Occupancy, density and the ecology of terrestrial British mammals

Mammals include species of ecological, economic and cultural importance. Determining the factors that drive their abundance and distribution, and developing effective management, rely on sustained and widespread monitoring. Britain boasts a rich history of detailed and informative studies of specific mammal populations. Over the same period, however, effective monitoring of the wide range of terrestrial mammal species has been lacking, exacerbated by the fact that many species occur at low densities, and are nocturnal or otherwise elusive. Consequently, calls for improved monitoring to underpin conservation and management have a long history.

Recent years have brought some notable declines in British mammals, including hedgehogs, weasels and wildcats. At the same time, invasive non-native species, including mammals such as muntjac and grey squirrel, continue to expand, causing problems for native species. Constant vigilance is required to avoid further invasions; the presence of the invasive greater white-toothed shrew, recently identified in north-east England, highlights the importance of improved monitoring systems.

In light of these concerns, the MammalWeb project was set up to encourage citizen scientists to engage in mammal monitoring by contributing camera trap images and associated metadata to a growing national database, and by helping with the task of classifying the resultant images. In this way, MammalWeb has assembled a database of millions of photos, videos and image classifications, from thousands of camera trap placements around Britain. This is a powerful resource but inference from these data is hampered by three issues: (i) long lags between data submission and data classification; (ii) known and unknown biases in camera placements; and (iii) the complex nature of the data, which demand elaborate analyses for robust inference. This project will tackle all three of these problems to produce timely and robust inferences with which to underpin insights for ecology, conservation and management.

Aims: (1) to use an array of camera traps to collect data on the occupancy and density of British mammals, calibrating, validating and improving inferences from citizen-led data collection; (2) to utilise recently-developed and high-performance AI models for image classification to develop efficient and prioritised workflows for robust image classification; (3) to analyse classified data from the MammalWeb dataset to answer questions regarding the occupancy and activity of British mammals, as well as their natural and anthropogenic drivers; and (4) to work with our end-user partners to showcase the use of ecological inferences to underpin management and policy.

Methods will include deploying and calibrating camera traps, using cutting-edge analytical techniques to infer occupancy and density of selected mammal species; working with partners to classify image contents using bespoke machine learning (convolutional neural networks -CNNs) for British and European mammals, improving training for underrepresented species, and modifying classification algorithms on MammalWeb in light of the outputs; and hierarchical Bayesian modelling to derive insights into the drivers of occupancy and activity, including multi-species occupancy modelling. Training will be provided by all supervisors and, in addition, we anticipate a placement at the National Wildlife Management Centre (APHA), liaising directly with end-users of the data.

This project is in competition with others for funding. Success will depend on the quality of applications received, relative to those for competing projects. If you are interested in applying, in the first instance contact the supervisor (Professor Philip Stephens), with a CV and covering letter, detailing your reasons for applying for the project.