Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

作者：Pablo José Negret, Moreno Di Marco, Laura Jane Sonter, Jonathan R. Rhodes, Hugh P. Possingham, Martine Maron · 发表于：Conservation Biology · 年份：2020 · DOI：10.1111/cobi.13522 · 被引用次数：67 · 研究领域：Economic and Environmental Valuation、Conservation, Biodiversity, and Resource Management、Land Use and Ecosystem Services

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures-ways of generating matching pairs at different scales-in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelati...