Can distribution models help refine inventory-based estimates of conservation priority? A case study in the Eastern Arc forests of Tanzania and Kenya

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Abstract:

Abstract Aim 

Data shortages mean that conservation priorities can be highly sensitive to historical patterns of exploration. Here, we investigate the potential of regionally focussed species distribution models to elucidate fine-scale patterns of richness, rarity and endemism. Location 

Eastern Arc Mountains, Tanzania and Kenya. Methods 

Generalized additive models and land cover data are used to estimate the distributions of 452 forest plant taxa (trees, lianas, shrubs and herbs). Presence records from a newly compiled database are regressed against environmental variables in a stepwise multimodel. Estimates of occurrence in forest patches are collated across target groups and analysed alongside inventory-based estimates of conservation priority. Results 

Predicted richness is higher than observed richness, with the biggest disparities in regions that have had the least research. North Pare and Nguu in particular are predicted to be more important than the inventory data suggest. Environmental conditions in parts of Nguru could support as many range-restricted and endemic taxa as Uluguru, although realized niches are subject to unknown colonization histories. Concentrations of rare plants are especially high in the Usambaras, a pattern mediated in models by moisture indices, whilst overall richness is better explained by temperature gradients. Tree data dominate the botanical inventory; we find that priorities based on other growth forms might favour the mountains in a different order. Main conclusions 

Distribution models can provide conservation planning with high-resolution estimates of richness in well-researched areas, and predictive estimates of conservation importance elsewhere. Spatial and taxonomic biases in the data are essential considerations, as is the spatial scale used for models. We caution that predictive estimates are most uncertain for the species of highest conservation concern, and advocate using models and targeted field assessments iteratively to refine our understanding of which areas should be prioritised for conservation.

Keywords: Biodiversity; conservation planning; endemism; rare species; sampling bias; spatial predictions

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1472-4642.2010.00668.x

Affiliations: 1: Royal Botanic Garden Edinburgh, 20A Inverleith Row, EH3 5LR, UK 2: Missouri Botanical Garden, P.O. Box 299, Saint Louis, MO 63166-0299, USA 3: KITE, Environment Department, University of York, Heslington, York YO10 5DD, UK 4: Centre for Clean Technology and Environmental Policy, University of Twente/CSTM, P.O. Box 217, 7500 AE Enschede, The Netherlands 5: Department of Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland 6: Department of Geography, King’s College London, Strand, London, WC2R 2LS, UK 7: Society for Environmental Exploration (Frontier), 50-52 Rivington Street, London EC2A 3QP, UK

Publication date: July 1, 2010

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