Geochemical and mineralogical characteristics of elephant geophagic soils in Udawalawe National Park,Sri Lanka

Geophagy or deliberate ingestion of soils was observed among Asian elephants (Elephas maximus) in the Udawalwe National Park, Sri Lanka, for several years. The geochemical and mineralogical composition of the clayey soil layers which are purposefully selected and eaten by elephants in the park were studied, in order to identify the possible reasons for elephant geophagy. The concentrations of major and trace elements were determined by means of X-ray fluorescence spectrometry in 21 soil samples from eight geophagic sites and six soil samples collected from four non-geophagic sites. The mineralogical composition of selected soil samples was investigated using X-ray diffractometry (XRD). These geochemical analyses revealed that geophagic soils in the study areas are deeply weathered and that most of the elements are leached from the soil layers under extreme weathering conditions. The XRD data showed that the soils of the area consisted mainly quartz, feldspar, and the clay minerals kaolinite, Fe-rich illite, and smectite. Although no significant geochemical differences were identified between geophagic and non-geophagic soils, a clear difference was observed in their clay mineralogical content. Soils eaten by elephants are richer in kaolinite and illite than non-geophagic soils, which contain a higher amount of smectite. It is suggested that elephants in Udawalawe National Park ingest soils mainly not to supplement the mineral contents of their forage but to detoxify unpalatable compounds in their diet.     

Wildlife impacts and vulnerable livelihoods in a transfrontier conservation landscape

Interactions between humans and wildlife resulting in negative impacts are among the most pressing conservation challenges globally. In regions of smallholder livestock and crop production, interactions with wildlife can compromise human well-being and motivate negative sentiment and retaliation toward wildlife, undermining conservation goals. Although impacts may be unavoidable when human and wildlife land use overlap, scant large-scale human data exist quantifying the direct costs of wildlife to livelihoods. In a landscape of global importance for wildlife conservation in southern Africa, we quantified costs for people living with wildlife through a fundamental measure of human well-being, food security, and we tested whether existing livelihood strategies buffer certain households against crop depredation by wildlife, predominantly elephants. To do this, we estimated Bayesian multilevel statistical models based on multicounty household data (n = 711) and interpreted model results in the context of spatial data from participatory land-use mapping. Reported crop depredation by wildlife was widespread. Over half of the sample households were affected and household food security was reduced significantly (odds ratio 0.37 [0.22, 0.63]). The most food insecure households relied on gathered food sources and welfare programs. In the event of crop depredation by wildlife, these 2 livelihood sources buffered or reduced harmful effects of depredation. The presence of buffering strategies suggests a targeted compensation strategy could benefit the region's most vulnerable people. Such strategies should be combined with dynamic and spatially explicit land-use planning that may reduce the frequency of negative human–wildlife impacts. Quantifying and mitigating the human costs from wildlife are necessary steps in working toward human–wildlife coexistence.     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

The Complex Links between Governance and Biodiversity

Abstract:  We argue that two problems weaken the claims of those who link corruption and the exploitation of natural resources. The first is conceptual and the second is methodological. Studies that use national-level indicators of corruption fail to note that corruption comes in many forms, at multiple levels, that may affect resource use quite differently: negatively, positively, or not at all. Without a clear causal model of the mechanism by which corruption affects resources, one should treat with caution any estimated relationship between corruption and the state of natural resources. Simple, atheoretical models linking corruption measures and natural resource use typically do not account for other important control variables pivotal to the relationship between humans and natural resources. By way of illustration of these two general concerns, we used statistical methods to demonstrate that the findings of a recent, well-known study that posits a link between corruption and decreases in forests and elephants are not robust to simple conceptual and methodological refinements. In particular, once we controlled for a few plausible anthropogenic and biophysical conditioning factors, estimated the effects in changes rather than levels so as not to confound cross-sectional and longitudinal variation, and incorporated additional observations from the same data sources, corruption levels no longer had any explanatory power.