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.
Research articles from the MammalWeb team
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.
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.
What is MammalWeb? MammalWeb is a "citizen science" platform intended to collate, validate and curate camera trap data that can inform us about the distribution and ecology of mammals.
Why monitor mammals? Mammals are often elusive. Often coming out at night, and not in great numbers, it is hard to monitor their populations, where they are distributed, and how they behave. Knowing these things is important for a wide range of applications, including conservation and the sustainable development our natural landscape.
What is the aim of MammalWeb? MammalWeb's aims include:
Read more about MammalWeb and the ten principles of citizen science.
Read more about MammalWeb's findings.
What are camera traps? Camera traps are devices that do not actually catch the animals, but detect and photograph any animal that moves in front of them. They are relatively easy to set up, and cause less stress to animals than traditional monitoring methods, like capturing and tagging. Camera trapping can help us to learn what animals occur in an area, when they are active, and how they time important seasonal events, such as reproduction. With careful development and analysis, camera traps can also tell us about the abundance of species in different areas and at different times. Battery-powered camera traps can be left in the wild taking pictures for months, so do not need constant checks.
Who can get involved? Anyone can get involved with MammalWeb, in one (or more) of three ways. First, any individual can also register for an account to start identifying what's pictured in the uploaded images (find out about Spotting to participate). Second, anyone who owns or can borrow a camera trap can upload image data to the site, along with information about where the images were obtained and over what period (find out about Trapping to participate). Third, organisations that already deploy camera traps (or those that are considering doing so) can contact us about setting up a 'project'; this will allow them to submit image data to the platform and to involve others in the job of classifying those data (find out about Projects to participate). Click the links in this paragraph for more information on any of these possibilities.
Read about MammalWeb's Terms and Conditions.
Who runs MammalWeb? MammalWeb was set up in collaboration between Durham University and Durham Wildlife Trust. It is run by MammalWeb Limited. MammalWeb Limited is a not-for-profit company limited by guarantee. Directors include representatives from Durham University's departments of Anthropology, Bioscience and Computer Science, Durham Wildlife Trust, the National Wildlife Management Centre and Manifesto Digital. Much of the hard work involved in setting up and running the platform, and in understanding the data is conducted by other scientists at Durham University, including the following:
Emily is conducting a PhD with a focus on improving the efficiency with which citizen science data - not just those generated by the MammalWeb project - can be verified. Emily will also be considering the impacts of confidence in the data on the downstream use of citizen science data.
Sian's PhD is focused on improving engagement, to get as many people as possible taking part in, and enjoying MammalWeb. Later on, Sian will use the data to answer questions about camera trapping methods, and their impacts on both engagement and ecological inferences.
Sammy is conducting a PhD with two major components. First, she has been conducting a rigorous, standardised survey of County Durham, to provide a comparison with the data submitted by participants. Second, she is also assessing ways to get schools and school children involved in monitoring.
Jonathan, who has a background in physics, is conducting a PhD on the use of image analysis and deep learning to help us extract more information more quickly from submitted photographs, thereby ensuring that the data can be put to use more quickly.
Alumni and other acknowledgements
Pen-Yuan Hsing completed his PhD in 2019. Pen's PhD was focused on the early design of MammalWeb and the recruitment and training of participants. Pen also showed how the data could be analysed to yield insights into spatial and temporal aspects of species' ecology.
A number of MBiol (degree with integrated masters) students have conducted research projects on MammalWeb, helping to inform the design and interpretation of the project. They include: Courtney Neal (2016), Bryony Jones (2018), Toby Atkinson-Coyle (2018) and Balint Ternyik (2020).
Undergraduates, interns and other contributors
We are also grateful to the wide variety of additional students, interns and others who have helped with the project from 2014 to the present. They include: Magnus Bower, Emma Brown, Florian Graber, David Jarrett, Hahyun Lee, Alice Miller, Emily Perrin, Emily Townley and Lucy Zhang.
Since its early creation by Steven Bradley, almost all of the work to add features and troubleshoot the platform has been conducted by Helen Chappell of Rhombus Technology. We are enormously grateful to Helen for her tireless work on the site and for her huge achievements in spite of the incompetence of the rest of the team!
Former Board Member
We are grateful to Simon Hodgkinson of the Smart Earth Network, one of the founding directors of MammalWeb CLG. Simon's faith in what we were trying to achieve, and his insistence on a more corporate structure to take that forward, were important drivers in creating the structure that now underpins MammalWeb.
1. Citizen science projects actively involve citizens in scientific endeavour that generates new knowledge or understanding.
Relative to taxa such as birds and butterflies, mammals have not enjoyed rigorous, sustained and community-wide monitoring over recent decades. Documenting their presence, absence and activity patterns in space and time yields data that can be analysed to gain new knowledge about aspects of the environment that determine where they occur and when they are active, as well as how that changes through time. These are not trivial questions and they require a critical mass of data, about which we understand the quality, biases and limitations. At MammalWeb, we are using the collated data to inform analyses that underpin that understanding. We can then progress to answering ecological questions of both pure and applied importance.
2. Citizen science projects have a genuine science outcome.
The insights gained from MammalWeb have been presented at national and international scientific conferences and have been used to underpin published scientific papers [hyperlink]. Ongoing work will develop that – and we anticipate that scientific outputs will accelerate over time, once our platform and protocols are sufficiently refined to attract and sustain widespread engagement.
3. Both the professional scientists and the citizen scientists benefit from taking part.
As scientists engaged in academia and practical conservation management, members of the MammalWeb team certainly benefit from the availability of data to inform management actions (such as the removal of invasive species), underpin scientific publications, and provide an important resource for training and education. From communication with participants, we also know that many benefit from the way that MammalWeb can be used to collate and manage their projects, from the challenges inherent in identifying wildlife, and from the satisfaction of contributing to the wider endeavour of monitoring our mammalian heritage.
4. Citizen scientists may, if they wish, participate in multiple stages of the scientific process.
One of MammalWeb’s unique facets from the outset was our focus on citizen scientists participating in both data collection (camera trapping) and data processing (image and video classification). In addition, some participants have gone on to author their own reports, co-author publications, or be interviewed in the national press about their work. We welcome ideas from participants about questions they would like us to answer using the data, or about questions they would like to answer using the data.
5. Citizen scientists receive feedback from the project.
All participants can explore the data using the Discover menu item. In addition, all are welcome to sign up to our monthly newsletters, which provide regular feedback on what we’ve been up to and what we’re finding out from the data. We also post findings and outcomes on social media and on our News page.
6. Citizen science is considered a research approach like any other, with limitations and biases that should be considered and controlled for.
We have invested significant effort in understanding the biases and limitations in the data, and much of our ongoing work is focused on how to reach reliable inferences most efficiently.
7. Citizen science project data and meta-data are made publicly available and, where possible, results are published in an open access format.
We have previously contributed data to local records centres but the best way to do that (at a level that can be considered reliable, and by a process that is efficient for the data recipients) is still under discussion. This is an area of development. In the meantime, our publications have made data available on open repositories, where relevant. Our open data policy is, however, sensitive to issues of privacy raised by contributing organisations and individuals.
8. Citizen scientists are acknowledged in project results and publications.
All participants are credited via group authorship, where publisher policies permit, or relevant acknowledgements where that is not an option. In addition, some contributors have co-authored papers with the MammalWeb team.
9. Citizen science programmes are evaluated for their scientific output, data quality, participant experience and wider societal or policy impact.
We aim to produce data with societal impact and outputs that educate and inform. Ultimately, our participants and the end-users of the collated data must be the judges of whether MammalWeb performs in these respects.
10. The leaders of citizen science projects take into consideration legal and ethical issues surrounding copyright, intellectual property, data sharing agreements, confidentiality, attribution, and the environmental impact of any activities.
We take these issues very seriously, as enshrined in our Terms and Conditions. We have active data sharing agreements with organisational contributors and have previously signed data sharing agreements with external researchers.