The use of environmental DNA (eDNA) has become an applicable noninvasive tool with which to obtain information about biodiversity. A subdiscipline of eDNA is iDNA (invertebrate-derived DNA), where genetic material ingested by invertebrates is used to characterize the biodiversity of the species that served as hosts. While promising, these techniques are still in their infancy, as they have only been explored on limited numbers of samples from only a single or a few different locations. In this study, we investigate the suitability of iDNA extracted from more than 3,000 haematophagous terrestrial leeches as a tool for detecting a wide range of terrestrial vertebrates across five different geographical regions on three different continents. These regions cover almost the full geographical range of haematophagous terrestrial leeches, thus representing all parts of the world where this method might apply. We identify host taxa through metabarcoding coupled with high-throughput sequencing on Illumina and IonTorrent sequencing platforms to decrease economic costs and workload and thereby make the approach attractive for practitioners in conservation management. We identified hosts in four different taxonomic vertebrate classes: mammals, birds, reptiles and amphibians, belonging to at least 42 different taxonomic families. We find that vertebrate blood ingested by haematophagous terrestrial leeches throughout their distribution is a viable source of DNA with which to examine a wide range of vertebrates. Thus, this study provides encouraging support for the potential of haematophagous terrestrial leeches as a tool for detecting and monitoring terrestrial vertebrate biodiversity.

Effective conservation of large carnivores requires reliable estimates of population density, often obtained through capture-recapture analysis, in order to prioritize investments and assess conservation intervention effectiveness. Recent statistical advances and development of user-friendly software for spatially explicit capture-recapture (SECR) circumvent the difficulties in estimating effective survey area, and hence density, from capture-recapture data. We conducted a camera-trapping study on leopards (Panthera pardus) in Mondulkiri Protected Forest, Cambodia. We compared density estimates using SECR with those obtained from conventional approaches in which the effective survey area is estimated using a boundary strip width based on observed animal movements. Density estimates from Chao heterogeneity models (3.8 AE SE 1.9 individuals/100 km 2 ) and Pledger heterogeneity models and models accounting for genderspecific capture and recapture rates (model-averaged density 3.9 AE SE 2.9 individuals/100 km 2 ) were similar to those from SECR in program DENSITY (3.6 AE SE 1.0/100 km 2 ) but higher than estimates from Jack-knife heterogeneity models (2.9 AE SE 0.9 individuals/100 km 2 ). Capture probabilities differed between male and female leopards probably resulting from differences in the use of human-made trails between sexes. Given that there are a number of biologically plausible reasons to expect gender-specific variation in capture probabilities of large carnivores, we recommend exploratory analysis of data using models in which gender can be included as a covariate affecting capture probabilities particularly given the demographic importance of breeding females for population recovery of threatened carnivores. ß 2011 The Wildlife Society.

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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.

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