#ibs2015 – Confronting uncertainty, biases and the unknown

The 2015 meeting of the International Biogeography Society just came to an end, and even for someone who wouldn’t traditionally consider themselves a ‘biogeographer’ there were many interesting topics and talks to see.

The focus of most talks was on biological patterns over space and/or time (or ‘deep time’, which is a fun phrase to throw around), and the talks emphasized how sophisticated statistical methodologies for such questions have become. The extent and complexity of approaches for making inferences from limited existing information, be it phylogenetic, distributional, or fossil and pollen records, is pretty amazing.

Such complicated inference needn't and shouldn't come at the cost of careful scientific work, and must include recognition of uncertainty and biases. The final sessions of the conference acted as an excellent (and at times provocative) reminder of this. For example, Joaquin Hortal advocated the development of ‘maps of ignorance’, which instead of showing the typical distributions of known information, highlight where information is missing and new sampling should be emphasized. Not only is information sometimes missing, but its value degrades over space and time. The value of a sample declines the further away you get from that site or the more different the spatial scale; samples over 50 years old may not represent current conditions any more. Predictions should consider or even incorporate this uncertainty.

Catherine Graham, David Nipperess, and Jon Chase all gave talks similarly emphasizing how fundamental consideration of scale and extent is. This is as true for phylogenetic community analysis (Graham, what extent or size of tree should be considered for analyses of community phylogenetics?); for rarefaction of phylogenetic diversity (Nipperess); or for measures of beta-diversity (Chase). Without this context, we are likely to be misunderstanding our results.

Finally, David Currie gave a damning critique of macroecology. Unfortunately, he said, macroecology seems to be a field where hypothesis testing is rare and conclusions are drawn based on spurious correlations with little explanatory and even less predictive ability. For example, why has the study of latitudinal gradients in richness progressed little beyond a list of possible correlates after more than 30 years of attention? Though Currie was focused on his own field, his comments were relevant to many ecological approaches. Currie expressed concerns about areas where scientific methods were being given short shift. In particular, he noted a lack of appropriate hypothesis testing and strong inference. Instead there is a tendency for studies to look for evidence in support of a hypothesis of interest, rather than attempting to falsify a hypothesis. Supporting evidence, sadly, does not actually increase the probability that a hypothesis is true, since the evidence could equally support some other, currently unconsidered, hypothesis. Further, correlations between variables of interest are at best a weak test of a hypothesis. The most important suggestions were that macroecologists and others should be testing the predictive ability of their hypotheses on new data sets: model fitting, in his opinion, is too often confused with model testing.