Dr. Dao (crouching on right) and team with Dr. Tovi Lehmann (with sandals), Dr. Yaro (with white cap), and Moussa Diallo (front).
The fact that mosquitoes are insects of massive importance is of little dispute. With malaria still killing almost half a million people annually and after recent outbreaks of Zika, dengue and West-Nile viruses the threat of mosquito-borne diseases is becoming common knowledge. The meme of ‘Mosquitoes are the No.1 killer of all time,’ is also growing more popular (I even heard it from my 8-year-old kid one day after he returned from school!). Yet, with all we think we know about the little bug(ger)s, it’s probably only the tip of the iceberg.
Much work was done over the past century to try to answer basic questions about mosquitoes like:
How big are their populations?
How long do they live?
Where do they go when we don’t see or feel them?
Different methods have been developed to provide insights and notions on the mosquitoes’ movements, survival, and populations estimates; but the limitations and conditions of these methods mean that our knowledge is still incomplete.
One of the gold-standard tools for answering questions like those above is Mark-Release-Recapture (MRR). It was developed almost a century ago and has been modified and remodified through the years, as different marking technologies became available. Continue reading →
Understanding Population Responses to Environmental Change
Rapid climatic change has increased interest about how populations respond to environmental change. This has broad applications, for example in the management of endangered and economically important species, the control of harmful species, and the spread of disease. At the population level changes in abundance are driven by changes in vital rates, such as survival and fecundity. So studies that track individual survival and reproduction over time can provide useful insights into the drivers of such changes. They allow us to make future population level predictions on things like abundance, extinction risk and evolutionary strategies.
Predicting the future isn’t a simple task though. Anyone whose washing has got soaked through after the weather forecast suggested the day would be dry and sunny will know that (though the accuracy of short term weather forecasts has increased dramatically in recent years). Ideally, if we want to predict what will happen to populations as their environment changes, we would identify the drivers of variation in their survival and reproduction. We do this by asking questions like ‘are years of low survival associated with high rainfall?’ But, this is not a simple task; identifying drivers and the time periods over which they act and accurately estimating their effects requires long-term demographic data. Continue reading →
Knowing how many individuals there are in a population is a fundamental objective in ecology and conservation biology. But estimating abundance is often extremely difficult. It’s particularly difficult in the management of exploited marine, anadromous and freshwater populations. In marine fisheries, abundance estimation traditionally relies on demographic models, costly and time consuming mark recapture (MR) approaches if they are feasible at all, and the relationship between fishery catches and effort (catch per unit effort or CPUE). CPUEs can be subject to bias and uncertainty. This is why they tend to be considered relatively unreliable and contentious.
Close-Kin Mark-Recapture: Reducing Bias and Uncertainty
There is an alternative method though. It’s known as “Close-Kin Mark-Recapture” (CKMR), and is grounded in genomics and was first proposed by Skaug in 2001. The method is based on the principle that an individual’s genotype can be considered a “recapture” of the genotypes of each of its parents. Assuming the sampling of offspring and parents is independent of each other, the number of Parent-Offspring pairs (POP) genetically identified in a large collection of both groups can be used to estimate abundance. Continue reading →
Understanding animal movement and population size is a challenge for researchers studying any megafauna species. Sea turtles though, add a whole additional level of complexity. These wide-ranging, swift, charismatic animals spend much of their time underwater and in remote places. When trying to track down and count turtles, this obstacle to understanding population size becomes a full-on barricade.
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 →
Analysis of datasets collected on marked individuals has spurred the development of statistical methodology to account for imperfect detection. This has relevance beyond the dynamics of marked populations. A couple of great examples of this are determining site occupancy or disease infection state.
The regular series of EURING-sponsored meetings (which began in 1986) have been key to this development. They’ve brought together biological practitioners, applied modellers and theoretical statisticians to encourage an exchange of ideas, data and methods.
This new cross-journal Special Feature between Methods in Ecology and Evolution and Ecology and Evolution, edited by Rob Robinson and Beth Gardner, brings together a collection of papers from the most recent EURING meeting. That meeting was held in Barcelona, Spain, 2017, and was hosted by the Museu de Ciènces Naturals de Barcelona. Although birds have provided a convenient focus, the methods are applicable to a wide range of taxa, from plants to large mammals. Continue reading →
“My interests lie at the intersection between ecology and statistics, particularly in demography, population ecology, species range dynamics and community ecology. My work addresses questions in conservation biology especially in relation to climate change. I’m particularly excited about the increasing availability of large data sets, such as those collected by citizen scientists, and the opportunities and challenges their analysis brings.”
The discovery of Chronic Wasting Disease (CWD) in Norway in 2016 has led to extensive measures and testing of deer in Norway. Since 2018 there have been similar measures within the EU. But how many deer need to be tested before we can be (almost) certain that a population is not infected by CWD?
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 →
In an age of rapid technological advances, ecologists need to keep abreast of how we can improve or reinvent the way we do things. Remote sensing technology and image analysis have been developing rapidly and have the potential to revolutionise how we count and estimate animal populations.
Using remotely sensed imagery isn’t new in ecology, but recent innovations mean we can use it for more things. Land use change and vegetation mapping are among the areas of ecology where remote sensing has been used extensively for some time. Estimating animal populations with remotely sensed imagery was also demonstrated more than 40 years ago by detecting indirect signs of an animal with some success: think wombat burrows and penguin poop.
A polar bear from a helicopter
Thanks to improved spatial and spectral resolution (see the text box at the bottom of the post for a definition), accessibility, cost and coverage of remotely sensed data, and software development we have now reached a point where we can detect and count individual animals in imagery. Many of the first studies to demonstrate automated and semi-automated techniques have taken computer algorithms from other disciplines, such as engineering or biomedical sciences, and applied them to automate counting of animals in remotely sensed imagery. It turns out that detecting submarines is not so different to detecting whales! And finding abnormal cells in medical imaging is surprisingly similar to locating polar bears in the arctic! Continue reading →