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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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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1766230920978759680 |