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Risk Assessment of UK Skylark Populations Using Life-History and Individual-Based Landscape Models

Abstract

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.

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Acknowledgments

We wish to acknowledge the Pesticides Safety Directorate, Department for Environment, Food and Rural Affairs, UK for funding the workshop.

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Correspondence to C. J. Topping.

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Topping, C., Sibly, R., Akçakaya, H. et al. Risk Assessment of UK Skylark Populations Using Life-History and Individual-Based Landscape Models. Ecotoxicology 14, 925–936 (2005). https://doi.org/10.1007/s10646-005-0027-3

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Keywords

  • life history
  • landscape models
  • populations
  • risk assessment