With the expansion of wind‐energy generation, there is a growing need to develop accurate and efficient methods to detect bat casualties resulting from turbine collision and barotrauma. We conducted a formal blinded trial comparing the abilities of search dogs and human observers to locate bat carcasses. Dogs located 73% (46/63) of bats, whereas humans found 20% (12/60). We therefore recommend search dogs as an effective means of monitoring bat fatalities, particularly when a high degree of search accuracy is important. This includes surveys for rare species, or cases where searches are limited in extent or duration, because the application of correction factors is problematic where very few or no casualties are found. The dogs averaged 40 min to complete a survey, which was <25% of the time taken by humans. At large sites, the high initial set‐up costs for search dogs can therefore be offset by the increased number of surveys that can be conducted within a given time. However, care must be taken with the selection and training of the dogs and handlers to produce consistent results. To allow fatality rates to be estimated from the number of casualties located, it is essential that assessments of the accuracy of the dog–handler team are made at each site. © 2013 The Wildlife Society.

BackgroundThe structuring of wild animal populations can influence population dynamics, disease spread, and information transfer. Social network analysis potentially offers insights into these processes but is rarely, if ever, used to investigate more than one species in a community. We therefore compared the social, temporal and spatial networks of sympatric Myotis bats (M. nattereri (Natterer's bats) and M. daubentonii (Daubenton's bats)), and asked: (1) are there long-lasting social associations within species? (2) do the ranges occupied by roosting social groups overlap within or between species? (3) are M. daubentonii bachelor colonies excluded from roosting in areas used by maternity groups?ResultsUsing data on 490 ringed M. nattereri and 978 M. daubentonii from 379 colonies, we found that both species formed stable social groups encompassing multiple colonies. M. nattereri formed 11 mixed-sex social groups with few (4.3%) inter-group associations. Approximately half of all M. nattereri were associated with the same individuals when recaptured, with many associations being long-term (>100 days). In contrast, M. daubentonii were sexually segregated; only a quarter of pairs were associated at recapture after a few days, and inter-sex associations were not long-lasting. Social groups of M. nattereri and female M. daubentonii had small roost home ranges (mean 0.2 km2 in each case). Intra-specific overlap was low, but inter-specific overlap was high, suggesting territoriality within but not between species. M. daubentonii bachelor colonies did not appear to be excluded from roosting areas used by females.ConclusionsOur data suggest marked species- and sex-specific patterns of disease and information transmission are likely between bats of the same genus despite sharing a common habitat. The clear partitioning of the woodland amongst social groups, and their apparent reliance on small patches of habitat for roosting, means that localised woodland management may be more important to bat conservation than previously recognised.

Policy makers require high‐level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy‐relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined ‘regions’ (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the ‘ideal’ and ‘minimal’ criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.