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

TY - JOUR

T1 - Can distribution models help refine inventory-based estimates of conservation priority?

T2 - A case study in the Eastern Arc forests of Tanzania and Kenya

AU - Platts, Philip J.

AU - Ahrends, Antje

AU - Gereau, Roy E.

AU - McClean, Colin J.

AU - Lovett, Jon C.

AU - Marshall, Andrew R.

AU - Pellikka, Petri K. E.

AU - Mulligan, Mark

AU - Fanning, Eibleis

AU - Marchant, Rob

PY - 2010/7

Y1 - 2010/7

N2 - AimData 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.LocationEastern Arc Mountains, Tanzania and Kenya.MethodsGeneralized 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.ResultsPredicted 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 conclusionsDistribution 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.

AB - AimData 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.LocationEastern Arc Mountains, Tanzania and Kenya.MethodsGeneralized 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.ResultsPredicted 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 conclusionsDistribution 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.

KW - Biodiversity

KW - conservation planning

KW - endemism

KW - rare species

KW - sampling bias

KW - spatial predictions

KW - SPECIES-AREA RELATIONSHIP

KW - BIODIVERSITY HOTSPOTS

KW - INDICATOR TAXA

KW - MOUNTAINS

KW - DIVERSITY

KW - AFRICA

KW - RICHNESS

KW - PATTERNS

KW - CLIMATE

KW - WORK

UR - http://www.scopus.com/inward/record.url?scp=77952939487&partnerID=8YFLogxK

U2 - 10.1111/j.1472-4642.2010.00668.x

DO - 10.1111/j.1472-4642.2010.00668.x

M3 - Article

SN - 1366-9516

VL - 16

SP - 628

EP - 642

JO - Diversity and Distributions

JF - Diversity and Distributions

IS - 4

ER -