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A survey on mathematical training for ecologists

Ecology today relies largely on statistical and theoretical modeling. Of course, there has always been a tradition of mathematical modelling in ecology, in fields as diverse as behavioural ecology and biogeography. In the last century however, mathematical models were mostly the prerogative of theoreticians and statisticians. With the increasing power and ease of use of computers, the situation is changing fast. Modelling is now on every ecologist's doorstep, and that makes good training all the more important.

Mathematical and computational models have 'percolated' throughout the entire network of ecological and evolutionary disciplines. Notably, statistical models are increasingly sophisticated to accommodate the non-standard, messy nature of all ecological datasets. Many ecologists use now the free statistical software and language R to analyze their data, and some even directly program their models using likelihood/Bayesian techniques. Modern statistical textbooks for ecologists (e.g. Bolker's Ecological Models and Data in R) emphasize that trend.

This means most young ecologists, and not just those with a mathematical bent, are using mathematical models. In this context, it is unclear whether most ecologists receive adequate quantitative training to do so. In particular, it has been remarked that a number of ecology students lack the basic maths that would help them tackle successfully data analysis and model building (e.g. Aaron M Ellison, and Brian Dennis. 2010. Paths to statistical fluency for ecologists. Frontiers in Ecology and the Environment 8: 362-370. doi:http://dx.doi.org/10.1890/080209). Some new modeling books recognize this problem, such as Matthiopoulos `How to be a quantitative ecologist?', and assume on purpose a very low mathematical background at the start, to make sure all ecological readers can follow.

In this context, it is time to assess how early-career ecologists view their ecological coursework and look back at their university education. Summarizing the mathematical/quantitative needs of today's early-career ecologists can help the design of tomorrow's ecological education. To our knowledge, there is not much data on the mathematical needs and experiences of the next generation of ecologists. Do they use and/or like mathematical models? Was there enough mathematical training during their education? Enough statistical training? Should professors add some? At what level? To find answers to these questions, we designed a short and anonymous questionnaire. If you have one minute, you can fill it at:

https://sites.google.com/site/mathematicsandecologysurvey/

Please help us to inform the ecological community!

Frederic Barraquand

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