Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.

Camera traps (CTs) are an increasingly popular method of studying animal behavior. However, the impact of cameras on detected individuals—such as from mechanical noise, odor, and emitted light—has received relatively little attention. These impacts are particularly important in behavioral studies in conservation that seek to ascribe changes in behavior to relevant environmental factors. In this article, we discuss three sources of bias that are relevant to conservation behavior studies using CTs: (a) disturbance caused by cameras; (b) variation in animal‐detection parameters across camera models; and (c) biased detection across individuals and age, sex, and behavioral classes. We propose several recommendations aimed at mitigating responses to CTs by wildlife. Our recommendations offer a platform for the development of more rigorous and robust behavioral studies using CT technology and, if adopted, would result in greater applied benefits for conservation and management.

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. 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. We describe MammalWeb, its background, its development and the novel approaches we have for participation. We consider the data collected by MammalWeb 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 accounting of bias relative to ad hoc observational data. Estimating abundance presents challenges, as for many camera‐trapping studies, but we discuss the potential of the data as they stand, and opportunities to advance their value for abundance estimation. Challenges remain to MammalWeb's central missions of enhancing engagement with and connection to nature, and delivering policy‐relevant data on Britain's wild mammals. We discuss these challenges and the opportunities they provide for advances in respect of engagement, science and financial security. Our approach reduces administrative burden and increases spatial coverage and, as such, MammalWeb provides a useful addition to existing case studies of citizen science camera‐trapping program design. We believe MammalWeb is an important step towards fulfilling calls for UK‐wide mammal monitoring and our description of challenges identifies an agenda for fulfilling that purpose.

Human encroachment on wildlife habitats is leading to increased fragmentation; hence, there is an increasing focus on improving connectivity between remaining habitat. Large, wide‐ranging species such as the African elephant, ( Loxodonta africana), are particularly vulnerable due to their extensive habitat requirements. Wildlife corridors have been created to facilitate movement, with little knowledge to date on whether they serve their intended function as transit routes, or whether they simply extend the available habitat for occupancy. We collected data on elephant behaviour in the Mount Kenya Elephant Corridor, with the aim of quantifying the utility of the corridor. A grid of 25 camera traps was used to survey the 478 ha corridor over 11 weeks. Cameras recorded over 43,000 photos with 694 separate events triggered by elephants. Patterns of use varied spatially and temporally, indicating that certain areas were treated as habitat extension, while others were predominantly for transit. These differences were likely due to variation in vegetation cover and levels of human disturbance. Corridor use differed amongst individuals, suggesting that use may depend on both the characteristics of the corridor itself and the social or resource needs of individual elephants.

Camera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high‐quality video, the majority of studies collect still images, in part because of concerns with video performance. We conducted a camera trap survey of a forested landscape in the UK, using a grid of paired camera traps, to quantify the impact of using video compared to photos on the outcomes of ecological research and for participation and engagement of citizen scientists. Ecological outputs showed no difference between photo and video datasets, but comparison between expert and citizen science classifications showed citizen scientists were able to classify videos more accurately (average accuracy of 95% for video, 86% for photo). Furthermore, citizen scientists were more likely to volunteer additional information on age (provided for 61% videos and 30% photos) and sex (provided for 63% videos and 45% photos) of animals in video footage. Concerns over slow trigger speeds for videos did not appear to affect our datasets or the inferences gained. When combined with citizen science, video datasets are likely to be of higher quality due to increased classification accuracy. Consequently, we encourage researchers to consider the use of video for future camera‐trapping projects.

Increasing concern over the unfavourable population trends and data gaps for many mustelids highlight the need to improve existing monitoring methods, to enable more accurate population assessments. Here, we investigated the efficacy of two camera trapping techniques for the detection of small mustelids, specifically least weasels Mustela nivalis, and stoats M. erminea in England. We deployed two concurrent camera trap techniques - a Mostela (a camera trap enclosed within a box) and an external camera trap at 20 sampling sites during summer 2021. We measured the number of daily detections over time and calculated the probability of detection and occupancy for each species. Our results highlighted the efficacy of the Mostela at detecting weasels whilst significantly reducing the amount of video footage to review. Stoats, however, were rarely detected with either technique, although the external camera appeared to be more reliable than the Mostela. European polecats Mustela putorius were regularly detected inside the Mostela and were detected at a similar rate as the external cameras after an initial period of avoidance. The data collected from the Mostela also highlighted the nocturnal activity of polecats and diurnal activity of weasels. Here, the Mostela provided an effective method to detect and estimate the occupancy of weasels and this may be just as efficient for polecats if deployed for long enough.