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.

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract The conservation of wide-ranging, territorial carnivores presents many challenges, not least the inadequacy of many protected areas in providing sufficient space to allow such species to maintain viable populations. As a result populations occurring outside protected areas may be of considerable importance for the conservation of some species, although the significance of these areas is poorly understood. Brown hyaenas Parahyaena brunnea are categorized as Near Threatened on the IUCN Red List and recent research suggests the species may be particularly vulnerable to habitat loss and the conversion of land to agriculture. Here we report on the population density and abundance of brown hyaenas in an area of commercial farmland in western Botswana. Mean brown hyaena density estimated from camera-trap surveys was 2.3 per 100 km 2 and from spoor surveys was 2.88 per 100 km 2 , which are comparable to estimates reported for protected areas. Estimated densities were higher on farms used for livestock production than on those used for game farming, suggesting that the species can tolerate land use change where reliable alternative food resources exist. Our results indicate that populations of brown hyaenas in non-protected areas comprise a significant proportion of the global population and that such areas may be of critical importance for their conservation.

Populations of large carnivores can persist in mountainous environments following extensive land use change and the conversion of suitable habitat for agriculture and human habitation in lower lying areas of their range. The significance of these populations is poorly understood, however, and little attention has focussed on why certain mountainous areas can hold high densities of large carnivores and what the conservation implications of such populations might be. Here we use the leopard (Panthera pardus) population in the western Soutpansberg Mountains, South Africa, as a model system and show that montane habitats can support high numbers of leopards. Spatially explicit capture-recapture (SECR) analysis recorded the highest density of leopards reported outside of state-protected areas in sub-Saharan Africa. This density represents a temporally high local abundance of leopards and we explore the explanations for this alongside some of the potential conservation implications.

To develop guidelines for the collection of independent field samples of scats for the quantification of wild dog (Lycaon pictus) diet we determined the passage rates of different wild dog prey items from feeding trials on a captive pack held at Marakele National Park, Limpopo Province. The minimum time to first detection was 5.5 hours after feeding (S.E. ± 1.52, n = 5) and prey items remained in the gut for an average of 79.4 hours (S.E. ± 6.00, n = 3). Differential passage rates of prey species were not pronounced. Observed passage rates were used to devise a sampling protocol for scats collected during a field study where scats were separated by a minimum period of 120 hours to ensure independence of samples. Comparison of the percentage occurrence of prey species in field-collected scats with the percentage occurrence from direct observations of kills illustrated the tendency for small prey to be underrepresented in the latter. However, the strong correlation between percentage occurrences in diet as determined by the two methods (r s = 0.85, P < 0.01, 13 d.f.) suggests that both methods can reliably determine the relative importance of prey in the diets of obligate carnivores such as wild dogs. The determination of maximum passage rates and subsequent guidelines for collection of independent faecal samples in the field could be a valuable tool for reducing inherent biases in carnivore diet studies.

1. In light of global biodiversity loss, there is an increasing need for large-scale wildlife monitoring. This is difficult for mammals, since they can be elusive and nocturnal. In the United Kingdom, there is a lack of systematic, widespread mammal monitoring, and a recognized deficiency of data. Innovative new approaches are required.2. We developed MammalWeb, a portal to enable UK-wide camera trapping by a network of citizen scientists and partner organizations. MammalWeb citizen scientists contribute to both the collection and classification of camera trap data.Following trials in 2013-2017, MammalWeb has grown organically to increase its geographic reach (e.g. ∼2000 sites in Britain). It has so far provided the equivalent of over 340 camera trap-years of wild mammal monitoring, and produced nearly 440,000 classified image sequences and videos, of which, over 180,000 are mammal detections.3. We describe MammalWeb, its background, its development and the novel approaches we have for participation. We consider the data collected by Mammal-Web participants, especially in light of their relevance to the main goals of wildlife monitoring: to provide spatial data, abundance data and temporal behavioural data. MammalWeb can complement existing approaches to mammal monitoring. Explicit accounting for spatial and temporal patterns in animal activity enables accountingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.