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A Description of the Biosis Model to Assess Risk of Bird Collisions With Wind Turbines
Wildlife Society Bulletin
Abstract
We describe the model of Biosis Propriety Limited for quantifying potential risk to birds of collisions with wind turbines. The description follows the sequence of the model's processes from input parameters, through modules of the model itself. Aspects of the model that differentiate it from similar models are the primary focus of the description. These include its capacity to evaluate risk for multi-directional flights by its calculation of a mean presented area of a turbine; its use of bird flight data to determine annual flux of movements; a mathematical solution to a typical number of turbines that might be encountered in a given bird flight; capacity to assess wind-farm configurations ranging from turbines scattered in the landscape to linear rows of turbines; and the option of assigning different avoidance rates to structural elements of turbines that pose more or less risk. We also integrate estimates of the population of birds at risk with data for numbers of their flights to predict a number of individual birds that are at risk of collision. Our model has been widely applied in assessments of potential wind-energy developments in Australia. We provide a case history of the model's application to 2 eagle species and its performance relative to empirical experience of collisions by those species. © 2013 The Wildlife Society.
... Consequently, if the presence of the turbine structure or the motion of the blades does draw some species in and repel others, the risks of collisions will be underestimated and overestimated, respectively. Avoidance rates are a key parameter in collision risk models used for birds (CRMs, see Section 5.6.1), and are both difficult to estimate and have a strong influence on predictions of collision rates (Smales et al. 2013). Therefore, an accurate understanding of these behavioural responses is important to the risk assessment process. ...
... The only study we reviewed that unconditionally (i.e. where it was not dependent on species, or data cleaning decisions) found a relationship between the pre-construction risk assessments and postconstruction mortalities was that of Smales et al. (2013), which focused on Wedge-tailed Eagles and Whitebellied Sea-Eagles at two facilities in Tasmania. While this paper is nominally about the collision risk modelling (CRM) approach, they demonstrated that collision estimates from the model (0.1-2.7 individuals per year, depending on assumed avoidance rates) matched up well with the mean annual number of individuals found during carcass searches. ...
... CRMs are most frequently used for raptors (12/17 case studies we identified, or 70%) but we also found examples of CRMs that were developed for shorebirds such as gulls (Everaert 2014;Masden et al. 2021), waterbirds (Sugimoto and Matsuda 2011), and in one case, for 27 different bird species in a proposed development area in the Western Ghats, India (Pande et al. 2013). CRMs are not typically used for bats because they rely on direct observations of flight behaviours and heights, and ideally also rates of turbine avoidance, and these are not readily observable for nocturnal species (though Smales et al. 2013 note that an unpublished CRM has been used for the Pacific Flying-fox in Fiji). ...
Assessment, mitigation and monitoring of
onshore wind turbine collision impacts on wildlife: A systematic review of the international peer-reviewed literature, and its relevance
to the Victorian context. Available at https://www.ari.vic.gov.au/__data/assets/pdf_file/0023/746060/ARI-Technical-Report-389-Systematic-review-of-onshore-wind-farm-collisions.pdf
... However, for the Bluff Point and Studland Bay wind farms in Tasmania, where substantial, rigorous and controlled programs of monitoring have been underway for 9 and 5 years, respectively, data are available for whitebellied sea-eagles Haliaeetus leucogaster and wedge-tailed eagles Aquila audax fleayi (Hydro Tasmania 2012). A fuller comparison of the model's results with actual collision rates for these two species at the two wind farms is provided in Smales et al. (2013). However, Table 2 shows the model's results at three avoidance rates for these species along with the mean annual number of actual collisions detected over the entire periods of operation of the two wind farms. ...
... The collision rates of WTE are within expectations of collision risk modelling for a range of avoidance rates using the Biosis collision risk model (see Smales et al. 2013 ), but the collision rate of WBSE is lower than that estimated by the modelling (Hydro Tasmania 2013 ; Smales et al. 2013 ). The documented collision rates of both species are also below the maximum estimated collision rates in the analysis conducted (see Hydro Tasmania 2000 ) and upon which the wind farms were approved and offsets for potential mortalities determined. ...
... The collision rates of WTE are within expectations of collision risk modelling for a range of avoidance rates using the Biosis collision risk model (see Smales et al. 2013 ), but the collision rate of WBSE is lower than that estimated by the modelling (Hydro Tasmania 2013 ; Smales et al. 2013 ). The documented collision rates of both species are also below the maximum estimated collision rates in the analysis conducted (see Hydro Tasmania 2000 ) and upon which the wind farms were approved and offsets for potential mortalities determined. ...
Greenhouse gasses are widely acknowledged as the primary cause of anthropogenically driven climate change. As part of its response to climate change, the New Zealand Government has adopted a target for renewable electricity generation of 90 % by 2025. Currently New Zealand has 16 wind farms in operation with a combined capacity of 622 MW. Wind-generated power, combined with a projected six-fold increase in wind generated electricity by 2030 has the potential to contribute significantly to New Zealand’s renewable targets. In New Zealand, wind energy developments require resource consent under the Resource Management Act 1991 involving the preparation of an Assessment of Environmental Effects to identify and address the risks of a proposal to the environment, including biodiversity. Quantitative methods require empirical data while more qualitative approaches can be based more on knowledge of the topography and sensitivity of the ecosystems. Adopting quantitative approaches can provide a structured approach to study design, data needs and analysis that objectively inform a risk assessment, decisions about the appropriateness of a development, the mitigation hierarchy and, when required, the development of biodiversity offsets. Stratified qualitative approaches that use ranked data of significant habitats and/or species of regional or national significance can also inform decision making. We illustrate these principles with case studies involving modelling of collision risk for the threatened New Zealand falcon based on radio tracking data, and the use of a risk envelope for a wind farm based on habitat and species assessments.
... Avian collision has received much attention as it is considered a very real threat to bird populations (Johnson et al., 2002;Krijgsveld et al., 2009) and a variety of methods have been developed to aid the assessment of the risk of collision. The methods can be categorised as those that measure and assess collisions empirically including direct and remote observations of bird flights in the development area (pre-and post-construction of the wind turbines) to assess flight behaviour, habitat use and flux of birds (Desholm and Kahlert, 2005;Desholm et al., 2006;Douglas et al., 2012) and corpse searches to document actual collisions (Winkelman, 1992;Huso and Dalthorp, 2014), and those which are more theoretical such as collision risk models which predict likely collisions (Holmstrom et al., 2011;Eichhorn et al., 2012;Smales et al., 2013). In addition to estimating collisions between birds and wind turbines, collision risk models (CRMs) are used in a range of other situations including marine mammals and marine renewable energy devices i.e. tidal stream turbines (Wilson et al., 2006), fish and turbines (Hammar and Ehnberg, 2013) and shipping collisions with moving and stationary objects (Montewka et al., 2010). ...
... The collision risk model developed by Biosis Propriety Limited has been widely used to assess wind-energy developments in Australia since 2002 (Smales et al., 2013). The model provides a predicted number of collisions between turbines and a local or migrating population of birds. ...
... The model also estimates collision risk as the sum of the average number of turbines encountered per flight within a scattered wind turbine array, rather than for all turbines in an array. The average number of turbines likely to be encountered is calculated using a topological, non-affine mapping technique (Smales et al., 2013). ...
With the increasing global development of wind energy, collision risk models (CRMs) are routinely used to assess the potential impacts of wind turbines on birds. We reviewed and compared the avian collision risk models currently available in the scientific literature, exploring aspects such as the calculation of a collision probability, inclusion of stationary components e.g. the tower, angle of approach and uncertainty. 10 models were cited in the literature and of these, all included a probability of collision of a single bird colliding with a wind turbine during passage through the rotor swept area, and the majority included a measure of the number of birds at risk. 7 out of the 10 models calculated the probability of birds colliding, whilst the remainder used a constant. We identified four approaches to calculate the probability of collision and these were used by others. 6 of the 10 models were deterministic and included the most frequently used models in the UK, with only 4 including variation or uncertainty in some way, the most recent using Bayesian methods. Despite their appeal, CRMs have their limitations and can be 'data hungry' as well as assuming much about bird movement and behaviour. As data become available, these assumptions should be tested to ensure that CRMs are functioning to adequately answer the questions posed by the wind energy sector.
... The difficulty in prediction has led to a recognized need to better understand and anticipate the number of birds killed by collisions with operating turbines[17]. There are a number of different Collision Risk Models (hereafter referred to as CRM, e.g.,[18][19][20]). Most existing methods of modeling predicted fatalities, however, do not incorporate uncertainty into their estimates and only provide point estimates from which to make management and conservation decisions. ...
... As a result, we can simulate directly from the posteriors[29]to obtain estimates of bird fatalities (F). Bird minutes (k) are positive, whole numbered counts, allowing us to assume the data are distributed according to a Poisson distribution (e.g.,[20]). From the conjugate family we chose a gamma distribution for the prior on λ, ensuring our estimate remains real-valued and positive and that the posterior on λ is also a gamma distribution. ...
... As with existing CRMs (e.g.,[19]), our model assumes the bird population is open, which is biologically realistic at the scale of an individual project. However, in most other ways the CRM presented here differs from other models currently in the literature (e.g.,[18][19][20]). Existing CRMs differentiate between a collision probability based on non-varying avian flight (i.e., a straight line at a constant height and speed) and an avoidance rate incorporating a bird's ability to evade a collision (e.g.,[19]). ...
Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers , policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chry-saetos) fatalities at a wind installation in the United States. Using pre
... Kann für die Vorhersage von Kadaverzahlen basierend auf Flugdichten (und WEA-spezifischen Parametern) eingesetzt werden. (Smales et al., 2013) Mechanistisches CRM. Von der Grundidee ähnlich wie Band et al. (2007), arbeitet aber mit "Bird flux densities" und erlaubt die Betrachtung beliebiger Einflugwinkel. ...
... Modell vonSmales et al. (2013): Angelehnt an die Vorgehensweise vonBand et al. (2007), es wird jedoch nicht die vom Einflugwinkel abhängige Durchflugszeit mit der entsprechenden dem Vogel präsentierten (u. U. ellipsoiden) Rotorfläche verrechnet, sondern der Effekt beliebiger Einflugwinkel explizit betrachtet, indem die mittlere dem Vogel präsentierte Rotorflächengröße berechnet wird. ...
... The methods can be broadly classified as those based on the pre-construction assessments (bird habitat use, abundance, flight behavior etc.) of the wind turbines in the development zones (Douglas et al. 2012) and post-construction assessments by fatality search operations around the sited turbines to document the actual number of collisions (Huso and Dalthorp, 2014), and lastly those that apply more theoretically to these assessments e.g. collision risk models, which predict collision probabilities (Smales et al. 2013;Eichhorn et al. 2012). ...
... Currently available avian collision risk models each has been developed with its own purpose (Madsen and Cook, 2016) exploring different aspects of calculating collision probability, by including different components of collision, e.g. bird phenology (Tucker 1996;Band 2012a;2012b), the configurations of the turbine (Band 2012a;2012b), bird behavior (McAdam, 2005), their angle of approach (Holmstorm et al. 2011, Smales et al. 2013), geometry of the wind farm (Bolker et al. 2014) and co-occurrence of birds and turbines in space (Eichhorn et al. 2012). There are many arrays of such models using different approaches on these components to assess the collision risks. ...
Although, it is well recognized that harnessing wind energy is highly indispensable, but collisions of birds at wind turbines has also developed simultaneously, concerning multiple bird species. With wind being strongly affected by the landscape and the behavior of birds also being strongly influenced by the landscape, the main objective of the thesis was to understand the relevance of interactions between wind energy infrastructures and bird species from an ecological perspective of the landscape. Utilizing the carcass collision datasets of the frequently-hit bird-groups paradoxically as proxies for species presence, collision sensitive ecological distances to different land-use types were ascertained, by employing multiple techniques of species distribution modelling (SDMs), to delineate their respective collision sensitive niche employing the capabilities of machine learning algorithms. The predicted areas were specialized and highly dispersed across the federal state, with raptors showing the broadest niche and significant overlaps with the other groups. Based on estimated collision probabilities of the assessed areas (between 0 and 1), further segregations differentiated only those areas with negligible collision probabilities, <0.05, which were interpreted as the actual "no risk areas, suggesting any further planned additions of wind turbines to be suitably positioned only in these “safer” areas. Additionally, these collision probabilities were translated to strike susceptibilities, by relating them to the regional density distributions of the species as well. Summarizing, these analyses praigmatically ascertained collision risk areas, and especially the collision sensitive distances from different land-use types to these areas, enabling the accurate guidance of future wind farm expansions in the landscape. Ultimately, formulating novel wind turbine allocation strategies to minimize avian collisions, making them as compatible as possible.
... • CRM by Tucker (Tucker 1996a, b) • CRM by Band et al. (2007)-first developed by the Scottish Natural Heritage (SNH 2000) • CRM by McAdam (2005) • CRM by Podolsky (2008) • Hamer model (Holmstrom et al. 2011) • Biosis model (Smales et al. 2013) • CRM by (Eichhorn et al. 2012) • CRM by Desholm (2006) • CRM by Bolker et al. (2014) • U.S. Fish and Wildlife Service model (USFWS 2013) All the considered CRMs include an estimate of the probability of collision with a wind turbine of a single bird while flying through the rotor swept area. However, they differ in terms of how the estimation is made and in the degree of complexity of the calculations, and rely upon previous knowledge on bird and wind turbine characteristics. ...
... The Podolsky model (2008) follows similar methods as Band et al. (2007) and Tucker (1996a) and also considers different angle flights approaches, adding a proportion of birds that avoid collision (avoidance behaviour) to the model; Holmstrom et al. (2011), also based on Tucker (1996a, b) and also considering the different oblique angles of approach, however, ignored avoidance behaviour. In the biosis model, Smales et al. (2013) considered the static components of the generator as others had done, but considered the possibility that birds could approach turbines from any direction. It already added turbine layout variable to the model, and estimated the sum of the average number of turbine encountered per flight within a scattered wind turbine array, rather than for all turbines in an array, calculating it using a topological non-affine mapping technique (Masden and Cook 2016). ...
This book presents a review of the state-of-the-art knowledge on the interactions between biodiversity and wind energy development, focused on the Portuguese reality. The volume addresses the particularities of the impact assessment procedures in Portugal, contrasting it with the international practices and presenting its main findings by covering the following broader themes: i) evaluation of spatial and temporal dynamics of wildlife affected by wind farms, including birds, bats and terrestrial mammals (in particularly Portuguese wolf population); ii) the methodologies used to assess impacts caused by this type of developments in biodiversity; iii) the best practice methodologies to implement an adaptive management approach to reconcile biodiversity and wind farms.
The knowledge presented in this book was gathered through the research and development activities developed by Bioinsight company (former Bio3 company) during the last 13 years and partially funded by a R&D project designated as “Integrated solutions for biodiversity management at wind farms: reduce and compensate bird and bat mortality” (acronym: Wind & Biodiversity), co-funded by the European Regional Development Fund (FEDER), under the Regional Operational Programme of Centre (Mais Centro).
This volume fills a void in the literature as a book giving insights on the best practices to install and manage a wind farm from a biodiversity management point of view, while establishing a commitment between economic sustainability and biodiversity conservation.
... z. B. Blary et al., 2023;Drewitt und Langston, 2008;Huso und Dalthorp, 2023;Smales et al., 2013, sowie jüngste Ergebnisse aus dem LIFE Eurokite-Projekt (noch unpubliziert)) -ein Sachverhalt der bereits hinreichend aus dem Kontext von Vogelschlag an Freileitungen bekannt ist (Liesenjohann et al., 2019;Mercker, 2021). ...
The “hybrid model” developed in the probabilistic pilot study has now been improved and finalized for the red kite in the form of the “Raumnutzungs-Kollisionsrisikomodell” (RKR model). The development of the RKR model was intensively supported by the Probabilistic Subgroup (UAG-2) and a project-accompanying working group (PAG).
The RKR model is able to reliably quantify both the spatial use of the breeding bird under consideration and the collision risks associated with specific wind turbines, given the local constellation (habitat, breeding site, real or planned wind turbine locations and parameters). All input parameters were determined empirically and validly based on a maximum data basis. The reliability of the forecasts of the RKR model was also tested and verified using data from various external studies on land use, bird strike numbers and/or residence times in the wind turbine risk area.
... The results from theoretical models, like the SOSS Band model, are constrained by the fact that the overall avoidance rate of birds (wind farm and wind turbine avoidance combined) is largely unknown (Band, 2012;Chamberlain et al., 2006;Cook et al., 2014;Fox et al., 2006). Given the lack of a better alternative, a standard set of estimates of overall avoidance rates is commonly implemented in the SOSS Band model, generally ranging between 95% and 99.5% (Band, 2012;Smales et al., 2013). Since these estimates are based on very limited field measurements (Cook et al., 2012) the reliability of these is generally uncertain. ...
Collision of birds with wind turbines is an important negative effect of wind energy generation. Assessments of the potential numbers of bird collisions are required prior to the construction of wind farms. Collision rate models (CRMs) are used as a tool to estimate numbers of collision victims for wind farm initiatives. In the past couple of decades various CRMs have been developed. These models are all based on the theoretical calculation of collision probabilities (theoretical models). In this paper we introduce an empirical model, the Flux Collision Model (FCM), in which actual knowledge of species (group)-specific collision probabilities collected in existing wind farms on land is used to calculate collision rates for planned wind farms. An important quality of the FCM is that it provides a means to use empirical information to assess collision rates in Environmental Impact Assessments (EIAs) for wind farm initiatives. In addition, no detailed information on bird behaviour close to the rotor is needed, as this information is already incorporated in the empirical collision probability. In two case studies, one offshore and one on land, we compare and discuss the use and performance of the empirical FCM and the theoretical SOSS Band model for predicting collision rates of birds at wind farm initiatives. To date, no actual collision rates are known for the offshore situation. Accordingly, in the FCM, collision probabilities derived from wind farms on land were used. Nevertheless, in the offshore case study, the results of the FCM were comparable with those of the SOSS Band model. Basic sensitivity analyses for both the FCM and the SOSS Band model showed that purely theoretically both models are equally sensitive to changes in avoidance rates. However, because lower values for avoidance are applied in the FCM (wind farm avoidance) than in the SOSS Band model (overall avoidance), in practice the effect of realistic variation in avoidance rates on the resulting collision rates is much smaller for the FCM than for the SOSS Band model. Our results show that the FCM provides a valuable addition to the existing suite of (theoretical) CRMs. The predictive value of the theoretical SOSS Band model is constrained by the limited availability of knowledge on species (group)-specific (wind turbine) avoidance rates, which is not the case for the FCM. By contrast, the reliability of the empirical FCM is determined only by variation in the availability and quality of information on species (group)-specific collision probabilities. The choice of which CRM to use (theoretical or empirical) seems not to depend on the location of the wind farm initiative as being offshore or on land, but on the availability and reliability of species (group)-specific information in existing wind farms. The availability of a reliable collision probability supports the use of the FCM, while the availability of information on (overall) avoidance rates in the absence of a species (group)-specific collision probability supports the use of a theoretical CRM like the SOSS Band model. Synthesis and applications. The predictive power of collision rate models relies in the first place on the quality of the input information, and second on the theoretical details of the model calculations. Although the FCM is less dependent on measurements of avoidance rates, an urgent need remains to obtain information on actual collision rates and corresponding collision probabilities as well as avoidance rates in existing wind farms both offshore and on land in order to accurately determine the impact of wind energy on bird populations.
... Although these mathematical models are based on numerous assumptions that, collectively, may lead to prediction error, recent studies suggest that there may be a correlation between actual post-construction collision results and the predictive ranges estimated by collision risk modeling. This has been especially true for bird impacts, where the available collision data set is more complete (Smales et al., 2013). ...
The search for clean, renewable energy sources has gained considerable momentum in response to rising concerns over the harmful effects of global warming. As more wind farm projects are designed, concerns over potential impacts to wildlife also continue to rise. This book is part two in a four-part series. It attempts to examine carefully the tools and methodologies currently available to help quantify the pre- and post-construction effects of wind farm projects on two significant wildlife groups, namely birds and bats. It also offers solutions to help mitigate the effects of wind farm placement and operation on these two delicate indicators of environmental health and quality. [more...]
... A second approach employs collision risk models (CRMs) to predict collision mortality rates. CRMs combine three-dimensional records of bird flight activity in the vicinity of proposed turbines with the probability of collision for birds passing through the rotor swept volume, incorporating the physics of moving turbine blades with the size and flight speed of the target species (Band et al., 2007;Smales et al., 2013). An open-access and peer reviewed CRM is the "Band CRM" (Band et al., 2007;Chamberlain et al., 2006) which, as in all CRMs, has so far been restricted to specific wind farm projects during the EIA process (e.g. ...
Harnessing wind energy is seen as an environmentally friendly strategy to combat climate change. However, adverse environmental impacts have come to light for species that are prone to collision with wind turbine blades, such as vultures, leading to a conflict between wind energy industry and conservation. Our study area epitomized such a conflict, containing the only population of cinereous vultures in south-eastern Europe while also being the location for substantial existing and planned wind farms. We used long-term remote telemetry data to produce a species-specific sensitivity map for guiding wind energy development and to estimate vulture collision mortality due to currently operating wind farms. Most operational wind farms were in the population core area and in the highest priority areas for vulture conservation. Collision mortality due to the thirteen operating wind farms was estimated by combining global position system (GPS) telemetry data on vulture space use with a collision risk model (CRM). Estimated mortality varied greatly according to the CRM's ‘avoidance rate’. Under the most likely avoidance rates annual predicted collision mortality was 5–11% of the population, creating risk of population decline. Collision mortality was expected almost exclusively in the population core area, rendering further future development plans there severely problematic for vulture population persistence. Our sensitivity map, as a conservation prioritization system, offered a spatially explicit solution to the conflict between wind energy development and vulture conservation. Combining spatial use models derived from telemetry data with collision mortality models offers a novel conservation tool for evaluating large scale wind energy development proposals.
... This has led to the development of collision risk models (e.g. Tucker 1996a; Band et al. 2007;Holmstrom et al. 2011;Smales et al. 2013). An advantage of such models is the ability to predict potential impacts of wind farm construction on bird mortality rates through pre-construction field surveys. ...
A new spreadsheet is presented, to be used as part of the Band model for estimating potential avian mortality due to wind turbine strike. The spreadsheet extends the Band collision risk spreadsheet by allowing for oblique approach angles and wind speed. The differences in the results between this new spreadsheet and the standard Band spreadsheet are given for two species, the white-tailed eagle Haliaeetus albicilla and the South Island pied oystercatcher Haematopus finschi, chosen for their contrasting sizes and flight characteristics. Under more representative conditions, the true risk for large birds is shown to be substantially greater than that calculated by the Band spreadsheet. Examples of how to use the new spreadsheet with bird survey and wind data are given.
... Hence I will use the term displacement also to include possible attraction effects. While displacement relates to reduced habitat utilization and thereby reduced abundance of birds within the wind-power plant footprint (Madders and Whitfield, 2006), it is often confounded with avoidance indicating flight behaviour to avert a potential collision with a wind-turbine (Smales et al., 2013). Although these two terms may be the same semantically, the term 'avoidance' often remains ambiguous both with regard to the type of response studied (e.g. ...
The construction and operation of wind-power plants may affect birds through collision mortality,
reduced habitat utilization due to disturbance, barriers to movement and habitat modifications, with
the nature and magnitude of those effects being site- and species-specific. Birds may however manage
these effects through fleeing, activity shifts or changed habitat utilization; usually termed avoidance.
Given the important role avoidance plays in estimating the impact wind-power development has on
birds, there is a pressing need to formalizing the avoidance process. Crucial in this context is to identify
the underlying mechanisms of behavioural responses by birds to wind-power plants and individual turbines.
To provide a better basis for and improved understanding of the underlying mechanisms for avoidance
a conceptual framework for wind-turbine avoidance is presented decomposing various forms of
avoidance at different spatial scales. Avoidance behaviour includes displacement (macro-avoidance),
anticipatory and impulsive evasion (meso-avoidance), and escape (micro-avoidance). For understanding
why particular responses occur with regard to wind-turbine disturbance this concept is applied to predation
risk theory. The risk-disturbance hypothesis elucidates possible trade-offs between avoiding perceived
risk and fitness-enhancing activities. The four behavioural responses are related to, respectively,
habitat selection, vigilance and fleeing (twice); from which specific predictions can be derived.
Formalizing the different forms of avoidance facilitates design of effects studies, enhances comparisons
among sites studied, and guide siting and mitigation strategies.
... European offshore wind energy development precedes the U.S. and already includes 69 wind farms in eleven countries (EWEA, 2014). Methods used to assess risk and effects to bird populations at multiple scales during pre-and post-construction included direct observation or video surveys along boat and aerial transects (Banks et al., 2005;Camphuysen et al., 2004), collision risk models (Band, 2012;Smales et al., 2013), and field experiments to assess turbine avoidance (Guillemette and Larsen, 2002). As research efforts in Europe have gained a greater understanding of effects from wind farms, the need for additional tools and techniques to appropriately assess impacts has become apparent (Bailey et al., 2014). ...
a r t i c l e i n f o Classification of birds and bats that use areas targeted for offshore wind farm development is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea is transect-surveys conducted by trained individuals in boats or planes, or analysis of imagery collected from aerial surveys. These methods can be costly and pose safety concerns so that observation times are limited to daylight hours and fair weather. We propose an alternative method based on analysis of thermal video that could be recorded autonomously. We present a framework for building models to classify birds and bats and their associated behaviors from their flight tracks. As an example, we developed a discriminant model for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-minute video clips. The agreement between model-and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and the number of different path types was reduced. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models could be improved if the distance between the camera and the target was known.
... In addition, there are more CRMs than just the Band model (e.g. the Biosis model: Smales et al. 2013), and an avoidance rate estimated by one CRM is not applicable to another . We should, at least, therefore refer to 'Band Avoidance Rates' to be clear, and if rates are derived under extensions of the Band model then they should be termed appropriately. ...
Currently, according to SNH (2010) guidance the recommended avoidance rate for swans under the Band Collision Risk Model (CRM) is 98 %. The objective of the present report is to evaluate available contemporary information on the most suitable value for an avoidance rate for swans that may encounter at operational onshore wind farms.
We highlight that the avoidance rates recommended by SNH (2010) consist largely or entirely of a Micro component and do not, as claimed by SNH (2010), encapsulate both potential Micro and Macro (displacement) components. If there is evidence or arguments to support the need for such Macro avoidance measures to be considered as relevant in assessments of wind farm proposals, then the avoidance rates of SNH (2010) should be regarded as minima. We refer to reviews and other studies which document that large wildfowl (including swans) are susceptible to displacement (Macro avoidance).
Whitfield (2010) argued that swans should probably be considered to have similar avoidance rates to geese. At that time, the SNH guidance recommended a 95 % avoidance rate for swans. On the basis of Whitfield (2010) the avoidance rate for swans was increased to 98 % in SNH (2010), although the avoidance rate for geese was given as 99 % in SNH (2010). SNH (2013) later recommended increasing the 99 % avoidance rate of SNH (2010) for geese, to 99.8 %.
Subsequent to Whitfield (2010), based on a later published study by Fijn and colleagues (2012) in a Dutch polder for Bewick’s swan, the present report derives an estimated avoidance rate (Micro avoidance only) of 99.7 % or, including displacement of flying birds (Macro avoidance), at 99.8 % . If reported displacement of feeding birds would have been included, the derived avoidance rate would have been still higher.
Despite some previous reviews (e.g. Rees 2012) and the findings of Fijn et al. (2012), it is apparent from recent information that feeding large wildfowl (including, likely, swans) are not always dissuaded from feeding within turbine arrays. Hence, we do not recommend that assessments of wind farm proposals that involve feeding swans within proposed arrays should de facto increase our derived 99.7 % and 99.8 % avoidance rates to yet higher rates.
In assessments of wind farm proposals where swans are flying across a proposed development area that intercepts a commuting route we would recommend that rates of 99.7 % and/or 99.8 % should be used (according to circumstance), and not 98 % (SNH 2010).
While we acknowledge that these rates are based empirically on only a single study, we present several corroborative lines of other evidence; and note that the study was fundamentally precautionary as regards swan mortality. Our recommended avoidance rates are applicable only to the original Band CRM and not to any subsequent model extensions.
... Although these mathematical models are based on numerous assumptions that, collectively, may lead to prediction error, recent studies suggest that there may be a correlation between actual post-construction collision results and the predictive ranges estimated by collision risk modeling. This has been especially true for bird impacts, where the available collision data set is more complete (Smales et al., 2013). ...
Thesis (M.A.)--Southern Illinois University at Carbondale, 1988. Includes abstract. Vita. Includes bibliographical references (leaves [48]-51).
Wind farms can have two broad potential adverse effects on birds via antagonistic processes: displacement from the vicinity of turbines (avoidance), or death through collision with rotating turbine blades. These effects may not be mutually exclusive. Using detailed data from 99 turbines at two wind farms in central Scotland and thousands of GPS-telemetry data from dispersing golden eagles, we tested three hypotheses. Before-and-after-operation analyses supported the hypothesis of avoidance: displacement was reduced at turbine locations in more preferred habitat and with more preferred habitat nearby. After-operation analyses (i.e. from the period when turbines were operational) showed that at higher wind speeds and in highly preferred habitat eagles were less wary of turbines with motionless blades: rejecting our second hypothesis. Our third hypothesis was supported, since at higher wind speeds eagles flew closer to operational turbines; especially–once more–turbines in more preferred habitat. After operation, eagles effectively abandoned inner turbine locations, and flight line records close to rotor blades were rare. While our study indicated that whole-wind farm functional habitat loss through avoidance was the substantial adverse impact, we make recommendations on future wind farm design to minimise collision risk further. These largely entail developers avoiding outer turbine locations which are in and surrounded by swathes of preferred habitat. Our study illustrates the insights which detailed case studies of large raptors at wind farms can bring and emphasises that the balance between avoidance and collision can have several influences.
Renewable energy continues to grow globally, and the number of offshore wind farms is set to increase. Whilst wind energy developments provide energy security and reduced carbon budgets, they may impact bird populations through collision mortality, habitat modification and avoidance. To date, avian collision mortality has received the most attention and collision risk models have been developed to estimate the potential mortality caused by wind turbines. The utility of these models relies not only on their underlying assumptions but also on the data available to ensure the predictions are informative. Using a stochastic collision risk model (sCRM; based on the Band collision risk model) as an example, we explore the importance of bird flight speed and consider how the assumptions of the model influence the sensitivity to flight speed. Furthermore we explore the consequences of using site-specific GPS-derived flight speed rather than a standard generic value, with Lesser Black-backed Gulls Larus fuscus as an example, and consider how this generic value is currently used. We found that the model was most sensitive to the parameters of bird density, non-avoidance rate and percentage of birds at collision risk height, as well as bird flight speed. Using site-specific flight speed data derived from GPS tags rather than a standard value reduced the predicted number of collisions. We highlight that within the model, both the estimation of the probability of collision (PColl) and the flux of birds are sensitive to the bird flight speed; this sensitivity acts in opposite directions but the two do not necessarily balance out. Therefore, when the sCRM is used as generally done, there is little difference in collision estimates if airspeeds (bird flight speed relative to air through which it is moving) are used rather than groundspeeds (bird flight speed relative to ground). Estimates of seabird collision rates in relation to offshore wind farms are impacting future offshore wind development. By using site specific flight speed estimates and, accounting for different speeds in relation to wind direction, we demonstrate that cumulative collision estimates can be affected, highlighting the need for more representative flight speed data and where possible site-specific data.
Crete has been characterized as an area with a high wind energy capacity due to its mountainous terrain and the strong prevailing winds throughout the year. At the same time, the island constitutes the last stronghold for vulture species in Greece, currently holding the largest insular population of Eurasian griffons (Gyps fulvus) worldwide (ca. 1000 individuals). Given the empirical data on the mortality of large raptors due to collisions with wind turbine blades, the aim of the present study was to predict the potential impact of wind energy installations on the griffon vulture population on the island. The study was developed in two steps, namely, (a) the spatial mapping of the existing and planned wind energy projects up to the year 2012 and the delineation of their risk area and (b) the calculation of the annual collision rate based on the expected number of vulture risk flights and the probability of being killed. Overall, the minimum number of fatalities due to collision of vultures to wind turbines was estimated at 84 individuals per year. However, this figure could drop by over 50% if the European network of the NATURA 2000 sites was set as an exclusion zone for wind energy facilities. The study pinpoints the need for proper siting of wind farms and the prerequisite of sensitivity mapping for vulnerable species prone to collision on wind turbines.
Wind farms present several impacts on wildlife. However, the scientific community, as well as environmental practitioners, have focused most of their attention on bat and bird collision and fatality impacts. As a result, an increasing variety of methods have been developed to assess the risk of collision in wind farms during both the pre-construction phase and the post-construction phase. This chapter provides a review of the main methodologies that are currently used to address collision risk in wind farms (collision risk models, indexes and other tools)at both the international level, as well as in Portugal. In this review, a brief description of each analysed methodology is presented, and a comparison of limitations, advantages and disadvantages is made, in light of Environmental Impact Assessments studies. Final remarks are given with regards to the Portuguese context in terms of methodologies currently in use, and new tendencies to assess collision risk. A case study is presented demonstrating a simple and practical method for estimating collision risk. Developed in Portugal and in use since 2007, this method is currently used to relate risky behaviour of the common kestrel observed across a Portuguese wind farm with turbines where higher levels of fatalities of the species occur.
The Tasmanian wedge-tailed eagle (WTE, Aquila audax fleayi) and the white-bellied sea-eagle (WBSE, Haliaeetus leucogaster) are present on the Bluff Point (37 Vestas V66 turbines) and Studland Bay (25 Vestas V90 turbines) Wind Farms in north-west Tasmania, Australia. These species have been intensively studied since the commencement of operations in 2002 and 2007, respectively, as part of compliance monitoring. Monitoring has included documenting collisions with turbines, breeding success surveys, and movement and behaviour studies. Additional investigations (outside regulatory requirements) have also been conducted, including targeted studies and trials of collision mitigation techniques. Both species of eagle have continued to use the sites during construction and operation of the wind farms. The average collision rates for WTE were 1.54 and 0.95 per year, and for WBSE 0.36 and 0 per year at Bluff Point and Studland Bay, respectively (calculated up to October 2012). These are below maximum rates estimated in collision risk modeling which formed part of the information for the assessment of the wind farms. The collision rate for WTE was constant across years, although there was some evidence the rate could be declining at Studland Bay. Analyses could not be conducted on WBSE due to small sample sizes. Seasonal and other temporal patterns were tested for in the collision data, but all evidence supported the theory that the strikes were independent and random in time, with no support found for some proposed theories about why eagles collide with turbines. A spatial analysis of collisions was not possible, again due to small sample sizes. Eagles continued to breed at the sites, with at least the same level of success as nests outside the wind farms. The observational studies provided useful data about how eagles interacted with turbines at these sites. These data were used to calculate turbine avoidance rates and to assess how rates changed with development of the wind farm and when turbines were operational or not.
Current knowledge about bird and bat collisions with wind turbines in Australia is limited by a lack of consistent monitoring methods and of publicly available information where data have been collected. An overview of information that is available for mortalities and for collision modelling is provided and it suggests that frequency of collisions is generally low and unlikely to have significant impacts on population of many species. The perceptions and paradigms within which wind turbine collisions are considered are compared with aviation fauna collisions in Australia. Assessment by approval authorities of potential and actual bird and bat collisions have generally not been well focused on whether the levels of mortality involved influence viability of populations of species of concern. This is despite important regulatory policy that is clearly intended to ensure this approach. There is a great deal of potential to improve our understanding of bird and bat collisions with turbines and recommendations are made to ensure that assessments of collision rates are focused on determining whether they have impacts on populations of threatened taxa.
Collision risk for birds remains a potential conservation issue and environmental barrier to the development of wind farms on land as well as at sea. Baseline and post-construction studies in Denmark carried out at coastal and marine wind farms during 2010–2012 have aimed at developing prediction tools which could pave the way for improved planning and siting of wind farms in relation to movements of birds. Detection of flight trajectories by means of visual observations is severely constrained, and thus field campaigns were undertaken using a combination of visual observations and radar- and rangefinder-based tracking. The collection of two- and three-dimensional track data was necessary to obtain useful information on the responses of migrating bird species to the wind farms, and on flight altitudes of the birds during different weather conditions and in relation to landscape components. To be able to assess general patterns in the migration behaviour of birds, we developed statistical models capable of explaining the differences in altitude based on relationships with wind and weather conditions and distance to coast. As these relationships in many cases were non-linear, the error structure of the data non-normally distributed, and the track data spatially and temporally auto-correlated we chose to use a generalized additive mixed modelling (GAMM) framework. The resulting models of the migration altitude of raptors and other groups of landbirds made it possible to assess the weather-dependent flight altitude at the wind farm sites. The studies provided strong indications that wind speed and direction as well as humidity, air clarity and air pressure are important predictors in general for all species in addition to distance to land and wind farm, and the birds favour tail winds and decreasing wind speed. Collision models display a variety of specific trends with rates of collisions of landbirds increasing during periods of head winds and reduced visibility, while the collision rates of seabirds typically increase during periods of tail winds and increased visibility. Our studies have shown that birds across a wide range of species show clear weather-dependent movements which can be predicted for specific spatial settings using statistical models. These findings stress the potential for intensifying the strategic planning processes related to wind farms.
Birds flying across a region containing a windfarm risk death from turbine
encounters. This paper describes a geometric model that helps estimate that
risk and a spreadsheet that implements the model.
This is a brief review of the main insights into bird migration provided
by radar. Radar is the main tool to study the flight behavior of
migratory birds under the influence of environmental factors, i.e., the
ecology of migratory flights, ranging from the large-scale pattern of
migration in relation to the distribution of land masses, geomorphology,
and weather systems down to the variation of flight behavior of single
birds in response to leading lines, obstacles, particular atmospheric
conditions, and flight phases.
This book is available for purchase, The 1993 version is available on the page at http://distancesampling.org/downloads/distancebook1993/index.html
A bird census method is presented that is designed for tall, structurally complex vegetation types, and rugged terrain. With this method the observer counts all birds seen or heard around a station, and estimates the horizontal distance from the station to each bird. Count periods at stations vary according to the avian community and structural complexity of the vege- tation. The density of each species is determined by inspecting a histogram of the number of individuals per unit area in concentric bands of predeter- mined widths about the stations, choosing the band (with outside radius x) where the density begins to decline, and summing the number of individuals counted within the circle of radius x and dividing by the area (m2). Although all observations beyond radius x are rejected with this procedure, coefficients of detectability may be determined for each species using a standard fixed maximum distance.
IN THE APPROXIMATELY 60 years since the dis- covery that birds were responsible for some of the puzzling radar echoes dubbed "angels" by the British (Lack and Varley 1945, Buss 1946), radar has proven to be a useful tool for the de- tection, monitoring, and quantifi cation of bird movements in the atmosphere (Eastwood 1967; Richardson 1979; Vaughn 1985; Bruderer 1997a, b). Radar has been a particularly valuable tool for descriptive studies of daily and seasonal patterns of bird migration, but the technique has also been used to answer important ques- tions about how birds orient during migration and the role of atmospheric structure in shaping fl ight strategies of birds. Within the last two de- cades, radar ornithology has played an increas- ingly important role in conservation of species that are migratory, endangered, threatened, or of special concern. TYPES OF RADAR
Sensory ecology investigates the information that underlies an animal’s interactions with its environment. A sensory ecology framework is used here to seek to assess why flying birds collide with prominent structures, such as power lines, fences, communication masts, wind turbines and buildings, which intrude into the open airspace. Such collisions occur under conditions of both high and low visibility. It is argued that a human perspective of the problems posed by these obstacles is unhelpful. Birds live in different visual worlds and key aspects of these differences are summarized. When in flight, birds may turn their heads in both pitch and yaw to look down, either with the binocular field or with the lateral part of an eye’s visual field. Such behaviour may be usual and results in certain species being at least temporarily blind in the direction of travel. Furthermore, even if birds are looking ahead, frontal vision may not be in high resolution. In general, high resolution occurs in the lateral fields of view and frontal vision in birds may be tuned for the detection of movement concerned with the extraction of information from the optical flow field, rather than the detection of high spatial detail. Birds probably employ lateral vision for the detection of conspecifics, foraging opportunities and predators. The detection of these may be more important than simply looking ahead during flight in the open airspace. Birds in flight may predict that the environment ahead is not cluttered. Even if they are facing forward, they may fail to see an obstacle as they may not predict obstructions; perceptually they have no ‘prior’ for human artefacts such as buildings, power wires or wind turbines. Birds have only a restricted range of flight speeds that can be used to adjust their rate of gain of visual information as the sensory challenges of the environment change. It is argued that to reduce collisions with known hazards, something placed upon the ground may be more important than something placed on the obstacle itself. Foraging patches, conspecific models or alerting sounds placed a suitable distance from the hazard may be an effective way of reducing collisions in certain locations. However, there is unlikely to be a single effective way to reduce collisions for multiple species at any one site. Warning or diversion and distraction solutions may need to be tailored for particular target species.
This is the first part of a study on flight characteristics of birds and presents an annotated list of flight speeds of 139 western Palearctic species. All measurements were taken with the same tracking radar and corrected for wind influence according to radar-tracked wind-measuring balloons. Graphical presentation of the birds' air speeds emphasizes the wide variation of speeds within species and allows easy comparison between taxonomic groups, species, and types of flight. Unlike theoretical predictions, speeds increase only slightly with size. The larger species seem to be increasingly limited to speeds close to their speed of minimum power consumption Vmp',. Released birds, apparently reluctant to depart with migratory speed, fly at considerably lower speeds than migrating conspecifics. While large birds seem to be limited to speeds around Vmp', smaller birds seem to be capable of selecting between various speeds, approaching predicted Vmp, when tending to remain airborne at low cost, but flying at much higher speeds when tending to make best progress at low cost (around predicted speed of maximum range Vmr,). Predictions of air speeds by aerodynamic models proved to be too low for small birds because the models do not account for the gain in speed attained by the reduction in profile drag during bounding flight of small passerines. The models predict excessive speeds for large birds because the power output available for flight seems to decline much more with size than previously assumed.
Since the early 1990s, marine wind farms have become a reality, with at least 13 000 offshore wind turbines currently proposed in European waters. There are public concerns that these man-made structures will have a significant negative impact on the many bird populations migrating and wintering at sea. We assess the degree of usefulness and the limitations of different remote technologies for studying bird behaviour in relation to bird–turbine collisions at offshore wind farms. Radar is one of the more powerful tools available to describe the movement of birds in three-dimensional space. Although radar cannot measure bird–turbine collisions directly, it offers the opportunity to quantify input data for collision models. Thermal Animal Detection System (TADS) is an infra red-based technology developed as a means of gathering highly specific information about actual collision rates, and also for parameterizing predictive collision models. TADS can provide information on avoidance behaviour of birds in close proximity to turbine rotor-blades, flock size and flight altitude. This review also assesses the potential of other (some as yet undeveloped) techniques for collecting information on bird flight and behaviour, both pre- and post-construction of the offshore wind farms. These include the use of ordinary video surveillance equipment, microphone systems, laser range finder, ceilometers and pressure sensors.
Bird flight has always intrigued mankind. This book provides an up-to-date account of our existing knowledge on the subject, offering new insights and challenging some established views. A brief history of the science of flight introduces the basic physical principles governing aerial locomotion. This is followed by a treatment of flight-related functional morphology, concentrating on the difference in shape of the arm and hand part of the wings, on the structure and function of tails, and on the shape of the body. The anatomy and mechanical properties of feathers receive special attention. Aerodynamic principles used by birds are explained in theory by simply applying Newton’s laws, and in practice by showing the direction and velocity of the attached flow around an arm wing cross section and of the leading edge vortex flow above a hand wing. The Archaeopteryx fossils remain crucial in our understanding of the evolution of bird flight despite the recent discovery of a range of well-preserved ancient birds. Avian Flight offers a novel insight into the interactions between wings and air which challenges established theories relating to the origin of bird flight. Take-off, flapping flight, gliding and landing are the basic ingredients of bird flight, and birds use a variety of flight styles from hovering to soaring. Flight muscles are the engines that generate the force required to keep the wings and tail in the gliding configuration and perform work during flapping motion. The energy required to fly can be estimated or measured directly, and a comparison of the empirical results provides insights into the trend in metabolic costs of flight of birds varying in shape and mass from hummingbirds to albatrosses. The book will be of interest to biologists, ornithologists, and bird watchers. It will also be of relevance and use to physicists, mathematicians, and engineers involved with aerodynamics.
The Tasmanian wedge-tailed eagle (WTE, Aquila audax fleayi) and the white-bellied sea-eagle (WBSE, Haliaeetus leucogaster) are present on the Bluff Point (37 Vestas V66 turbines) and Studland Bay (25 Vestas V90 turbines) Wind Farms in north-west Tasmania, Australia. These species have been intensively studied since the commencement of operations in 2002 and 2007, respectively, as part of compliance monitoring. Monitoring has included documenting collisions with turbines, breeding success surveys, and movement and behaviour studies. Additional investigations (outside regulatory requirements) have also been conducted, including targeted studies and trials of collision mitigation techniques. Both species of eagle have continued to use the sites during construction and operation of the wind farms. The average collision rates for WTE were 1.54 and 0.95 per year, and for WBSE 0.36 and 0 per year at Bluff Point and Studland Bay, respectively (calculated up to October 2012). These are below maximum rates estimated in collision risk modeling which formed part of the information for the assessment of the wind farms. The collision rate for WTE was constant across years, although there was some evidence the rate could be declining at Studland Bay. Analyses could not be conducted on WBSE due to small sample sizes. Seasonal and other temporal patterns were tested for in the collision data, but all evidence supported the theory that the strikes were independent and random in time, with no support found for some proposed theories about why eagles collide with turbines. A spatial analysis of collisions was not possible, again due to small sample sizes. Eagles continued to breed at the sites, with at least the same level of success as nests outside the wind farms. The observational studies provided useful data about how eagles interacted with turbines at these sites. These data were used to calculate turbine avoidance rates and to assess how rates changed with development of the wind farm and when turbines were operational or not.
IN THE APPROXIMATELY 60 years since the discovery that birds were responsible for some of the puzzling radar echoes dubbed "angels" by the British (Lack and Varley 1945, Buss 1946), radar has proven to be a useful tool for the detection, monitoring, and quantification of bird movements in the atmosphere (Eastwood 1967; Richardson 1979; Vaughn 1985; Bruderer 1997a, b). Radar has been a particularly valuable tool for descriptive studies of daily and seasonal patterns of bird migration, but the technique has also been used to answer important questions about how birds orient during migration and the role of atmospheric structure in shaping flight strategies of birds. Within the last two decades, radar ornithology has played an increasingly important role in conservation of species that are migratory, endangered, threatened, or of special concern.
A regularly raised concern for wind farms is the number of species and rate of bird and bat collisions with turbines. Australian regulators require, at some operating wind farms, the monitoring of bird and bat collisions. Although monitoring is becoming more commonplace, the area recommended for searching beneath turbines is inconsistent, with many guidelines both in Australia and overseas being based on conjecture rather than empirical evidence. This has the potential to bias survey results, reduce confidence in the data collected, and preclude meaningful comparisons between sites. By having a measure of the range of the fall zone of a bird or bat, a survey can be designed that ensures that an adequate area is being searched and that the cost of searching outside the area where birds and bats can fall is minimised. This article outlines a model that describes the fall zone of bats and birds of various sizes after colliding with different sized turbines, by applying a Monte-Carlo approach to ballistics theory. The modelling results are benchmarked with data from two Australian wind farms and one from the USA, for which data were available. The results indicate the size of the search area required around already constructed turbines, and the search area required to estimate levels of background mortality for control zones for pre-commission mortality surveys.
Understanding the interaction between eagles and wind farms is essential for the development of strategies to minimize collision risk, and to quantify avoidance rates for collision risk modeling. The purpose of our study was to measure the avoidance rates of Tasmanian wedge-tailed eagles (Aquila audax fleayi) and white-bellied sea-eagles (Haliaeetus leucogaster) using a new method, and to examine factors affecting these rates. We conducted eagle surveys at the Musselroe Wind Farm (undeveloped and used as a control); Studland Bay Wind Farm during commissioning and operational stages; and Bluff Point Wind Farm during the operational stage, all in northern Tasmania, Australia. Observers documented flight tracks and behavior of eagles over 875 days during the period 2006–2008. Both species demonstrated a distinct avoidance of the turbines, preferring to fly midway between them. Avoidance rates were 81%–97%, and differed significantly between species and sites, with white-bellied sea-eagles avoiding at a higher rate than wedge-tailed eagles. Eagles at Bluff Point had a higher avoidance rate than those at Studland Bay, even though the sites were only 3 km apart. Both species altered their avoidance rates in response to stages in the wind-farm development, but only the wedge-tailed eagle altered its rate in response to weather conditions, demonstrating a higher avoidance rate during wet and windy conditions. Our study found that the interaction of eagles and wind turbines is complex, which highlights the need for further study of avoidance rates in species at different sites. © 2013 The Wildlife Society.
A mathematical model for collisions between birds and propeller-type turbine rotors identifies the variables that can be manipulated to reduce the probability that birds will collide with the rotor. This study defines a safety index--the clearance power density--that allows rotors of different sizes and designs to be compared in terms of the amount of wind energy converted to electrical energy per bird collision. The collision model accounts for variations in wind speed during the year and shows that for model rotors with simple, one-dimensional blades, the safety index increases in proportion to rotor diameter, and variable speed rotors have higher safety indexes than constant speed rotors. The safety index can also be increased by enlarging the region near the center of the rotor hub where the blades move slowly enough for birds to avoid them. Painting the blades to make them more visible might have this effect. Model rotors with practical designs can have safety indexes an order of magnitude higher than those for model rotors typical of the constant speeds rotors in common use today. This finding suggests that redesigned rotors could have collision rates with birds perhaps an order of magnitude lower than today`s rotors, with no reduction in the production of wind power. The empirical data that exist for collisions between raptors, such as hawks and eagles, and rotors are consistent with the model: the numbers of raptor carcasses found beneath large variable speed rotors, relative to the numbers found under small constant speed rotors, are in the proportions predicted by the collision model rather than in proportion to the areas swept by the rotor blades. However, uncontrolled variables associated with these data prevent a stronger claim of support for the model.
When a bird flies through the disk swept out by the blades of a wind turbine rotor, the probability of collision depends on the motions and dimensions of the bird and the blades. The collision model in this paper predicts the probability for birds that glide upwind, downwind, an across the wind past simple one-dimensional blades represented by straight lines, and upwind and downwind past more realistic three-dimensional blades with chord and twist. Probabilities vary over the surface of the disk, and in most cases, the tip of the blade is less likely to collide with a bird than parts of the blade nearer the hub. The mean probability may be found by integration over the disk area. The collision model identifies the rotor characteristics that could be altered to make turbines safer for birds.
Bird flight has always intrigued mankind. This book provides an up-to-date account of the existing knowledge on the subject, offering new insights and challenges some established views. A brief history of the science of flight introduces the basic physical principles governing aerial locomotion. This is followed by chapters on the flight-related functional morphology. The anatomy of the flight apparatus includes the wings, tail, and body. Treatment of the wings emphasizes the difference in shape of the arm and hand part. The structural complexity and mechanical properties of feathers receive special attention. Aerodynamic principles used by birds are explained in theory by applying Newton's laws, and in practice by showing the direction and velocity of the flow around the arm and hand wing. The Archaeopteryx fossils remain crucial to the understanding of the evolution of bird flight despite the recent discovery of a range of well-preserved ancient birds. A novel hypothesis explaining the enigmatic details of the Archaeopteryx remains and challenges established theories regarding the origin of bird flight. Take-off, flapping flight, gliding, and landing are the basic ingredients of bird flight, and birds use a variety of flight styles from hovering to soaring. Muscles are the engines that generate the forces required to control the wings and tail, and to work during flapping motion. The energy required to fly can be estimated or measured directly, and a comparison of the empirical results, provides insights into the trend in metabolic costs of flight of birds varying in shape and mass from hummingbirds to albatrosses.
The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.
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