A framework for deriving measures of chronic anthropogenic disturbance: Surrogate, direct, single and multi-metric indices in Brazilian Caatinga

摘要

The development of multi-metric indices of chronic anthropogenic disturbance (CAD) from disparate disturbance indicators represents a major challenge for understanding the impacts of CAD on biodiversity, especially in tropical dry areas where livelihoods of local populations are highly dependent on natural resources. We present a conceptual framework for deriving variably integrated, multi-metric measures of CAD from disparate disturbance indicators. Our framework has three steps: (1) identifying the main sources of CAD in the target region, and quantifying them using data of varying levels of spatial and intensity precision; (2) classifying the sources of disturbance into general disturbance pressures, and deriving an index for each; and (3) combining the individual disturbance pressure indices into a fully integrated index that characterizes the overall level of CAD. We apply this framework to Catimbau National Park in the Brazilian Caatinga, using 12 primary data sources to derive disturbance pressure indices relating to livestock, wood extraction and people pressure. The meaningfulness of pressure and overall CAD indices were validated by reference to variation in ant communities. Our analysis revealed notable findings. First, indirect measures from the geographic and socio-ecological context were poorly correlated with direct, field-based measurements, and were therefore of questionable reliability. Second, the three main disturbance pressures were largely independent of each other, which points to complex patterns of resource use by local communities. Third, different weightings of component disturbance pressure indices had little influence on the Global index, making our Global CAD index somewhat insensitive to assessments of the relative importance of different disturbance pressures. Finally, our results caution against a reliance on multivariate ordination to derive integrated indices of disturbance from disparate data sources. Our multi-scale integration of disturbance data can facilitate the analysis of the resource use effects on biodiversity, contributing to effective conservation management and sustainable livelihood development.