No abstract

Protected areas (PAs) have long been criticized as creations of and for an elite few, where associated costs, but few benefits, are borne by marginalized rural communities. Contrary to predictions of this argument, we found that average human population growth rates on the borders of 306 PAs in 45 countries in Africa and Latin America were nearly double average rural growth, suggesting that PAs attract, rather than repel, human settlement. Higher population growth on PA edges is evident across ecoregions, countries, and continents and is correlated positively with international donor investment in national conservation programs and an index of park-related funding. These findings provide insight on the value of PAs for local people, but also highlight a looming threat to PA effectiveness and biodiversity conservation.

Species distribution models are used for a range of ecological and evolutionary questions, but often are constructed from few and/or biased species occurrence records. Recent work has shown that the presence‐only model Maxent performs well with small sample sizes. While the apparent accuracy of such models with small samples has been studied, less emphasis has been placed on the effect of small or biased species records on the secondary modeling steps, specifically accuracy assessment and threshold selection, particularly with profile (presence‐only) modeling techniques. When testing the effects of small sample sizes on distribution models, accuracy assessment has generally been conducted with complete species occurrence data, rather than similarly limited (e.g. few or biased) test data. Likewise, selection of a probability threshold – a selection of probability that classifies a model into discrete areas of presences and absences – has also generally been conducted with complete data. In this study we subsampled distribution data for an endangered rodent across multiple years to assess the effects of different sample sizes and types of bias on threshold selection, and examine the differences between apparent and actual accuracy of the models. Although some previously recommended threshold selection techniques showed little difference in threshold selection, the most commonly used methods performed poorly. Apparent model accuracy calculated from limited data was much higher than true model accuracy, but the true model accuracy was lower than it could have been with a more optimal threshold. That is, models with thresholds and accuracy calculated from biased and limited data had inflated reported accuracy, but were less accurate than they could have been if better data on species distribution were available and an optimal threshold were used.

Accurate, reliable, and efficient monitoring methods for detecting changes in the distribution and abundance of wildlife populations are the cornerstone of effective management. Aerial surveys of active burrow sites and ground counts of open burrows have been used to estimate distribution and abundance, respectively, of a number of rodent species. We compared the efficacy of these and other methods for estimating distribution, abundance, and population growth of the endangered giant kangaroo rat (Dipodomys ingens) to determine the best practices for monitoring. Specifically, we compared aerial surveys, rapid expert assessments, and live-trapping for estimating giant kangaroo rat range, and burrow counts and live-trapping for estimating abundance and growth. We carried out the study in the Carrizo Plain National Monument, California, USA, from 2007 to 2011. Expert rapid assessment of sites performed nearly as well as trapping in determining range extent, while aerial surveys provided estimates of total range extent but with less precision. Active burrow counts were adequate to determine relative abundance averaged over multiple years, but were not reliable as an estimate of annual population size or growth. ß 2012 The Wildlife Society.

Defining historical baselines is critical for species conservation. Under the niche reduction hypothesis, species in decline may be restricted disproportionately from parts of their environmental niche. This bias likely has important implications for modeling species’ distributions if only contemporary occurrences (i.e. post‐range reduction) are used, because suitable habitat will be classified as unsuitable. Unfortunately, robust historical occurrence data is rarely available for sensitive species. In this study, we documented historical locations of the endangered, keystone giant kangaroo rat Dipodomys ingens by examining aerial imagery for burrow mounds. These burrow mounds are readily identifiable and distinguishable from other soil disturbances. We found giant kangaroo rat burrows well outside the currently accepted estimate of their historical distribution. Following the niche reduction hypothesis, we found that giant kangaroo rats have been extirpated from the flattest, hottest, driest parts of their range due to agricultural conversion. This reduction in their realized niche led to significant changes between historical and contemporary models of their distribution. We found that giant kangaroo rats may have occupied up to 56% more habitat historically than currently believed. Our results provide new guidance for managers working on restoration and habitat protection for this ecosystem engineer. This study highlights the critical importance of modeling historical distributions using the entire environmental niche once occupied by species of conservation need.