Nest-site preferences of Eleonora’s Falcon (Falco eleonorae) on uninhabited islets of the Aegean Sea using GIS and species distribution models

Abstract

Eleonora's Falcon breeds colonially on small islands of the Mediterranean Sea and Macaronesia. Despite the wealth of papers highlighting the importance of nesting characteristics on this species' breeding performance, few have addressed the issue of nest-site selection explicitly. In this paper, we develop presence-absence and presencepseudoabsence models to predict nest occurrence as a function of the topography of the nesting territory. Nest occurrence data were available for nine uninhabited islets of the Aegean Sea, within which the majority of the global population of Eleanora's Falcon is encountered. Our findings suggest that the presence of conspecifics together with certain topographic features according to the surface area of the islet being studied can be used to predict nest occurrence on uninhabited islets of the Aegean Sea. We conclude that predictive models characterized by flexibility and/or the use of absence data that also consider nest clustering can result in robust predictions of the nest occurrence of Eleonora's Falcons in Greek breeding colonies and eventually facilitate future monitoring schemes. Since this is the first time nest-site preferences of Eleonora's Falcon have been analyzed using species distribution models, we encourage the application of similar methodologies to other areas within the species' breeding range to further validate our findings.

On the brink: status and breeding ecology of Eleonora’s FalconFalco eleonoraein Algeria

“…data). It has widely been reported that older individuals usually nest earlier and attain higher reproductive success than do younger, inexperienced, or low quality conspecifics (Newton 1979, Ferrer and Bisson 2003, Ristow and Wink 2004, Kassara et al 2012, but see Zabala and Zuberogoitia 2015). Therefore, the apparent delayed laying date in Srigina, despite corresponding to a different year, might suggest that this colony represents a suboptimal habitat, which is mainly occupied by young or low-quality birds.…”

Drifting baited stereo‐videography: a novel sampling tool for surveying pelagic wildlife in offshore marine reserves

“…Due to its consistently competitive performance compared to other classical, more established presence-only and machine learning techniques (Elith et al 2006, Gastón andGarcía-Viñas 2011), MaxEnt has gained prominence in wildlife research and is seeing a rapidly growing number of applications in studies of both v www.esajournals.org terrestrial and marine mobile mega-vertebrates such as sharks (Dambach andRö dder 2011, Sequeira et al 2012), seabirds (Friedlaender et al 2011), turtles (McClellan et al 2014, dolphins (Thorne et al 2012, Gomez andCassini 2015), baleen whales (Bombosch et al 2014), leopards (McCarthy et al 2015, falcons (Kassara et al 2012), owls (Carroll 2010, Isaac et al 2013, foxes (Cleve et al 2011), or African wild dogs (Whittington-Jones et al 2014), among many other examples. MaxEnt is founded ''on the bedrock of probability theory'' (Brierley et al 2003), and a complete description of the mechanics underlying the algorithm is given by and Baldwin (2009).…”

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“…for foraging or nesting; Brambilla and Saporetti 2014) or to benchmark established conservation concepts (such as the umbrella species concept, Fourcade et al 2017). More explicitly, the increasing availability of fine-scale information has helped conservationists to evaluate species-specific human impacts (Braunisch et al 2011, Coppes et al 2017 or to identify (potential) nest sites (Kassara et al 2011, Brambilla et al 2013, Heuck et al 2013. However, when such fine-scaled information is not available, downscaling information from coarser scales (such as from atlas data) could offer possible alternatives (but also poses new challenges; see Niamir et al 2011, Bombi and D'Amen 2012, Keil et al 2013Keil et al , 2014 for Figure 2.…”