Dynamics of range margins for metapopulations under climate change.

We link spatially explicit climate change predictions to a dynamic metapopulation model. Predictions of species' responses to climate change, incorporating metapopulation dynamics and elements of dispersal, allow us to explore the range margin dynamics for two lagomorphs of conservation concern. Although the lagomorphs have very different distribution patterns, shifts at the edge of the range were more pronounced than shifts in the overall metapopulation. For Romerolagus diazi (volcano rabbit), the lower elevation range limit shifted upslope by approximately 700 m. This reduced the area occupied by the metapopulation, as the mountain peak currently lacks suitable vegetation. For Lepus timidus (European mountain hare), we modelled the British metapopulation. Increasing the dispersive estimate caused the metapopulation to shift faster on the northern range margin (leading edge). By contrast, it caused the metapopulation to respond to climate change slower, rather than faster, on the southern range margin (trailing edge). The differential responses of the leading and trailing range margins and the relative sensitivity of range limits to climate change compared with that of the metapopulation centroid have important implications for where conservation monitoring should be targeted. Our study demonstrates the importance and possibility of moving from simple bioclimatic envelope models to second-generation models that incorporate both dynamic climate change and metapopulation dynamics.

Case Study Part 2: Probabilistic modelling of long-term effects of pesticides on individual breeding success in birds and mammals.

Long term exposure of skylarks to a fictitious insecticide and of wood mice to a fictitious fungicide were modelled probabilistically in a Monte Carlo simulation. Within the same simulation the consequences of exposure to pesticides on reproductive success were modelled using the toxicity-exposure-linking rules developed by R.S. Bennet et al. (2005) and the interspecies extrapolation factors suggested by R. Luttik et al. (2005). We built models to reflect a range of scenarios and as a result were able to show how exposure to pesticide might alter the number of individuals engaged in any given phase of the breeding cycle at any given time and predict the numbers of new adults at the season's end.

Case Study Part 1: How to calculate appropriate deterministic long-term toxicity to exposure ratios (TERs) for birds and mammals.

In the European Union, first-tier assessment of the long-term risk to birds and mammals from pesticides is based on calculation of a deterministic long-term toxicity/exposure ratio (TER(lt)). The ratio is developed from generic herbivores and insectivores and applied to all species. This paper describes two case studies that implement proposed improvements to the way long-term risk is assessed. These refined methods require calculation of a TER for each of five identified phases of reproduction (phase-specific TERs) and use of adjusted No Observed Effect Levels (NOELs) to incorporate variation in species sensitivity to pesticides. They also involve progressive refinement of the exposure estimate so that it applies to particular species, rather than generic indicators, and relates spraying date to onset of reproduction. The effect of using these new methods on the assessment of risk is described. Each refinement did not necessarily alter the calculated TER value in a way that was either predictable or consistent across both case studies. However, use of adjusted NOELs always reduced TERs, and relating spraying date to onset of reproduction increased most phase-specific TERs. The case studies suggested that the current first-tier TER(lt )assessment may underestimate risk in some circumstances and that phase-specific assessments can help identify appropriate risk-reduction measures. The way in which deterministic phase-specific assessments can currently be implemented to enhance first-tier assessment is outlined.

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.

Using Red List Indices to measure progress towards the 2010 target and beyond.

The World Conservation Union (IUCN) Red List is widely recognized as the most authoritative and objective system for classifying species by their risk of extinction. Red List Indices (RLIs) illustrate the relative rate at which a particular set of species change in overall threat status (i.e. projected relative extinction-risk), based on population and range size and trends as quantified by Red List categories. RLIs can be calculated for any representative set of species that has been fully assessed at least twice. They are based on the number of species in each Red List category, and the number changing categories between assessments as a result of genuine improvement or deterioration in status. RLIs show a fairly coarse level of resolution, but for fully assessed taxonomic groups they are highly representative, being based on information from a high proportion of species worldwide. The RLI for the world's birds shows that that their overall threat status has deteriorated steadily during the years 1988-2004 in all biogeographic realms and ecosystems. A preliminary RLI for amphibians for 1980-2004 shows similar rates of decline. RLIs are in development for other groups. In addition, a sampled index is being developed, based on a stratified sample of species from all major taxonomic groups, realms and ecosystems. This will provide extinction-risk trends that are more representative of all biodiversity.

Linking population-level risk assessment with landscape and habitat models.

Ecological risk assessment at the population level often involves predicting the effects of a particular change in the land-use patterns on the viability of native species. A common method of addressing such questions is modeling the metapopulation dynamics of the species in the landscape. However, the landscape and, as a result, the spatial structure of the metapopulation usually do not remain unchanged, thus the assessment of viability must incorporate the dynamic nature of the landscape. A new link being developed between a metapopulation modeling program (RAMAS) and a landscape dynamics program (LANDIS) will allow the transitional dynamics of the landscape to be incorporated into assessment of viability and threat. This approach combines methods of landscape prediction with those of metapopulation simulation. The link between the landscape model and metapopulation model is provided by statistical models of habitat suitability for the species in focus.

Predictive accuracy of population viability analysis in conservation biology.

Population viability analysis (PVA) is widely applied in conservation biology to predict extinction risks for threatened species and to compare alternative options for their management. It can also be used as a basis for listing species as endangered under World Conservation Union criteria. However, there is considerable scepticism regarding the predictive accuracy of PVA, mainly because of a lack of validation in real systems. Here we conducted a retrospective test of PVA based on 21 long-term ecological studies--the first comprehensive and replicated evaluation of the predictive powers of PVA. Parameters were estimated from the first half of each data set and the second half was used to evaluate the performance of the model. Contrary to recent criticisms, we found that PVA predictions were surprisingly accurate. The risk of population decline closely matched observed outcomes, there was no significant bias, and population size projections did not differ significantly from reality. Furthermore, the predictions of the five PVA software packages were highly concordant. We conclude that PVA is a valid and sufficiently accurate tool for categorizing and managing endangered species.

Underestimation of mutual interference of predators.

The usual method of estimating the mutual interference constant, m, assumes a linear (type I) functional response of predators. In the cases where the response is not linear, the application of the method introduces a bias in the estimation of the searching efficiencies. It is shown that, as a consequence, the value of m is underestimated. A new method is proposed, which allows for a type II functional response due to a handling time. A comparative analysis of 15 data sets from the literature shows that the proposed method gives values of m that are consistently higher than those estimated by the traditional method. The new method calculates the parameters with nonlinear regression and provides standard errors for the estimates. Therefore, the reliability of the searching efficiencies, the handling time and the constant m can be quantified. Very few of the interference constants are significantly different from m=1. This special value implies that the functional response is a function of the ratio of prey and predator densities. These empirical findings support the suggestion of Arditi and Ginzburg (1989) that the functional response might often be ratio-dependent, especially in complex and heterogeneous situations.