Prediction of species geographical ranges

The use of climate matching to improve the success rate of introductions of biological control agents into new environments is well-established (DeBach, 1964). Similarly, there have been robust examples where the risk of establishment of invasive species has been successfully defined a priori using climatic modelling. These include: Leptinotarsa decemlineata (Say) in Europe (Sutherst et al., 1991), Amblyomma variegatum (Fabricius) and A. hebraeum (Koch) in Zimbabwe (Bruce & Wilson, 1998); Chrysomya bezziana (Villeneuve) in Ethiopia (Hall & Wall, 1995) and Boophilus microplus (Canestrini) in east and southern Africa (Sutherst, 2001). Samways et al. (1999) claim to have tested ‘how accurate predictions of range change might be before entertaining global climatic change’. They attempted to do this by using climate matching to predict the success of establishment of fifteen species of ladybirds (Coccinellidae, Chilocorus spp.), which had been the subject of efforts to spread them beyond their native ranges to enhance biological control. The ‘percent correct predictions of establishment’ was the criterion used to test their hypothesis, expressed also as ‘predicting species climatic tolerances’. After achieving an apparently low success rate, they concluded that ‘even in the absence of climate change, range cannot always be determined, which means that most predictions of range change with climate change are likely to be wrong’. I discuss here how such a statement demonstrates weak scientific inference. Samways et al. used the CLIMEX model (Sutherst & Maywald, 1985; Sutherst et al., 1995, 1999) and its associated ‘Match Climates’, climate-matching algorithm to make their predictions. The CLIMEX model is a simulation model of moderate complexity for inferring the responses of a species to climate from its geographical distribution. Once response functions have been fitted, the model can be run with meteorological data from other parts of the world to estimate the species response to new climatic environments. The potential range, as determined by climate, can then be estimated. The model parameter values constitute the hypotheses on the climatic factors that determine the species population growth, and survival during adverse seasonal conditions, and so limit the geographical distribution. Alternatively, the meteorological data base can be manipulated to create scenarios of climate change. Samways et al. attempted to explain the success or otherwise of particular introductions of Chilocorus species to new environments based on their estimated potential climatic range. This assumes that both the claims of the predictive success of climate matching, in this case using CLIMEX, and the base rates for establishment of exotic introductions are both 100%. However, Smith et al. (1999) showed that low base rates for establishment of exotic species influence the reliability of predictive tools. In the field of biological control, using arthropods, the base rates are in fact quite high, at around 65% (Julien et al., 1984; Waterhouse & Sands, 2001). Nevertheless such a suboptimal base rate caps the maximum success rate for predictions below the accuracy that is estimated on the assumption that all introductions into suitable climates will be successful. Sutherst & Maywald (1985) stated a caveat that users of CLIMEX need to exclude non-climatic factors limiting the distribution before assuming that climate is the only factor. The CLIMEX model, or other climate-matching tools, do not pretend to predict the outcome of particular introduction events. They define the role of climate as a factor in determining the potential for establishment when all other factors are not included. In addition, the CLIMEX software includes a facility for comparing meteorological data from different places (Match Climates). Samways et al. also used this algorithm in their efforts to explain the outcomes of introductions. A comprehensive response to correct errors in Samways et al. would have required many weeks of literature reviews and re-calculations of their analysis of each species, which was not practical. Rather, I point out examples of the main types of factual and methodological errors, inappropriate assumptions and omissions in the paper and show why the results of their analyses are invalid and their conclusions are not logical. I then investigate re-fitting of the parameter values of the CLIMEX model for one species – C. cacti (Linnaeus 1767) – to illustrate how the modelling is recommended to be carried out. GUEST EDITORIAL Journal of Biogeography, 30, 805–816