Camera trap distance sampling for terrestrial mammal population monitoring: lessons learnt from a UK case study

Accurate and precise density estimates are crucial for effective species management and conservation. However, efficient monitoring of mammal densities over large spatial and temporal scales is challenging. In the United Kingdom, published density estimates for many mammals, including species considered to be common, are imprecise. Camera trap distance sampling (CTDS) can estimate densities of multiple species at a time and has been used successfully in a small number of studies. However, CTDS has typically been used over relatively homogeneous landscapes, often over large time scales, making monitoring changes (by repeating surveys) difficult. In this study, we deployed camera traps at 109 sites across an area of 2725 km2 of varied habitat in North-East England, United Kingdom. The 4-month survey generated 51 447 photos of wild mammal species. Data were sufficient for us to use CTDS to estimate the densities of eight mammal species across the whole-survey area and within four specific habitats. Both survey-wide and habitat-specific density estimates largely fell within previously published density ranges and our estimates were amongst the most precise produced for these species to date. Lower precision for some species was typically due to animals being missed by the camera at certain distances, highlighting the need for careful consideration of practical and methodological decisions, such as how high to set cameras and where to left-truncate data. Although CTDS is a promising methodology for determining densities of multiple species from one survey, species-specific decisions are still required and these cannot always be generalized across species types and locations. Taking the United Kingdom as a case study, our study highlights the potential for CTDS to be used on a national scale, although the scale of the task suggests that it would need to be integrated with a citizen science approach

Economical crowdsourcing for camera trap image classification

Camera trapping is widely used to monitor mammalian wildlife but creates large image datasets that must be classified. In response, there is a trend towards crowdsourcing image classification. For high‐profile studies of charismatic faunas, many classifications can be obtained per image, enabling consensus assessments of the image contents. For more local‐scale or less charismatic communities, however, demand may outstrip the supply of crowdsourced classifications. Here, we consider MammalWeb, a local‐scale project in North East England, which involves citizen scientists in both the capture and classification of sequences of camera trap images. We show that, for our global pool of image sequences, the probability of correct classification exceeds 99% with about nine concordant crowdsourced classifications per sequence. However, there is high variation among species. For highly recognizable species, species‐specific consensus algorithms could be even more efficient; for difficult to spot or easily confused taxa, expert classifications might be preferable. We show that two types of incorrect classifications – misidentification of species and overlooking the presence of animals – have different impacts on the confidence of consensus classifications, depending on the true species pictured. Our results have implications for data capture and classification in increasingly numerous, local‐scale citizen science projects. The species‐specific nature of our findings suggests that the performance of crowdsourcing projects is likely to be highly sensitive to the local fauna and context. The generality of consensus algorithms will, thus, be an important consideration for ecologists interested in harnessing the power of the crowd to assist with camera trapping studies