Collaboration for conservation: Assessing countrywide carnivore occupancy dynamics from sparse data

Aim Assessing the distribution and persistence of species across their range is a crucial component of wildlife conservation. It demands data at adequate spatial scales and over extended periods of time, which may only be obtained through collaborative efforts, and the development of methods that integrate heterogeneous datasets. We aimed to combine existing data on large carnivores to evaluate population dynamics and improve knowledge on their distribution nationwide. Location Botswana. Methods Between 2010 and 2016, we collated data on African wild dog, cheetah, leopard, brown and spotted hyaena and lion gathered with different survey methods by independent researchers across Botswana. We used a multi‐species, multi‐method dynamic occupancy model to analyse factors influencing occupancy, persistence and colonization, while accounting for imperfect detection. Lastly, we used the gained knowledge to predict the probability of occurrence of each species countrywide. Results Wildlife areas and communal rangelands had similar occupancy probabilities for most species. Large carnivore occupancy was low in commercial farming areas and where livestock density was high, except for brown hyaena. Lion occupancy was negatively associated with human density; lion and spotted hyaena occupancy was high where rainfall was high, while the opposite applied to brown hyaena. Lion and leopard occupancy remained constant countrywide over the study period. African wild dog and cheetah occupancy declined over time in the south and north, respectively, whereas both hyaena species expanded their ranges. Countrywide predictions identified the highest occupancy for leopards and lowest for the two hyaena species. Main Conclusions We highlight the necessity of data sharing and propose a generalizable analytical method that addresses the challenges of heterogeneous data common in ecology. Our approach, which enables a comprehensive multi‐species assessment at large spatial and temporal scales, supports the development of data‐driven conservation guidelines and the implementation of evidence‐based management strategies nationally and internationally.