Modelling the spatial distribution of cetaceans in New Zealand waters
Abstract 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...
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Online Access: | http://dx.doi.org/10.1111/ddi.13035 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.13035 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13035 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ddi.13035 |
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crwiley:10.1111/ddi.13035 2024-06-23T07:51:49+00:00 Modelling the spatial distribution of cetaceans in New Zealand waters Stephenson, Fabrice Goetz, Kimberly Sharp, Ben R. Mouton, Théophile L. Beets, Fenna L. Roberts, Jim MacDiarmid, Alison B. Constantine, Rochelle Lundquist, Carolyn J. Sarmento Cabral, Juliano NIWA Coasts and Oceans Programme New Zealand Ministry of Primary Industries 2020 http://dx.doi.org/10.1111/ddi.13035 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.13035 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13035 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ddi.13035 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Diversity and Distributions volume 26, issue 4, page 495-516 ISSN 1366-9516 1472-4642 journal-article 2020 crwiley https://doi.org/10.1111/ddi.13035 2024-05-31T08:13:36Z Abstract 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 km 2 ) 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 ... Article in Journal/Newspaper Blue whale Wiley Online Library Bounty Trough ENVELOPE(178.070,178.070,-45.499,-45.499) New Zealand Diversity and Distributions 26 4 495 516 |
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Open Polar |
collection |
Wiley Online Library |
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crwiley |
language |
English |
description |
Abstract 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 km 2 ) 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 ... |
author2 |
Sarmento Cabral, Juliano NIWA Coasts and Oceans Programme New Zealand Ministry of Primary Industries |
format |
Article in Journal/Newspaper |
author |
Stephenson, Fabrice Goetz, Kimberly Sharp, Ben R. Mouton, Théophile L. Beets, Fenna L. Roberts, Jim MacDiarmid, Alison B. Constantine, Rochelle Lundquist, Carolyn J. |
spellingShingle |
Stephenson, Fabrice Goetz, Kimberly Sharp, Ben R. Mouton, Théophile L. Beets, Fenna L. Roberts, Jim MacDiarmid, Alison B. Constantine, Rochelle Lundquist, Carolyn J. Modelling the spatial distribution of cetaceans in New Zealand waters |
author_facet |
Stephenson, Fabrice Goetz, Kimberly Sharp, Ben R. Mouton, Théophile L. Beets, Fenna L. Roberts, Jim MacDiarmid, Alison B. Constantine, Rochelle Lundquist, Carolyn J. |
author_sort |
Stephenson, Fabrice |
title |
Modelling the spatial distribution of cetaceans in New Zealand waters |
title_short |
Modelling the spatial distribution of cetaceans in New Zealand waters |
title_full |
Modelling the spatial distribution of cetaceans in New Zealand waters |
title_fullStr |
Modelling the spatial distribution of cetaceans in New Zealand waters |
title_full_unstemmed |
Modelling the spatial distribution of cetaceans in New Zealand waters |
title_sort |
modelling the spatial distribution of cetaceans in new zealand waters |
publisher |
Wiley |
publishDate |
2020 |
url |
http://dx.doi.org/10.1111/ddi.13035 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.13035 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13035 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ddi.13035 |
long_lat |
ENVELOPE(178.070,178.070,-45.499,-45.499) |
geographic |
Bounty Trough New Zealand |
geographic_facet |
Bounty Trough New Zealand |
genre |
Blue whale |
genre_facet |
Blue whale |
op_source |
Diversity and Distributions volume 26, issue 4, page 495-516 ISSN 1366-9516 1472-4642 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1111/ddi.13035 |
container_title |
Diversity and Distributions |
container_volume |
26 |
container_issue |
4 |
container_start_page |
495 |
op_container_end_page |
516 |
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1802642958086832128 |