Can internal range structure predict range shifts?

Poleward and uphill range shifts are a common-but variable-response to climate change. We lack understanding regarding this interspecific variation; for example, functional traits show weak or mixed ability to predict range shifts. Characteristics of species' ranges may enhance prediction of range shifts. However, the explanatory power of many range characteristics-especially within-range abundance patterns-remains untested. Here, we introduce a hypothesis framework for predicting range-limit population trends and range shifts from the internal structure of the geographic range, specifically range edge hardness, defined as abundance within range edges relative to the whole range. The inertia hypothesis predicts that high edge abundance facilitates expansions along the leading range edge but creates inertia (either more individuals must disperse or perish) at the trailing range edge such that the trailing edge recedes slowly. In contrast, the limitation hypothesis suggests that hard range edges are the signature of strong limits (e.g. biotic interactions) that force faster contraction of the trailing edge but block expansions at the leading edge of the range. Using a long-term avian monitoring dataset from northern Minnesota, USA, we estimated population trends for 35 trailing-edge species and 18 leading-edge species and modelled their population trends as a function of range edge hardness derived from eBird data. We found limited evidence of associations between range edge hardness and range-limit population trends. Trailing-edge species with harder range edges were slightly more likely to be declining, demonstrating weak support for the limitation hypothesis. In contrast, leading-edge species with harder range edges were slightly more likely to be increasing, demonstrating weak support for the inertia hypothesis. These opposing results for the leading and trailing range edges might suggest that different mechanisms underpin range expansions and contractions, respectively. As data and state-of-the-art modelling efforts continue to proliferate, we will be ever better equipped to map abundance patterns within species' ranges, offering opportunities to anticipate range shifts through the lens of the geographic range.