Implicit assumptions underlying simple harvest models of marine bird populations can mislead environmental management decisions.

Assessing the potential impact of additional mortality from anthropogenic causes on animal populations requires detailed demographic information. However, these data are frequently lacking, making simple algorithms, which require little data, appealing. Because of their simplicity, these algorithms often rely on implicit assumptions, some of which may be quite restrictive. Potential Biological Removal (PBR) is a simple harvest model that estimates the number of additional mortalities that a population can theoretically sustain without causing population extinction. However, PBR relies on a number of implicit assumptions, particularly around density dependence and population trajectory that limit its applicability in many situations. Among several uses, it has been widely employed in Europe in Environmental Impact Assessments (EIA), to examine the acceptability of potential effects of offshore wind farms on marine bird populations. As a case study, we use PBR to estimate the number of additional mortalities that a population with characteristics typical of a seabird population can theoretically sustain. We incorporated this level of additional mortality within Leslie matrix models to test assumptions within the PBR algorithm about density dependence and current population trajectory. Our analyses suggest that the PBR algorithm identifies levels of mortality which cause population declines for most population trajectories and forms of population regulation. Consequently, we recommend that practitioners do not use PBR in an EIA context for offshore wind energy developments. Rather than using simple algorithms that rely on potentially invalid implicit assumptions, we recommend use of Leslie matrix models for assessing the impact of additional mortality on a population, enabling the user to explicitly define assumptions and test their importance.

Flight heights obtained from GPS versus altimeters influence estimates of collision risk with offshore wind turbines in Lesser Black-backed Gulls Larus fuscus.

The risk posed by offshore wind farms to seabirds through collisions with turbine blades is greatly influenced by species-specific flight behaviour. Bird-borne telemetry devices may provide improved measurement of aspects of bird behaviour, notably individual and behaviour specific flight heights. However, use of data from devices that use the GPS or barometric altimeters in the gathering of flight height data is nevertheless constrained by a current lack of understanding of the error and calibration of these methods. Uncertainty remains regarding the degree to which errors associated with these methods can affect recorded flight heights, which may in turn have a significant influence on estimates of collision risk produced by Collision Risk Models (CRMs), which incorporate flight height distribution as an input. Using GPS/barometric altimeter tagged Lesser Black-backed Gulls Larus fuscus from two breeding colonies in the UK, we examine comparative flight heights produced by these devices, and their associated errors. We present a novel method of calibrating barometric altimeters using behaviour characterised from GPS data and open-source modelled atmospheric pressure. We examine the magnitude of difference between offshore flight heights produced from GPS and altimeters, comparing these measurements across sampling schedules, colonies, and years. We found flight heights produced from altimeter data to be significantly, although not consistently, higher than those produced from GPS data. This relationship was sustained across differing sampling schedules of five minutes and of 10 s, and between study colonies. We found the magnitude of difference between GPS and altimeter derived flight heights to also vary between individuals, potentially related to the robustness of calibration factors used. Collision estimates for theoretical wind farms were consequently significantly higher when using flight height distributions generated from barometric altimeters. Improving confidence in telemetry-obtained flight height distributions, which may then be applied to CRMs, requires sources of errors in these measurements to be identified. Our study improves knowledge of the calibration processes for flight height measurements based on telemetry data, with the aim of increasing confidence in their use in future assessments of collision risk and reducing the uncertainty over predicted mortality associated with wind farms.

Quantifying avian avoidance of offshore wind turbines: Current evidence and key knowledge gaps.

The risk of collision between birds and turbines is seen as one of the key issues in the planning process for offshore wind farms. In some cases, predictions of collision risk have led to projects either being withdrawn from the planning process, or refused planning consent. Despite this, the evidence base on which collision risk is assessed is extremely limited and assessments rely on models which can be highly sensitive to assumptions, notably about bird collision avoidance behaviour. We present a synthesis of the current state of knowledge about collision risk and avoidance behaviour in seabirds. Evidence suggests species-specific responses to turbines and that in order to avoid collision, most birds adjust their flight paths at some distance from the turbines, rather than making last-second adjustments. We highlight the key gaps in knowledge and make recommendations for future data collection.