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

Jonathan Gaudreau,Liliana Perez,Saeed Harati

doi:10.3390/ijgi7090335

Jonathan Gaudreau, Liliana Perez + Show 1 more

Open Access

https://doi.org/10.3390/ijgi7090335

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Abstract

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.

Highlights

During the last two decades, climate change has been receiving growing attention in environmental studies, and it has been named as one of the main possible drivers of ecological change in the upcoming decades [1,2,3]
We identified the explanatory variables linked to the spatial distribution of 37 boreal bird species via statistical modeling methods applied to multivariate data, such as redundancy canonical analysis (RDA) and variation partitioning
The two statistical modeling methods implemented in our research require different inputs; the redundancy canonical analysis (RDA) needs a dataset where the observations are noted by sampling site

Summary

Introduction

During the last two decades, climate change has been receiving growing attention in environmental studies, and it has been named as one of the main possible drivers of ecological change in the upcoming decades [1,2,3]. Terrestrial ecosystems have to adapt to early springs and fauna and flora have already started migrating, in most cases towards higher latitudes or elevations, where topography allows [4,5,6,7]. Similar research projects completed in North America, for bird species, conclude that migration in the direction of higher latitudes or elevations has been observed when topography allows it [8,9,10]. This hypothesis is corroborated by studies developed in Antarctica, the Arctic and northern hemisphere temperate areas [3,10]. If temperatures continue to rise, in the boreal areas, forest ecosystems may be facing considerable changes [11,12,13] which could in consequence facilitate shifts and reductions in suitable bird habitats [14]

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