B for Biology: Not just counting sheep

As some of my co-bloggers have mentioned before, Biostatistics has been closely associated lately with studies in the health sciences and has somehow forgotten the wider biological side of things. I will be focusing today on ecological and environmental matters and the statistical approach to this kind of problems.

According to Smith (Ecological Statistics; Encyclopedia of Environmetrics, Vol. 2, pp 589-602; John Wiley & Sons, 2002), ecological Statistics can be defined as the area of Statistics that focuses on ecological problem solving, where Ecology can be understood as the scientific study of the distribution and abundance of organisms. It will cover, therefore, “sampling, assessment, and decission making for both policy and research” (Patil, G.P. , Environmental and Ecological Statistics; Encyclopedia of Environmetrics, Vol. 2, pp 672-674; John Wiley & Sons, 2002), and will require of advanced techniques to ensure the correct modelling of complex univariate and multivariate relationships (often nonlinear) from both spatial and temporal perspectives.

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To fully understand this field of study, we would initially need to make a clear distinction between single species and multispecies analysis, two diametrically opposed approaches calling for different statistical strategies.

The former is mainly based on measurements of the species abundance and performance (survival, growth, and recruitment). As such, it encounters an old dilemma: how to keep observational bias to a minimum? Petersen and transect methods are used to monitor wildlife census and avoid this and other biases, and advanced methodology like mixed models, flexible regression techniques, spatial and temporal statistics, and Bayesian inference are applied in the analysis itself.

Multispecies analysis on the other hand, deals with the complicated interactions and dependencies existing in the various ecosystems. The notions of diversity – measuring global changes in different species as a community, and mostly criticized for the potential lack of ecological relevance of some of the measures – and integrity – metrics accounting for a certain ecosystem unimpaired state; read this interesting article for further discussion on the difference between health and integrity –  are its main pillars. Multivariate analysis of ecosystems includes methods like correspondence analysis and redundancy analysis, amongst many others.

It is worth noticing that the latter is nowadays a major focus of research as a direct consequence of an increasing public awarenes of the need to preserve endangered ecosystems in order to ensure the whole planet´s good health.

In the end it is not just about counting sheep but how to count them, ensuring representativity, and considering issues like diversity and integrity in their relationships with other species.

Note: as a proof of the importance of this broad area on its own, there is a multidisciplinary journal dedicated to the topic, “Environmental and Ecological Statistics”, and an exhaustive R Task View called “Analysis of Ecological and Environmental Data” available here. “Analyzing Ecological Data” (2007) by Zuur, Ieno & Smith is also highly recommended.

Have you faced any of these problems? Any tips? Many thanks for your comments!!

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2 thoughts on “B for Biology: Not just counting sheep

  1. Very interesting post!
    Two quotes about non-linearity:

    I am convinced that the nations and people who master the new sciences of complexity will become the economic, cultural, and political superpowers of the next century (Heinz Pagels)

    [Non-linear modelling] is the most important idea since the idea of Jesus Christ (Gregory Bateson)

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