Ecological impact assessment in data-poor systems: a case study on metapopulation persistence.
Legislation on the protection of biodiversity (e.g., European Union Habitat and Bird Directives) increasingly requires ecological impact assessment of human activities. However, knowledge and understanding of relevant ecological processes and species responses to different types of impact are often incomplete. In this paper we demonstrate with a case study how impact assessment can be carried out for situations where data are scarce but some expert knowledge is available. The case study involves two amphibian species, the great crested newt (Triturus cristatus) and the natterjack toad (Bufo calamita) in the nature reserve the Meinweg in the Netherlands, for which plans are being developed to reopen an old railway track called the Iron Rhine. We assess the effects of this railway track and its proposed alternatives (scenarios) on the metapopulation extinction time and the occupancy times of the patches for both species using a discrete-time stochastic metapopulation model. We quantify the model parameters using expert knowledge and extrapolated data. Because of our uncertainty about these parameter values, we perform a Monte Carlo uncertainty analysis. This yields an estimate of the probability distribution of the model predictions and insight into the contribution of each distinguished source of uncertainty to this probability distribution. We show that with a simple metapopulation model and an extensive uncertainty analysis it is possible to detect the least harmful scenario. The ranking of the different scenarios is consistent. Thus, uncertainty analysis can enhance the role of ecological impact assessment in decision making by making explicit to what extent incomplete knowledge affects predictions.
Etienne
Rampal S
RS
Community and Conservation Ecology Group, University of Groningen, PO Box 14, 9750 AA, Haren, The Netherlands. r.etienne@biol.rug.nl
Vos
Claire C
CC
Jansen
Michiel J W
MJ
D000818:Animals; D002025:Bufonidae; D003247:Conservation of Natural Resources; D003657:Decision Making; D004463:Ecology; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011157:Population Dynamics; D012455:Salamandridae