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.

Human–wildlife conflict (HWC) is a key topic in conservation and agricultural research. Decision makers need evidence‐based information to design sustainable management plans and policy instruments. However, providing objective decision support can be challenging because realities and perceptions of human–wildlife interactions vary widely between and within rural, urban, and peri‐urban areas. Land users who incur costs through wildlife argue that wildlife‐related losses should be compensated and that prevention should be subsidized. Supporters of human–wildlife coexistence policies, such as urban‐dwelling people, may not face threats to their livelihoods from wildlife. Such spatial heterogeneity in the cost and benefits of living with wildlife is germane in most contemporary societies. This Special Section features contributions on wildlife‐induced damages that range from human perspectives (land use, psychology, governance, local attitudes and perceptions, costs and benefits, and HWC and coexistence theory) to ecological perspectives (animal behavior). Building on current literature and articles in this section, we developed a conceptual model to help frame HWC and coexistence dimensions. The framework can be used to determine damage prevention implementation levels and approaches to HWC resolution. Our synthesis revealed that inter‐ and transdisciplinary approaches and multilevel governance approaches can help stakeholders and institutions implement sustainable management strategies that promote human–wildlife coexistence.

Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species.

Summary: Although many reintroduction schemes for the Eurasian lynx Lynx lynx in Germany have been discussed, the implications of connectivity between suitable patches have not been assessed. We introduce an individual‐based, spatially explicit dispersal model to assess the probability of a dispersing animal reaching another suitable patch in the complex heterogeneous German landscape, with its dense transport system. The dispersal model was calibrated using telemetric data from the Swiss Jura and based on a map of potential lynx dispersal habitat. Most suitable patches could be interconnected by movements of dispersing lynx within 10 years of reintroduction. However, when realistic levels of mortality risks on roads were applied, most patches become isolated except along the German–Czech border. Consequently, patch connectivity is limited not so much by the distribution of dispersal habitat but by the high mortality of dispersing lynx. Accordingly, rather than solely investing in habitat restoration, management efforts should try to reduce road mortality. Synthesis and applications. Our approach illustrates how spatially explicit dispersal models can guide conservation efforts and reintroduction programmes even where data are scarce. Clear limits imposed by substantial road mortality will affect dispersing lynx as well as other large carnivores, unless offset by careful road‐crossing management or by the careful selection of release points in reintroduction programmes.