The use of occurrence data to predict the effects of climate change on insects.

Experimental information on the roles played by climatic factors in determining the ecology and distribution of insect species is scarce. This has stimulated the increasing use of the climatic characteristics of the localities in which the species are observed to derive predictions under different climatic scenarios (the so called species-distribution models or SDMs). This text reviews the main limitations of these correlative models when they are applied to organisms, such as insects, that are characterized by a high degree of collector bias and incompleteness. It is argued that SDMs must rely solely on presence information, rejecting the use of background or pseudoabsences, and that we are not predicting the future distribution of a species but exploring the future location of the climatic conditions in which a species was observed. The scarcity and bias of the available occurrence information in insects as well as our ignorance about the non-climatic factors delimiting species ranges forces us to be extremely careful. It is therefore desirable to avoid the use of central tendency measures reflecting supposed optimum niche conditions because they are particularly dependent on the quantity and biases of the occurrence information. The use of simple algorithms and procedures aimed at extracting information on environmental limits from the available occurrences would be more convenient in this case.