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

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