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Detecting climate change induced range shifts: Where and how should we be looking?

Authors

  • LUKE P. SHOO,

    1. School of Environmental and Applied Sciences, Griffith University Gold Coast, PMB 50 Gold Coast Mail Centre, Bundall, QLD 9726, Australia (Email: L.Shoo@griffith.edu.au),
    2. Cooperative Research Centre for Tropical Rainforest Ecology and Management Cairns, Queensland, Australia
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  • STEPHEN E. WILLIAMS,

    1. School of Tropical Biology, James Cook University of North Queensland, Townsville, Queensland, Australia and
    2. Cooperative Research Centre for Tropical Rainforest Ecology and Management Cairns, Queensland, Australia
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  • JEAN-MARC HERO

    1. School of Environmental and Applied Sciences, Griffith University Gold Coast, PMB 50 Gold Coast Mail Centre, Bundall, QLD 9726, Australia (Email: L.Shoo@griffith.edu.au),
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Abstract

Abstract:  Global climate warming is expected to cause systematic shifts in the distribution of species and consequently increase extinction risk. Conservation managers must be able to detect, measure and accurately predict range shifts in order to mitigate impacts on biodiversity. However, important responses to climate change may go unnoticed or be dismissed if we fail to collect sufficient baseline data and apply the most sensitive analytical tests. Here we use randomizations of a contemporary data set on rainforest birds of north-eastern Australia to quantify the sensitivity of three measures for assessing range shifts along altitudinal gradients. We find that smaller range shifts are detectable by analysing change in the mean altitude of presence records rather than upper or lower range boundaries. For a moderate survey effort of 96 surveys, measurements of change in the mean altitude of 34 species have the capacity to provide strong inference for a mean altitudinal range shift as small as 40 m across the species assemblage. We also show that range shifts measured at range boundaries can be potentially misleading when differences in sampling effort between contemporary and historical data sets are not taken into account.

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