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      Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change

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          Abstract

          Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.

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          Most cited references7

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          Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment.

          The Millennium Ecosystem Assessment (MA) introduced a new framework for analyzing social-ecological systems that has had wide influence in the policy and scientific communities. Studies after the MA are taking up new challenges in the basic science needed to assess, project, and manage flows of ecosystem services and effects on human well-being. Yet, our ability to draw general conclusions remains limited by focus on discipline-bound sectors of the full social-ecological system. At the same time, some polices and practices intended to improve ecosystem services and human well-being are based on untested assumptions and sparse information. The people who are affected and those who provide resources are increasingly asking for evidence that interventions improve ecosystem services and human well-being. New research is needed that considers the full ensemble of processes and feedbacks, for a range of biophysical and social systems, to better understand and manage the dynamics of the relationship between humans and the ecosystems on which they rely. Such research will expand the capacity to address fundamental questions about complex social-ecological systems while evaluating assumptions of policies and practices intended to advance human well-being through improved ecosystem services.
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            Niches, models, and climate change: assessing the assumptions and uncertainties.

            As the rate and magnitude of climate change accelerate, understanding the consequences becomes increasingly important. Species distribution models (SDMs) based on current ecological niche constraints are used to project future species distributions. These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities, the quality and quantity of data, and mismatches between the scales of modeling and data. We illustrate the application of SDMs using two climate models and two distributional algorithms, together with information on distributional shifts in vegetation types, to project fine-scale future distributions of 60 California landbird species. Most species are projected to decrease in distribution by 2070. Changes in total species richness vary over the state, with large losses of species in some "hotspots" of vulnerability. Differences in distributional shifts among species will change species co-occurrences, creating spatial variation in similarities between current and future assemblages. We use these analyses to consider how assumptions can be addressed and uncertainties reduced. SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. Doing this will require that the sources and magnitudes of uncertainty are documented, and that conservationists and resource managers be willing to act despite the uncertainties. The alternative, of ignoring the future, is not an option.
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              Why is the choice of future climate scenarios for species distribution modelling important?

              Species distribution models (SDMs) are common tools for assessing the potential impact of climate change on species ranges. Uncertainty in SDM output occurs due to differences among alternate models, species characteristics and scenarios of future climate. While considerable effort is being devoted to identifying and quantifying the first two sources of variation, a greater understanding of climate scenarios and how they affect SDM output is also needed. Climate models are complex tools: variability occurs among alternate simulations, and no single 'best' model exists. The selection of climate scenarios for impacts assessments should not be undertaken arbitrarily - strengths and weakness of different climate models should be considered. In this paper, we provide bioclimatic modellers with an overview of emissions scenarios and climate models, discuss uncertainty surrounding projections of future climate and suggest steps that can be taken to reduce and communicate climate scenario-related uncertainty in assessments of future species responses to climate change.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                24 November 2014
                : 9
                : 11
                : e113749
                Affiliations
                [1 ]Fenner School of Environment and Society, College of Medicine, Biology and Environment, Australian National University, Canberra, Australian Capital Territory, Australia
                [2 ]Antarctic Climate & Ecosystems Cooperative Research Centre, Hobart, Tasmania, Australia
                [3 ]Centre for the Environment, University of Tasmania, Hobart, Tasmania, Australia
                [4 ]Griffith Climate Change Response Program, Griffith University, Gold Coast, Queensland, Australia
                [5 ]Australia Research Council, Centre of Excellence in Climate System Science, Hobart, Tasmania, Australia
                [6 ]The Commonwealth Scientific and Industrial Research Organisation, Oceans & Atmosphere Flagship, Hobart, Tasmania, Australia
                University of New England, Australia
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: LLP RMBH. Performed the experiments: LLP RMBH SH GL. Analyzed the data: LLP RMBH SH GL BM ECL. Contributed reagents/materials/analysis tools: LLP RMBH SH GL BM SFG ECL NLB. Wrote the paper: LLP RMBH GL BM.

                Article
                PONE-D-14-41407
                10.1371/journal.pone.0113749
                4242662
                25420020
                65ca9822-c553-44fa-b612-b96efaff4b02
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 September 2014
                : 29 October 2014
                Page count
                Pages: 21
                Funding
                This research is an output from the Landscapes and Policy Research Hub. The hub is supported through funding from the Australian Government's National Environmental Research Programme and involves researchers from the University of Tasmania (UTAS), The Australian National University (ANU), Murdoch University, the Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC), Griffith University and Charles Sturt University (CSU). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Biological Data Management
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Computer Applications
                Computer-Assisted Instruction
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Ecology and Environmental Sciences
                Conservation Science
                Environmental Management
                Environmental Protection
                Habitats
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Models
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. The modelled projections are available through the Tasmanian Partnership for Advanced Computing (TPAC) portal ( https://dl.tpac.org.au/tpacportal/). The literature review data were submitted as a database in the supplementary informant ion, as a ZIP file.

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