MammalWeb's findings

Many people are interested in the wildlife around them and many use camera traps for a variety of purposes. Ultimately, MammalWeb is intended to harness the efforts of those individuals and to ensure that, regardless of the purpose for which camera traps are deployed, the data don't just remain on individual SD cards and hard drives, but contribute towards a wider effort to document our wild mammals (and any other species of mammal and bird that are caught in images and videos).

Collating data in this way is not ideally suited to answering one or two specific science questions but, over time, it helps us to build up a picture of mammal distributions and what affects those, as well as mammal activity and its drivers. Moreover, with careful analysis, many specific questions can be answered along the way. These include methodological questions about citizen science, engagement and survey techniques, ecological questions about species associations, activity and occurrence, and management questions about the occurrence and whereabouts of non-native species, pest species or species of conservation concern. Moreover, you never know when routine monitoring might reveal a pattern that you weren't expecting to find, hinting at some previously undetected process.

The initial focus must be on how to build engagement with the project and how to interpret the data. The outputs and links below give some idea of the progress we are making, and the findings being generated along the way. All of the links should be accessible but please let us know (at This email address is being protected from spambots. You need JavaScript enabled to view it.) if you cannot access an article of interest.

Research articles from the MammalWeb team

This article is the baseline description of the MammalWeb project, describing its inception, growth and potential to contribute to national mammal monitoring. In the paper, we consider the challenges and opportunities of a citizen science project on a national scale, noting the close linkages between science, engagement and funding that represent fundamental challenges to all citizen science projects.

This article describes the outcomes of the two Forest of Dean camera trapping projects, based on fieldwork conducted as part of Sian's PhD. Sian collected photo and video from paired camera traps, placed on a systematic grid covering the whole of the Forest of Dean. Using the data collected, she was able to compare the outcomes for ecology, for the accuracy of citizen scientist classifications, and for apparent engagement using the two different approaches. She found that the two approaches did not differ in their implications for ecological research, but that videos were classified more accurately and appeared to be more engaging than photos.

This article describes a rigorous and systematic camera trapping survey of County Durham undertaken by Sammy as part of her PhD. Sammy used the cutting edge technique of Camera Trap Distance Sampling (developed by Eric Howe and colleagues) to estimate densities of the commoner medium-large wild mammals that occur in the county. Estimated densities of focal species were within expected ranges and were more precise than many previous estimates of density for mammals within the UK. Sammy went on to consider how this approach might be used at a national scale, highlighting that a citizen science element would be essential.

This piece, led by Pen during his PhD research, describes the process and outcomes of outreach work focused around using the MammalWeb platform in a secondary school. It features school pupils and MammalWeb citizen scientists as co-authors and establishes some of the wider benefits that can arise from taking MammalWeb into schools. The journal's style prevented group authorship being attributed to volunteers, specifically, but accepted that "the MammalWeb citizen science project" could be credited.

This is a review that focuses on how citizen science can contribute to camera trapping image data processing, and how - given the constraints on that processing - deep learning (or "artificial intelligence") might be integrated with citizen scientists' efforts to make data processing more efficient. The research did not draw on MammalWeb data.

This piece, also led by Pen during his PhD, was a preliminary exploration of how classifications of image data submitted by citizen scientists can be interpreted, and how they affect the confidence we can have in what species feature in an image sequence. Pen showed that contributors have generally high accuracy but that, nonetheless, it can take from 7 to 9 classifications per sequence to have high confidence in what is featured. On the face of it, this finding is bleak. However, species-specific assessments show that most species can be determined with confidence much more rapidly (typically, with only a few classifications). Difficulties arise in classifying sequences featuring small rodents, as these are often mistaken for 'empty' sequences. This is less of a worry, as small rodents are not really the focus of camera trapping. Nevertheless, with better and more efficient approaches to image classification, we will be able to make more of the data, more rapidly.

News articles

This article focuses on the GAP project which is led by PhD student Sammy. For this project, Sammy has been working with a group of students who are taught outside of mainstream school due to experiences with severe depression and anxiety. This article highlights some of the positive impacts the project is having on the student's involved, particularly during the challenging times of the national lockdown during the Covid-19 pandemic. You can read more about the project, and classify photos taken by students at GAP here.

This press release was published by the British Ecological Society (BES) following the presentation Sammy gave at the BES Annual Meeting 2019. It gives an overview of the preliminary results from Sammy's project engaging 43 primary schools across north east England in the MammalWeb project. Preliminary findings suggest that school pupils who participated in the project increased their knowledge of UK mammals and their connection to nature. You can read more about the project, and classify photographs taken by the schools involved here.

This article focuses on the experiences of one of our long-standing contributors, Roland Ascroft, on being involved in the MammalWeb project. Roland was one of our first contributors to MammalWeb back in 2015 and since then has uploaded and classified thousands of camera trap images. As this article highlights, Roland has captured a large range of mammals and birds on camera traps he has deployed in his local area in County Durham, including some more surprising species! 

This article was written by PhD student Sian. The article gives an overview of why mammal monitoring is important and how citizen scientists and camera traps can help us study mammals. The article also highlights some of the findings from the MammalWeb project thus far.

Articles from our contributors

This article was written by one of the students involved in the GAP project which is being led by PhD student Sammy (read more about the GAP project here). Lily writes of her experiences with the project, including deploying camera traps at Gosforth park nature reserve and classifying images as part of her home education during the Covid-19 pandemic.

This article details the findings from the Highland Red Squirrel project, led by Dr Louise de Raad at the University of the Highlands and Islands. For this project, MammalWeb participants helped to classify camera trap photographs of squirrel nest boxes. The aim of the project was to understand the impact of, and potential mitigation for, forest operations on red squirrels.

This article was written by long-standing contributor to MammalWeb, Roland Ascroft. In the article Roland writes about the species he has captured on camera traps in his local woods in County Durham.