The behavior of animals potentially affects their survival and reproduction. The activity budget of the Critically Endangered giraffe Giraffa camelopardalis camelopardalis has been investigated in populations across Africa and found to be influenced by body size, diet and sex. Foraging patterns show how an animal chooses to forage in its environment, and is influenced by resource availability, competition and predation risk. The activity budget and foraging patterns of Nubian giraffes vary considerably between ecosystems. The Nubian giraffe is a recently identified subspecies G. camelopardalis camelopardalis). This Critically Endangered giraffes which includes Rothschild’s giraffes, occurs only within Kenya, Uganda, Ethiopia, and Southern Sudan. We observed the behavior of a population of Nubian giraffes in Lake Nakuru National Park, Kenya, to assess seasonal activity budgets and foraging patterns. In the wet and dry seasons, giraffes spent approximately the same amount of time (53 and 57%, respectively) foraging. Movement and resting duration decreased slightly from the dry to the wet season (22 to 20% and 25 to 22%, respectively. Across both seasons, Vachellia xanthophloea (67%), Maytenus senegalensis (19%), and Solanum incanum (9%) made up the bulk of the giraffe’s diet. In the dry season, giraffe additionally foraged on Maerua triphylla (2%), Vachellia gerrardi (2%), and Grewia similis (1%); in the wet season, they added Vachellia abyssinica (2%) and Rhus natalensis (2%) to their diet. The most utilized browsing height was 3.5 m below their average height.. Overall, season did not appear to influence the Nubian giraffe’s activity time budget or foraging patterns in Lake Nakuru National Park. Improved knowledge of the behavioral patterns of this subspecies will allow managers to manage and conserve the species and its habitat better. For instance, planting perennial plants in all vegetation types used by giraffes in the park minimizes browsing pressure on the already over browsed Vachellia.

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.