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Flight behaviour characteristics such as flight altitude and avoidance behaviour determine the species-specific collision risk of birds with wind turbines. However, traditional observational methods exhibit limited positional accuracy. High-resolution GPS telemetry represents a promising method to overcome this drawback. In this study, we used three-dimensional GPS tracking data including high-accuracy tracks recorded at 3-s intervals to investigate the collision risk of breeding male Montagu's Harriers Circus pygargus in the Dutch–German border region. Avoidance of wind turbines was quantified by a novel approach comparing observed flights to a null model of random flight behaviour. On average, Montagu's Harriers spent as much as 8.2 h per day in flight. Most flights were at low altitude, with only 7.1% within the average rotor height range (RHR; 45–125 m). Montagu's Harriers showed significant avoidance behaviour, approaching turbines less often than expected, particularly when flying within the RHR (avoidance rate of 93.5%). For the present state, with wind farms situated on the fringes of the regional nesting range, collision risk models based on our new insights on flight behaviour indicated 0.6–2.0 yearly collisions of adult males (as compared with a population size of c. 40 pairs). However, the erection of a new wind farm inside the core breeding area could markedly increase mortality (up to 9.7 yearly collisions). If repowering of the wind farms was carried out using low-reaching modern turbines (RHR 36–150 m), mortality would more than double, whereas it would stay approximately constant if higher turbines (RHR 86–200 m) were used. Our study demonstrates the great potential of high-resolution GPS tracking for collision risk assessments. The resulting information on collision-related flight behaviour allows for performing detailed scenario analyses on wind farm siting and turbine design, in contrast to current environmental assessment practices. With regard to Montagu's Harriers, we conclude that although the deployment of higher wind turbines represents an opportunity to reduce collision risk for this species, precluding wind energy developments in core breeding areas remains the most important mitigation measure.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.