Mapping wildlife: integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS
Z. Austin A , S. Cinderby B , J. C. R. Smart A , D. Raffaelli A and P. C. L. White A CA Environment Department, University of York, Heslington, York, YO10 5DD, UK.
B Stockholm Environment Institute, University of York, Heslington, York, YO10 5DD, UK.
C Corresponding author. Email: pclw1@york.ac.uk
Wildlife Research 36(7) 553-564 https://doi.org/10.1071/WR08153
Submitted: 28 October 2008 Accepted: 23 June 2009 Published: 28 October 2009
Abstract
Context. Some species that are perceived by certain stakeholders as a valuable resource can also cause ecological or economic damage, leading to contrasting management objectives and subsequent conflict between stakeholder groups. There is increasing recognition that the integration of stakeholder knowledge with formal scientific data can enhance the information available for use in management. This is especially true where scientific understanding is incomplete, as is frequently the case for wide-ranging species, which can be difficult to monitor directly at the landscape scale.
Aims. The aim of the research was to incorporate stakeholder knowledge with data derived from formal quantitative models to modify predictions of wildlife distribution and abundance, using wild deer in the UK as an example.
Methods. We use selected predictor variables from a deer–vehicle collision model to estimate deer densities at the 10-km square level throughout the East of England. With these predictions as a baseline, we illustrate the use of participatory GIS as a methodological framework for enabling stakeholder participation in the refinement of landscape-scale deer abundance maps.
Key results. Stakeholder participation resulted in modifications to modelled abundance patterns for all wild deer species present in the East of England, although the modifications were minor and there was a high degree of consistency among stakeholders in the adjustments made. For muntjac, roe and fallow deer, the majority of stakeholder changes represented an increase in density, suggesting that populations of these species are increasing in the region.
Conclusions. Our results show that participatory GIS is a useful technique for enabling stakeholders to contribute to incomplete scientific knowledge, especially where up-to-date species distribution and abundance data are needed to inform wildlife research and management.
Implications. The results of the present study will serve as a valuable information base for future research on deer management in the region. The flexibility of the approach makes it applicable to a range of species at different spatial scales and other wildlife conflict issues. These may include the management of invasive species or the conservation of threatened species, where accurate spatial data and enhanced community involvement are necessary in order to facilitate effective management.
Additional keywords: deer–vehicle collisions, geographic information systems, management conflict resolution, predictive spatial model, stakeholder participation.
Acknowledgements
This research was funded by the Natural Environment Research Council (NERC). We are grateful to all of the interviewees that participated in the research, to J. Langbein, A. Ward and the British Deer Society for providing data and to E. Willis and A. Owen for providing GIS knowledge and expertise. We are also grateful to two anonymous referees whose comments helped to improve the original manuscript.
Abbot, J. , Chambers, R. , Dunn, C. , Harris, T. , Merode, E. D. , Porter, G. , Townsend, J. , and Weiner, D. (1998). Participatory GIS: opportunity or oxymoron? PLA Notes 33, 27–34.
Chase, L. C. , Siemer, W. F. , and Decker, D. J. (2002). Designing stakeholder involvement strategies to resolve wildlife management controversies. Wildlife Society Bulletin 30, 937–950.
Cooke, A. , and Farrell, L. (2001). Impact of muntjac deer (Muntiacus reevesi) at Monks Wood National Nature Reserve, Cambridgeshire, eastern England. Forestry 74, 241–250.
| Crossref | GoogleScholarGoogle Scholar |
Côté, S. D. , Rooney, T. P. , Tremblay, J. P. , Dussault, C. , and Waller, D. M. (2004). Ecological impacts of deer overabundance. Annual Review of Ecology Evolution and Systematics 35, 113–147.
| Crossref | GoogleScholarGoogle Scholar |
Farrell, M. C. , and Tappe, P. A. (2007). County-level factors contributing to deer–vehicle collisions in Arkansas. Journal of Wildlife Management 71, 2727–2731.
| Crossref | GoogleScholarGoogle Scholar |
Hall, G. P. , and Gill, K. P. (2005). Management of wild deer in Australia. The Journal of Wildlife Management 69, 837–844.
| Crossref | GoogleScholarGoogle Scholar |
Irvine, R. J. , Fiorini, S. , McLeod, J. , Turner, A. , Van der Wal, R. , Armstrong, H. , Yearley, S. , and White, P. C. L. (2009). Can managers inform models? Integrating local knowledge into models of red deer habitat use. Journal of Applied Ecology 46, 344–352.
| Crossref | GoogleScholarGoogle Scholar |
Krumm, C. E. , Conner, M. M. , and Miller, M. W. (2005). Relative vulnerability of chronic wasting disease infected mule deer to vehicle collisions. Journal of Wildlife Diseases 41, 503–511.
| PubMed |
Mallick, S. A. , Hocking, G. J. , and Driessen, M. M. (1998). Road-kills of the eastern barred bandicoot (Perameles gunnii) in Tasmania: an index of abundance. Wildlife Research 25, 139–145.
| Crossref | GoogleScholarGoogle Scholar |
McCall, M. K. , and Minang, P. A. (2005). Assessing participatory GIS for community-based natural resource management: claiming community forests in Cameroon. Geographical Journal 171, 340–356.
| Crossref | GoogleScholarGoogle Scholar |
Morellet, N. , Gaillard, J.-M. , Hewison, A. J. M. , Ballon, P. , Boscardin, Y. , Duncan, P. , Klein, F. , and Maillard, D. (2007). Indicators of ecological change: new tools for managing populations of large herbivores. Journal of Applied Ecology 44, 634–643.
| Crossref | GoogleScholarGoogle Scholar |
Putman, R. J. , and Moore, N. P. (1998). Impact of deer in lowland Britain on agriculture, forestry and conservation habitats. Mammal Review 28, 141–164.
| Crossref | GoogleScholarGoogle Scholar |
Radeloff, V. C. , Pidgeon, A. M. , and Hostert, P. (1999). Habitat and population modelling of roe deer using an interactive geographic information system. Ecological Modelling 114, 287–304.
| Crossref | GoogleScholarGoogle Scholar |
Stewart, A. J. A. (2001). The impact of deer on lowland woodland invertebrates: a review of the evidence and priorities for future research. Forestry 74, 259–270.
| Crossref | GoogleScholarGoogle Scholar |
White, P. C. L. , Ford, A. E. S. , Clout, M. N. , Engeman, R. M. , Roy, S. , and Saunders, G. (2008). Alien invasive vertebrates in ecosystems: pattern, process and the social dimension. Wildlife Research 35, 171–179.
| Crossref | GoogleScholarGoogle Scholar |
Yamada, K. , Elith, J. , McCarthy, M. , and Zerger, A. (2003). Eliciting and integrating expert knowledge for wildlife habitat modelling. Ecological Modelling 165, 251–264.
| Crossref | GoogleScholarGoogle Scholar |