Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Scales, Kylie L.; Miller, Peter I.; Ingram, Simon N.; Hazen, Elliott L.; Bograd, Steven J.; Phillips, Richard A.. 2016 Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models. Diversity and Distributions, 22 (2). 212-224. 10.1111/ddi.12389

Before downloading, please read NORA policies.

[thumbnail of Scales et al 2016 - Identifying predictable foraging habitats AAM.pdf]

Preview

Text
This article has been accepted for publication and will appear in a revised form in Diversity & Distributions, published by Wiley. Copyright John Wiley & Sons Ltd.
Scales et al 2016 - Identifying predictable foraging habitats AAM.pdf - Accepted Version
Download (7MB) | Preview

Official URL: http://dx.doi.org/10.1111/ddi.12389

Abstract/Summary

Aim 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 (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location Bird Island, South Georgia; Southern Atlantic Ocean. Methods GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results Predictable foraging habitats identified by EENM 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-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ 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. Main conclusions EENM 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 EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1111/ddi.12389
Programmes:	BAS Programmes > BAS Programmes 2015 > Ecosystems
ISSN:	13669516
Additional Keywords:	albatross, Boosted Regression Trees, front map, generalized additive models, habitat model, Random Forest, satellite remote sensing
Date made live:	03 Nov 2015 14:03 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/512138

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1111/ddi.12389

Programmes:

BAS Programmes > BAS Programmes 2015 > Ecosystems

ISSN:

13669516

Additional Keywords:

albatross, Boosted Regression Trees, front map, generalized additive models, habitat model, Random Forest, satellite remote sensing

Date made live:

03 Nov 2015 14:03 +0 (UTC)