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 →
A salamander having its skin swabbed to test for Bsal infection.
Imagine you’re at the doctor’s office. You’re waiting to hear back on a critical test result. With recent emerging infectious diseases in human populations, you are worried you may be infected after a sampling trip to a remote field site. The doctor walks in. You sit nervously, sensing a slight tremble in your left leg. The doctor confidently declares, “Well, your tests results came back negative.” In that moment, you let out a sigh of relief, the kind you feel throughout your body. Then, thoughts start flooding your mind. You wonder– what are the rates of false negatives associated with the test? How sensitive is the diagnostic test to low levels of infection? The doctor didn’t sample all of your blood, so how can they be sure I’m not infected? Is the doctor’s conclusion right?
Now, let’s say I’m the doctor and my patient is an amphibian. I don’t have an office where the amphibian can come in and listen to me explain the diagnosis or the progression of disease − BUT I do regularly test amphibians in the wild for a fatal fungal pathogen, known as Batrachochytrium dendrobatidis (commonly known as Bd). Diseases like Bd are among the leading causes of the approximately one-third of amphibian species that are threatened, near threatened, or vulnerable to extinction. To test for Bd, and the recently emerged sister taxonBatrachochytrium salamandrivorans (hereafter referred to as: Bsal), disease ecologists rely on non-invasive skin swabs. Continue reading →
Sea otters (Enhydra lutris) are an apex predator of the nearshore marine ecosystem – the narrow band between terrestrial and oceanic habitat. During the commercial maritime fur trade in the 18th and 19th centuries, sea otters were nearly hunted to extinction across their range in the North Pacific Ocean. By 1911, only a handful of small isolated populations remained.
But sea otter populations have recovered in many areas due to a few changes. The International Fur Seal Treaty in 1911 and the Marine Mammal Protection Act (1972) protected sea otters from most human harvest. Wildlife agencies helped sea otter colonisation by transferring them to unoccupied areas. Eventually, sea otters began to increase in abundance and distribution, and they made their way to Glacier Bay, a tidewater glacier fjord and National Park in southeastern Alaska. Continue reading →