 AG n = 12, YLG n = 11). The plasma values of birds during the breeding period differed significantly between Audouin’s and yellow-legged gull (MANOVA, Wilk’s lambda, F2,45 = 12.27, p < 0.001), between the two years (MANOVA, Wilk’s lambda, F2,45 = 12.27, p = 0.02), but the interaction species*year was not significant (MANOVA, Wilk’s lambda, F2,45 = 1, 39, p = 0.260). A factorial ANOVA for each stable isotope revealed that Audouin’s gull had significantly lower values for both carbon and nitrogen (Table 3). Overall, 2015 revealed a significant lower value for nitrogen (Table 3). Table 3. Stable isotope ratios and factorial ANOVA results of carbon (δ13C) and nitrogen (δ15N) in plasma of Audouin’s (AG) and yellow-legged (YLG) gulls, for 2015 (AG n = 15; YLG n = 12) and 2016 (AG n = 12; YLG n = 11). Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Values are mean ± SD, with sample size in parenthesis. Significant effects are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Tukey corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Table 3. Stable isotope ratios and factorial ANOVA results of carbon (δ13C) and nitrogen (δ15N) in plasma of Audouin’s (AG) and yellow-legged (YLG) gulls, for 2015 (AG n = 15; YLG n = 12) and 2016 (AG n = 12; YLG n = 11). Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Years AG YLG Statistics F p Main effect δ13C (‰) 2015 –18.6 ± 0.4 (15) –18.5 ± 0.3 (12) Species F1,46 = 22.7 ≤ 0.001 AG < YLG Year F1,46 = 1.9 = 0.177 – 2016 –18.0 ± 0.5 (12) –18.0 ± 1.4 (11) Species * Year F1,46 = 1.1 = 0.308 – δ15N (‰) 2015 +12.8 ± 0.5 +13.2 ± 0.5 Species F1,46 = 17.6 = 0.0001 AG < YLG 2016 +13.0 ± 0.4 +13.3 ± 1.6 Year F1,46 = 8.3 = 0.006 2015 < 2016 Species * Year F1,46 = 2.8 = 0.098 – Values are mean ± SD, with sample size in parenthesis. Significant effects are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Tukey corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). SIBER analysis revealed a significantly lower isotopic niche in 2016, when fisheries almost completely ceased for one week, Audouin’s gull exhibited a significantly narrower isotopic niche when compared with yellow-legged gulls (SEAB: p = 0.03), against the regular fishing intensity period of 2015. Furthermore, in 2016 yellow-legged gull showed an important isotopic niche segregation from Audouin’s gull for both 2015 and 2016 (Figure 2): the overlap was lower between yellow-legged gull in 2016 with Audouin’s gull in 2015 (4.3%), but also with Audouin’s gull in 2016 (5.6%), and higher within Audouin’s gull between both years (27.5%). At-sea distribution and environmental variables During LFA, Audouin’s gulls spent more time at-sea, foraged farther from the colony, closer to areas with high fishery activity, that were deeper and had a higher chl a concentration, and overall were more consistent in the use of habitat (i.e. higher % overlap with congeners) when compared with yellow-legged gulls during both LFA and NFA. Also during LFA, yellow-legged gulls foraged closer to fishing ports when compared with, to Audouin's gulls during both LFA and NFA (Figure 3a;Tables 4 and 5). Table 4. Mean (±SD) foraging trip characteristics of yellow-legged (YLG) and Audouin’s (AG) gulls in May (incubation period) of 2015 between the two fishery activity period (NFA and LFA). Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 FA—core Foraging Area; 50% Kernel Utilization Distribution. Environmental predictors for 06/05/2015–17/05/2015. a http://www.worldportsource.com/ports/MAR.php. b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPublic/). Table 4. Mean (±SD) foraging trip characteristics of yellow-legged (YLG) and Audouin’s (AG) gulls in May (incubation period) of 2015 between the two fishery activity period (NFA and LFA). Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 Normal Fishery Activity Low Fishery Activity Variables YLG AG YLG AG Foraging trip characteristics N tracks [N birds] 102 [6] 69 [6] 35 [6] 16 [6] Trip duration (h) 1.8 ± 0.6 3.7 ± 1.2 2.2 ± 1.3 3.9 ± 1.6 Time spent flying per day−1 (h) 5.2 ± 2.1 6.8 ± 2.2 3.9 ± 1.7 8.3 ± 2.8 % of time spent in FA 33.1 ± 9.5 43.2 ± 9.8 23.7 ± 8.8 51.7 ± 7.2 Max. distance to colony (km) 25.1 ± 5.6 48.2 ± 11.1 15.2 ± 7.5 54.9 ± 12.4 Min. distance to fishing harbours (km)a 25.6 ± 7.6 45.8 ± 6.4 9.4 ± 2.9 50.9 ± 9.8 Min. distance to very high fishing intensity areas (km)b 22.4 ± 4.3 20.2 ± 4.7 28.9 ± 3.7 15.9 ± 2.9 Spatial ecology parameters FA overlaps within the same fishing period and species (%) 75.4 ± 9.3 89.1 ± 9.9 58.7 ± 12.1 91.2 ± 7.2 FA overlaps between fishing periods and within the same species (%) 69.9 ± 7.5 79.2 ± 8.8 — — FA overlaps within the same fishing period and between species (%) 40.1 ± 9.2 17.2 ± 8.4 Habitat of FA (within FA) Bathymetry (BAT; m) 167.2 ± 14.8 245. ± 24.2 88.2 ± 20.9 322.1 ± 19.3 Chlorophyll a concentration (Chl a; mg m−3) 1.2 ± 0.8 2.0 ± 0.5 0.9 ± 0.4 2.4 ± 0.9 Sea surface temperature (SST; ºC) 20.8 ± 3.5 19.8 ± 3.3 20.1 ± 2.4 19.2 ± 2.3 FA—core Foraging Area; 50% Kernel Utilization Distribution. Environmental predictors for 06/05/2015–17/05/2015. a http://www.worldportsource.com/ports/MAR.php. b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPublic/). Table 5. Generalized Linear Mixed Models (GLMMs) testing the effect of gull species (yellow-legged gulls—YLG and Audouin gulls—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on foraging trip characteristics, spatial ecology parameters, and habitat characteristics of FA. Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – FA—core Foraging Area; 50% Kernel Utilization Distribution (50 Kernel UD). Environmental predictors for May 2015. The individual was used as a random effect to avoid pseudo-replication issues. Significant results are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Bonferroni corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). a http://www.worldportsource.com/ports/MAR.php b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPubl. Table 5. Generalized Linear Mixed Models (GLMMs) testing the effect of gull species (yellow-legged gulls—YLG and Audouin gulls—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA) and their interaction on foraging trip characteristics, spatial ecology parameters, and habitat characteristics of FA. Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – Species Fishery Activity period Species *Fishery Activity period Variables GLMM p Main effect GLMM p Main effect GLMM p Main effect Foraging trip characteristics N tracks [N birds] – – – – – – – – – Trip duration (h) F3,218 = 8.41 <0.001 AG > YLG F3,218 = 2.62 0.05 NFA < LFA F3,218 = 2.0 0.09 – Time spent flying per day−1(h) F3,218 = 3.90 0.01 AG > YLG F3,218 = 2.01 0.09 – F3,218 = 1.87 0.14 – % of time spent in FA F3,218 = 5.42 <0.001 AG > YLG F3,218 = 1.75 0.17 – F3,218 = 3.05 0.03 AG LFA > all others Max. distance to colony (km) F3,218 = 4.11 0.01 AG > YLG F3,218 = 1.14 0.34 – F3,218 = 3.91 0.01 AG LFA > all others Min. distance to fishing ports (km)a F3,218 = 3.37 0.02 AG > YLG F3,218 = 4.15 0.01 NFA < LFA F3,218 = 3.88 0.01 YLG LFA < all others Min. distance to very high fishing intensity areas (km)b F3,218 = 3.91 0.01 AG < YLG F3,218 = 1.46 0.22 – F3,218 = 2.89 0.04 AG LFA < all others Spatial ecology parameters FA overlaps within the same fishery activity period and species (%) F3, 218 = 4.98 0.001 AG > YLG F3, 218 = 1.69 0.18 – F3, 218 = 3.37 0.02 AG LFA > all others FA overlaps between fishery activity periods and within the same species (%) – – – – – – – – – FA overlaps within the same fishery activity period and between species (%) – – – – – – – – – Habitat of FA (within FA) Bathymetry (m) F3, 69 = 4.09 0.01 AG > YLG F3, 69 = 1.38 0.26 – F3, 69 = 3.57 0.02 AG LFA > all others Chlorophyll a concentration (mg m−3) F3, 69 = 6.69 <0.001 AG > YLG F3, 69 = 1.19 0.33 – F3, 69 = 3.18 0.03 AG LFA > all others Sea Surface Temperature (ºC) F3, 69 = 1.64 0.19 – F3, 69 = 2.04 0.12 – F3, 69 = 1.21 0.30 – FA—core Foraging Area; 50% Kernel Utilization Distribution (50 Kernel UD). Environmental predictors for May 2015. The individual was used as a random effect to avoid pseudo-replication issues. Significant results are shown in bold. Main effect was evaluated with post-hoc multiple comparisons Bonferroni corrected tests. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). a http://www.worldportsource.com/ports/MAR.php b See depicting the outputs of the EU Blue Hub project—the first high-resolution map of fishing intensity areas covering all EU waters (https://bluehub.jrc.ec.europa.eu/mspPubl. Figure 3. View largeDownload slide (a) GPS locations of yellow-legged (YLG; black; n = 6 birds) and Audouin’s (AG; grey (red in the online version); n = 6 birds) gulls foraging movements during NFA (n = 102 and 69 foraging trips, respectively) and LFA period (n = 35 and 16 foraging trips, respectively) of May 2015, overlaid with fishing intensity (https://bluehub.jrc.ec.europa.eu/mspPublic/). 1, very low; 2, low; 3, medium; 4, high; 5, very high. (b) Time spend per day (%) during the two fishery activity periods by each gull species in the six main foraging habitats. Treat. Station = Water treatment station. (c) Detail view (from panel A) of YLG (black) and AG (grey (red in the online version)); using (1) refuse dump, (2) water treatment station, and (3) fishing port habitats. Figure 3. View largeDownload slide (a) GPS locations of yellow-legged (YLG; black; n = 6 birds) and Audouin’s (AG; grey (red in the online version); n = 6 birds) gulls foraging movements during NFA (n = 102 and 69 foraging trips, respectively) and LFA period (n = 35 and 16 foraging trips, respectively) of May 2015, overlaid with fishing intensity (https://bluehub.jrc.ec.europa.eu/mspPublic/). 1, very low; 2, low; 3, medium; 4, high; 5, very high. (b) Time spend per day (%) during the two fishery activity periods by each gull species in the six main foraging habitats. Treat. Station = Water treatment station. (c) Detail view (from panel A) of YLG (black) and AG (grey (red in the online version)); using (1) refuse dump, (2) water treatment station, and (3) fishing port habitats. In relation to foraging patterns, the GLM analysis revealed that Audouin’s gull presented mainly a marine foraging behaviour during both fishery activity periods (NFA and LFA); as did the yellow-legged gull (Figures 3b and 4; Table 6). The Sea was used significantly more at Night Time than during Day Time (Table 6), and significantly more by Audouin’s gull, which showed nocturnal behaviour (night time period, n = 6; Figure 4a), in contrast to the yellow-legged gull (Figure 4b;Table 6). During the NFA period, yellow-legged gull foraged significantly more at sea (Figures 3b and 4b;Table 6). The lagoon was used as a resting site, significantly more during the NFA period (Figure 4; Table 6). Audouin’s gull rested significantly more during the Day Time period (10–18 h), whereas the yellow-legged gull rested significantly more during the Night Time period (Figure 4; Table 6). The water treatment station was used significantly more during the LFA period (Figures 3b and 4; Table 6). The beach was used significantly more during the NFA at Night Time (Figure 4; Table 6). In relation to alternative foraging habitats, such as refuse dumps and fishing ports, these were only used by yellow-legged gull (Figures 3b, 3c, and 4). Table 6. GLMs testing the effect of gull species (yellow-legged gull—YLG and Audouin’s gull—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA), daily period (night time vs. day time) on the use of the different habitats (Beach, Sea, Lagoon, and Water treatment station). Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Significant results are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Table 6. GLMs testing the effect of gull species (yellow-legged gull—YLG and Audouin’s gull—AG), fishery activity period (Normal Fishery Activity—NFA and Low Fishery Activity—LFA), daily period (night time vs. day time) on the use of the different habitats (Beach, Sea, Lagoon, and Water treatment station). Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Beach Sea Lagoon Water treatment station β±SE |Z| P Main β±SE |Z| p Main β±SE |Z| p Main β±SE |Z| p Main Species 18.69 ± 602.21 0.03 0.98 – −0.49 ± 0.08 −7.31 0.0001 YLG < AG −1.88 ± 0.29 −6.56 0.0001 YLG < AG 0.18 ± 0.17 1.08 0.28 – Fishery activity period −0.68 ± 0.46 −1.49 0.14 – −0.08 ± 0.06 −1.22 0.22 – 0.71 ± 0.13 5.62 0.0001 NFA > LFA −1.42 ± 0.28 −5.09 0.0001 NFA < LFA Daily period 16.85 ± 602.21 0.03 0.98 – 0.31 ± 0.06 5.39 0.0001 Night time > day time −21.53 ± 760.77 −0.03 0.98 – −19.59 ± 1176.52 −0.02 0.99 – Species*Fishery activity period 0.26 ± 0.42 0.62 0.54 – 0.26 ± 0.08 3.41 0.0001 YLG NFA > All others 0.56 ± 0.33 1.70 0.09 – 0.38 ± 0.36 1.07 0.29 – Species*Daily period −17.27 ± 602.21 −0.03 0.98 – −0.55 ± 0.08 −7.20 0.0001 YLG night time < all others 4.56 ± 0.45 10.23 0.0001 YLG night time > all others −0.37 ± 1405.79 0.000 1.000 – Fishery activity period *Daily period 0.60 ± 0.25 2.47 0.01 NFA night time > all others 0.04 ± 0.07 0.56 0.58 – 18.09 ± 760.76 0.02 0.98 – 1.23 ± 1405.79 0.001 0.999 Significant results are shown in bold. Statistical analyses were carried out in R (Version 3.4.0) (R Core Team, 2015). Figure 4. View largeDownload slide Percentage of time spent (within 2 h slots) in different foraging habitats by: a—Audouin gulls (AG) and b—yellow-legged gulls (YLG) during normal and LFA period. Treat.station = water treatment station. Figure 4. View largeDownload slide Percentage of time spent (within 2 h slots) in different foraging habitats by: a—Audouin gulls (AG) and b—yellow-legged gulls (YLG) during normal and LFA period. Treat.station = water treatment station. Discussion This study assessed differences in feeding, trophic, and foraging ecology of two gull species, Audouin’s and yellow-legged gull, between two periods of differing fishery activity (NFA vs. LFA). We established dietary, spatial, and temporal segregation between the species. Audouin’s gull showed strict marine foraging behaviour, contrasting with the generalist behaviour of the yellow-legged gull, which foraged in several habitats and on a wide range of prey. The two gull species fed mostly on highly commercial species, such as Sardina pilchardus, more frequently during the NFA period than the LFA period. On the other hand, they fed more frequently on species with low commercial value, such as Scomberesox saurus/Belone belone during the LFA period than the NFA period. Additionally, when fisheries almost completely ceased for one week (LFA—2016), according to species-specific differences Audouin’s gull exhibited a narrower isotopic niche (food/foraging-specialist) and yellow-legged gull a wider isotopic niche (food/foraging-generalist). However, no significant differences were found in diet and trophic ecology between species*fishery activity period. Nevertheless, as a result of LFA, gull species revealed foraging activity differences between the two fishery activity periods. Overall, there was visible dietary, spatial and temporal segregation between the two species; mostly attributed to their diet and habitat preferences. These differences can be related to the availability of anthropogenic resources, such as fishery discards. Dietary segregation: differences between week periods The two gull species consumed a diverse range of fish species, including demersal prey which is only available through fishery discards (Furness et al., 2007; Votier et al., 2010). However, the specialized feeding behaviour of Audouin’s gull was evident from the pellet analysis. During the two consecutive years, and the two fishery activity periods, their main prey species were Scomberesox saurus/Belone belone, which are epipelagic fish that could be “naturally” caught (Arcos and Oro, 2002) or provided by nocturnal fisheries (purse-seiner discards; Borges et al., 2001; Gonçalves et al., 2008). Similar to Audouin’s gull, yellow-legged gull fed mainly on fish, during both fishery activity periods (NFA vs. LFA), taking Sardina pilchardus and Trachurus sp., which were also the most landed fish species in the Algarve (Supplementary Table S2) and the most frequently discarded by Portuguese fisheries (Borges et al., 2001; Erzini et al., 2002; Fernandes et al., 2015). Terrestrial items, such as refuse and insects were mostly taken by yellow-legged gull, in line with the findings of González-Solís et al., (1997a). During NFA, the two gull species fed mostly on commercial fish species (Supplementary Table S2), for example Sardina pilchardus; which could be caught in association with fisheries activities, and as fishery discards (Oro et al., 1996; Ruiz et al., 1996) by yellow-legged gull (Ramos et al., 2009; Foster et al., 2017) and also naturally caught in the case of Audouin’s gull (Pedrocchi et al., 2002). During LFA, the two gulls’ species fed more frequently on epipelagic prey with low commercial value (Table 1), such as Scomberesox saurus/Belone Belone; both species could be taken from fisheries discards (Borges et al., 2001; Erzini et al., 2002; Stratoudakis and Marçalo, 2002; Gonçalves et al., 2008; Vázquez-Rowe et al., 2012), and also caught naturally by Audouin’s gull (Arcos and Oro, 2002). Contrary to our expectations, during the LFA period, we did not detect a significant increase in the consumption of refuse or terrestrial prey by yellow-legged gulls as reported in previous studies (Oro et al., 1995; González-Solís et al., 1997a; Ceia et al., 2014). This result could be explained through a combination of three factors: (1) the low accessibility to refuse dumps (Duhem et al., 2005) and travelling distance to refuse dumps, as shown by Ramos et al., (2009) in different Mediterranean islands, (2) low population numbers of both species in our study area, which even under LFA could allow high dependence on fishery discards, or (3) the fact that refuse items do not present hard remains and thus are not detectable in pellets. The plasma δ13C and δ15N values for the incubation period suggest trophic segregation between the two gull species. Using SIBER, specialized feeding and foraging behaviour was evident for Audouin’s gull (Figure 2), which exhibited a narrow isotopic niche during the two periods of differing fishery activity (2015 vs. 2016), and individuals exhibited higher intra-specific niche overlap. However, Audouin’s gull niche width decreased during the LFA period (2016), which can be attributed to its specialized fish-based diet (i.e. feeding mainly on fish and foraging at-sea). The yellow-legged gull, similarly to the Audouin’s gull, demonstrated a narrow isotopic niche width during the NFA period (2015). Between the two fishery activity periods (2015 vs. 2016), there was a shift in yellow-legged gull isotopic niche and therefore in its foraging ecology; though we were unable to detect a strong switch in diet with the pellet analysis (see above). However, consistent with a generalist feeding strategy, in the LFA period (2016), yellow-legged gulls broadened their isotopic niche, suggesting a possible decrease in their main fish food resources (Ceia et al., 2014). During the LFA Period (2016), Audouin’s gull exhibited a narrow niche and yellow-legged gull a broader niche, in concordance with the species-specific difference in foraging ecology, such as foraging ability, dietary, and habitat preferences (Wilson, 2010; Navarro et al., 2013). We found significant differences in δ13C stable isotopic values between gull species; higher for yellow-legged than Audouin’s gull, which suggests a spatial segregation between species (see below). δ15N values revealed differences between species, with higher values for 2016 than 2015, which could be attributed to the consumption of alternative prey (e.g. refuse) and high trophic level prey, mostly by yellow-legged gull (Figure 2; Forero and Hobson, 2003,;Navarro et al., 2009; Ceia et al., 2014), which strongly suggests dietary segregation. We are aware that other possible variables, such as poor weather conditions at-sea and strong winds, could influence the behavioural foraging ecology of the birds. However, if we assume an effect of strong winds it should have affected mostly yellow-legged gulls, because Audouin’s gulls only foraged at-sea and restricted their isotopic niche, contrary to yellow-legged gulls that changed their niche during the period without fishery discards. Therefore, we believe that variation in fishing intensity and consequently the strong reduction in the availability of fishery discards had a much stronger influence on the diet and foraging patterns of gulls. Spatial and temporal distribution of gulls between periods of different fishery intensities Using the tracking devices, we confirmed the specialist foraging behaviour of Audouin’s gull in both fishery activity periods Audouin’s gull is characterized as a specialist species (Pedrocchi et al., 2002), and foraging exclusively within marine areas, mostly during Night Time, for both fishery activity periods. However, these patterns fully coincide with the departure of purse-seine vessels, suggesting that such fishery activities could define the daily foraging patterns of this species (González-Solís, 2003; Bécares et al., 2015). Furthermore, the association with purse seines could facilitate their natural fishing behaviour (exploiting vertical migration of epipelagic and pelagic species under the vessels’ lights) or the exploitation of discards (Arcos and Oro, 2002; Mañosa et al., 2004). Moreover, during the LFA period Audouin’s gull foraged over significantly deeper and more productive waters (higher Chl a concentration); such pelagic and productive habitats are also targeted by commercial fisheries (Ramos et al., 2013). Similarly to Audouin’s gull, yellow-legged gull individuals foraged mainly at-sea, contrasting with other studies (e.g. Christel et al., 2012). They foraged mostly during NFA and in the Day Time periods, which fit with fishing activity schedules, in contrast to Audouin’s gull daily patterns, suggesting the existence of spatial and temporal segregation between the two species (González-Solís, 2003). As expected, and in agreement with δ13C values, yellow-legged gulls foraged inland with some individuals foraging at fishing ports and refuse dumps, in agreement with what has been reported for other populations of this species (Ceia et al., 2014; Navarro et al., 2016). Similarly to Mediterranean colonies, when fishery activity decreases, foraging in alternative habitats, for example refuse dumps, increases (González-Solís et al., 1997a). However, the lower availability of refuse dumps in the vicinity of our study area may explain the low number of individuals exploiting those areas (Bertellotti et al., 2001). Conservation implications Former studies report that under LFA, both Audouin’s and yellow-legged gulls usually shift their distribution to forage on alternative habitats and prey (González-Solís et al., 1997a, b; Ceia et al., 2014; Alonso et al., 2015; Bécares et al., 2015). However, in our study, both species kept foraging mostly in marine habitats. These results may be explained by the relatively small population numbers of both species in our study area (fewer than 2000 breeding pairs for each species; unpublished data) when compared with highly populated Mediterranean colonies, where competition for resources should be much higher, thus forcing birds to use alternative foraging habitats and prey (Oro et al., 1997; Duhem et al., 2008; Catry et al., 2010; Meirinho et al., 2014; Bécares et al., 2015). The populations of both Audouin’s gull and yellow-legged gull in our study area have increased about 300 breeding pairs annually for the last 5 years (unpublished data); the high fishing activities close to the breeding colonies should help to explain this, given their high dependence on commercial fish species and the fact that both species foraging movements and daily patterns synchronized with the fisheries activities in the area. Under the coming scenario of the EU discard ban policy, to be applied onward and thus banning the fisheries discards at-sea (https://ec.europa.eu/fisheries/cfp/fishing_rules/discards/); these Audouin’s gull and yellow-legged gull populations may suffer a decline due to their high dependence on fisheries resources, as revealed by Foster et al. (2017) for other gull population. However, competition for food resources should increase and lead to (the larger) yellow-legged gull predating on (the smaller) Audouin’s gull, a previously documented behaviour (Martínez-Abraín et al., 2003; Catry et al., 2004; Alonso et al., 2015). A discard ban policy should lead to an increase in the use of land resources by yellow-legged gull, which will potentiate more conflicts with wildlife and humans, e.g. individuals turning into urban dwellers, breeding in roofs and terraces, and potential spread of diseases through the contamination of water reservoirs with faecal matter (Belant, 1997; Bertellotti et al., 2001; Rock, 2005; Charles and Linklater, 2013; Alm et al., 2018). This new fishery policy should be implemented gradually, and closely monitored in order to facilitate species adaptation and to minimize possible negative effects of opportunistic yellow-legged gulls on more specialized gull and tern species. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Acknowledgements VHP, FRC, and JGC acknowledge their fellowships (SFRH/BPD/85024/2012, SFRH/BPD/95372/2013, and PD/BD/127991/2016, respectively) attributed by the ‘Fundação para a Ciência e Tecnologia’ (FCT; Portugal) and the European Social Fund (POPH, EU). This project benefited from the strategic project UID/MAR/04292/2013 granted by FCT to MARE. We are thankful for the fishery landings data supplied by Dados Estatísticos 2015/2016, Docapesca—Portos e Lotas, S.A. Logistic support was also provided by the Ria Formosa Natural Park (ICNF). References Afán I. , Navarro J. , Cardador L. , Ramírez F. , Kato A. , Rodríguez B. , Ropert-Coudert Y. , et al. 2014 . Foraging movements and habitat niche of two closely related seabirds breeding in sympatry . Marine Biology , 161 : 657 – 668 . Google Scholar CrossRef Search ADS Alm E. W. , Daniels-Witt Q. R. , Learman D. R. , Ryu H. , Jordan D. W. , Gehring T. M. , Santo Domingo J. 2018 . 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ICES Journal of Marine ScienceOxford University Press

Published: Jul 30, 2018

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