Predicting foraging hotspots for Yelkouan Shearwater in the Black Sea

Author’s Accepted Manuscript Predicting foraging hotspots Shearwater in the Black Sea

for

Yelkouan

María Pérez Ortega, Süreyya İsfendiyaroğlu

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PII: DOI: Reference:

S0967-0645(16)30193-X http://dx.doi.org/10.1016/j.dsr2.2016.07.007 DSRII4106

To appear in: Deep-Sea Research Part II Received date: 17 September 2015 Revised date: 7 July 2016 Accepted date: 7 July 2016 Cite this article as: María Pérez Ortega and Süreyya İsfendiyaroğlu, Predicting foraging hotspots for Yelkouan Shearwater in the Black Sea, Deep-Sea Research Part II, http://dx.doi.org/10.1016/j.dsr2.2016.07.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Predicting foraging hotspots for Yelkouan Shearwater in the Black Sea María Pérez Ortegaa, Süreyya İsfendiyaroğlua,b a

b

Doğa Derneği, BirdLife Turkey, Mebusan Yokuşu, Alçakdam Yokuşu 24/3 Fındıklı. Beyoğlu / Istanbul

Istanbul University, Faculty of Forestry, Department of Forest Entomology and Protection, 34473, Bahçeköy-Sariyer, Istanbul, Turkey [email protected],[email protected]

Abstract The Yelkouan shearwater (Puffinus yelkouan Acerbi, 1827) is a vulnerable species endemic to the Mediterranean Region, but there is little information of its ecology particularly when at sea. In this study, we assessed the habitat use by Yelkouan shearwater in the Black Sea during the breeding (March-July) and non-breeding(August-February) periods of 2013, using boat-based surveys and shore-based counts. We created a species distribution model (SDM) based on the environmental variables that most accurately reflected the oceanographic habitat of this species in order to delineate foraging hotspots. Our habitat modelling analyses suggest that Yelkouan shearwaters respond to complex bio-physical coupling, as evidenced by their association with oceanographic variables. Foraging Yelkouan shearwaters mainly occurred on the western Black Sea continental shelf, indicating that Yelkouan shearwaters were foraging in shallow, cold and coastal waters. In the non-breeding period, Yelkouan Shearwater occurred beyond the Black Sea continental shelf, a wide pelagic extension of sea, indicating that shearwaters foraged in deep, warm and pelagic waters. These results are consistent with earlier studies, which identified the Black Sea as an important congregation site for Mediterranean Yelkouan shearwater populations outside the breeding season. This study demonstrates how the integration of boat-based survey data, shore-based counts and modelling can provide a wider understanding of the linkage between marine ecosystems that is mediated by marine megafauna such as pelagic seabirds.

Key words: Habitat modelling, MaxEnt, Yelkouan shearwater, foraging range, Black Sea, Mediterranean

1. Introduction Marine environments have received little attention relative to terrestrial areas, and less than 3% of the ocean’s surface is included in marine protected areas (MPAs) (IUCN and UNEPWCMC, 2013). Consequently seabirds have received protection in many of their breeding colonies, but few sites at sea are protected (Grémillet and Boulinier, 2009) although these include key foraging sites for seabirds species (Arcos et al., 2012; Harris et al., 2007; Hyrenbach et al., 2006; Louzao et al., 2006; Nur et al., 2011). The dynamic nature of the marine environment, the lack of obvious boundaries and the requirement of large extensions for effective protection have been among the major challenges regarding the identification of such areas (Alpine and Hobday, 2007; Game et al., 2009; Hyrenbach et al., 2000). Pelagic sites are of particular importance as they comprise marine areas remote from land where pelagic seabirds regularly gather in large numbers, whether to feed or for other purposes (e.g. moulting). These areas usually attract pelagic seabird species due to their higher food availability, which may be associated with specific oceanographic features, such as the presence of seamounts or high primary productivity (Fric et al., 2012). Recent studies have focused monitoring seabirds at sea, with extensive long-term surveys along with new tracking technologies and analytical tools (Amorim et al., 2009; Arcos et al., 2012; Camphuysen et al., 2012; Lascelles et al., 2012; Le Corre et al., 2012; Louzao et al., 2009; Montevecchi et al., 2012; Oppel et al., 2012). These efforts are promoted by BirdLife International who has set among its priorities the extension of the Important Bird Area (IBA) Programme to the marine environment (BirdLife International, 2004, 2010). The selection and delineation of these sites has been particularly effective for the identification of biodiversity conservation priorities and for the most efficient use of limited financial resources (Fric et al., 2012). The “Marine IBA toolkit” compiled by Birdlife, defines standardised methods and techniques to be used in the process of marine IBA identification and delineation(BirdLife International 2010; Delord et al., 2014). The toolkit also combines use of several data sources, such as satellite tracking data, at-sea surveys data and habitat modelling. The Mediterranean and Black Seas are two of the four marine regions targeted by the European Marine Strategy Framework Directive (MSFD, 2008/56/EC), an ambitious conservation instrument for European marine ecosystems and species. Both the Mediterranean and Black Seas are heavily impacted by human activities (Micheli et al., 2013). In particular, the Black Sea is a unique marine environment, representing the largest land-locked basin in the world. Its waters are in a state of almost complete isolation from the world ocean, as result of the restricted exchange with the Mediterranean Sea through the Turkish Straits System (the Bosphorus, Dardanelles Straits and the Sea of Marmara) (Özsoy and Ünlüata, 1997). It is an important breeding ground for many seabird species (Chernichko, 1993; Nankinov, 1996; Schogolev et al., 2005). The ornithological importance of the Black Sea is due to its geographic location, salubrious climate and the abundance of food (Nankinov, 1996). A total of 41 species of seabirds, 24 of which breed, occur in the Black Sea. The most numerous breeding species are the Mediterranean gull (Larus melanocephalus) (over 90% of the global population), Common tern (Sterna hirundo), Sandwich tern(Sterna sandvicensis), Slender-billed gull (Larus genei) and Yellow-legged gull (Larus cachinnans) (Nankinov, 1996). Moreover, the Southern Black Sea is an important wintering site for the Yelkouan

shearwater (Puffinus yelkouan Acerbi, 1827), an endemic species to the Mediterranean region (Raine et al., 2012). The Yelkouan Shearwater current global population is estimated to be between 10,815 and 53,574 breeding pairs, mainly concentrated in Malta, Italy, Greece, France, Croatia and Tunisia (Bourgeois and Vidal 2008; Bourgeois et al., 2013). This species breeds on rocky coastal and offshore islets and on the mainland. In the non-breeding season it disperses widely within the Mediterranean and Black Seas, often congregating in large flocks (Snow and Perrins 1998). Up to 55,682 individuals were counted at sea passing through the Bosphorus, indicating that a significant part of the global population enters the Black Sea for foraging and moulting (Fric et al., 2012; Şahin et al., 2012). Telemetry studies concluded that a large number of Mediterranean Yelkouan shearwaters, from Malta and France, were longdistance migrants moving to the Black Sea to spend several months of the non-breeding period (Militão et al., 2013; Raine et al., 2012; Péron et al., 2013). That Yelkouan shearwaters from different Mediterranean populations gather in the Black Sea illustrates how mobile marine megafauna can link different ecosystems, but may face different threats in each. Yelkouan shearwaters feed on shoals of small pelagic fish (Bourgeois et al., 2011; Péron et al., 2013) but also on fishery discards (Arcos et al., 2001). Despite the extensive research effort, there are many knowledge gaps on its ecology and the threats looming on the species, particularly when at sea (Bourgeois et al., 2011). Yelkouan shearwater populations have declined in recent years and got extirpated in parts of their former distribution range. The main drivers of these changes include predation by introduced species, fisheries by-catch, illegal hunting, human disturbance, light and noise pollution, and habitat destruction (Bourgeois and Vidal 2008; Borg et al., 2010; Oppel et al., 2011; Bonnaud et al., 2012; Micheli et al., 2013). In 2012 the Red List status of the species was upgraded by the International Union for Conservation of Nature (IUCN) to ‘Vulnerable’ (BirdLife International, 2012) because Derhé (2012) predicted a population decline of 30% within the next 54 years (three generations). In this paper we assessed the habitat use by the Yelkouan shearwater as a priority in the design of marine zoning strategies to protect seabirds in the Southern Black Sea Coast. Although the latter is an important wintering area for this shearwater and other seabird species (Doğa Derneği, 2014), marine IBAs have yet to be identified in the Southern Black Sea Coast. To fill this gap, we characterized the oceanographic habitat of the Yelkouan shearwater along the Black Sea during breeding and non-breeding seasons. The aim of our research is to identify Yelkouan shearwater hotspots in the Black Sea, where important foraging sites are found, as a first step for an inventory of Marine Important Bird Areas along the Southern Black Sea coast. This study focuses on boat-based surveys, shore-based counting and species distribution models to characterize the oceanographic habitat of the Yelkouan shearwater during its breeding and non-breeding season of 2013 and to evaluate the habitat use by this species.

2.

Methods

2.1. Study Area We studied the distribution and oceanographic habitat of the Yelkouan shearwater along the Southern Black Sea coast, within the territorial waters of Romania, Bulgaria, Turkey, Ukraine and in pelagic international waters (Fig. 1a). The study area encompassed 1,438 km2, with 42% corresponding to the continental shelf (depth <200 m), and the rest covering the continental slope and the deep sea depression down to a depth of 2,200 m. Overall the continental shelf is very wide (>200 km) in the north and east (Romanian and Bulgarian waters), and narrows to the south, being only a narrow intermittent strip with an approximate width of 2km along the Anatolian coast (Turkey). 2.2. Overview of the foraging hotspots identification process The identification process of foraging hotspots for Yelkouan shearwater is outlined in Fig.2.

2.3. Data collection Prior to data collection, a training program was organized for volunteers and professional staff in order to standardize the data collection effort. In November 2013, participants from Bulgaria, Romania and Turkey participated to European Seabirds At Sea (ESAS) training, held by experts from Helenic Ornithological Society. Boat based observations were all conducted by experienced Non-Governmental Organizations (NGO) staff and volunteers. Shore based data were all collected by a subset of experienced volunteers, coordinated by NGO staff. 2.3.1. Boat-based observations Boat-based observations constituted the largest dataset for Yelkouan shearwater distribution, abundance and behaviour. At-sea observations were carried out during the breeding (March to July) and (August to February) non-breeding seasons of 2013. 37 boatbased surveys were conducted by trained observers and researchers from three NGOs of Romania, Bulgaria and Turkey along predefined routes within territorial waters of three countries and in international waters from January to November of 2013 (Fig. 1a, b and Table 1). Seabird surveys followed the ESAS methodology proposed by Tasker et al., (1984) and Camphuysen and Garthe (2004): birds were counted within a 300 m strip transect band, at one or two sides ahead of the vessel depending on census conditions and a 90º or 180º angle from the ship’s bow. All flying seabirds were counted using the ‘snapshot method’ and bird observations were summed over 5 min periods. Based on recorded vessel speed and the nominal width of the survey transect we then calculated the area surveyed (in km2), and

density of birds as the total number of observed birds divided by the area covered (in birds per km2). In Turkey, 17 ESAS surveys were carried out from Zonguldak to Evpatoria and Sevastopol (Ukraine) from March to November of 2013 using RO-RO1 (Roll-On/Roll-Off) Ships covering a distance of 435 km, approximately. Since each trip took around 16 to 17 hours, the observers operated during daylight. From January to October 2013, 13 boat-based surveys were performed in Bulgaria surveying the southern coast between Burgas and Rezovo, and the northern coast between Kavarna and Tyulenovo. Surveys were conducted in Romania starting in April and finishing in November 2013, with 7 boat-based transects covering the whole Romanian shore. Research vessels were used to carry out at-sea observations. 2.3.2. Shore-based counts Coastal counts took place from fixed locations in each country (Fig. 3) during 2012, 2013 and 2014 to cover all the seasons (from November 2012 to January 2014). Information collected by trained observers and researchers during shore-based counts included the number of seabirds observed per species, their behaviour, the flight direction and location or distance from the vantage point using binoculars or telescopes (see Table 2). In the present study, only data from 2013 were used in order to match them with boat-based data. 2.4. Data processing and exploratory analysis Observation data were processed, analysed and then arranged into GIS layers in order to identify hotspots. We chose a “seasonal” approach to prediction rather than producing month-by-month predictions because the survey coverage was adequate at the seasonal scale, but in general, surveys did not provide adequate month-by-month coverage of the study area to justify 12 monthly predictions per year. Yelkouan shearwater mating and courtship takes place in January and February, with laying and incubation spanning March and April (Borg et al., 2010). The last chicks fledge by the end of July, after which colonies are deserted to start the migration from the Mediterranean Sea. Birds have been recorded returning to the colonies as early as midOctober to begin preparing nest sites for the following breeding season (Borg et al., 2002). We have divided our data into two fundamental classes as non-breeding and breeding season. We have taken in to account from March to July as breeding period, comprising the period initiated by egg laying until the fledging of last chicks. We then labelled the rest of the year as non-breeding period, including January and February, when the courtship takes place. Boat-based data was represented selecting the highest 5% of seabird densities i.e. corresponding to the 95th percentiles of all positive records (presence data), as well as values

1

RO-RO ships: vessels that are used to carry wheeled cargo.

above the mean of positive values, to reduce the confusion created by a large number of observations when plotted in a map. Shore-based data was expressed in total number of birds using the area. The number of Yelkouan shearwaters from shore-based counts was estimated from direct counts and it was not extrapolated to periods of the day that were not sampled. Therefore the estimates are considered to represent a lower limit value and the actual number of individuals using these areas is expected to be higher. Spatial autocorrelation is frequently encountered in ecological data, and not properly accounting for spatial correlation can influence the statistical inference of species distribution models (Dormann, 2007; Dormann et al., 2007; Lichstein et al., 2002). We explored whether there was spatial autocorrelation by calculating Moran’s I (Moran, 1950) prior to habitat modelling. Moran’s I ranges from -1 (perfect dispersion) to +1 (perfect correlation), with values around zero indicative of a random spatial pattern. Significance of the Moran’s I values was performed in ArcGIS 10.2 trough Spatial Autocorrelation (Global Moran's I) and Incremental Spatial Autocorrelation tools. 2.5. Habitat modelling Information on habitat was compiled and used to build Species Distribution Models (SDMs). Habitat variables were selected on the basis of data availability and potential biological relevance, after a literature review and preliminary modelling trials. Selected variables included both static and dynamic features (Table 3). Static features consisted of a set of measures related to the topography of the marine environment (bathymetry, distance to coast and distance to continental slope). These variables were calculated from GEBCO bathymetry data (BODC and NERC, 2015) and coastline data (APRS, 2009). Dynamic features were derived from a time series of remote sensing data, in particular monthly Terra MODIS SST (Sea Surface Temperature in °C) and Aqua MODIS Chl-a (Chlorophyll-a concentration in , as a proxy of biological production) imagery from Ocean Color Web (Feldman and McClain, 2007). The temporal definition of these variables was necessarily linked to the timing of the oceanographic surveys. Thus, we selected SST and Chl-a for the breeding and non-breeding period, using the months representative of each one. Also SST and Chl-a was considered for the three seasons previous to the surveys, given it is unlikely Yelkouan shearwater distribution responds instantaneously to changes in these two variables (Arcos et al., 2012; Louzao et al., 2009). A free open-source geoprocessing toolbox named Marine Geospatial Ecology Tools (Roberts et al., 2010) was used to convert the satellite images download from Terra MODIS SST and Aqua MODIS Chl-a into ArcGIS Raster files. To build the models, all data sources were resampled to a resolution of 2.5 minutes of arc (’), i.e. about 4.5 km pixels. 2.5.1. Maximum Entropy modelling We related Yelkouan Shearwater occurrence to seven explanatory environmental variables: bathymetry, distance to coast, distance to continental slope, Chl-a and SST for the survey period 2013, and Chl-a and SST for the three (3-months) seasons previous to the surveys.

SDMs were based on boat-based data, for the most ecologically relevant seasons (breeding and non-breeding) for 2013, according to the availability of oceanographic surveys (Table 1). Maximum entropy modelling method, implemented in MaxEnt software (version 3.3, http://www.cs.princeton.edu/~schapire/maxent/) (Arcos et al., 2012; Fric et al., 2012; Louzao et al., 2009; Oppel et al., 2012; Phillips et al., 2006; Pittman and Brown 2011; Thaxter et al., 2012) was used. The basic principle of the statistical approach implemented in MaxEnt is the estimation of the probability of presence using maximum entropy (that is, the most spread out or the most uniform distribution) given a set of conditions (the environmental characteristics of the site where the species is detected) (Phillips et al., 2006). Outputs were projected to the resolution fixed by environmental information (4.5 km pixels). Default parameterization of MaxEnt was used to develop the SDMs, limiting the response to environmental variables to linear and quadratic functions. SDMs were run on the 80% of training data with 20% of the sample records as testing data to assess the predictive ability of the SDM. The predictive ability of the models was assessed by using the AUC (Area Under the Curve) generated between the SDMs predictions and presence data from surveys used for model building. The AUC of the ROC (Receiver Operating Characteristics) curve provides a measure of the models predictive capability ranging between 0.5 (null predictive power) and 1 (a perfect predictive model) (Boyce et al., 2002). MaxEnt calculated several measures of variable importance: (1) relative gain contribution per variable to the model (a goodness-of-fit measure similar to deviance, Phillips et al. (2006)), (2) variable response curves for single and marginal variable models, and (3) a jackknife procedure to assess AUC/gain changes when excluding a variable. 2.5.2. Mapping the hotspots areas We used the results from SDMs to create maps of hotspot for each studied season using ArcGIS 10.2. The ASCII files obtained through MaxEnt software were converted into Raster files obtaining maps with probabilities of Yelkouan presence, where each output cell had a ‘habitat suitability index (HIS)’ which ranged from 0 (low) to 1 (high). To highlight the adequate seabird areas, firstly we defined as presence areas those with habitat suitability values above the lowest ten percentile (Arcos et al., 2012; Escalante et al., 2013). Secondly, within the presence areas, three scores of habitat suitability (Suitable, Good and Optimal) were defined using Natural Breaks (Jenks) classification method, which seeks to reduce the variance within classes and maximize the variance between classes (Jenks, 1967). Once all available spatial information (boat-based and shore-based counts, and habitat suitability models) had been arranged, hotspots for Yelkouan shearwater were identified. To this end, all available spatial data layers, including maps of densities, shore-based counts as well as areas exhibiting moderate, good and optimal conditions deduced by modelling were overlaid separately in ArcGIS 10.2 for each studied season for 2013.

3. Results 3.1. Boat-based surveys We surveyed a total of 77,286 transects units (5-min counts) along 37 transects over a total of 33 days (21 in breeding and 12 in non-breeding season), corresponding to a total linear effort of 2,428 km and covering over 1,438.7 km2 (Table 1). We have observed 2,753 individual Yelkouan shearwaters in total. Shearwater densities showed significant differences between both seasons. The maximum densities varying between 1,540 and 22 shearwaters per 100 km2 for breeding and non-breeding season respectively (Table 4). Overall, most of these sightings occurred along the Romanian and Bulgarian coasts. To illustrate the spatial distribution of shearwater aggregations, we plotted density data for each period separately (Fig. 4a, b). During the breeding season shearwaters were more concentrated along the coasts of Romania and Bulgaria, with densities higher than 171 birds/km2, and lower densities were located offshore between Turkey and Crimea. During the non-breeding season, the maximum densities (15 birds/km2) were obtained along the Bulgarian and Turkish coasts, while in Romania the densities were the lowest. Apart from the areas near to the Bulgarian coast, deep areas between Turkey and Crimea showed densities above average (1-15 birds/km2). 3.2. Shore-based Counts We counted 20,758 Yelkouan shearwaters in the three countries during 2013. The maximum numbers of Yelkouan shearwaters were recorded along the Romanian and Bulgarian coast, where the census effort was greater during the breeding season. Out of 20,284 shearwaters observed during the breeding season, 8,257 were registered in Vadu (Romania) and 4,777 in Kaliakra (Bulgaria). In the non-breeding season, 474 individuals were recorded in total. The largest groups were seen in Rezovo (Bulgaria) with 432 individuals, followed by Romania with only 2 individuals. 3.3. Spatial autocorrelation We found no evidence of spatial autocorrelation in Yelkouan shearwater data in both studied seasons at the selected scale of analysis. At-sea surveys yielded small magnitude Moran’s I values (0.15 in breeding season and -0.48 in non-breeding season), suggestive of weak aggregated spatial patterns. Moran's I correlograms for Yelkouan shearwater in both season showed small values: from +0.16 to +0.01 in the breeding season and from +0.1 to +0.05 in the non-breeding season (Figure S1a, S1b). Thus correlograms revealed that the selected spatial scale of analysis yielded independent observations, suitable for habitat modelling.

3.4. Modelling foraging probability In total, 2 SDMs were generated for the Yelkouan shearwater, one in each studied season (breeding and non-breeding). In both cases, SDM performance (i.e. evaluation based on data used to build the models) was very good, with all SDMs achieving AUC values above 0.9 (excellent discrimination power) (Table 5). 3.4.1. Breeding season Yelkouan shearwater habitat selection during the breeding period was mainly determined by three variables (see Table 6): distance to coast, SST of the three seasons previous to the surveys and bathymetry accounting for relative gain contributions of 53%, 20.4%, and 13.1%, respectively (combined 86.5%). Habitat suitability responded in different and characteristic ways to each significant variable. Correlations between explanatory variables complicated the interpretation of marginal response curves. Therefore it was necessary to check the single-variable curves. During this season (see Fig. 5a) habitat suitability decreased with increasing distance to the coast. The sea surface temperature curve showed a very low and constant habitat suitability until 18-19ºC where it increased significantly. Its single-variable response curve (i.e. only taking into account SST in a MaxEnt model) showed decreased suitability with increasing temperature (waters near the coast were colder than pelagic waters). Suitable habitat decreased with depth, although the single-variable response curve showed habitat suitability increased in both the deepest and most shallow areas. Jackknife test of variable importance showed that “sea surface temperature of the three seasons previous to the surveys” was the variable most important in the breeding season. Predictive habitat model in the breeding season (Fig. S2a), showed the best habitat were near to the coast in cold waters. 3.4.2. Non-breeding season During the non-breeding period, Yelkouan shearwater habitat selection was mainly determined by five variables (see Table 6): distance to coast, bathymetry, distance to continental slope, Chl-a concentration from non-breeding period and sea surface temperature for the three seasons previous to the surveys with the following contributions, 29.2%, 24.6%, 10.7%, 10.6% and 10.1% respectively (combined 85.2%). As in the breeding season, correlations between explanatory variables complicated the interpretation of marginal response curves, and justified to check the single-variable curves. Habitat suitability during this season (Fig. 5b) decreased with increasing distance from the coast, although not as steep as in breeding season and with higher HIS values for the same distance. Its single-variable response curve showed a gradual decrease. The marginal curve for bathymetry indicated optimal habitat around 2,000 meters then a decrease, whereas its single curve showed a stable suitability along 2,500 and 500 meters, and then a decline. Habitat suitability increased both in areas adjacent to continental slope and faraway, and declined with increasing Chl-a concentration. Habitat suitability for Yelkouan shearwater increased with increasing sea

surface temperature to a certain extent (14-15ºC) and then decreased. Jackknife test of variable importance showed that “distance to coast” was the most important variable. At a wider scale, during the non-breeding season habitat suitability was associated with warmer waters, somewhat more distant from the coast in a wide bathymetric range with low Chlorophyll concentration, in areas both near to continental slope and faraway. Predictive habitat model in the non-breeding season (Fig. S2b), indicated that grounds with better predicted conditions were observed both in coastal and off-shore areas. Once suitability habitat maps were reclassified in three categories (red colour optimal, orange good and yellow suitable areas) and overlaid with shore-based counts and shearwater densities, predicted hotspots showed slight seasonal variations (Fig. 6).

4. Discussion Yelkouan shearwater habitat suitability during the breeding season was mainly characterized by distance to coast, sea surface temperature of the 3 seasons previous to the surveys and bathymetry. Yelkouan shearwater preferred cool waters near to the coast, in very deep or very shallow areas. The shallow region, up to 50-m isobaths, and estuarine areas especially is the most nutrient enriched region of the Black Sea (Churilova et al., 2011). Suitable habitat in deep waters were quite unexpected, but this is due to the continental shelf reaching an abrupt termination at Sakarya Canyon (in front of the Turkish coast), where the depth suddenly increases from 100m to 1,500m (Özsoy and Ünlüata, 1997). Arcos et al., (2012) remarked how ‘overall productivity of the environment is conditioned by SST during the previous year’. Our analysis showed that SST from the three seasons prior to the surveys was the second most important variable to the model, confirming the earlier findings. However, the contribution of the Chl-a and distance to continental slope was not significant in our model, in contrast to other studies (e.g. Arcos et al., 2009; Le Fevre 1986; Longhurst 1998; Louzao et al., 2006; Louzao et al., 2008). We expected that higher Chl-a concentration, which is an indicator of primary productivity and possibly food availability for the seabirds (i.e. pelagic fish), would influence Yelkouan shearwater distribution. The lack of effect may stem from the relatively few number of at-sea surveys carried out during the breeding period. Thus, the number of occurrence localities may be too low to reliably estimate model parameters (Stockwell and Peterson, 2002). Another reason could be that Chl-a is a proxy for primary productivity, while seabirds, such as Yelkouan shearwaters, are top-predators and usually feed 2 to 3trophic levels higher up the food chain: correlations (or their lack there-of) between seabird distribution and indices of primary productivity can consequently be quite misleading (Gremillet and Boulinier, 2009). We identified hotspots near the coasts of Romania and Bulgaria (Fig 6a). Although the optimal areas were not situated closer to the coast according to our model, there were significantly large flocks of Yelkouan shearwaters, observed along the coast line. This should rather be interpreted as a migration pathway along the coast. Moreover our model suggested the Bulgarian coast as an important foraging area, consistent with shore-based counts. The Western range of the Turkish Black Sea coast proved to be more suitable than the Eastern Turkish coast. Foraging range of the species in the breeding period mainly comprised the

West Black Sea continental shelf, where spawning grounds of key prey species Anchovy (Engraulis encrasicolus), Whiting (Merlangius (merlangus) euxinus), Mediterranean sand smelt (Atherina hepsetus) and Black Sea horse mackerel (Trachurus mediterraneus) are also found (Bat et al., 2005; İsmen, 2001; Öztürk et al., 2011; Schismenou et al., 2008; Yankova, 2011). Yelkouan shearwater coastal behaviour in the breeding season was consistent with the predicted potential breeding habitats. Since this species breeds on rocky coastal and offshore islets (BirdLife International 2015; Péron et al., 2013), this might indicate undiscovered breeding colonies in Romania, Bulgaria and Turkey (Bourgeois and Vidal 2008; Şahin et al., 2012). This coastal foraging behavior is consistent with the first telemetry study conducted by Péron et al. (2013) at a French colony of Yelkouan shearwaters. Spatial segregation between immature (non-breeding) and breeding adults during all or part of the year occurs in many species. Yelkouan shearwaters have a long lifespan and start breeding at age 2 or 3. The flocks in Black Sea might include of non-breeding, potentially estivating, immature birds. A similar segregation was recently documented in the closely related Manx shearwaters (Puffinus puffinus) (Fayet et al., 2015). Despite the more restricted spatial coverage of the vessel-based surveys, habitat modelling identified additional important foraging hotspots off theTurkish coast and along Crimean shores. These areas are characterized by greater depth and sea surface temperature, explaining the high habitat suitability index obtained in deep waters and around 18ºC (Ivanov and Belokopytov, 2013). In the non-breeding season, Yelkouan shearwater suitable habitat was mainly influenced by distance to coast, bathymetry, distance to continental slope, Chl-a concentration during the non-breeding period and SST of the 3 seasons prior to the surveys. In contrast to the breeding period, Yelkouan shearwater preferred warmer waters near the coast, although their preferred locations are also located in remote seas with a wide bathymetric range (-2,500m to -500m) and low Chl-a concentration. They were found either closer to or away from the continental slope without any indication of a significant preference. Non breeding behavior (Fig. 6b) appears to be more pelagic than during the breeding season. During the non-breeding period, Yelkouan shearwaters disperse widely within the Black Sea, often congregating in large flocks (Şahin et al., 2012; Snow and Perrins 1998). The Black Sea is an important region for Mediterranean shearwaters to spend their non-breeding period (Bourgeois and Vidal, 2007; Péron et al., 2013; Raine et al., 2012; Şahin et al., 2012). Telemetry studies carried out in 2012 by Raine et al., and Militão et al., highlight the importance of the Black Sea for Maltese and French colonies during the non-breeding period. Yelkouan shearwater dispersal during non-breeding can be influenced by the movements of its preys. According to Nankinov (2001) “…during summer, the Black Sea anchovy and the European sprat (Sprattus sprattus) are distributed in vast areas and live in the upper layers of the water column…”. In autumn Yelkouan Shearwater follow the shoals of the Black Sea anchovy and horse mackerel, which migrate from the open sea mainly to the north, towards the Crimean Peninsula and to east towards the Caucasus and in some years to south along the west Black Sea coast (Nankinov 2001; Yankova 2011; Yankov 2014). Foraging range of the Yelkouan Shearwater during both breeding and non-breeding seasons responded to complex bio-physical coupling illustrated by their association with

oceanographic variables. Species Distribution Models allowed us to determine the relationship between the Yelkouan shearwater distribution and its habitats in the Black Sea, to assess the temporal variability of these habitats and to predict suitable habitats. The present study illustrates the need for repeated surveys extending the study area and standardizing the sampling locations during contrasting oceanographic conditions to validate habitat suitability models developed for a specific area (Forney 2000; Raymond and Woehler 2003). The integration of vessel and coastal data and modelling techniques provided a unique opportunity for identifying key marine areas for Yelkouan shearwater in the Black Sea. To our knowledge, this is the first study to quantitatively assess Yelkouan shearwater marine habitat use on the basis of both static and dynamic habitat variables. Future modelling work in the Black Sea should focus on increasing and unifying the effort in coastal surveys. Following the approach of previous studies (Arcos et al., 2009; Fric et al., 2012; Ramírez et al., 2008), a greater understanding would be gained with data from different years including new variables such as sea fronts, upwellings and prey distribution to understand the small-scale interactions between them, and the congregation pattern and behaviour of Yelkouan shearwaters within the high-use foraging grounds identified in the present study. Additionally, satellite tracking and small-scale surveys along the frontal systems where the species concentrates (e.g., Begg and Reid 1997; Hyrenbach et al., 2002) should also be taken into consideration.

5. Conclusions This study provides an example of an endangered species that faces a double jeopardy with different threats and pressures in two ecosystems. Yelkouan shearwater creates a linkage across two different regions, the Mediterranean and the Black Seas, both of which are under pressure (Micheli et al., 2013). Through this work we have adopted the ESAS methodology and predictive modelling that were implemented for the first time in the Black Sea. It shows the value of predictive models as an exploratory tool to identify additional potential habitats of highly endangered species in otherwise poorly surveyed areas. Hotspots with suitable habitats should be targeted for future ship-based surveys. Our results provide new relevant insights for defining the oceanographic habitat and for predicting the distribution of this species during non-breeding and breeding periods. Moreover, our model of the non-breeding period identified which parts of sea are preferred by the Mediterranean Yelkouan shearwater population during these months when they are away from their breeding colonies. Since this work addresses hotspots that are vital to Yelkouan shearwater, it should be considered as a preliminary effort for marine important bird area delineation in Black Sea. Some of these sites should be designated marine protected areas by regional governments, where good environmental status of the sites are maintained, and human activities such as fisheries are managed by an ecosystem based approach as recommended in the European Marine Strategy Framework Directive (MSFD). The Black Sea Basin countries are also parties to other international treaties such as, CBD (Convention on

Biodiversity), the 1992 Bucharest Convention and the 1995 Barcelona Convention revealing future cooperation opportunities with non-EU countries. A major challenge in the implementation of the MSFD is to attain the necessary scientific knowledge of the elements that define the state of the marine environment. In the present study, combining scientific data (vessel, coastal data and modelling techniques) from different countries provide the necessary knowledge to advance in the conservation of a seabird in a poorly-known ecosystem under great pressures. Additionally we have managed to increase the local capacity from none to tens of volunteers and experts, who have contributed to the fieldwork in Bulgaria, Romania and Turkey. This also demonstrates the successful dissemination of LIFE project experience from Greece to Southern Black Sea Coast. This underscore the need for holistic conservation instrument, such as the MSFD that aims at cooperation between Member and Non-Member States. In this sense, conserving the vulnerable Yelkouan shearwater, exemplifies the collaborative spirit of the European MSFD.

Acknowledgements This research is framed within the international project ‘07.020400/2012/617393/SUB/D2 Preparing the basis for an inventory of Marine Important Bird Areas along the southern Black Sea Coast (Romania, Bulgaria and Turkey)’ carried out between 2012 and 2014. We thank many people their help in the development of this work: Hayri Dağli from Doğa Derneği for managing the mentioned project, Emil Todorov, Lavinia Raducescu and Ciprian Fantana from Romanian Ornithologial Society and Anna Staneva, Minko Madjarov and Stoycho Stoychev from Bulgarian Society for the Protection of Birds for help, support and collaboration during this project. Similarly, we thank Jakob Fric, Aris Manolopoulos and Thanos Kastritis from the Hellenic Ornithological Society who helped design marine surveys and supervise habitat modelling. In addition special acknowledgement to Matthieu Authier and Levent Erkol for their helpful feedbacks and revisions of this manuscript, and two anonymous reviewers whose comments greatly improved this manuscript. Thanks to all those who contributed to data collection and continued support, in particular Can Yeniyurt, Evrim Tabur, Turan Cetin and Derya Engin. And finally, but not the less important, we thank to all volunteers who participated actively at-sea and coastal surveys, specially: Merve Ünal, Gökhan Gürbüz and Hamdi Kılıç.

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Fig.2. Outline of the process of foraging hotspots identification.

Fig. 3. Vantage points in each country (Romania in black, Bulgaria in yellow and Turkey in red color) Fig. 4. Yelkouan Shearwater densities in (a) breeding season and (b) non-breeding season. Densities above average depicted by medium circles, while the highest 5% of densities by large circles. Zoom on specific areas are shown.

Fig. 5. Variable response curves for the most predictive variables performing MaxEnt models in (a) breeding season and (b) non-breeding season. At each period, upper tables show marginal curves (the curve shows how the prediction changes as each environmental variable is varied, keeping all other environmental variables at their average sample value) and tables below indicate single-variable response curves (each curves represents a different model, namely, a Maxent model created using only the corresponding variable).

Fig. 6. Suitability values converted into three categories, suitable in light pink, good in salmon pink and optimal in maroon. Overlaid of boat surveys, coastal counts and habitat modelling layers in (a) breeding season and (b) non-breeding season. Note the boat surveys data are expressed in density values while coastal counts in total numbers.

SUPPLEMENTARY INFORMATION Fig. S1. Results of the spatial autocorrelation of vessel-based survey in (a) breeding and (b) non-breeding season. No evidence of significant spatial autocorrelation was found. The solid, horizontal line at zero represents a baseline showing no spatial pattern. Fig. S2. Categorization of habitat models for Yelkouan Shearwater for each studied season, (a) breeding and (b) non-breeding. The figures show habitat suitability values on a continuum from 0 (low) to 1 (optimal).

Team

5- min counts Distance (km) Area (km2)

Period

Turkey

12,582

1004.38

602.63

March 2013-November 2013

Bulgaria

60,668

1055.73

633.44

January 2013- October 2013

Rumania

4,036

367.79

202.63

April 2013-November 2013

Total

77,286

2,427.9

1,438.7

33 days

Table 1 Boat surveys effort conducted within the study area by the three teams in their territorial waters: number of transect units (5-min counts), distance and area surveyed (during transects) and period.

March 2013 - January 2014

Nº vantage points 6

Nº days of observations 15

Bulgaria

December 2012 - November 2013

41

25

Rumania

November 2012 - November 2013

12

23

Country

Period

Turkey

Table 2 Coastal Counts effort conducted within the study area by the three teams in the coast: covered period, number of sighting points and number of days of observations.

Variable

Spatial Temporal Resolution* Resolution

Source

Indicative of the following processes

Bathymetry (m)

0.5'

Distance to coast (km) Static

Dynamic

Distance to continental slope (km) Sea Surface Temperature (SST, ºC) Chlorophyll-a concentration (Chl-a, mg/m3)

Constant

GEBCO (www.bodc.ac.uk)

Coastal vs. pelagic distribution

0.5'

Constant

Derived from coast line data

0.5'

Constant

Derived from GEBCO bathymetry

Onshore-offshore distribution patterns Presence of upwellings

2.5'

Monthly

Terra Modis (http://oceancolor.gsfc.nasa.gov/)

Water mass distribution

2.5'

Monthly

Aqua Modis (http://oceancolor.gsfc.nasa.gov/)

Ocean productivity domains

*1'=0.01665 of latitude/longitude (approx.=1.75 km)

Table 3 Environmental data collected and used for modelling seabird distribution.

Breeding

Number recorded 2,476

Maximum density (birds/100km2) 1,540

Non-breeding

277

22

Season

Table 4 Summary of the Yelkouan Shearwater observations showing the total number of birds sighted and the maximum density (birds/100km2).

AUC Season Training data

Test data

Breeding

0.96

0.95

Non-breeding

0.92

0.96

Table 5 Area under the curve (AUC) for training and test data in each seasonal model.

Season

Variable Permutation importance (%) coastdist

53

sst_bppr

20.4

bathy chl_bp13 sst_bp13

13.1 6.2 5.9

consldist

1.4

chl_bppr

0.1

coastdist bathy consldist chl_nb13 sst_nbpr Non-Breeding chl_nbpr sst_nb13

29.2 24.6 10.7 10.6 10.1 7.6 7.2

Breeding

Table 6 For each studied season, the permutation importance for each variable are shown. Note: ‘coastdist’ is distance to coast.; ‘bathy’ is bathymetry’; ‘consldist’ is distance to continental slope’; ‘chl’ is chlorophyll; ‘sst’ is Sea Surface Temperature; ‘bp’ is breeding period; ‘nb’ is non-breeding period; ‘bppr’ is previous to the breeding period; ‘nbpr’ is previous to nonbreeding period.