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A successful community‐level strategy for conservation prioritization
Author(s) -
Arponen Anni,
Moilanen Atte,
Ferrier Simon
Publication year - 2008
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/j.1365-2664.2008.01513.x
Subject(s) - species richness , maximization , representation (politics) , biodiversity , cluster analysis , range (aeronautics) , selection (genetic algorithm) , prioritization , computer science , ecology , scale (ratio) , environmental resource management , environmental science , geography , machine learning , mathematics , mathematical optimization , biology , engineering , management science , cartography , politics , political science , law , aerospace engineering
Summary1 Regions of the world with highest biodiversity and greatest conservation needs are often simultaneously data poor. An effective surrogate strategy would be invaluable for conservation prioritization in such regions. Large‐scale environmental data are readily available, but the effectiveness of environmental surrogate strategies for conservation planning has not been confirmed. In this study, we compare a range of such strategies. 2 Environmental surrogacy is based on the idea that by covering a wide range of different environmental conditions, one also achieves high species representation. The effectiveness of this strategy may be enhanced by using community‐level modelling techniques, in conjunction with best‐available biological (species distribution) data, to calibrate the relationship between environmental gradients and community richness and composition. We develop a novel approach, called maximization of complementary richness, which accounts for gradients in species richness and non‐constant turnover rates of community composition in environmental space. 3 We show that our novel technique can achieve notably higher species representation than what is achieved using past approaches. Simple strategies, such as direct use of environmental data only or environmental clustering, achieved species representation only slightly better than random selection of sites. P‐median selection from ordinations of community composition had intermediate performance. Performance of our new maximization of complementary richness technique was closer to the representation levels of optimal reserve networks than to random selection of sites. 4 Synthesis and applications . We found that there are three critical components that are relevant for success: a good model for community turnover (compositional dissimilarity), a good model for species richness, and a selection procedure that appropriately utilizes both turnover and richness information. By taking these into account, one can achieve reasonable levels of species representation. We conclude that using surrogates based on community‐level modelling is a highly promising strategy for cost‐effective conservation prioritization.