Earlier in this chapter we saw how environmental factors may affect insect development. Here we exam- ine some predictive models of how insect abundance and distribution change with abiotic factors. These models have application to past climate reconstruction, and increasingly are tested for veracity against range changes modulated by present, on-going climate change.
6.11.1 Modeling climatic effects on insect distributions
The abundance of any poikilothermic species is determined largely by proximate ecological factors including the population densities of predators and competitors (section 13.4) and interactions with hab- itat, food availability, and climate. Although the distri- butions of insect species result from these ecological factors, there is also a historical component. Ecology Climate and insect distributions 171
Fig. 6.13 Solitary and gregarious females of the migratory locust, Locusta migratoria(Orthoptera: Acrididae). The solitaria adults have a pronounced pronotal crest and the femora are larger relative to the body and wing than in the gregaria adults. Intermediate morphologies occur in the transiens (transient stage) during the transformation from solitaria to gregaria or the reverse.
determines whether a species can continue to live in an area; history determines whether it does, or ever had the chance to live there. This difference relates to timing; given enough time, an ecological factor becomes a historical factor. In the context of present-day studies of where invasive insects occur and what the limits of their spread might be, history may account for the original or native distribution of a pest. However, knowledge of ecology may allow prediction of potential or future distributions under changed environmental conditions (e.g. as a result of the “greenhouse effect”;
see section 6.11.2) or as a result of accidental (or inten- tional) dispersal by humans. Thus, ecological know- ledge of insect pests and their natural enemies, especially information on how climate influences their development, is vital for the prediction of pest out- breaks and for successful pest management.
There are many models pertaining to the population biology of economic insects, especially those affecting major crop systems in western countries. One example of a climatic model of arthropod distribution and abund- ance is the computer-based system called CLIMEX (developed by R.W. Sutherst and G.F. Maywald), which allows the prediction of an insect’s potential relative abundance and distribution around the world, using ecophysiological data and the known geographical distribution. An annual “ecoclimatic index” (EI), des- cribing the climatic favorability of a given location for permanent colonization of an insect species, is derived
from a climatic database combined with estimates of the response of the organisms to temperature, mois- ture, and day length. The EI is calculated as follows (Fig. 6.14). First, a population growth index (GI) is determined from weekly values averaged over a year to obtain a measure of the potential for population increase of the species. The GI is estimated from data on the seasonal incidence and relative abundance in different parts of the species’ range. Second, the GI is reduced by incorporation of four stress indices, which are measures of the deleterious effects of cold, heat, dry, and wet.
Commonly, the existing geographical distribution and seasonal incidence of a pest species are known but biological data pertaining to climatic effects on develop- ment are scanty. Fortunately, the limiting effects of climate on a species usually can be estimated reliably from observations on the geographical distribution.
The climatic tolerances of the species are inferred from the climate of the sites where the species is known to occur and are described by the stress indices of the CLIMEX model. The values of the stress indices are progressively adjusted until the CLIMEX predictions agree with the observed distribution of the species.
Naturally, other information on the climatic tolerances of the species should be incorporated where possible because the above procedure assumes that the present distribution is climate limited, which might be an oversimplification.
Fig. 6.14 Flow diagram depicting the derivation of the “ecoclimatic index”
(EI) as the product of population growth index and four stress indices. The EI value describes the climatic favorability of a given location for a given species.
Comparison of EI values allows different locations to be assessed for their relative suitability to a particular species. (After Sutherst & Maywald 1985.)
Such climatic modeling based on world data has been carried out for tick species and for insects such as the Russian wheat aphid, the Colorado potato beetle, screw-worm flies, biting flies of Haematobia species, dung beetles, and fruit flies (Box 6.3). The output has great utility in applied entomology, namely in epide- miology, quarantine, management of insect pests, and entomological management of weeds and animal pests (including other insects).
In reality, detailed information on ecological perform- ances may never be attained for many taxa, although such data are essential for the autecological-based distribution models described above. Nonetheless, there are demands for models of distribution in the absence of ecological performance data. Given these practical constraints, a class of modeling has been developed that accepts distribution point data as surrogates for “performance (process) characteristics”
of organisms. These points are defined bioclimatically, and potential distributions can be modeled using some flexible procedures. Analyses assume that current species distributions are restricted (constrained) by bioclimatic factors. A suite of models developed in Australia (e.g. BIOCLIM, developed by Henry Nix and colleagues) allow estimation of potential constraints on species distribution in a stepwise process. First, the sites at which a species occurs are recorded and the climate estimated for each data point, using a set of bioclimatic measures based on the existing irregular network of weather stations across the region under considera- tion. Factors such as annual precipitation, seasonality of precipitation, precipitation of the driest quarter, minimum temperature of the coldest period, maximum temperature of the warmest period, and elevation appear to be particularly influential and are likely to have wide significance in determining the distribution of poikilothermic organisms. From this information a bioclimatic profile is developed from the pooled climate per site estimates, providing a profile of the range of climatic conditions at all sites for the species. Next, the bioclimatic profiles so produced are matched with clim- ate estimates at other mapped sites across a regional grid to identify all other locations with similar climates.
Specialized software then can be used to measure similarity of sites, with comparison being made via a digital elevation model with fine resolution. All loca- tions within the grid with similar climates to the species-profile form a predicted bioclimatic domain.
This is represented spatially (mapped) as a “predicted potential distribution” for the taxon under considera-
tion, in which isobars (or colors) represent different degrees of confidence in the prediction of presence.
The estimated potential distribution of the chirono- mid midge genus Austrochlus (Diptera) based on data points in south-western Australia is shown in Fig. 6.15.
Based on climatic (predominantly seasonal rainfall) parameters, dark locations show high probability of occurrence and light grey show less likelihood. The model, based on two well-surveyed, partially sympatric species from south-western Australia, predicts the occurrence of an ecologically related taxon in central Australia, which has been since discovered within the predicted range. The effectiveness of bioclimatic modeling in predicting distributions of sister taxa, as shown here and in other studies, implies that much speciation has been by vicariance, with little or no ecological divergence (section 8.6).
6.11.2 Climatic change and insect distributions
The modeling techniques above lend themselves to back-tracking, allowing reconstruction of past species distributions based on models of previous climate and/
or reconstruction of past climates based on postglacial fossil remains representing past distributional informa- tion. Such studies were based originally on pollen remains (palynology) from lake benthic cores, in which rather broad groups of pollens, with occasional indica- tive species, were used to track vegetational changes through time, across landscapes, and even associated with previous climates. More refined data came from preserved ostracods, beetles (especially their elytra), and the head capsules of larval chironomids. These remnants of previous inhabitants derive from short- lived organisms that appear to respond rapidly to climatic events. Extrapolation from inferred bioclimatic controls governing the present-day distributional range of insect species and their assemblages to those same taxa preserved at time of deposition allows recon- structions of previous climates. For example, major features from the late Quaternary period include a rapid recovery from extreme conditions at the peak of last glaciation (14,500 years ago), with intermittent reversal to colder periods in a general warming trend.
Verification for such insect-based reconstructions has come from independent chemical signals and con- gruence with a Younger Dryas cold period (11,400 – 10,500 years ago), and documented records in human Climate and insect distributions 173
history such as a medieval 12th century warm event and the 17th century Little Ice Age when “Ice Fairs”
were held on the frozen River Thames. Inferred changes in temperatures range from 1 to 6°C, sometimes over just a few decades.
Confirmation of past temperature-associated biotic changes leads to the advocacy of such models to pre- dict future range changes. For example, estimates for disease-transmitting mosquitoes and biting midges under different climate-change scenarios have ranged Box 6.3 Climatic modeling for fruit flies
The Queensland fruit fly, Bactrocera tryoni, is a pest of most commercial fruits. The females oviposit into the fruit and larval feeding followed by rotting quickly destroys it. Even if damage in an orchard is insignific- ant, any infestation is serious because of restrictions on interstate and overseas marketing of fruit-fly-infested fruit.
CLIMEX has been used by R.W. Sutherst and G.F.
Maywald to describe the response of B. tryoni to Australia’s climate. The growth and stress indices of CLIMEX were estimated by inference from maps of the geographical distribution and from estimates of the rel- ative abundance of this fly in different parts of its range in Australia. The map of Australia depicts the ecoclim- atic indices (EI) describing the favorableness of each site for permanent colonization by B. tryoni. The area of each circle is proportional to its EI. Crosses indicate that the fly could not permanently colonize the site.
The potential survival of B. tryonias an immigrant pest in North America can be predicted using CLIMEX by climate-matching with the fly’s native range.
Accidental transport of this fly could lead to its estab- lishment at the point of entry or it might be taken to other areas with climates more favorable to its persistence. Should B. tryonibecome established in North America, the eastern seaboard from New York to Florida and west to Kansas, Oklahoma, and Texas in the USA, and much of Mexico are most at risk. Canada and most of the central and western USA are unlikely to support permanent colonization. Thus, only certain regions of the continent are at high risk of infestation by B. tryoni and quarantine authorities in those places should maintain appropriate vigilance. (After Sutherst &
Maywald 1991.)
from nạve estimates of increased range of disease vec- tors into populated areas currently disease-free (where vectors actually already exist in the absence of the virus) to sophisticated models accounting for altered development rates for vector and arbovirus, and altered environments for larval development. Future levels of predicted climate change remain unclear, allowing certain policy makers to deny its existence or its biotic significance. However, by the turn of the millennium Europe had warmed 0.8°C in the 20th century and realistic expectations are for a further increase of between 2.1 and 4.6°C mean global change in this century, along with commensurate variation in other climatic factors such as seasonality and rainfall. That predicted changes in distributions of insects are occur- ring is evident from studies of individual species, but the generality of these examples has been unclear.
However, a study of species of western European butterflies (limited to non-migrants and excluding monophagous and/or geographically restricted taxa) is quite conclusive. Significant northward extension of ranges is demonstrated for many taxa (65% of 52 species), with some stasis (34%), and retraction south from an earlier northern limit for only one species. Data for the southern boundary, limited to 40 species, revealed retraction northward for 22%, stasis for 72%, and southward extension for only one species. The sub-
set of the data for which sufficient historical detail was known for both northern and southern boundaries comprised 35 species: of these 63% shifted northward, 29% were stable at both boundaries, 6% shifted south- wards, and one species extended both boundaries.
For the many species whose boundaries moved, an observed range shift of from 35 to 240 km in the past 30 –100 years coincides quite closely with the (north) polewards movement of the isotherms over the period. That such range changes have been induced by a modest temperature increase of <1°C surely is a warning of the dramatic effects of the ongo- ing “global warming” over the next century.
FURTHER READING
Binnington, K. & Retnakaran, A. (eds.) (1991) Physiology of the Insect Epidermis.CSIRO Publications, Melbourne.
Carroll, S.B. (1995) Homeotic genes and the evolution of arthropods and chordates. Nature 376, 479 – 85.
Chapman, R.F. (1998) The Insects. Structure and Function, 4th edn. Cambridge University Press, Cambridge.
Daly, H.V. (1985) Insect morphometrics. Annual Review of Entomology30, 415 –38.
Danks, H.V. (ed.) (1994) Insect Life Cycle Polymorphism:
Theory, Evolution and Ecological Consequences for Seasonality and Diapause Control. Kluwer Academic, Dordrecht.
Further reading 175
Fig. 6.15 Modeled distribution for Austrochlus species (Diptera:
Chironomidae) based on presence data.
Black, predicted presence within 98%
confidence limits; pale grey, within 95%
confidence. (After Cranston et al. 2002.)