Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change

Adaptation to climate change requires prediction of its impacts, especially on ecosystems. In this work we simulated the change in bird species richness in the boreal forest of Quebec, Canada, under climate change scenarios. To do so, we first analyzed which geographical and bioclimatic variables we...

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Published in:ISPRS International Journal of Geo-Information
Main Authors: Jonathan Gaudreau, Liliana Perez, Saeed Harati
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2018
Subjects:
Online Access:https://doi.org/10.3390/ijgi7090335
https://doaj.org/article/7944e26111e74eb09b41edbd6441f656
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spelling ftdoajarticles:oai:doaj.org/article:7944e26111e74eb09b41edbd6441f656 2023-05-15T18:41:25+02:00 Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change Jonathan Gaudreau Liliana Perez Saeed Harati 2018-08-01T00:00:00Z https://doi.org/10.3390/ijgi7090335 https://doaj.org/article/7944e26111e74eb09b41edbd6441f656 EN eng MDPI AG http://www.mdpi.com/2220-9964/7/9/335 https://doaj.org/toc/2220-9964 2220-9964 doi:10.3390/ijgi7090335 https://doaj.org/article/7944e26111e74eb09b41edbd6441f656 ISPRS International Journal of Geo-Information, Vol 7, Iss 9, p 335 (2018) climate change boreal Quebec biogeography species richness bioclimatic modelling redundancy analysis (RDA) ecological change random forest (RF) Geography (General) G1-922 article 2018 ftdoajarticles https://doi.org/10.3390/ijgi7090335 2022-12-31T13:20:55Z Adaptation to climate change requires prediction of its impacts, especially on ecosystems. In this work we simulated the change in bird species richness in the boreal forest of Quebec, Canada, under climate change scenarios. To do so, we first analyzed which geographical and bioclimatic variables were the strongest predictors for the spatial distribution of the current resident bird species. Based on canonical redundancy analysis and analysis of variance, we found that annual temperature range, average temperature of the cold season, seasonality of precipitation, precipitation in the wettest season, elevation, and local percentage of wet area had the strongest influence on the species’ distributions. We used these variables with Random Forests, Multivariate Adaptive Regression Splines and Maximum Entropy models to explain spatial variations in species abundance. Future species distributions were calculated by replacing present climatic variables with projections under different climate change pathways. Subsequently, maps of species richness change were produced. The results showed a northward expansion of areas of highest species richness towards the center of the province. Species are also likely to appear near James Bay and Ungava Bay, where rapid climate change is expected. Article in Journal/Newspaper Ungava Bay James Bay Directory of Open Access Journals: DOAJ Articles Canada Ungava Bay ENVELOPE(-67.489,-67.489,59.498,59.498) ISPRS International Journal of Geo-Information 7 9 335
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic climate change
boreal Quebec
biogeography
species richness
bioclimatic modelling
redundancy analysis (RDA)
ecological change
random forest (RF)
Geography (General)
G1-922
spellingShingle climate change
boreal Quebec
biogeography
species richness
bioclimatic modelling
redundancy analysis (RDA)
ecological change
random forest (RF)
Geography (General)
G1-922
Jonathan Gaudreau
Liliana Perez
Saeed Harati
Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change
topic_facet climate change
boreal Quebec
biogeography
species richness
bioclimatic modelling
redundancy analysis (RDA)
ecological change
random forest (RF)
Geography (General)
G1-922
description Adaptation to climate change requires prediction of its impacts, especially on ecosystems. In this work we simulated the change in bird species richness in the boreal forest of Quebec, Canada, under climate change scenarios. To do so, we first analyzed which geographical and bioclimatic variables were the strongest predictors for the spatial distribution of the current resident bird species. Based on canonical redundancy analysis and analysis of variance, we found that annual temperature range, average temperature of the cold season, seasonality of precipitation, precipitation in the wettest season, elevation, and local percentage of wet area had the strongest influence on the species’ distributions. We used these variables with Random Forests, Multivariate Adaptive Regression Splines and Maximum Entropy models to explain spatial variations in species abundance. Future species distributions were calculated by replacing present climatic variables with projections under different climate change pathways. Subsequently, maps of species richness change were produced. The results showed a northward expansion of areas of highest species richness towards the center of the province. Species are also likely to appear near James Bay and Ungava Bay, where rapid climate change is expected.
format Article in Journal/Newspaper
author Jonathan Gaudreau
Liliana Perez
Saeed Harati
author_facet Jonathan Gaudreau
Liliana Perez
Saeed Harati
author_sort Jonathan Gaudreau
title Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change
title_short Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change
title_full Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change
title_fullStr Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change
title_full_unstemmed Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change
title_sort towards modelling future trends of quebec’s boreal birds’ species distribution under climate change
publisher MDPI AG
publishDate 2018
url https://doi.org/10.3390/ijgi7090335
https://doaj.org/article/7944e26111e74eb09b41edbd6441f656
long_lat ENVELOPE(-67.489,-67.489,59.498,59.498)
geographic Canada
Ungava Bay
geographic_facet Canada
Ungava Bay
genre Ungava Bay
James Bay
genre_facet Ungava Bay
James Bay
op_source ISPRS International Journal of Geo-Information, Vol 7, Iss 9, p 335 (2018)
op_relation http://www.mdpi.com/2220-9964/7/9/335
https://doaj.org/toc/2220-9964
2220-9964
doi:10.3390/ijgi7090335
https://doaj.org/article/7944e26111e74eb09b41edbd6441f656
op_doi https://doi.org/10.3390/ijgi7090335
container_title ISPRS International Journal of Geo-Information
container_volume 7
container_issue 9
container_start_page 335
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