Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

Camera traps typically generate large amounts of bycatch data of non-target species that are secondary to the study's objectives. Bycatch data pooled from multiple studies can answer secondary research questions; however, variation in field and data management techniques creates problems when pooling data from multiple sources. Multi-collaborator projects that use standardized methods to answer broad-scale research questions are rare and limited in geographical scope. Many small, fixed-term independent camera trap studies operate in poorly represented regions, often using field and data management methods tailored to their own objectives. Inconsistent data management practices lead to loss of bycatch data, or an inability to share it easily. As a case study to illustrate common problems that limit use of bycatch data, we discuss our experiences processing bycatch data obtained by multiple research groups during a range-wide assessment of sun bears Helarctos malayanus in Southeast Asia. We found that the most significant barrier to using bycatch data for secondary research was the time required, by the owners of the data and by the secondary researchers (us), to retrieve, interpret and process data into a form suitable for secondary analyses. Furthermore, large quantities of data were lost due to incompleteness and ambiguities in data entry. From our experiences, and from a review of the published literature and online resources, we generated nine recommendations on data management best practices for field site metadata, camera trap deployment metadata, image classification data and derived data products. We cover simple techniques that can be employed without training, special software and Internet access, as well as options for more advanced users, including a review of data management software and platforms. From the range of solutions provided here, researchers can employ those that best suit their needs and capacity. Doing so will enhance the usefulness of their camera trap bycatch data by improving the ease of data sharing, enabling collaborations and expanding the scope of research.

Context Understanding ranging behaviour and habitat selection of threatened species is crucial for the development of conservation strategies and the design of conservation areas. Our understanding of the actual needs of the critically endangered Sumatran elephant in this context is insufficient. Aims Provide reliable subspecies-specific information on home range size and habitat selection of Sumatran elephants. Methods Using both the new area-corrected autocorrelated kernel density estimation (AKDEC) and two commonly applied conventional methods, the home range sizes of nine Sumatran elephants were estimated. Elephant habitat selection was studied using Manly’s selection ratios. Key results AKDEC home ranges of adults ranged from 275 km2 to 1352 km2. Estimates obtained using conventional KDE and minimum convex polygon (MCP) ranged between 156 km2 and 997 km2. Overall habitat selection was significant for both slope and land-cover type, whereas individual preferences varied to some extent. On the basis of global selection ratios, we found natural forest, pulpwood plantations and gentle slopes (≤4°) to be significantly selected, whereas most areas affected by human activities and steeper slopes were avoided by the majority of animals included in the study. Conclusions As expected, AKDEC estimates were much larger than those obtained using conventional methods because conventional methods have a tendency to underestimate home range size when confronted with autocorrelated movement data and produce estimates that refer to the limited study period only, whereas AKDEC estimates include the predicted animal’s long-term space use. The extremely large AKDEC estimate obtained for a subadult male most likely represents a combination of population dispersal range and temporary home range rather than its final adult home range. Regardless, it appears that Sumatran elephants roam over much larger areas than previously assumed. Natural forests and relatively flat areas are of great importance for Sumatran elephants. The observed intensive use of pulpwood plantations by one individual is likely because of limited availability of alternative suitable habitats. Implications A landscape-wide approach to elephant conservation that takes large home ranges into account, is required, and should include forest protection and restoration and elephant friendly management of existing pulpwood concessions, with special focus on areas with relatively gentle slopes.

Reliable baseline information necessary for the monitoring and conservation of Sumatran elephants is scarce. We here combine non-invasive molecular genetics methods and capture-recapture modeling to estimate elephant population size, distribution, sex ratio, and age structure for the Bukit Tigapuluh landscape in Sumatra, Indonesia. Two separate subpopulations were found, for which we estimated a population size of 99 (95% CI = [86, 125], PCCL = 38.59%) and 44 elephants (95% CI = [37, 56], PCCL = 43.18%), respectively. Low elephant densities are likely the result of patchy habitat usage and anthropogenically increased mortality, the latter assumption being supported by strong skews in both sex ratio and age structure as well as direct evidence of elephant killing. Still, the Bukit Tigapuluh landscape currently holds the largest known population of elephants in central Sumatra, representing one of the most important areas for their conservation in Indonesia. Conservation of both the elephant population and their habitat in this region should thus be of high priority. We identified several threats to the population, including (i) the risk of inbreeding and subsequent loss of genetic diversity, (ii) illegal elephant killing, and (iii) the lack of protected habitat. In order to overcome these challenges we suggest: (i) the implementation of a meta-population management program, (ii) monitoring and safeguarding elephants and improving law enforcement, and (iii) providing sufficient safe habitat to mitigate human-elephant-conflict (HEC) and ensure elephant survival.