Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches
Abstract Aim Accurate predictions of cetacean distributions are essential to their conservation but are limited by statistical challenges and a paucity of data. This study aimed at comparing the capacity of various statistical algorithms to deal with biases commonly found in nonsystematic cetacean s...
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crwiley:10.1111/ddi.12782 2024-06-23T07:54:33+00:00 Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire Elith, Jane World Wildlife Fund International Fund for Animal Welfare 2018 http://dx.doi.org/10.1111/ddi.12782 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.12782 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12782 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Diversity and Distributions volume 24, issue 11, page 1657-1673 ISSN 1366-9516 1472-4642 journal-article 2018 crwiley https://doi.org/10.1111/ddi.12782 2024-06-11T04:38:58Z Abstract Aim Accurate predictions of cetacean distributions are essential to their conservation but are limited by statistical challenges and a paucity of data. This study aimed at comparing the capacity of various statistical algorithms to deal with biases commonly found in nonsystematic cetacean surveys and to evaluate the potential for citizen science data to improve habitat modelling and predictions. An endangered population of humpback whales ( Megaptera novaeangliae ) in their breeding ground was used as a case study. Location New Caledonia, Oceania. Methods Five statistical algorithms were used to model the habitat preferences of humpback whales from 1,360 sightings collected over 14 years of nonsystematic research surveys. Three different background sampling approaches were tested when developing models from 625 crowdsourced sightings to assess methods accounting for citizen science spatial sampling bias. Model evaluation was conducted through cross‐validation and prediction to an independent satellite tracking dataset. Results Algorithms differed in complexity of the environmental relationships modelled, ecological interpretability and transferability. While parameter tuning had a great effect on model performances, GLM s generally had low predictive performance, SVM s were particularly hard to interpret, and BRT s had high descriptive power but showed signs of overfitting. MAXENT and especially GAM s provided a valuable complexity trade‐off, accurate predictions and were ecologically intelligible. Models showed that humpback whales favoured cool (22–23°C) and shallow waters (0–100 m deep) in coastal as well as offshore areas. Citizen science models converged with research survey models, specifically when accounting for spatial sampling bias. Main conclusions Marine megafauna distribution models present specific challenges that may be addressed through integrative evaluation, independent testing and appropriately tuned statistical algorithms. Specifically, controlling overfitting is a priority when ... Article in Journal/Newspaper Megaptera novaeangliae Wiley Online Library Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Diversity and Distributions 24 11 1657 1673 |
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Wiley Online Library |
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crwiley |
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English |
description |
Abstract Aim Accurate predictions of cetacean distributions are essential to their conservation but are limited by statistical challenges and a paucity of data. This study aimed at comparing the capacity of various statistical algorithms to deal with biases commonly found in nonsystematic cetacean surveys and to evaluate the potential for citizen science data to improve habitat modelling and predictions. An endangered population of humpback whales ( Megaptera novaeangliae ) in their breeding ground was used as a case study. Location New Caledonia, Oceania. Methods Five statistical algorithms were used to model the habitat preferences of humpback whales from 1,360 sightings collected over 14 years of nonsystematic research surveys. Three different background sampling approaches were tested when developing models from 625 crowdsourced sightings to assess methods accounting for citizen science spatial sampling bias. Model evaluation was conducted through cross‐validation and prediction to an independent satellite tracking dataset. Results Algorithms differed in complexity of the environmental relationships modelled, ecological interpretability and transferability. While parameter tuning had a great effect on model performances, GLM s generally had low predictive performance, SVM s were particularly hard to interpret, and BRT s had high descriptive power but showed signs of overfitting. MAXENT and especially GAM s provided a valuable complexity trade‐off, accurate predictions and were ecologically intelligible. Models showed that humpback whales favoured cool (22–23°C) and shallow waters (0–100 m deep) in coastal as well as offshore areas. Citizen science models converged with research survey models, specifically when accounting for spatial sampling bias. Main conclusions Marine megafauna distribution models present specific challenges that may be addressed through integrative evaluation, independent testing and appropriately tuned statistical algorithms. Specifically, controlling overfitting is a priority when ... |
author2 |
Elith, Jane World Wildlife Fund International Fund for Animal Welfare |
format |
Article in Journal/Newspaper |
author |
Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire |
spellingShingle |
Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
author_facet |
Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire |
author_sort |
Derville, Solene |
title |
Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
title_short |
Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
title_full |
Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
title_fullStr |
Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
title_full_unstemmed |
Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
title_sort |
finding the right fit: comparative cetacean distribution models using multiple data sources and statistical approaches |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1111/ddi.12782 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.12782 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12782 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Gam |
geographic_facet |
Gam |
genre |
Megaptera novaeangliae |
genre_facet |
Megaptera novaeangliae |
op_source |
Diversity and Distributions volume 24, issue 11, page 1657-1673 ISSN 1366-9516 1472-4642 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/ddi.12782 |
container_title |
Diversity and Distributions |
container_volume |
24 |
container_issue |
11 |
container_start_page |
1657 |
op_container_end_page |
1673 |
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1802646735141470208 |