The recently published paper by Jane Elith and Catherine Graham et.al.”Novel methods improve prediction of species’ distributions from occurrence data” (EG06) is sure to be a landmark study in the field. EG06 compares 16 modeling methods using 226 well-surveyed species in 6 regions of the world. Measures of statistical skill on held back data show a spread from a wide range of methods including: the older methods such as BIOCLIM, DOMAIN, through GARP, GLM and GAM to the newer arrivals from machine learning MAXENT, BRT and community based method GDM, prompting the conclusion “novel methods improve prediction”. The work of a great many people is appreciated, as these results will no doubt be very helpful to many biodiversity modellers in the future.

Why novel methods work

EG06 attributes the success of the newer methods to representing complexity of the relationships of species to their environment.

One feature that they all share in common is a high level of flexibility in fitting complex responses

The same thing was found in the early 80’s when novel machine learning methods — particularly neural nets, decision trees (CART), and genetic algorithms (GARP) — were first used for species distribution modeling. When BIOCLIM and GLM were the only species distribution methods, these early experiments showed heuristic approaches from machine learning would benefit the field.

The opposing view still widely held is that approaches should be based strictly on ecological theory, such as using BIOCLIM to represent the Hutchinsonian niche. This view is valid too, if your primary aim is to evaluate the theory and not necessarily maximize accuracy. This is a familiar theme in ML — that real world performance requires heuristic complexity at the expense of theoretical elegance. Happily the species prediction problem has come to the attention of the leading edge researchers in present day ML, and both theory and practice will benefit from the interplay.

Historic progression of models

In addition to the complexity dimension, the range of statistical skill across methods also represents an historic progression. GARP in the early 80’s used genetic algorithms to combine the major methods of the time, BIOCLIM, GLM and surrogate into a multi-model rule-set. The strategy of using multiple models for prediction is also used in the highest performing method BST in EG06 (Boosted Regression Trees). Under ideal conditions the ensemble approach in GARP would be expected to be better than the worst of the methods it uses (BIOCLIM), but no better than the best of the methods (GLM), and this is shown in EG06. It is likely that other approaches with high performance such as boosted regression trees (BRT) have evolved experiences with from earlier regression tree algorithms such as classification and regression trees (CART).

Unanswered issues

The major unanswered issue in species distribution modeling is environmental data selection. In EG06, selection of environmental data reflects the typical practices:

The environmental data used for each region were determined according to their relevance to the species being modeled (Austin 2002) as determined by the data provider (Tables 1 and 3).

EG06 does n