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Projecting species’ range expansion dynamics: sources of systematic biases when scaling up patterns and processes
Article first published online: 24 JUL 2012
DOI: 10.1111/j.2041-210X.2012.00235.x
© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society
Additional Information
How to Cite
Bocedi, G., Pe’er, G., Heikkinen, R. K., Matsinos, Y. and Travis, J. M. J. (2012), Projecting species’ range expansion dynamics: sources of systematic biases when scaling up patterns and processes. Methods in Ecology and Evolution, 3: 1008–1018. doi: 10.1111/j.2041-210X.2012.00235.x
Publication History
- Issue published online: 11 DEC 2012
- Article first published online: 24 JUL 2012
- Received 18 January 2012; accepted 18 June 2012 Handling Editor: Robert Freckleton
Keywords:
- biases;
- dispersal;
- dynamic modelling;
- individual-based model;
- map resolution;
- population dynamics;
- range expansion;
- scaling
Summary
1. Dynamic simulation models are a promising tool for assessing how species respond to habitat fragmentation and climate change. However, sensitivity of their outputs to impacts of spatial resolution is insufficiently known.
2. Using an individual-based dynamic model for species’ range expansion, we demonstrate an inherent risk of substantial biases resulting from choices relating to the resolution at which key patterns and processes are modelled.
3. Increasing cell size leads to overestimating dispersal distances, the extent of the range shift and population size. Overestimation accelerates with cell size for species with short dispersal capacity and is particularly severe in highly fragmented landscapes.
4. The overestimation results from three main interacting sources: homogenisation of spatial information, alteration of dispersal kernels and stabilisation/aggregation of population dynamics.
5. We urge for caution in selecting the spatial resolution used in dynamic simulations and other predictive models and highlight the urgent need to develop upscaling methods that maintain important patterns and processes at fine scales.