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Pitch the niche – taking responsibility for the concepts we use in ecology and species distribution modelling


  • Greg J. McInerny,

    Corresponding author
    1. Computational Ecology and Environmental Science Group, Computational Science Laboratory, Microsoft Research, Cambridge, UK
    • Department of Computer Science, University of Oxford, Oxford, UK
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  • Rampal S. Etienne

    1. Community and Conservation Ecology Group, Centre for Ecological and Evolutionary Studies, University of Groningen, Groningen, The Netherlands
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Correspondence: Greg J. McInerny, Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.



In a discussion it is often easier to staunchly reject or offer resolute support for an idea. This third paper on the niche concept aims to develop a balanced argument by exploring general principles for determining an appropriate level for pitching the niche concept that will guide better use and less abuse of niche concepts. To do this we first have to accept that niche concepts are not necessarily essential for ecology. Rather than to improve niche concepts, our aim should then be to pitch the niche in terms of ecology. This aim helps us develop an ‘ultimate goal of the niche’ by which we can evaluate the concepts we use. For species distribution modelling, there has been a focus on the niche as an equilibrium outcome that perhaps has less relevance for disequilibrium situations (e.g. climate change projections). As is the case for much of ecology, more causal explanations of species' distributions use alternative terminologies and less frequently use the word ‘niche’. We suggest that niche concepts that are better aligned with the rest of ecology could arise from taking more responsibility for our own implementations, and by explaining our models with terms other than niche. A general, holistic niche concept promotes this view and promotes practical thinking about what we are modelling and how we interpret those models, which in turn should help inspire and support innovative modelling approaches in species distribution modelling.

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