A study led by researchers at the University of Southampton has used data collected by volunteer bird watchers to study how the importance of wildlife habitat management depends on changing temperatures for British birds.
The team studied data from the British Trust for Ornithology’s Bird Atlas 2007 – 11 on the abundance of the Eurasian jay over the whole of Great Britain. The University of Southampton researchers focused on jays for this trial as they are a species of bird known to frequent a mixture of different natural environments. Continue reading →
Researchers from EPFL and the University of Zurich have developed a model that uses data from sensors worn by meerkats to gain a more detailed picture of how animals behave in the wild.
Advancement in sensor technologies has meant that field biologists are now collecting a growing mass of ever more precise data on animal behaviour. Yet there is currently no standardised method for determining exactly how to interpret these signals. Take meerkats, for instance. A signal that the animal is active could mean that it is moving; alternatively, it could indicate that it is digging in search of its favourite prey, scorpions. Likewise, an immobile meerkat could be resting – or keeping watch.
In an effort to answer these questions, researchers from EPFL’s School of Engineering Laboratory of Movement Analysis and Measurement (LMAM) teamed up with colleagues from the University of Zurich’s Population Ecology Research Group to develop a behavior recognition model. The research was conducted in affiliation with the Kalahari Research Centre. Continue reading →
Mathematicians and conservationists from the UK, Africa and the United States have used machine-learning and citizen science techniques to accurately count wildebeest in the Serengeti National Park in Tanzania more rapidly than is possible using traditional methods.
Evaluating wildebeest abundance is currently extremely costly and time-intensive, requiring manual counts of animals in thousands of aerial photographs of their habitats. From those counts, which can take months to complete, wildlife researchers use statistical estimates to determine the size of the population. Detecting changes in the population helps wildlife managers make more informed decisions about how best to keep herds healthy and sustainable. Continue reading →
SCR models simultaneously estimate the detection function and density of individual activity centres. A half-normal detection model is generally used.
The estimation of population size is one of the primary goals and challenges in wildlife ecology. Within the last decade and a half, a new class of tools has emerged, allowing us to estimate abundance and other key population parameters in specific areas. So-called spatial capture-recapture (SCR) models are growing in popularity not only because they can map abundance, but also because they can be fitted to data collected from a variety of monitoring methods. For example, the ever increasing use of non-invasive monitoring methods, such as camera trapping and non-invasive genetic-sampling, is one of the reason that makes SCR models so popular.
One other strengths of SCR models is the ability to make population level inferences. But the wider the region you’re monitoring, the greater the computational burden, challenging the use of such methods at really large scale. Continue reading →
Imagine you’re the manager of a national park. One that’s rich in endemic biodiversity found nowhere else on the planet. It’s under the influence of multiple human pressures causing irreversible declines in the biodiversity, possibly even leading to the extinction of some of the species. You’re working with a complex system of multiple species and threats, limited knowledge of which threats are causing the biggest declines and limited resources. How do you decide what course of action to take to conserve the biodiversity of the park? This is the dilemma faced by biodiversity managers across the globe.
Expert judgement is used to predict current and future trends for Koala populations across Australia
New technologies provide ecologists with unprecedented means for informing predictions and decisions under uncertainty. From drones and apps that capture data faster and cheaper than ever before, to new methods for modelling, mapping and sharing data.
But what do you do when you don’t have data (or the data you have is incomplete or uninformative), but decisions need to be made?
In real-life situations, it is far more common for decisions to be based on a comparison between things that can’t be judged on the same standards. Whether you’re choosing a dish or a house or an area to prioritise for conservation you need to weigh up completely different things like cost, size, feasibility, acceptability, and desirability.
Those three examples of decisions differ in terms of complexity – you’d need specific expert knowledge and/or the involvement of other key stakeholders to choose conservation prioritisation areas, but probably not to pick a dish. The bottom line is they all require evaluating different alternatives to achieve the desired goal. This is the essence of multi-criteria decision analysis (MCDA). In MCDA the pros and cons of different alternatives are assessed against a number of diverse, yet clearly defined, criteria. Interestingly, the criteria can be expressed in different units, including monetary, biophysical, or simply qualitative terms. Continue reading →
Focus Group Discussions: What are They and Why Use Them?
A focus group discussion with local farmers in Trans Mara district, Kenya, carried out by Tobias O. Nyumba (co-author)
To paraphrase Nelson Mandela: ultimately, conservation is about groups of people. On a global scale it’s our collective human footprint that drives habitat destruction and species extinction, and the joint action of large groups that makes positive change. At a smaller scale, groups of people make decisions about conservation policy or management. In turn, communities of people feel the positive or negative effects of these actions, directly or indirectly. From global to local scales, groups of people make changes and groups of people feel the effects of those changes.
To improve conservation action and understand how decisions affect communities on the ground we need to talk to those communities. This is where focus group discussions become an asset to conservation research. They bring participants together in the same place where they can draw from their own personal beliefs and experiences, and those of other group members in a collective discussion. The researcher takes more of a backseat (facilitator) role in focus group discussions compared to interviews, allowing the group conversation to evolve organically. We can get a more holistic view of a situation from this method than from one-on-one interviews alone. Also, as respondents are interviewed at the same time and in the same place, travelling times and costs can be reduced for the researcher. Continue reading →
Understanding key habitat requirements is critical to the conservation of species at risk. For highly mobile species, discerning what is key habitat as opposed to areas that are simply being traversed (perhaps in the search for key habitats) can be challenging. For seabirds, in particular, it can be difficult to know which areas in the sea represent key foraging grounds. Devices that record birds’ diving behaviour can help shed light on this, but they’re expensive to deploy. In contrast, devices that record the birds’ geographic position are more commonly available and have been around for some time.
In their recent study entitled ‘Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds,’ Ella Browning and her colleagues made use of a rich dataset on 399 individual birds from three species, some equipped with both global positioning (GPS) and depth recorder devices, others with GPS only. The data allowed them to test whether deep learning methods can identify when the birds are diving (foraging) based on GPS data alone. Results were highly promising, with top models able to distinguish non-diving and diving behaviours with 94% and 80% accuracy. Continue reading →