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.

Summary1. Camera trapping is a widely applied method to study mammalian biodiversity and is still gaining popularity. It can quickly generate large amounts of data which need to be managed in an efficient and transparent way that links data acquisition with analytical tools. 2. We describe the free and open-source R package camtrapR, a new toolbox for flexible and efficient management of data generated in camera trap-based wildlife studies. The package implements a complete workflow for processing camera trapping data. It assists in image organization, species and individual identification, data extraction from images, tabulation and visualization of results and export of data for subsequent analyses. There is no limitation to the number of images stored in this data management system; the system is portable and compatible across operating systems. 3. The functions provide extensive automation to minimize data entry mistakes and, apart from species and individual identification, require minimal manual user input. Species and individual identification are performed outside the R environment, either via tags assigned in dedicated image management software or by moving images into species directories. 4. Input for occupancy and (spatial) capture-recapture analyses for density and abundance estimation, for example in the R packages unmarked or secr, is computed in a flexible and reproducible manner. In addition, survey summary reports can be generated, spatial distributions of records can be plotted and exported to GIS software, and single-and two-species activity patterns can be visualized. 5. camtrapR allows for streamlined and flexible camera trap data management and should be most useful to researchers and practitioners who regularly handle large amounts of camera trapping data.

Radical revision of tiger taxonomy for a pragmatic and scientifically sound approach to tiger conservation management.

Habitat degradation and hunting have caused the widespread loss of larger vertebrate species (defaunation) from tropical biodiversity hotspots. However, these defaunation drivers impact vertebrate biodiversity in different ways and, therefore, require different conservation interventions. We conducted landscape-scale camera-trap surveys across six study sites in Southeast Asia to assess how moderate degradation and intensive, indiscriminate hunting differentially impact tropical terrestrial mammals and birds. We found that functional extinction rates were higher in hunted compared to degraded sites. Species found in both sites had lower occupancies in the hunted sites. Canopy closure was the main predictor of occurrence in the degraded sites, while village density primarily influenced occurrence in the hunted sites. Our findings suggest that intensive, indiscriminate hunting may be a more immediate threat than moderate habitat degradation for tropical faunal communities, and that conservation stakeholders should focus as much on overhunting as on habitat conservation to address the defaunation crisis.

Assessing spatiotemporal interactions between species is of fundamental interest to behavioural and community ecology. Observer‐independent methods such as camera trapping facilitate the study of interactions, but analyses are hampered by the lack of comparative assessment of available approaches. We present a flexible and expandable framework to simulate and explore spatiotemporal interactions between species from camera trapping data with well‐defined properties, and compare methods to detect such interactions in a two‐species system with two types of (spatio)temporal interactions: spatiotemporal avoidance (of a site by a species after the presence of another species) and temporal segregation (shifts in daily activity patterns between species), across a range of daily activity patterns and interaction strengths. For spatiotemporal avoidance, we analysed time intervals between species records using linear models, the Mann–Whitney U‐test, a permutation test and a test based on randomly generated records. For temporal segregation, we applied a permutation test. Statistical power (the ability to detect an existing effect) for detecting spatiotemporal avoidance between species was strongly affected by interaction strength, highest for linear models and reliable above 50 records per species. Reliably detecting strong temporal segregation required fewer records (10–20 records) but depended heavily on the underlying activity pattern. All tests were valid (uniform distribution of P‐values under the null hypothesis) even at low sample sizes above a minimum of 10 records per species. Linear models were the most suitable approach to analyse spatiotemporal avoidance and can easily correct for other sources of variation in interactions. The framework presented here can help to improve survey design in camera trapping and be extended to more complex settings (e.g. with imperfect detection). In addition, it allows researchers to validate the methods used for inference of spatiotemporal interactions from camera trapping data in their specific circumstances.