Estimating management costs of protected areas: A novel approach from the Eastern Arc Mountains, Tanzania

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• 作者：Jonathan M.H. Green^a ; ^{jmhg2@cam.ac.uk} ; Neil D. Burgess^b ; ^c ; Rhys E. Green^a ; ^d ; Seif S. Madoffe^e ; Pantaleo K.T. Munishi^e ; Evarist Nashanda^f ; R. Kerry Turner^g ; Andrew Balmford^a
• 关键词：Eastern Arc Mountains ; Tanzania ; Protected areas ; Management costs ; Population pressure ; Conservation planning
• 刊名：Biological Conservation
• 出版年：2012
• 期刊代码：13_00063207
• 类别：env
• 出版时间：June, 2012
• 卷：150
• 期：1
• 页码：5-14
• 文件大小：599 K

摘要

Despite chronic underfunding for conservation and the recognition that funds must be invested wisely, few studies have analysed the direct costs of managing protected areas at the spatial scales needed to inform local site management. Using a questionnaire survey we collected data from protected area managers in the Eastern Arc Mountains (EAMs) of Tanzania to establish how much is currently spent on reserve management and how much is required to meet conservation objectives. We use an information theoretic approach to model spatial variation in these costs using a range of plausible, spatially explicit predictor variables, including a novel measure of anthropogenic pressure that measures the human pressure that accrues to any point in the landscape by taking into account all people in the landscape, inversely weighted by their distance to that point.

Our models explain over 75%of variation in actual spend and over 40%of variation in necessary spend. Population pressure is a variable that has not been used to model protected area management costs before, yet proved to be considerably better at predicting both actual and necessary spend than other measures of anthropogenic pressure.

We use our results to estimate necessary spend at a 9 km² resolution across the EAM and highlight those areas where the management costs of effective management are predicted to be high. This information can be used by conservation planners in the region and can be estimated for future scenarios of population growth and migration.