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
[1]
I. Forno,et al.
NATIVE PARASITOIDS AND PATHOGENS ATTACKING SAMEA MULTIPLICALIS GUENÉE (LEPIDOPTERA: PYRALIDAE) IN QUEENSLAND
,
1987
.
[2]
R. W. Sutherst,et al.
A computerised system for matching climates in ecology
,
1985
.
[3]
J. Smith,et al.
Maintenance of water balance in the twice-stabbed ladybird beetle, Chilocorus cacti (Coleoptera: Coccinellidae), despite heavy infestation by the mite, Hemisarcoptes cooremani (Acari: Acariformes)
,
1999
.
[4]
P. Debach,et al.
Biological control of insect pests and weeds
,
1967
.
[5]
R. Sutherst.
Climate Variability, Seasonal Forecasting and Invertebrate Pests — The Need for a Synoptic View
,
2000
.
[6]
Allan P. Dodd.
The Control and Eradication of Prickly-Pear in Australia
,
1936
.
[7]
K. R. Reddy,et al.
Crop ecosystem responses to climatic change: pests and population dynamics.
,
2000
.
[8]
C. Stern.
CONCLUDING REMARKS OF THE CHAIRMAN
,
1950
.
[9]
D. Kriticos,et al.
A comparison of systems to analyze potential weed distributions.
,
2001
.
[10]
V. Hattingh,et al.
Global climate change and accuracy of prediction of species’ geographical ranges: establishment success of introduced ladybirds (Coccinellidae, Chilocorus spp.) worldwide
,
1999
.
[11]
H. Nix,et al.
The climatic factor in Australian grassland ecology
,
1970
.
[12]
P. M. Room,et al.
Nitrogen and establishment of a beetle for biological control of the floating weed Salvinia in Papua New Guinea
,
1985
.
[13]
R. W. Sutherst,et al.
The geographical distribution of the Queensland fruit fly, Bactrocera (Dacus) tryoni, in relation to climate
,
1998
.
[14]
R. Sutherst,et al.
Implications of global change and climate variability for vector-borne diseases: generic approaches to impact assessments.
,
1998,
International journal for parasitology.
[15]
O. H. Swezey.
Records of Introduction of Beneficial Insects into the Hawaiian Islands
,
1923
.
[16]
D. Hilburn,et al.
The Coccinellidae (Coleoptera) of Bermuda
,
1990
.
[17]
R. W. Sutherst,et al.
Estimating vulnerability under global change: modular modelling of pests
,
2000
.
[18]
R. Wall,et al.
Myiasis of humans and domestic animals.
,
1995,
Advances in parasitology.
[19]
J. D. Kerr,et al.
Biological control of weeds: an evaluation
,
1984
.
[20]
M. Sallam.
Classical Biological Control of Arthropods in Australia
,
2002
.
[21]
Susan P. Worner.
Use of Models in Applied Entomology: The Need for Perspective
,
1991
.
[22]
R. Sutherst,et al.
Potential Geographical Distribution of the Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae), with Emphasis on Argentina and Australia
,
2002
.
[23]
N. Mills.
Biological control of forest aphid pests in Africa
,
1990
.
[24]
D. Kriticos,et al.
Gleditsia triacanthos L. (Caesalpiniaceae), another thorny, exotic fodder tree gone wild.
,
1994
.
[25]
R. Gordon.
The Coccinellidae (Coleoptera) of America, north of Mexico
,
1985
.
[26]
R. Sutherst,et al.
The vulnerability of animal and human health to parasites under global change.
,
2001,
International journal for parasitology.
[27]
R. Sutherst.
The dynamics of hybrid zones between tick (Acari) species.
,
1987,
International journal for parasitology.
[28]
R. Sutherst,et al.
From CLIMEX to PESKY, a generic expert system for pest risk assessment
,
1991
.
[29]
Wolfgang Cramer,et al.
The effects of fragmentation and disturbance of rainforest on ground‐dwelling small mammals on the Robertson Plateau, New South Wales, Australia
,
1996,
Journal of Biogeography.
[30]
G. Hoogenboom.
Climate Change and Global Crop Productivity
,
2002
.
[31]
W. M. Lonsdale,et al.
When to Ignore Advice: Invasion Predictions and Decision Theory
,
1999,
Biological Invasions.
[32]
M. Williamson,et al.
The Varying Success of Invaders
,
1996
.