Threatened species impact assessments: survey effort requirements based on criteria for cumulative impacts

Abstract

AbstractAimEnvironmental impact assessments (EIAs) often involve establishing whether a species of concern is present at the site considered for development. When surveys falsely conclude that sites are unoccupied, species prevalence in the region is cumulatively reduced. We argue that setting an acceptable level of induced decline in species occurrence provides a defensible strategy to determine minimum survey effort requirements. We investigate methods for setting such requirements.LocationEastern Australia, although we demonstrate methods applicable wherever species detection data are available to inform survey design.MethodsWe use probability theory to investigate required survey effort when aiming to limit decline in species occurrence. We use optimization tools to provide a method that, in addition, minimizes overall survey costs. We demonstrate the methods using data for an Australian gliding marsupial.ResultsA method based on ensuring a constant probability of occupied site misclassification directly links with a prescribed acceptable decline in occurrence. Optimization results indicate that, under particular conditions, a cost‐efficient survey effort allocation can be achieved by setting a constant posterior probability of occupancy at sites where the species is not detected, provided the target level is set in accordance with the acceptable decline in occurrence. Our results provide a critical examination of the approach recently proposed by Wintle et al. (2012) for determining minimum survey effort requirements.Main conclusionsEIA survey effort requirements should explicitly link uncertainty in establishing species absence with the broader consequences of failing to detect species presence in places subject to proposed impacts. A direct method, which involves keeping a constant probability of occupied site misclassification, only requires information about species detectability. Alternatively, a method that minimizes overall survey costs can be used. This approach also requires occupancy probability estimates so its performance relies on availability of an informative species distribution model.