Aim Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. Location Borneo, Southeast Asia. Methods We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. Results Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main Conclusions We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.

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Illegal hunting of resident and migratory herbivores is widespread in the Serengeti National Park, Tanzania. To devise effective strategies to reduce levels of hunting, information is required on why people are involved in illegal hunting and the role of bushmeat in the local economy. Participation in hunting may be influenced by measures of relative wealth, including livestock ownership, means of generating cash income and access to alternative sources of meat. Data came from 300 individuals responding to a questionnaire in 10 villages, from responses by 359 people in 24 group discussions in another 12 villages, and from 552 people arrested and interviewed in the National Park. A smaller proportion of individual respondents (32%) than group respondents (57%) volunteered that they participated in illegal hunting. Most individual and group respondents were subsistence farmers who considered bushmeat to be a source of protein and a means of generating cash income. Three-quarters of those arrested participated in hunting primarily to generate cash income and a quarter claimed that they only hunted to obtain food. Participation in illegal hunting decreased as wealth in terms of the number of sheep and goats owned increased. People with access to alternative means of generating income or acquiring protein were also less likely to be involved in illegal hunting. Arrested respondents were typically young adult males with low incomes and few or no livestock. Illegal hunting was not reduced by participation in community-based conservation pro-grammes. Results suggested that between 52 000 and 60 000 people participated in illegal hunting within protected areas, and that many young men (approximately 5200) derived their primary source of income from hunting.

Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of ‘movement ecology’. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide ‘mobile links’ between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through ‘equalizing’ and ‘stabilizing’ mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.