An evolutionary modelling approach to understanding the factors behind plant invasiveness and community susceptibility to invasion.

Ecologists have had limited success in understanding which introduced species may become invasive. An evolutionary model is used to investigate which traits are associated with invasiveness. Translocation experiments were simulated in which species were moved into similar but evolutionarily younger communities. The main findings were that species that had previously been the most abundant in their original communities have significantly higher rates of establishment than did species that had previously occurred at low abundance in their original community. However, if establishment did occur, previously abundant and previously low-abundant species were equally likely to become dominant and were equally likely to exclude other species from their new community. There was a suggestion that the species that were most likely to establish and exclude others were 'genetically' different. When species that had evolved in different simulations (but with identical environmental conditions) were transplanted into communities that had also evolved in different simulations of the same conditions, the outcomes were difficult to predict. Observed rates of establishment and subsequent competitive dominance were observed to be species- and community combination-specific. This evolutionary study represents a novel in silico attempt to tackle invasiveness in an experimental framework and may provide a new methodology for tackling these issues.

Population-level impacts of pesticide-induced chronic effects on individuals depend more on ecology than toxicology.

The current method for assessing long-term risk of pesticides to mammals in the EU is based on the individual rather than the population-level and lacks ecological realism. Hence there is little possibility for regulatory authorities to increase ecological realism and understanding of risks at the population-level. Here we demonstrate how, using ABM modelling, assessments at the population-level can be obtained even for a pesticide with complex long-term effects such as epigenetic transmission of reproductive depression. By objectively fitting nonlinear models to the simulation outputs it was possible to compare population depression and recovery rates for a range of scenarios in which toxicity and exposure factors were varied. The system was differentially sensitive to the various factors, but vole ecology and behaviour were at least as important predictors of population-level effects as toxicology. This emphasises the need for greater focus on animal ecology in risk assessments.

Population-level assessment of risks of pesticides to birds and mammals in the UK.

It is generally acknowledged that population-level assessments provide a better measure of response to toxicants than assessments of individual-level effects. Population-level assessments generally require the use of models to integrate potentially complex data about the effects of toxicants on life-history traits, and to provide a relevant measure of ecological impact. Building on excellent earlier reviews we here briefly outline the modelling options in population-level risk assessment. Modelling is used to calculate population endpoints from available data, which is often about individual life histories, the ways that individuals interact with each other, the environment and other species, and the ways individuals are affected by pesticides. As population endpoints, we recommend the use of population abundance, population growth rate, and the chance of population persistence. We recommend two types of model: simple life-history models distinguishing two life-history stages, juveniles and adults; and spatially-explicit individual-based landscape models. Life-history models are very quick to set up and run, and they provide a great deal of insight. At the other extreme, individual-based landscape models provide the greatest verisimilitude, albeit at the cost of greatly increased complexity. We conclude with a discussion of the implications of the severe problems of parameterising models.

Risk assessment of UK skylark populations using life-history and individual-based landscape models.

Following a workshop exercise, two models, an individual-based landscape model (IBLM) and a non-spatial life-history model were used to assess the impact of a fictitious insecticide on populations of skylarks in the UK. The chosen population endpoints were abundance, population growth rate, and the chances of population persistence. Both models used the same life-history descriptors and toxicity profiles as the basis for their parameter inputs. The models differed in that exposure was a pre-determined parameter in the life-history model, but an emergent property of the IBLM, and the IBLM required a landscape structure as an input. The model outputs were qualitatively similar between the two models. Under conditions dominated by winter wheat, both models predicted a population decline that was worsened by the use of the insecticide. Under broader habitat conditions, population declines were only predicted for the scenarios where the insecticide was added. Inputs to the models are very different, with the IBLM requiring a large volume of data in order to achieve the flexibility of being able to integrate a range of environmental and behavioural factors. The life-history model has very few explicit data inputs, but some of these relied on extensive prior modelling needing additional data as described in Roelofs et al. (2005, this volume). Both models have strengths and weaknesses; hence the ideal approach is that of combining the use of both simple and comprehensive modeling tools.