1. Demographic consequences of human-induced rapid environmental change (HIREC) have been widely documented for many populations. The mechanisms underlying such patterns, however, are rarely investigated and yet are critical to understand for effective conservation and management. 2. We investigated the mechanisms underlying reduced avian nest survival with intensification of natural gas development, an increasing source of human-induced rapid environmental change globally. We tested the hypothesis that energy development increased the local activity of important nest predator species, thereby elevating nest predation rates. During 2011-2012, we surveyed predators and monitored 668 nests of Brewer's sparrows Spizella breweri (BRSP), sagebrush sparrows Artemisiospiza nevadensis (SASPs) and sage thrashers Oreoscoptes montanus (SATHs) breeding at twelve sites spanning a gradient of habitat loss from energy development in western Wyoming, USA. 3. Nine species, representing four mammalian and three avian families, were video-recorded depredating eggs and nestlings. Important nest predator species differed across songbird species, despite similar nesting habitats. Approximately 75% of depredation events were by rodents. 4. Consistent with our predictions, detections of most rodent nest predators increased with surrounding habitat loss due to natural gas development, which was associated with increased probability of nest predation for our three focal bird species. 5. An altered nest predator assemblage was therefore at least partly responsible for elevated avian nest predation risk in areas with more surrounding energy development. 6. Synthesis and applications. We demonstrate one mechanism, that is the local augmentation of predators, by which human-induced rapid environmental change can influence the demography of local populations. Given the accelerating trajectory of global energy demands, an important next step will be to understand why the activity and/or abundance of rodent predators increased with surrounding habitat loss from energy development activities.
Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m 3 ha -1 ). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (< 15 m 3 ha -1 ). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbonbased payments for ecosystem service schemes.
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