Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches
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 a...
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Online Access: | https://archimer.ifremer.fr/doc/00860/97181/106054.pdf https://archimer.ifremer.fr/doc/00860/97181/106055.pdf https://archimer.ifremer.fr/doc/00860/97181/106056.pdf https://archimer.ifremer.fr/doc/00860/97181/106057.pdf https://archimer.ifremer.fr/doc/00860/97181/106058.pdf https://doi.org/10.1111/ddi.12782 https://archimer.ifremer.fr/doc/00860/97181/ |
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ftarchimer:oai:archimer.ifremer.fr:97181 2023-12-10T09:50:37+01:00 Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire 2018-11 application/pdf https://archimer.ifremer.fr/doc/00860/97181/106054.pdf https://archimer.ifremer.fr/doc/00860/97181/106055.pdf https://archimer.ifremer.fr/doc/00860/97181/106056.pdf https://archimer.ifremer.fr/doc/00860/97181/106057.pdf https://archimer.ifremer.fr/doc/00860/97181/106058.pdf https://doi.org/10.1111/ddi.12782 https://archimer.ifremer.fr/doc/00860/97181/ eng eng Wiley https://archimer.ifremer.fr/doc/00860/97181/106054.pdf https://archimer.ifremer.fr/doc/00860/97181/106055.pdf https://archimer.ifremer.fr/doc/00860/97181/106056.pdf https://archimer.ifremer.fr/doc/00860/97181/106057.pdf https://archimer.ifremer.fr/doc/00860/97181/106058.pdf doi:10.1111/ddi.12782 https://archimer.ifremer.fr/doc/00860/97181/ info:eu-repo/semantics/openAccess restricted use Diversity And Distributions (1366-9516) (Wiley), 2018-11 , Vol. 24 , N. 11 , P. 1657-1673 citizen science generalized regression humpback whales machine learning species distribution models support vector machines text Article info:eu-repo/semantics/article 2018 ftarchimer https://doi.org/10.1111/ddi.12782 2023-11-14T23:51:12Z 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, GLMs generally had low predictive performance, SVMs were particularly hard to interpret, and BRTs had high descriptive power but showed signs of overfitting. MAXENT and especially GAMs provided a valuable complexity trade-off, accurate predictions and were ecologically intelligible. Models showed that humpback whales favoured cool (22-23 degrees 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 Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) Diversity and Distributions 24 11 1657 1673 |
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Open Polar |
collection |
Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) |
op_collection_id |
ftarchimer |
language |
English |
topic |
citizen science generalized regression humpback whales machine learning species distribution models support vector machines |
spellingShingle |
citizen science generalized regression humpback whales machine learning species distribution models support vector machines Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches |
topic_facet |
citizen science generalized regression humpback whales machine learning species distribution models support vector machines |
description |
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, GLMs generally had low predictive performance, SVMs were particularly hard to interpret, and BRTs had high descriptive power but showed signs of overfitting. MAXENT and especially GAMs provided a valuable complexity trade-off, accurate predictions and were ecologically intelligible. Models showed that humpback whales favoured cool (22-23 degrees 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 ... |
format |
Article in Journal/Newspaper |
author |
Derville, Solene Torres, Leigh G. Iovan, Corina Garrigue, Claire |
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 |
https://archimer.ifremer.fr/doc/00860/97181/106054.pdf https://archimer.ifremer.fr/doc/00860/97181/106055.pdf https://archimer.ifremer.fr/doc/00860/97181/106056.pdf https://archimer.ifremer.fr/doc/00860/97181/106057.pdf https://archimer.ifremer.fr/doc/00860/97181/106058.pdf https://doi.org/10.1111/ddi.12782 https://archimer.ifremer.fr/doc/00860/97181/ |
genre |
Megaptera novaeangliae |
genre_facet |
Megaptera novaeangliae |
op_source |
Diversity And Distributions (1366-9516) (Wiley), 2018-11 , Vol. 24 , N. 11 , P. 1657-1673 |
op_relation |
https://archimer.ifremer.fr/doc/00860/97181/106054.pdf https://archimer.ifremer.fr/doc/00860/97181/106055.pdf https://archimer.ifremer.fr/doc/00860/97181/106056.pdf https://archimer.ifremer.fr/doc/00860/97181/106057.pdf https://archimer.ifremer.fr/doc/00860/97181/106058.pdf doi:10.1111/ddi.12782 https://archimer.ifremer.fr/doc/00860/97181/ |
op_rights |
info:eu-repo/semantics/openAccess restricted use |
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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1784895808243499008 |