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Machine Learning for Conservation Planning in a Changing Climate

Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
Author to whom correspondence should be addressed.
Sustainability 2020, 12(18), 7657;
Received: 21 July 2020 / Revised: 31 August 2020 / Accepted: 12 September 2020 / Published: 16 September 2020
Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others. View Full-Text
Keywords: sage-grouse; climate change; machine learning; species distribution modeling sage-grouse; climate change; machine learning; species distribution modeling
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MDPI and ACS Style

Mosebo Fernandes, A.C.; Quintero Gonzalez, R.; Lenihan-Clarke, M.A.; Leslie Trotter, E.F.; Jokar Arsanjani, J. Machine Learning for Conservation Planning in a Changing Climate. Sustainability 2020, 12, 7657.

AMA Style

Mosebo Fernandes AC, Quintero Gonzalez R, Lenihan-Clarke MA, Leslie Trotter EF, Jokar Arsanjani J. Machine Learning for Conservation Planning in a Changing Climate. Sustainability. 2020; 12(18):7657.

Chicago/Turabian Style

Mosebo Fernandes, Ana C., Rebeca Quintero Gonzalez, Marie A. Lenihan-Clarke, Ezra F. Leslie Trotter, and Jamal Jokar Arsanjani. 2020. "Machine Learning for Conservation Planning in a Changing Climate" Sustainability 12, no. 18: 7657.

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