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Biodiversity Conservation Informatics Group
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5. Evaluation of conservation outcome: habitat suitability, connectivity and persistence
Researchers: Atte Moilanen, Jussi Laitila and Federico Montesino Pouzols
Selection of reserve networks requires data on the distributions of species or communities of conservation importance. It is nowadays widely accepted that reserve selection based on just presence or presence/absence records of certain species may result in reserve networks that are small and fragmented, and which will not ensure the long-term persistence of biodiversity (Cabeza & Moilanen, 2001). Reasons for this can be found in the type of data used: a snap-shot of the occurrence of biodiversity, without consideration of site quality variation.
As landscapes and species communities are dynamic in space and time, site quality varies as well. Reserve selection methods need to account for landscape dynamics and connectivity, to avoid selecting small (cheap) fragments as a reserve, which in practice are sinks from large (but expensive) habitat patches nearby, which remain unprotected and might therefore be lost (Cabeza, 2003; Cabeza & Moilanen, 2003; Van Teeffelen et al. 2005). Zonation (see topic 3 is a framework that can easily account for variable site quality. Variation in site quality can be taken into account by predicting species distribution based on environmental information, with so called habitat models or species distribution models (see Elith et al. 2006 for review). Predictions from these models give insight into site quality differences throughout the landscape. However, these models typically consider only local variables, whereas connectivity to surrounding habitat and populations are of major importance to population dynamics and therefore population persistence.
Cabeza (2003) was among the first to combine connectivity measures and habitat models in reserve selection. We have investigated a number of different (species-specific) ways of including connectivity effects into spatial prioritization (Cabeza 2003; Moilanen 2005a, 2005b; Moilanen et al. 2005; Moilanen and Wintle 2006, 2007; Moilanen et al. 2008). While connectivity can be deal with semi-rigorously, the translation from habitat amount, habitat quality and connectivity persistence remains a difficult question, which requires application of techniques such as metapopulation models or PVA simulation models.
There is substantial knowledge in the MRG about the influences of spatial pattern to connectivity and persistence, and our collaborators including Dr Jane Elith (Melbourne), Dr John Leathwick (NIWA, NZ) and Dr Simon Ferrier (CSIRO) are substantially experienced in the development of habitat models. Habitat quality, connectivity and persistence are fundamental quantities of population biology and conservation, and they will continue to be basic building blocks of the analyses we develop.
Cabeza, M. 2003. Habitat loss and connectivity of reserve networks in probability approaches to reserve design. Ecology Letters, 6:665-672.
Cabeza, M., Araujo, M.B., Wilson, R.J., Thomas, C.D., Cowley, M.J.R. and A. Moilanen. 2004b. Combining probabilities of occurrence with spatial reserve design. Journal of Applied Ecology, 41:252-262.
Cabeza, M., and A. Moilanen. 2001. Design of reserve networks and the persistence of biodiversity. Trends in Ecology and Evolution, 16:242-248.
Cabeza, M., and A. Moilanen. 2003. Site-selection algorithms and habitat loss. Conservation Biology, 17:1402-1413.
Cabeza M., Moilanen, A. and H.-P. Possingham. 2004a. Metapopulaton dynamics and reserve network design. Pages 541-564 in I. Hanski and O. Gaggiotti, eds. Metapopulation ecology, genetics, and evolution. Academic press.
Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K. S., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S. and N.E. Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurence data. Ecography, 29, 129-151.
Hodgson, J., Moilanen, A., Wintle, B.A., and C. D. Thomas. 2011. Habitat area, quality and connectivity: striking the balance for efficient conservation. J. Applied Ecology, 48:148-152.
Hodgson, J., Thomas, C.D., Wintle, B.A. and A. Moilanen. 2009. Climate change, connectivity and conservation decision making - back to basics. Journal of Applied Ecology, 46: 964-969.
Lehtomäki, J., Tomppo, E., Kuokkanen, P. Hanski, I., and A. Moilanen. 2009. Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest Ecology and Management, 258: 2439-2449.
Moilanen, A. 2005a. Methods for reserve selection: interior point search. Biological Conservation, 124: 485-492.
Moilanen, A. 2005b. Reserve selection using nonlinear species distribution models. American Naturalist, 165: 695-706.
Moilanen, A., Franco, A.M.A., Early, R., Fox, R., Wintle, B., and C.D. Thomas. 2005. Prioritising multiple use landscapes for conservation: methods for large multi species planning problems. Proc. R. Soc. Lond. B Biol. Sci., 272: 1885-1891.
Moilanen, A., Leathwick, J.R., and J. Elith. 2008a. A method for freshwater conservation prioritization. Freshwater Biology, 53: 577-592.
Moilanen, A., and M. Nieminen. 2002. Simple connectivity measures in spatial ecology. Ecology 84:1131-1145.
Moilanen, A. and B. A. Wintle. 2007. The boundary quality penalty a quantitative method for approximating species responses to fragmentation in reserve selection. Conservation Biology, 21: 355-364.
Van Teeffelen, A., Cabeza, M. and A. Moilanen. 2006. Connectivity, probabilities and persistence: comparing reserve selection strategies. Biodiversity and Conservation, 15, 899-919.