Abstract
<jats:title>Abstract</jats:title><jats:sec><jats:title>Aim</jats:title><jats:p>Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote‐sensing and ensemble ecological niche models (<jats:styled-content style="fixed-case">EENM</jats:styled-content>s) to identify predictable foraging habitats for a globally important population of the grey‐headed albatross (<jats:styled-content style="fixed-case">GHA</jats:styled-content>) <jats:italic>Thalassarche chrysostoma</jats:italic>.</jats:p></jats:sec><jats:sec><jats:title>Location</jats:title><jats:p>Bird Island, South Georgia; Southern Atlantic Ocean.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p><jats:styled-content style="fixed-case">GPS</jats:styled-content> and geolocation‐immersion loggers were used to track at‐sea movements and activity patterns of <jats:styled-content style="fixed-case">GHA</jats:styled-content> over two breeding seasons (<jats:italic>n </jats:italic>= 55; brood‐guard). Immersion frequency (landings per 10‐min interval) was used to define foraging events. <jats:styled-content style="fixed-case">EENM</jats:styled-content> combining Generalized Additive Models (<jats:styled-content style="fixed-case">GAM</jats:styled-content>), MaxEnt, Random Forest (<jats:styled-content style="fixed-case">RF</jats:styled-content>) and Boosted Regression Trees (<jats:styled-content style="fixed-case">BRT</jats:styled-content>) identified the biophysical conditions characterizing the locations of foraging events, using time‐matched oceanographic predictors (Sea Surface Temperature, <jats:styled-content style="fixed-case">SST</jats:styled-content>; chlorophyll <jats:italic>a</jats:italic>, chl‐<jats:italic>a</jats:italic>; thermal front frequency, <jats:italic><jats:styled-content style="fixed-case">TF</jats:styled-content>req</jats:italic>; depth). Model performance was assessed through iterative cross‐validation and extrapolative performance through cross‐validation among years.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Predictable foraging habitats identified by <jats:styled-content style="fixed-case">EENM</jats:styled-content> spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl‐<jats:italic>a </jats:italic>><jats:italic> </jats:italic>0.5 mg m<jats:sup>−3</jats:sup>) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that <jats:styled-content style="fixed-case">GHA</jats:styled-content> exploit enhanced foraging opportunities associated with frontal systems and objectively identify the <jats:styled-content style="fixed-case">APFZ</jats:styled-content> as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi‐model ensembles was superior to that of single‐algorithm models, and cross‐validation among years indicated reasonable extrapolative performance.</jats:p></jats:sec><jats:sec><jats:title>Main conclusions</jats:title><jats:p><jats:styled-content style="fixed-case">EENM</jats:styled-content> techniques are useful for integrating the predictions of several single‐algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of <jats:styled-content style="fixed-case">EENM</jats:styled-content> for use with movement data in identifying at‐sea habitats of wide‐ranging marine predators, with clear implications for conservation and management.</jats:p></jats:sec>
Original language | English |
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Pages (from-to) | 212-224 |
Number of pages | 0 |
Journal | Diversity and Distributions |
Volume | 22 |
Issue number | 2 |
Early online date | 29 Oct 2015 |
DOIs | |
Publication status | Published - Feb 2016 |