In the face of rapid anthropogenic climate change, species interactions add an unwelcome element of uncertainty and complexity. Many authors have emphasised the particular perils faced by species with specialist interactions, such as mutualists, or highly specialised consumers. If the temporal or spatial responses to climate change of interacting species are different, interactions could be disrupted - with grave implications for one or both species.

Despite the interest in species interactions, competition has seen less focus in this regard. An exception is Tom's work on competition between domestic and wild ungulates in the alps, in which he showed that Alpine chamois retreat to higher altitudes in the face of both high temperatures and grazing sheep - but that the effect of the presence of competitors is far more pronounced than the predicted effect of climate change. Thus, managing competition might be one way to mitigate for the consequences of climate change.

Last year, Francesco Ferretti visited the CEG from the University of Siena, Italy, on a Senior Fellowship sponsored by the Institute of Advanced Studies. One purpose of his visit was to work on analysing data on climate change and interactions between Apennine chamois (Fig. 1, above) and red deer. Red deer are currently expanding into the Apennines, where they compete with Apennine chamois (a subspecies of Pyrenean chamois recognised to be vulnerable to extinction). Francesco wanted to know if the impact of climate change would exacerbate the effect of competition (because the reduction in resources owing to climate change might make competition more intense), ameliorate the impact of competition (perhaps by leading to greater niche divergence between the species), or if the two would act independently. He brought with him data on foraging behaviour of chamois in an area that red deer have already expanded into, as well as data from an area that red deer have yet to reach. The results of his analyses have recently been published in the journal Current Zoology.

Francesco showed that climatic factors (temperature and rainfall) exerted a strong effect on chamois feeding behaviour. However, the presence or absence of red deer also impacted on the bite rate of chamois (a good indicator of the rate at which they take in food):

Importantly, in the context of Francesco's original question, there was no evidence of an interaction between the weather conditions and the presence of red deer. Thus, the two challenges appear to operate independently.

Francesco also showed that kid survival (indexed by the ratio of females to kids versus the ratio of females to yearlings in the different sites) was lower in the site with red deer than in the site without. Based on what is known about adult survival, we estimated that populations would only be stable (population growth rate, λ = 1) when kid survival was approximately 36%. In fact, the best estimate for kid survival was 49% in the site with no red deer but 27% in the site with red deer. Even given the uncertainty in that survival rate, there is a 95% chance that the survival rate in the site with red deer is too low for the chamois population to be self-sustaining (see Fig. 3). In general, increasing frequencies of drought conditions are likely to imperil Apennine chamois and related species - but that threat will be more pronounced in the presence of competitors, such as red deer, which are increasingly numerous throughout Europe.

The MammalWeb project keeps many of us (including Phil, Pen and Sammy) pretty busy. Happily, the first paper from the project has just been published in the journal Remote Sensing in Ecology and Conservation! Here, we talk about the motivation for the project and the findings of the paper.

To conserve biodiversity effectively, we need to know where and in what abundance it occurs. Breeding bird surveys, which happen in many countries every year, are a great example of how high-quality biodiversity data can underpin science and policy. In contrast to birds, however, many mammal species are elusive and surprisingly poorly documented. Motion-sensing camera traps can change this, owing to the relative ease with which they can be set up across a wide area to observe and document mammals in a non-intrusive way. As a result, camera trapping is a highly active focus of research in ecology and conservation.

A major challenge for camera trapping is dealing with the sheer volume of data that can be produced. Even modest studies can rapidly generate data sets numbering tens or hundreds of thousands of images. Someone must look at each photo and record the animals captured in it. This classification process can be a huge drain on a researcher’s time and can significantly delay the ecological insights that camera trapping can provide.
In recent years, many researchers have turned to online crowdsourcing platforms where anyone who is interested can help with data processing, which includes classifying camera trap photos. For example, the highly successful Snapshot Serengeti project attracted tens of thousands of participants to classify more than a million camera trap photos. An important trick of the trade is to ask multiple participants to classify each photo. This way, researchers can aggregate those “votes” to calculate a consensus classification. Once a consensus is achieved for a photo, it can be “retired” (i.e., no longer shown to visitors) so that users can look at other images in the dataset.

Motivated by the need to find better ways to monitor mammals in the United Kingdom, CEG staff collaborated with staff at Durham Wildlife Trust, plus many volunteers from around the country, to start MammalWeb, a citizen science project for monitoring wild mammals in north-east England. The project is unusual, in that MammalWeb citizen scientists can participate in one or both of two ways: by being a “Trapper” who sets up camera traps and uploads photos and associated data to our web platform; and by being a “Spotter” who logs in to help classify those photos (Fig. 1). One challenge for MammalWeb is that we have a much smaller group of Spotters (hundreds of users) than big, international projects like Snapshot Serengeti (tens of thousands of users). Therefore, we wanted to see if there is a way to arrive at those consensus classifications in an even more economical way, so that user effort can be focused on examining photos requiring more scrutiny. If we can do this, crowd-sourced camera trapping projects big and small can all benefit.

We started by looking at a “gold standard” subset of images for which we already know the species pictured. By comparing our user-submitted classifications to this gold standard, we can get an idea of how accurate our Spotters are. According to this, MammalWeb Spotters have over a 90% chance of correctly identifying the presence of an animal (if it is indeed present) for 10 out of 16 frequently-seen species. Where user classifications are not correct, the reasons seem to depend on the type of species. For example, classification accuracy for small rodents is lower because, often, they are simply missed by a Spotter. Other species are more frequently mis-identified rather than missed altogether. An example of this is the brown hare (Fig. 2), which is often confused with the European rabbit.

We also calculated the confidence we can have in a consensus classification, given the number and types of user classifications that underlie it. We found, for example, that very few classifications saying a badger is present are needed for us to be confident that it really is there; this is because badgers are fairly easy to identify. However, for more “ambiguous” species, such as the brown hare, we need to have more people look at the photos before we can be certain about whether or not it is there. Users are extremely unlikely to provide “false positive” classifications, suggesting that a species is pictured when the image sequence actually contains no wildlife.  Hence, even when many classifications suggest that an image sequence is devoid of wildlife, a single dissenter is more likely to be correct.

What all this means is that, when crowdsourcing the classification of camera trap photos and calculating consensus classifications, it may be helpful to factor in (1) differences in detectability between species, and (2) the relative influence of different types of incorrect classifications (where species have been missed versus where they have been misclassified). Together, these solutions can better focus user classification efforts on those photos requiring more scrutiny.

As projects like MammalWeb, Snapshot Serengeti and eMammal gather a large body of classified camera trap photos, they can be used as training data to aid machine learning algorithms to automatically classify wildlife photos. The first steps look very promising, emphasising how critical it is for researchers to share their data and results so that we can build on each other’s progress to address the need for large scale monitoring in this time of rapid ecological change.

More generally, the MammalWeb project has also demonstrated that citizen science is not limited to scientists crowdsourcing, or “outsourcing”, their work to volunteers. MammalWeb citizen scientists have not only been instrumental in setting up camera traps to observe wild mammals, but have also taken the initiative and started their own wildlife surveys. Some use the data they collect to inform public planning and engage policy makers, while others develop and deliver camera trapping workshops to other wildlife groups. Can citizen science camera trapping be as successful as other citizen-initiated remote sensing projects such as aerial mapping?

Pen has been honoured for his science outreach work, winning the 'Science Postgraduate Excellence in Outreach Award'. The award recognises outstanding contributions to public engagement with science.​ Pen has been involved in many outreach activities for the project MammalWeb, a joint project between the University and the Durham Wildlife Trust where members of the public help with mammal monitoring by deploying camera traps. Pen has worked with students from Belmont Community School in Durham, teaching them about camera trapping, and allowing them to do their own field work and research with the camera traps. Together, they made a video to explain more about MammalWeb and their experiences with it. You can watch the full video by clicking here, or a shorter version of the video here.

Pen giving a demonstration at a public Science Day.