When individual differences matter - intraspecific variation in 2016

Maybe it is just confirmation bias, but there seems to have been an upswing in the number of cool papers on the role of intraspecific variation in ecology. For example, three new papers highlight the importance of variation among individuals for topics ranging from conservation, coexistence, and community responses to changing environments. All are worth a deeper read.

‘An Anthropocene map of genetic diversity’ asks how intraspecific variation is distributed globally, a simple but important question. Genetic diversity in a species is an important predictor of their ability to adapt to changing environments. For many species, however, as their populations decline in size, become fragmented, or experience strong selection related to human activities, genetic diversity may be in decline. Quantifying a baseline for global genetic diversity is an important goal. Further, with the rise of ‘big data’ (as people love to brand it) it is now an accessible one: there are now millions of genetic sequences in GenBank and associated GPS coordinates. 

Many of the global patterns in genetic diversity agree with those seen for other forms of diversity: for example, some of the highest levels are observed in the tropical Andes and Amazonia, and there is a peak in the mid-latitudes and human presence seems to decrease genetic diversity.

From Miraldo et al. (2016): Map of uncertainty. Areas in green represent high sequence availability and taxonomic coverage (of all species known to be present in a cell). All other colors represent areas lacking important data.

The resulting data set represents ~ 5000 species, so naturally the rarest species and the least charismatic are underrepresented. The authors identify this global distribution of ignorance, highlighting just how small our big data still is.

Miraldo, Andreia, et al. "An Anthropocene map of genetic diversity." Science353.6307 (2016): 1532-1535.


In ‘How variation between individuals affects species coexistence’, Simon Hart et al. do the much needed work to answer the question of how intraspecific variation fits into coexistence theory. Their results reinforce the suggestion that in general, intraspecific variation should making coexistence more difficult, since it increases the dominance of superior competitors, and reduces species' niche differentiation. (Note this is a contrast to the argument Jim Clark has made with individual trees, eg. Clark 2010)

Hart, Simon P., Sebastian J. Schreiber, and Jonathan M. Levine. "How variation between individuals affects species coexistence." Ecology letters (2016).


The topic of evolutionary rescue is an interesting, highlighting (see work from Andy Gonzalez and Graham Bell for more details) the ability of populations to adapt to stressors and changing environments, provided enough underlying additive genetic variation and time is available. It has been suggested that phenotypic plasticity can reduce the chance of evolutionary rescue, since it reduces selection on genetic traits. Alternatively, by increasing survival time following environmental change, it may aid evolutionary rescue. Ashander et al. use a theoretical approach to explore how plasticity interacts with a change in environmental conditions (mean and predictability/autocorrelation) to affect extinction risk (and so the chance of evolutionary rescue). Their results provide insight into how the predictability of new environments, through an affect on stochasticity, in turn changes extinction risk and rescue.

Ashander, Jaime, Luis-Miguel Chevin, and Marissa L. Baskett. "Predicting evolutionary rescue via evolving plasticity in stochastic environments." Proc. R. Soc. B. Vol. 283. No. 1839. The Royal Society, 2016.

The problematic effect of small effects

Why do ecologists often get different answers to the same question? Depending on the study, for example, the relationship between biodiversity and ecosystem function could be positive, negative, or absent (e.g. Cardinale et al. 2012). Ecologists explain this in many ways - experimental issues and differences, context dependence. However, it may also be due to an even simpler issue, that of the statistical implications of small effect sizes.

This is the point that Lemoine et al. make in an interesting new report in Ecology. Experimental data from natural systems (e.g. for warming experiments, BEF experiments) is often highly variable, has low replication, and effect sizes are frequently small. Perhaps it is not surprising we see contradictory outcomes, because data with small true effect sizes are prone to high Type S (reflect the chance of obtaining the wrong sign for an effect) and Type M (the amount by with an effect size must be overestimated in order to be significant). Contradictory results arise from these statistical issues, combined with the idea that papers that do get published early on may simply have found significant effects by chance (the Winner's Curse). 

Power reflects the chance of failing to correctly reject the null hypothesis (Ho). The power of ecological experiments increases with sample size (N), since uncertainty in data decreases with increasing N. However, if your true effect size is small, studies with low power have to significantly overestimate the effect size to have a significant p-value. This is the result of the fact that if the variation in your data is large and your effect size is small, the critical value for a significant z-score is quite large. Thus for your results to be significant, you need to observe an effect larger than this critical value, which will be much larger than the true effect size. It's a catch-22 for small effect sizes: if your result is correct, it very well may not be significant; if you have a significant result, you may be overestimating the effect size. 

From Lemoine et al. 2016. 

The solution to this issue is clearly a difficult one, but the authors make some useful suggestions. First, it's really the variability of your data, more than the sample size, that raises the Type M error. So if your data is small but beautifully behaved, this may not be a huge issue for you (but you must be working in a highly atypical system). If you can increase your replication, this is the obvious solution. But the other solutions they see are cultural shifts when we publish statistical results. As with many other, the authors suggest we move away from reliance on p-values as a pass/fail tool for results. In addition to reporting p-values, they suggest we report effect sizes and their error rates. Further, that this be done for all variables regardless of whether the results are significant. Type M error and power analyses can be reported in a fashion meant to inform interpretation of results: “However, low power (0.10) and high Type M error (2.0) suggest that this effect size is likely an overestimate. Attempts to replicate these findings will likely fail.” 

Lemoine, N. P., Hoffman, A., Felton, A. J., Baur, L., Chaves, F., Gray, J., Yu, Q. and Smith, M. D. (2016), Underappreciated problems of low replication in ecological field studies. Ecology. doi: 10.1002/ecy.1506