Sian E. Green

Global efforts to mitigate anthropogenic pressures on biodiversity and ecosystems will often be realised through management at landscape-scales (i.e., in the range of 100s–1000 s km²). In consequence, we need to measure biodiversity responses at landscape-scales to ensure mitigations are effectively protecting and restoring ecosystems. Yet many countries currently lack monitoring programmes that can generate indicators of biodiversity at these scales. Localised monitoring (e.g., 1 km²) is often amalgamated into national-scale indicators, however, this leaves a substantial gap in the middle of this spatial gradient, limiting availability of information at decision-relevant scales. Here, using the United Kingdom as a case study, we explored the suitability of seven sources of biodiversity data which could be used to construct landscape-scale indicators. We surveyed 70, mostly UK-based, monitoring experts for their opinions on structured and unstructured in-person surveys, camera traps, eDNA, drones, passive acoustic recorders, and satellite remote sensing. We assessed data source utility to construct indicators reflecting Essential Biodiversity Variables, i.e., as holistic measures of taxa or ecosystems rather than assessments of individual management interventions. All seven data sources were deemed suitable, and experts expected developments in technology and infrastructure to greatly increase this potential over the next decade. However, there are technical, analytical, logistical and financial barriers to establishing monitoring networks that could yield the requisite data for landscape-scale indicators. Resolving these issues requires substantial research, policy commitment and investment, but landscape-scale indicators will be essential for the UK to undertake adaptive management and monitor nature recovery.

As camera traps have become more widely used, extracting information from images at the pace they are acquired has become challenging, resulting in backlogs that delay the communication of results and the use of data for conservation and management. To ameliorate this, artificial intelligence (AI), crowdsourcing to citizen scientists and combined approaches have surfaced as solutions. Using data from the UK mammal monitoring initiative MammalWeb, we assess the accuracies of classifications from registered citizen scientists, anonymous participants and a convolutional neural network (CNN). The engagement of anonymous volunteers was facilitated by the strategic placement of MammalWeb interfaces in a natural history museum with high footfall related to the ‘Dippy on Tour’ exhibition. The accuracy of anonymous volunteer classifications gathered through public interfaces has not been reported previously, and here we consider this form of citizen science in the context of alternative forms of data acquisition. While AI models have performed well at species identification in bespoke settings, here we report model performance on a dataset for which the model in question was not explicitly trained. We also consider combining AI output with that of human volunteers to demonstrate combined workflows that produce high accuracy predictions. We find the consensus of registered users has greater overall accuracy (97%) than the consensus from anonymous contributors (71%); AI accuracy lies in between (78%). A combined approach between registered citizen scientists and AI output provides an overall accuracy of 96%. Further, when the contributions of anonymous citizen scientists are concordant with AI output, 98% accuracy can be achieved. The generality of this last finding merits further investigation, given the potential to gather classifications much more rapidly if public displays are placed in areas of high footfall. We suggest that combined approaches to image classification are optimal when the minimisation of classification errors is desired.

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.

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.

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.

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.

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.