Modelling fish physico-thermal habitat selection using functional regression
In this paper, a new fish habitat modelling approach is introduced using the full probability density functions (PDF), rather than single measurements or central tendency metrics, to describe each predictor. To model habitat selection using PDFs, functional regression models (FRM) are used to allow...
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ftdatacite:10.6084/m9.figshare.13818257 2023-05-15T15:32:46+02:00 Modelling fish physico-thermal habitat selection using functional regression Boudreault, Jérémie St-Hilaire, André Chebana, Fateh Bergeron, Normand E. 2021 https://dx.doi.org/10.6084/m9.figshare.13818257 https://tandf.figshare.com/articles/journal_contribution/Modelling_fish_physico-thermal_habitat_selection_using_functional_regression/13818257 unknown Taylor & Francis https://dx.doi.org/10.1080/24705357.2020.1840313 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Marine Biology Inorganic Chemistry FOS Chemical sciences Text article-journal Journal contribution ScholarlyArticle 2021 ftdatacite https://doi.org/10.6084/m9.figshare.13818257 https://doi.org/10.1080/24705357.2020.1840313 2021-11-05T12:55:41Z In this paper, a new fish habitat modelling approach is introduced using the full probability density functions (PDF), rather than single measurements or central tendency metrics, to describe each predictor. To model habitat selection using PDFs, functional regression models (FRM) are used to allow for the inclusion of curves or functions (smoothed empirical PDFs) in regression models compared to scalars or vectors in classical contexts. The benefits of FRM are exemplified by comparing results with those obtained using generalized additive models (GAM), one of the most recent and performing models in the field. Abundance of juvenile Atlantic salmon sampled at 26 sites (75 m-long x river width) of the Sainte-Marguerite River (Quebec, Canada) was modelled with PDFs of four potential predictors: flow velocity, water depth, substrate size and water temperature. The latter has been less frequently used in habitat modelling and the results showed that it was the most significant predictor. Overall, FRM explained more of the variability in habitat selection than GAM (+14.9% for fry and +8.1% for 1+ parr), mainly due to their ability to use complete distributions of the habitat variables rather than aggregated values (mean). A leave-one-out cross validation showed that both GAM and FRM had similar performance to predict fish abundance. The use of FRM in fish habitat modelling is innovative and its potential should be further developed, especially in the current context where habitat variables are becoming increasingly easy to obtain due to rapid progress of remote measurement techniques. Text Atlantic salmon DataCite Metadata Store (German National Library of Science and Technology) Canada Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Marguerite ENVELOPE(141.378,141.378,-66.787,-66.787) Marguerite River ENVELOPE(-109.929,-109.929,57.560,57.560) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Marine Biology Inorganic Chemistry FOS Chemical sciences |
spellingShingle |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Marine Biology Inorganic Chemistry FOS Chemical sciences Boudreault, Jérémie St-Hilaire, André Chebana, Fateh Bergeron, Normand E. Modelling fish physico-thermal habitat selection using functional regression |
topic_facet |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences 69999 Biological Sciences not elsewhere classified Marine Biology Inorganic Chemistry FOS Chemical sciences |
description |
In this paper, a new fish habitat modelling approach is introduced using the full probability density functions (PDF), rather than single measurements or central tendency metrics, to describe each predictor. To model habitat selection using PDFs, functional regression models (FRM) are used to allow for the inclusion of curves or functions (smoothed empirical PDFs) in regression models compared to scalars or vectors in classical contexts. The benefits of FRM are exemplified by comparing results with those obtained using generalized additive models (GAM), one of the most recent and performing models in the field. Abundance of juvenile Atlantic salmon sampled at 26 sites (75 m-long x river width) of the Sainte-Marguerite River (Quebec, Canada) was modelled with PDFs of four potential predictors: flow velocity, water depth, substrate size and water temperature. The latter has been less frequently used in habitat modelling and the results showed that it was the most significant predictor. Overall, FRM explained more of the variability in habitat selection than GAM (+14.9% for fry and +8.1% for 1+ parr), mainly due to their ability to use complete distributions of the habitat variables rather than aggregated values (mean). A leave-one-out cross validation showed that both GAM and FRM had similar performance to predict fish abundance. The use of FRM in fish habitat modelling is innovative and its potential should be further developed, especially in the current context where habitat variables are becoming increasingly easy to obtain due to rapid progress of remote measurement techniques. |
format |
Text |
author |
Boudreault, Jérémie St-Hilaire, André Chebana, Fateh Bergeron, Normand E. |
author_facet |
Boudreault, Jérémie St-Hilaire, André Chebana, Fateh Bergeron, Normand E. |
author_sort |
Boudreault, Jérémie |
title |
Modelling fish physico-thermal habitat selection using functional regression |
title_short |
Modelling fish physico-thermal habitat selection using functional regression |
title_full |
Modelling fish physico-thermal habitat selection using functional regression |
title_fullStr |
Modelling fish physico-thermal habitat selection using functional regression |
title_full_unstemmed |
Modelling fish physico-thermal habitat selection using functional regression |
title_sort |
modelling fish physico-thermal habitat selection using functional regression |
publisher |
Taylor & Francis |
publishDate |
2021 |
url |
https://dx.doi.org/10.6084/m9.figshare.13818257 https://tandf.figshare.com/articles/journal_contribution/Modelling_fish_physico-thermal_habitat_selection_using_functional_regression/13818257 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) ENVELOPE(141.378,141.378,-66.787,-66.787) ENVELOPE(-109.929,-109.929,57.560,57.560) |
geographic |
Canada Gam Marguerite Marguerite River |
geographic_facet |
Canada Gam Marguerite Marguerite River |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_relation |
https://dx.doi.org/10.1080/24705357.2020.1840313 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.13818257 https://doi.org/10.1080/24705357.2020.1840313 |
_version_ |
1766363261162225664 |