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Can managers inform models? Integrating local knowledge into models of red deer habitat use
Article first published online: 3 MAR 2009
DOI: 10.1111/j.1365-2664.2009.01626.x
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Additional Information
How to Cite
Irvine, R. J., Fiorini, S., Yearley, S., McLeod, J. E., Turner, A., Armstrong, H., White, P. C. L. and Van Der Wal, R. (2009), Can managers inform models? Integrating local knowledge into models of red deer habitat use. Journal of Applied Ecology, 46: 344–352. doi: 10.1111/j.1365-2664.2009.01626.x
Publication History
- Issue published online: 3 MAR 2009
- Article first published online: 3 MAR 2009
- Received 31 July 2008; accepted 13 January 2009Handling Editor: Simon Thirgood
Keywords:
- natural resource management;
- deer;
- GIS;
- participation;
- habitat use;
- shelter;
- range use;
- local knowledge
Summary
- 1Many ecologically based wildlife-habitat models provide only limited explanations of the observed data because they do not take account of the way in which key factors driving distribution interact with local management. If models are to be credible tools for developing solutions for wildlife management, they need to integrate scientific knowledge with the wealth of knowledge held by those who manage these resources.
- 2In this study, we develop a participatory approach to integrate local knowledge from deer managers with formal scientific understanding and ecological spatial data in a simple Geographic Information System (GIS) to predict red deer Cervus elaphus L. distribution in the uplands of Scotland. We evaluate the extent to which the predictions are improved by this process.
- 3The initial GIS prediction matched both managers’ experience of deer locations and the independently derived deer point count data in around 50% of all cases.
- 4An analysis of interviews with managers indicated that for red deer, shelter provided by habitat characteristics was more important than topographic shelter or the forage value of the habitat. Disturbance, slope and elevation were also important. Analysis of the underlying spatial characteristics of those areas preferred by deer, as defined by managers, indicated similar relative importance of these factors in driving deer distribution.
- 5The model was modified to incorporate the managers’ knowledge and new predictions were evaluated against existing deer distribution data. The match between point counts and areas predicted by the model as being highly suitable for deer increased from around 50% to around 80%.
- 6Synthesis and applications. Our evaluations demonstrate the validity of using local knowledge which can substantially improve the predictions from simple spatial models of deer habitat suitability. Our approach enables knowledge from different sources and at different spatial scales to be combined to give realistic predictions of deer distribution at an appropriate scale. Such participatory approaches to wildlife-habitat model development have the potential to improve communication and consensus across ownership boundaries where different management objectives exist.