Prof. David Borchers
I'm a statistician at the University of St Andrews, specialising in developing statistical methods to solve problems in ecology: mostly methods of estimating wildlife population abudance, distribution and population trajectories over time.
See links above for more on my Research, PhD Students, and Publications. There are also a few Images and Videos relating to my research.
Postgraduate study in Statistical Ecology?
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We have an MSc in Statistical Ecology that started in 2020.
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Some Ongoing Projects and Collaborators
I and colleageus are working with the Global Snow Leopard and Ecosystem Protection Programme, The Snow Leopard Trust, and snow leopard range countries to complete the first range-wide assessment of snow leopard populations, using spatial capture-recapture methods.
I and colleageus are working with the IUCN Section on Small Apes, Rainforest Connection, Association Anoulak,the University of Auckland and an electronics engineer to develop customised hardware and software for effecient acoustic spatial capture-recapture methods to survey gibbons and other vocalising species.
I hold an associate position at the University of Cape Town and collaborate with various members of the Statistics in Ecology, Environment and Conservation research group there.
Some Recent Research
Acoustic Spatial Capture-Recapture Methods:
I am working with Rachel Fewster, Ben Stevenson and Paul van Dam-Bates, to develop spatial capture-recapture methods for situations in which capture histories are not known. This situation occurs quite often with acoustic surveys because it is often impossible to identify individuals when they are heard and not seen. With Erica Ye and Yuheng Wang, I am also looking into integrating AI methods for species identification from acoustic surveys, and methods for dealing with false positives (often generated by machine learning call identification methods).
How to Interpret Spatial Capture-Recapture Desity Estimates:
In this work, we highlight a common misinterpretation of spatial capture-recapture estimated density surfaces, and contrast this to correct interpretations. To make our point, we treat the Mona Lisa as a density surface and then try to reconstruct it using the incorrect and correct interpretations of the estimated surfaces.
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The figure above, taken from the paper, shows a monochrome Mona Lisa density surface (left) a sample of activity centres taken from this surface (middle) and the blurred Mona Lisa that we use as a prediction covariate (right).