Modelling the Geographical Range of a Species with Variable Life-History

Abstract

We show how a climatic niche model can be used to describe the potential geographic distribution of a pest species with variable life-history, and illustrate how to estimate biogeographic pest threats that vary across space. The models were used to explore factors that affect pest risk (irrigation and presences of host plant). A combination of current distribution records and published experimental data were used to construct separate models for the asexual and sexual lineages of Rhopalosiphum padi (Linnaeus) (Hemiptera: Aphididae). The two models were combined with knowledge of host plant presence to classify the global pest risk posed by R. padi. Whilst R. padi has a relatively limited area in which sexual lineages can persist year round, a much larger area is suitable for transient sexual and asexual lineages to exist. The greatest risk of establishment of persistent sexual and asexual populations is in areas with warm temperate climates. At the global scale the models show very little difference in risk patterns between natural rainfall and irrigation scenarios, but in Australia, the amount of land suitable for persistent asexual and transient sexual populations decreases (by 20%) if drought stress is no longer alleviated by irrigation. This approach proved useful for modelling the potential distribution of a species that has a variable life-history. We were able to use the model outputs to examine factors such as irrigation practices and host plant presence that altered the nature (transient or permanent) and extent of pest risk. The composite niche maps indicate pest risk in terms that are useful to both biosecurity agencies and pest managers.

Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change

“…This combination of data from different sources allows for testing and validation of the model using both empirical data and field observations (e.g. Macfadyen and Kriticos 2012). Some modelling attempts have incorporated using statistical tests of goodness of fit with known distribution points (e.g.…”

“…Thus, SDMs that rely on at least a partial mechanistic understanding of the species-environment relationship are perhaps more appropriate tools in predicting distributions under climate change and biological invasion scenarios. One such SDM is CLIMEX (Hearne Scientific Software Pty Ltd, Australia) (Sutherst & Maywald 1985;Sutherst et al 2007) which employs a semi-mechanistic approach to examine the relationship between climate, species distributions and patterns of growth (Macfadyen and Kriticos, 2012). CLIMEX models are typically fitted using a combination of empirically measured life history parameters (e.g.…”

“…This GI is then combined with stress indices (e.g. hot/cold and wet/dry) to calculate an Ecoclimatic Index (EI) (Sutherst et al 2007;Macfadyen and Kriticos 2012). The EI can then be determined across spatially explicit climate data to estimate the suitability of each grid cell (ranging from completely unsuitable to optimal) (see methods; Sutherst et al 2007).…”

Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly)

“…For example, it might help identify sites where a pest is not expected to overwinter even if the summer climate is suitable. This is important since some distribution (spatial) records may be unreliable or the record may have corresponded with a rare or ephemeral observation of the species (Macfadyen and Kriticos 2012).…”

Predicting the timing of first generation egg hatch for the pest redlegged earth mite Halotydeus destructor (Acari: Penthaleidae)

“…Among others, they are used to determine when control tactics, such as pesticide applications, should be used (Doddala et al 2013) and to improve the efficiency of scouting operations (Samietz et al 2013). If the relationship between the biology of the pest species and abiotic factors is clear then models can also predict how the distribution and abundance of the pest will change under future climatic conditions (Hill et al 2012;Macfadyen and Kriticos 2012). Although the use of crop models in decision-support tools aimed at farmers is becoming more common (e.g., Zinyengere et al 2011), predictive models for arthropod pest (and disease control) are still infrequently utilised except within the context of participatory learning activities that encourage change of farmer practice by simulating different management options (Phung et al 2013).…”