Olive baboons (Papio anubis) use fixed, secure and naturally-occurring sleeping sites such as tall trees and rocky cliffs, as protection from predators. Although these sleeping sites are safe, they can be limited in availability. We documented olive baboons’ use of 45m tall high-tension electrical pylons (towers) as sleeping sites in Laikipia, Kenya. These observations suggest that olive baboons may use anthropic infrastructure for sleeping sites in areas where naturally occurring cliffs and tall trees are limited.

With the Sixth Mass Extinction becoming an increasingly intractable juggernaut, it is vital to understand the patterns and drivers of local extinction and colonization events of at-risk species under global change. Here, we examine whether observed local colonization and extinctions of giant pandas (Ailuropoda melanoleuca) during three time periods (1985-1988, 1998-2002, 2011-2014) are correlated with protected area status, local rarity and abiotic factors (i.e., climate and land-use variables). We observed a decreased rate of local extinction and a relatively stable rate of local colonization through time, and fount that (i) local extinctions were more likely and local colonization less likely in areas with high local rarity, harsh abiotic (e.g., low proportion of forest cover) and unprotected area status, and (ii) the relative importance of these factors changed over time. These findings provide insights into the factors governing panda recovery and showcase how conservation action can reverse fortunes and facilitate species recovery.

1. Energetics are a key driver of animal decision-making, as survival depends on the balance between foraging benefits and movement costs. This fundamental perspective is often missing from habitat selection studies, which mainly describe correlations between space use and environmental features, rather than the mechanisms behind these correlations. To address this gap, we present a new modelling framework, the energy selection function (ESF), to assess how moving animals choose habitat based on energetic considerations. 2. The ESF considers that the likelihood of an animal selecting a movement step depends directly on the corresponding energetic gains and costs. The parameters of the ESF measure selection for energetic gains and against energetic costs; when estimated jointly, these provide inferences about foraging and movement strategies. The ESF can be implemented easily with standard conditional logistic regression software, allowing for fast inference. We outline a workflow, from data-gathering to statistical analysis, and use a case study of polar bears (Ursus maritimus) as an illustrative example. 3. We show how defining gains and costs at the scale of the movement step allows us to include detailed information about resource distribution, landscape resistance, and movement patterns. We demonstrate this in the polar bear case study, in which the results show how cost-minimization may arise in species that inhabit environments with an unpredictable distribution of energetic gains.4. The ESF combines the energetic consequences of both movement and resource selection, thus incorporating a key aspect of evolutionary behaviour into habitat selection analysis. Because of its close links to existing habitat selection models, the ESF is widely applicable to any study system where energetic gains and costs can be derived, and has immense potential for methodological extensions.

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail-cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail-cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy are poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photo was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photo using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g. false species or false empty), and reason for the false classification (e.g. misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail-camera set up protocol with a burst of three consecutive photos, a short field of view, and consider appropriate camera sensitivity. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.