MEPS 551:261-275 (2016)  -  DOI: https://doi.org/10.3354/meps11736

Environmental correlates of nearshore habitat distribution by the Critically Endangered Māui dolphin

Solène Derville1,2,3,4,*, Rochelle Constantine5, C. Scott Baker4,5, Marc Oremus5, Leigh G. Torres4

1Département de Biologie, École Normale Supérieure de Lyon, Université de Lyon, UCB Lyon1, 46 Allée d’Italie, 69364 Lyon, France
2UMR ENTROPIE (IRD, Université de La Réunion, CNRS), Laboratoire d’Excellence-CORAIL, 101 Promenade Roger Laroque, BPA5, 98848 Nouméa Cedex, Nouvelle-Calédonie
3Sorbonne Universités, UPMC Université Paris 06, IFD-ED129, 4 Place Jussieu, 75252 Paris Cedex 05, France
4Marine Mammal Institute, Department of Fisheries and Wildlife, Oregon State University, Newport, HMSC, 2030 SE Marine Science Drive, Oregon 97365, USA
5School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
*Corresponding author:

ABSTRACT: Effective management of space-use conflicts with anthropogenic activities is contingent upon reliable knowledge of a species’ ecology. The Māui dolphin Cephalorhynchus hectori maui is endemic to New Zealand and is listed as Critically Endangered, mainly as a result of fisheries bycatch. Despite conservation efforts, the population was estimated at 55 animals in 2011. Here we investigate environmental correlates of Māui dolphin nearshore distribution, using 119 encounters with Māui dolphin groups during boat-based, coastal surveys across 4 summers (2010, 2011, 2013, 2015). We describe the nearshore distribution using a kernel density analysis with differential smoothing on the x- and y-axes to account for the nearshore preference of the dolphins and the survey design. In all years, dolphins were encountered consistently in a restricted area (4 year area of overlap: 87.3 km2). We modelled habitat preference with boosted regression trees, using presence/absence of dolphins relative to static and dynamic environmental predictors. An index of coastal turbidity was created based on a near-linear relationship between Secchi disk measurements and log-transformed remotely sensed chl a concentration. Sea surface temperature (SST; 22.6% contribution), turbidity (22.2%), distance to major watersheds (17%), depth (14.5%), distance to minor watersheds (13.3%) and distance to the coast (10.4%) partly explained Māui dolphin distribution. We detected a match between predicted areas of high nearshore habitat suitability around North Island and historical sightings (76.2% overlap), thus highlighting potential areas of Māui dolphin recovery. Our study presents methods broadly applicable to distribution analyses, and demonstrates an evidence-based application toward managing Māui dolphin habitat.


KEY WORDS: Habitat selection · Kernel density analysis · Boosted regression trees · Remote sensing · Turbidity · Māui dolphins


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Cite this article as: Derville S, Constantine R, Baker CS, Oremus M, Torres LG (2016) Environmental correlates of nearshore habitat distribution by the Critically Endangered Māui dolphin. Mar Ecol Prog Ser 551:261-275. https://doi.org/10.3354/meps11736

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