FN Archimer Export Format
PT J
TI Modelling the spatial distribution of cetaceans in New Zealand waters
BT 
AF Stephenson, Fabrice
   Goetz, Kimberly
   Sharp, Ben R.
   Mouton, Theophile
   Beets, Fenna L.
   Roberts, Jim
   MacDiarmid, Alison B.
   Constantine, Rochelle
   Lundquist, Carolyn J.
   Sarmento Cabral, Juliano
AS 1:1;2:2;3:3;4:4;5:1;6:2;7:2;8:5,6;9:1,6;10:;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;
C1 National Institute of Water and Atmospheric Research (NIWA) Hamilton, New Zealand
   National Institute of Water and Atmospheric Research (NIWA) Wellington ,New Zealand
   Fisheries New Zealand Ministry for Primary Industries Wellington ,New Zealand
   Marine Biodiversity, Exploitation, and Conservation (MARBEC) UMR 9190 Université de Montpellier Montpellier, France
   School of Biological Sciences University of Auckland Auckland ,New Zealand
   Institute of Marine Science University of Auckland Auckland ,New Zealand
C2 NIWA, NEW ZEALAND
   NIWA, NEW ZEALAND
   FISHERIES NEW ZEALAND, NEW ZEALAND
   UNIV MONTPELLIER, FRANCE
   UNIV AUCKLAND, NEW ZEALAND
   UNIV AUCKLAND, NEW ZEALAND
UM MARBEC
IN WOS Cotutelle UMR
   DOAJ
   copubli-int-hors-europe
IF 5.139
TC 36
UR https://archimer.ifremer.fr/doc/00606/71827/70345.pdf
   https://archimer.ifremer.fr/doc/00606/71827/70346.pdf
LA English
DT Article
DE ;boosted regression tree models;cetacean distribution;New Zealand;relative environmental suitability models;spatial management;species distribution models
AB Aim Cetaceans are inherently difficult to study due to their elusive, pelagic and often highly migratory nature. New Zealand waters are home to 50% of the world's cetacean species, but their spatial distributions are poorly known. Here, we model distributions of 30 cetacean taxa using an extensive at‐sea sightings dataset (n > 14,000) and high‐resolution (1 km2) environmental data layers. Location New Zealand's Exclusive Economic Zone (EEZ). Methods Two models were used to predict probability of species occurrence based on available sightings records. For taxa with <50 sightings (n = 15), Relative Environmental Suitability (RES), and for taxa with ≥50 sightings (n = 15), Boosted Regression Tree (BRT) models were used. Independently collected presence/absence data were used for further model evaluation for a subset of taxa. Results RES models for rarely sighted species showed reasonable fits to available sightings and stranding data based on literature and expert knowledge on the species' autecology. BRT models showed high predictive power for commonly sighted species (AUC: 0.79–0.99). Important variables for predicting the occurrence of cetacean taxa were temperature residuals, bathymetry, distance to the 500 m isobath, mixed layer depth and water turbidity. Cetacean distribution patterns varied from highly localised, nearshore (e.g., Hector's dolphin), to more ubiquitous (e.g., common dolphin) to primarily offshore species (e.g., blue whale). Cetacean richness based on stacked species occurrence layers illustrated patterns of fewer inshore taxa with localised richness hotspots, and higher offshore richness especially in locales of the Macquarie Ridge, Bounty Trough and Chatham Rise. Main conclusions Predicted spatial distributions fill a major knowledge gap towards informing future assessments and conservation planning for cetaceans in New Zealand's extensive EEZ. While sightings datasets were not spatially comprehensive for any taxa, these two best available approaches allow for predictive modelling of both more common, and of rarely sighted, cetacean species with limited available information. 
PY 2020
PD APR
SO Diversity And Distributions
SN 1366-9516
PU Wiley
VL 26
IS 4
UT 000510589200001
BP 495
EP 516
DI 10.1111/ddi.13035
ID 71827
ER

EF