Modelling habitat use and distribution of golden eagles <Emphasis Type="Italic">Aquila chrysaetos</Emphasis> in a low-density area of the Iberian Peninsula

We analyse the current situation of the Golden eagle (Aquila chrysaetos) in the region of Galicia in NW Spain. At present, the entire Galician population (five pairs) is located within an area of about 2000 km² in the province of Ourense. To identify high-priority areas for golden eagle conservation, we derived predictive models of habitat suitability using logistic regression and a Geographic Information System (GIS). Specifically, to model the distribution of the breeding population we considered topographic features, land use and degree of humanization, using a 10 × 10 km grid. Presence/absence of golden eagle nests was used as the dependent variable; analyses were performed both considering current nesting areas and considering old nesting areas (1960s and 70s). At the spatial scale considered, the best predictors of habitat suitability for breeding were topographical variables indicative of rugged relief. For current nesting areas the most parsimonious model included maximum altitude. We consider that the predictive models obtained may be of use for the monitoring and conservation management of the golden eagle population in this region. Conservation problems associated with habitat constraints such as food supply, availability of nesting sites, changes in land use and human disturbance are discussed.     

Potential impact of wind farms on territories of large eagles in southeastern Spain

Although wind farms in Spain have increased in numbers in recent years, their impact on birds, particularly large raptors, has received relatively little attention in the scientific literature. We study the potential impact of 72 wind energy developments planned for the south-east of Spain covering 128 golden eagle and 152 Bonelli’s eagle territories using nearest neighbour distances (NND) as an indicator of potential future interactions (abandonment, displacement and collision risk). Our analyses indicate low levels of potential interactions between wind farms and large eagles, and suggest that, of the two species studied, golden eagles will be the more affected because a greater proportion of wind farms will be constructed close to the breeding territories of this species. In the light of these findings, we discuss various management strategies in order to improve the compatibility of harvesting wind energy with the conservation of both species.     

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.     

Prioritizing Avian Species for Their Risk of Population-Level Consequences from Wind Energy Development

Recent growth in the wind energy industry has increased concerns about its impacts on wildlife populations. Direct impacts of wind energy include bird and bat collisions with turbines whereas indirect impacts include changes in wildlife habitat and behavior. Although many species may withstand these effects, species that are long-lived with low rates of reproduction, have specialized habitat preferences, or are attracted to turbines may be more prone to declines in population abundance. We developed a prioritization system to identify the avian species most likely to experience population declines from wind facilities based on their current conservation status and their expected risk from turbines. We developed 3 metrics of turbine risk that incorporate data on collision fatalities at wind facilities, population size, life history, species’ distributions relative to turbine locations, number of suitable habitat types, and species’ conservation status. We calculated at least 1 measure of turbine risk for 428 avian species that breed in the United States. We then simulated 100,000 random sets of cutoff criteria (i.e., the metric values used to assign species to different priority categories) for each turbine risk metric and for conservation status. For each set of criteria, we assigned each species a priority score and calculated the average priority score across all sets of criteria. Our prioritization system highlights both species that could potentially experience population decline caused by wind energy and species at low risk of population decline. For instance, several birds of prey, such as the long-eared owl, ferruginous hawk, Swainson’s hawk, and golden eagle, were at relatively high risk of population decline across a wide variety of cutoff values, whereas many passerines were at relatively low risk of decline. This prioritization system is a first step that will help researchers, conservationists, managers, and industry target future study and management activity.     

Predicting Bald Eagle Collision at Wind Energy Facilities

Bald eagles (Haliaeetus leucocephalus) are currently protected in the United States under the Bald and Golden Eagle Protection Act of 1940 and Migratory Bird Treaty Act of 1918. Given these protections and the increasing development of wind energy throughout the United States, it is important for regulators and the wind industry to understand the risk of bald eagle collisions with wind turbines. Prior probability distributions for eagle exposure rates and collision rates have been developed for golden eagles (Aquila chrysaetos) by the United States Fish and Wildlife Service (USFWS). Given similar information has not been available for bald eagles, the current recommendation by the USFWS is to use the prior probability distributions developed using data collected on golden eagles to predict take for bald eagles. But some evidence suggests that bald and golden eagles may be at different risk for collision with wind turbines and the prior probability distributions developed for golden eagles may not be appropriate for bald eagles. We developed prior probability distributions using data collected at MidAmerican Energy Company's operating wind energy facilities in Iowa, USA, from December 2014 to March 2017 for bald eagle exposure rates and collision rates. The prior probability distribution for collision rate developed for bald eagles has a lower mean collision rate and less variability relative to that developed for golden eagles. We determined that the prior probability distributions specific to bald eagles from these operating facilities are a better starting point for predicting take for bald eagles at operating wind energy facilities in an agricultural landscape than those developed for golden eagles. © 2021 The Wildlife Society.     

Avian top predator and the landscape of fear: responses of mammalian mesopredators to risk imposed by the golden eagle

Top predators may induce extensive cascading effects on lower trophic levels, for example, through intraguild predation (IGP). The impacts of both mammalian and avian top predators on species of the same class have been extensively studied, but the effects of the latter upon mammalian mesopredators are not yet as well known. We examined the impact of the predation risk imposed by a large avian predator, the golden eagle (Aquila chrysaetos, L.), on its potential mammalian mesopredator prey, the red fox (Vulpes vulpes, L.), and the pine marten (Martes martes, L.). The study combined 23 years of countrywide data from nesting records of eagles and wildlife track counts of mesopredators in Finland, northern Europe. The predation risk of the golden eagle was modeled as a function of territory density, density of fledglings produced, and distance to nearest active eagle territory, with the expectation that a high predation risk would reduce the abundances of smaller sized pine martens in particular. Red foxes appeared not to suffer from eagle predation, being in fact most numerous close to eagle nests and in areas with more eagle territories. This is likely due to similar prey preferences of the two predators and the larger size of foxes enabling them to escape eagle predation risk. Somewhat contrary to our prediction, the abundance of pine martens increased from low to intermediate territory density and at close proximity to eagle nests, possibly because of similar habitat preferences of martens and eagles. We found a slightly decreasing trend of marten abundance at high territory density, which could indicate that the response in marten populations is dependent on eagle density. However, more research is needed to better establish whether mesopredators are intimidated or predated by golden eagles, and whether such effects could in turn cascade to lower trophic levels, benefitting herbivorous species.     

Forecasting suitable areas for wind turbine occurrence to proactively improve wildlife conservation

The United States is rapidly expanding production of renewable energy to meet increased energy demands and reduce greenhouse gas emissions. Wind energy is at the forefront of this transition. A central challenge is understanding the nexus between wind energy development and its capacity for negative effects on wildlife causing population declines and habitat loss. Collaboration among conservationists and developers, early in the planning process, is crucial for minimizing wind-wildlife conflicts. Such collaborations require data showing where wind and wildlife impacts occur. To meet this challenge and inform decision-making, we provide natural resource agencies and stakeholders information regarding where future wind turbines may occur, and the potential affects on natural resource management, including the conservation of priority species and their habitats. We developed a machine learning model predicting suitability of wind turbine occurrence (hereafter, wind turbine suitability) across an eight-state region in the United States, representing some of the richest areas of wind potential. Our model incorporates predictor variables related to infrastructure, land ownership, meteorology, and topography. We additionally created a constraint layer indicating areas where wind would likely not be developed because of zoning, protected lands, and restricted federal agency proximity guidelines. We demonstrate how the predictive wind turbine suitability model informs conservation planning by incorporating animal movement models, relative abundance models coupled with spatial conservation planning software, and population density models for three exemplar, high priority species often affected by wind energy: whooping cranes (Grus americana), golden eagles (Aquila chrysaetos), and lesser prairie-chickens (Tympanuchus pallidicinctus). By merging the wind turbine and biological models, we identified conservation priority areas (i.e., places sharing high suitability for wind turbines and species use), and places where wind expansion could minimally affect these species. We use our “species-wind turbine occurrence relationships” to demonstrate applications, illustrating how forecasting areas of wind turbine suitability promotes wildlife conservation. These relationships inform wind energy siting to reduce negative ecological impacts while promoting environmental and economic viability.     

Collision risk of Montagu’s Harriers Circus pygargus with wind turbines derived from high-resolution GPS tracking

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.     

The Potential Wind Power Resource in Australia: A New Perspective

Australia’s wind resource is considered to be very good, and the utilization of this renewable energy resource is increasing rapidly: wind power installed capacity increased by 35% from 2006 to 2011 and is predicted to account for over 12% of Australia’s electricity generation in 2030. Due to this growth in the utilization of the wind resource and the increasing importance of wind power in Australia’s energy mix, this study sets out to analyze and interpret the nature of Australia’s wind resources using robust metrics of the abundance, variability and intermittency of wind power density, and analyzes the variation of these characteristics with current and potential wind turbine hub heights. We also assess the extent to which wind intermittency, on hourly or greater timescales, can potentially be mitigated by the aggregation of geographically dispersed wind farms, and in so doing, lessen the severe impact on wind power economic viability of long lulls in wind and power generated. Our results suggest that over much of Australia, areas that have high wind intermittency coincide with large expanses in which the aggregation of turbine output does not mitigate variability. These areas are also geographically remote, some are disconnected from the east coast’s electricity grid and large population centers, which are factors that could decrease the potential economic viability of wind farms in these locations. However, on the eastern seaboard, even though the wind resource is weaker, it is less variable, much closer to large population centers, and there exists more potential to mitigate it’s intermittency through aggregation. This study forms a necessary precursor to the analysis of the impact of large-scale circulations and oscillations on the wind resource at the mesoscale.     

A Population Estimate for Golden Eagles in the Western United States

ABSTRACT Researchers have suggested golden eagle (Aquila chrysaetos) populations may be declining in portions of their range. However, there are few baseline data describing golden eagle populations across their range in the western United States. We used aerial line transect distance methodology with a double-observer modification to estimate golden eagle population numbers in 4 bird conservation regions of the western United States. We conducted surveys from 16 August to 8 September 2003, after most golden eagles had fledged and before fall migration. The goal of our sampling strategy was to provide >80% power (α = 0.1) to detect an annual rate of total population change >3% per year over a 20-year period. We observed 172 golden eagles across 148 transects and estimated 27,392 golden eagles (90% CI: 21,352-35,140) occurred in the study area during the late summer and early fall of 2003. Following the surveys, we used Monte Carlo simulation to determine the statistical power to detect trends in the golden eagle populations if yearly surveys were continued over a 20-year monitoring period. The simulation indicated the desired power could be achieved under the current methodology and sample size. The methods utilized in this study can be implemented for other raptor species when population estimates that include nonbreeding members of a population are needed. The results of this study can be utilized by professionals to help manage golden eagle populations and to develop conservation strategies.     

基于MAXENT和ZONATION的加拿大一枝黄花入侵重点监控区确定

外来入侵植物对本地生态系统及其生物多样性构成严重的威胁,要有效地控制外来植物入侵,首先应该明确植物入侵的高度风险区.以加拿大一枝黄花(Solidago canadensis)为对象,以其广泛发生的安徽、江苏、浙江和上海华东3省1市为研究区域,综合考虑了土地利用变化、人类活动干扰、土壤性质、气候和地形等影响因子,采用MAXENT模型预测其潜在分布及其对主要影响因子的响应,并结合空间优化软件ZONATION识别出需要重点布控的入侵风险区。结果表明:1)影响加拿大一枝黄花分布的主要环境因子及其百分比贡献率分别为:距主要道路距离(29.4%)、土地利用变化(16.9%)、降水的季节性变异(15.9%)、人口密度(9.5%)与最干季均温(6.2%)。2)从影响因子的响应曲线分析得出,加拿大一枝黄花的发生概率随着距主要道路距离的增大而迅速减小;在耕地转化成的城乡居民点及工矿用地、水域转化成的草地、城乡居民点及工矿用地转化成的林地、草地与城乡居民点及工矿用地相互转换频繁的区域和城乡居民点及工矿用地保持不变的区域,其发生概率明显较高;其发生概率随着降水季节性变异的增大而快速减小至0.4,之后缓慢减小;随着人口密度的增大,其发生概率起初急剧升高,人口密度超过4千人/km~2后又缓慢地小幅下降;随着最干季均温的增大,其发生概率逐渐减小,在2.4℃附近达最小,之后逐渐增大。3)加拿大一枝黄花的入侵风险区面积为130433 km~2。其中,一级风险区主要分布在太湖流域、沿杭州湾地区、浙江沿海以及内陆地势较低的耕地及居民点区域;二级风险区主要分布在一级风险区的外缘,尤其是江苏南部的长江沿岸地区。三级风险区则广泛分布在江苏的南部和东部,安徽的中东部,浙江的北部和东部。     

Bottom‐up processes drive reproductive success in an apex predator

One of the central goals of the field of population ecology is to identify the drivers of population dynamics, particularly in the context of predator–prey relationships. Understanding the relative role of top‐down versus bottom‐up drivers is of particular interest in understanding ecosystem dynamics. Our goal was to explore predator–prey relationships in a boreal ecosystem in interior Alaska through the use of multispecies long‐term monitoring data. We used 29 years of field data and a dynamic multistate site occupancy modeling approach to explore the trophic relationships between an apex predator, the golden eagle, and cyclic populations of the two primary prey species available to eagles early in the breeding season, snowshoe hare and willow ptarmigan. We found that golden eagle reproductive success was reliant on prey numbers, but also responded prior to changes in the phase of the snowshoe hare population cycle and failed to respond to variation in hare cycle amplitude. There was no lagged response to ptarmigan populations, and ptarmigan populations recovered quickly from the low phase. Together, these results suggested that eagle reproduction is largely driven by bottom‐up processes, with little evidence of top‐down control of either ptarmigan or hare populations. Although the relationship between golden eagle reproductive success and prey abundance had been previously established, here we established prey populations are likely driving eagle dynamics through bottom‐up processes. The key to this insight was our focus on golden eagle reproductive parameters rather than overall abundance. Although our inference is limited to the golden eagle–hare–ptarmigan relationships we studied, our results suggest caution in interpreting predator–prey abundance patterns among other species as strong evidence for top‐down control.     

Induced changes in island fox (Urocyon littoralis) activity do not mitigate the extinction threat posed by a novel predator

Prey response to novel predators influences the impacts on prey populations of introduced predators, bio-control efforts, and predator range expansion. Predicting the impacts of novel predators on native prey requires an understanding of both predator avoidance strategies and their potential to reduce predation risk. We examine the response of island foxes (Urocyon littoralis) to invasion by golden eagles (Aquila chrysaetos). Foxes reduced daytime activity and increased night time activity relative to eagle-na?ve foxes. Individual foxes reverted toward diurnal tendencies following eagle removal efforts. We quantified the potential population impact of reduced diurnality by modeling island fox population dynamics. Our model predicted an annual population decline similar to what was observed following golden eagle invasion and predicted that the observed 11% reduction in daytime activity would not reduce predation risk sufficiently to reduce extinction risk. The limited effect of this behaviorally plastic predator avoidance strategy highlights the importance of linking behavioral change to population dynamics for predicting the impact of novel predators on resident prey populations.     

Multiplex PCR amplification of 13 microsatellite loci for <Emphasis Type="Italic">Aquila chrysaetos</Emphasis> in forensic applications

The golden eagle (Aquila chrysaetos) is an endangered raptor, which is threatened mainly by illegal egg and nestling robbery. Here we describe a fluorescently labeled, multiplex PCR method using 13 microsatellite markers, which provides a powerful tool for the individual identification and parentage testing of the Golden eagle. This test should be applicable to both forensic analysis and population studies. Fifteen polymorphic loci from A. chrysaetos were cross-amplified. Subsequent PCR condition optimization led to the successful co-amplification of 13 different loci in a single PCR reaction. Fifty samples from wild-living individuals and 89 samples from captive-bred individuals were examined. The results indicated that both populations have similar levels of moderate inbreeding, unsurprising in a small population. This probability of excluding a random individual in parentage analysis was 0.9912 for the wild population and 0.9932 in the captive-bred one in the case that both the individual and its mother were examined together. The probability of identity was estimated to be 3 × 10⁻⁸ for the wild and 4 × 10⁻⁸ for the captive-bred populations. Given the size of the Slovak golden eagle population, this test should therefore be sufficient to reliably identify individual raptors and assess parentage in both conservation studies and forensic analysis.     

Regional-scale modelling of the cumulative impact of wind farms on bats

Wind farms are steadily growing across Europe, with potentially detrimental effects on wildlife. Indeed, cumulative impacts in addition to local effects should be considered when planning wind farm development at a regional scale, and mapping the potential risk to bats at this scale would help in the large-scale planning of wind turbines and focus field surveys on vulnerable areas. Although modelling offers a powerful approach to tackle this goal, its application has been thus far neglected. We developed a simple regional-scale analysis in an area of central Italy (Molise region) that is undergoing considerable wind farm development. We implemented species distribution models (SDMs) for two bat species vulnerable to wind farm impact, Nyctalus leisleri and Pipistrellus pipistrellus. We developed risk maps by overlaying SDMs for the two species with turbine locations, assessed the alteration of the landscape patterns of foraging habitat patches determined by the wind turbines, and identified highly vulnerable areas where wind farm construction would be particularly risky. SDMs were statistically robust (AUC ≥0.8 for both species) and revealed that 41 % of the region offers suitable foraging habitat for both species. These areas host over 50 % of the existing or planned wind farms, with 21 % of the turbines located within 150 m of forest edges, suggesting an increase in fatality risk. The alterations in suitable foraging patches consisted of a 7.7 % increase in the number of patches, a 10.7 % increase in the shape index, and a 8.1 % decrease in the mean patch area. The region’s western portion, which is most suitable to both species, requires careful consideration with regard to future wind farm planning.     

Collision risk and micro-avoidance rates of birds with wind turbines in Flanders

Capsule Local factors can lead to strong variation in mortality rate and collision risk that obscures possible effects of turbine size in wind farms.

Aims The impact of bird collisions was studied at eight land-based wind farm sites with a total of 66 small to large turbines in order to assess the mortality rate and collision risk.

Methods Searches for collision fatalities were performed under all turbines with a minimum search interval of 14 days. Mortality rate was calculated with corrections for available search area, scavenging and search efficiency. Flight movements of birds crossing five of the wind farm sites were recorded during a minimum of four days per site. Actual collision risk was then calculated as the number of collision fatalities relative to the average surveyed flight intensity.

Results Mortality rate was 21 birds per turbine per year on average. Most fatalities were local common species (e.g. gulls) but rarer species were also found (e.g. terns, raptors and waders). Collision risk of gulls was 0.05% and 0.08% on average for birds, respectively, flying at turbine and rotor height through the wind farms (0.09% and 0.14% maximum). Large gulls had a significant higher collision risk than small gulls at rotor height. Mortality rate and collision risk were not significantly related to turbine size. The results were integrated in a widely used collision risk model to obtain information of micro-avoidance, i.e. the proportion of birds that fly through the wind farm but avoid passing through the rotor swept area of the turbines. For gulls, this micro-avoidance was 96.1% and 96.3% on average for birds, respectively, flying at turbine and rotor height through the wind farms.

Conclusion The results indicate that local factors can lead to strong variation in mortality rate and collision risk that obscures possible effects of turbine size in wind farms. However, large turbines have more installed capacity (MW), so repowering wind farms with larger but fewer wind turbines, could reduce total mortality at certain locations.     

Wind turbine fatalities approach a level of concern in a raptor population

Mortality from collisions with increasing numbers of wind turbines is a potential hazard to raptor populations, but the actual effects on a population scale have rarely been studied based on field data. We estimated annual collision numbers for Red Kites Milvus milvus in the German federal state of Brandenburg (29,483 km²). A hierarchical model considering carcass persistence rate, searcher efficiency and the probability that a killed animal falls into a searched area was applied to results of carcass searches at 617 turbines. Collision risk varied significantly with season. The model estimated 308 (95% CrI 159–488) Red Kite fatalities at 3044 turbines operating during 2012, representing 3.1% of the estimated post-breeding population of 9972 individuals. Using the potential biological removal (PBR) method, mortality thresholds of 4.0% were obtained for migratory Red Kite populations. This level of mortality may be reached when turbine numbers increase within a few years. Since wind turbine collisions may affect Red Kites throughout the global range, a more detailed assessment of the actual impacts on populations is needed, especially because the PBR does not account for the predominance of adult birds among the collision victims.     

Golden eagle use of winter roadkill and response to vehicles in the western United States

Vehicle collisions are a significant source of wildlife mortality worldwide, but less attention has been given to secondary mortality of roadkill scavengers, such as the golden eagle (Aquila chrysaetos). We sought to quantify golden eagle winter use of roadkill mammal carcasses and eagle flushing from vehicles in Oregon, Utah, and Wyoming, USA, as proxies for strike risk, using motion-sensitive cameras. We monitored 160 carcasses and captured 2,146 eagle–vehicle interactions at 58 carcasses (1–240 observations/carcass) during winters of 2016–2017, 2017–2018, and 2018–2019. We used generalized linear mixed models, which suggested that eagle use of carcasses declined with time since camera deployment but increased with distance to road. Flushing from vehicles decreased with carcass distance to road but was higher in the morning, in response to larger vehicles and vehicles in the closest lane, and in the Oregon study area. We suggest that roadkill distance to road is the easiest factor to manipulate with the dual benefits of increasing food availability to golden eagles and decreasing flush-related vehicle strike risk. We recommend that roadkill be moved at least 12 m from the road to increase eagle use and decrease flushing 4-fold relative to behavior observed at the road edge. Because flushing from roadkill is believed to be the primary cause of eagle–vehicle strikes, informed roadkill management has the potential to reduce human-caused mortality of golden eagles.     

Genetic characteristics of endangered Japanese golden eagles (Aquila chrysaetos japonica) based on mitochondrial DNA D-loop sequences and karyotypes

Mitochondrial DNA D-loop sequences (472 bases) for endangered Japanese golden eagles (Aquila chrysaetos japonica) were investigated to evaluate-intrapopulational genetic variations. Among 23 golden eagles, including origin-known eagles caught in the wild and origin-unknown eagles, 10 variable sites were found in the 472 base-sequences. From the nucleotide substitutions, five haplotypes of D-loop sequences were identified, indicating the occurrence of at least three maternal lineages in golden eagles around Japan. Distribution patterns of D-loop haplotypes suggested a wide genetic communication between local populations around Japan prior to a recent habitat fragmentation and a decrease in the population size. In addition, cytogenetic analysis showed that a karyotype specific to the Japanese golden eagle is consistently 2n=62 including eight microchromosomes. Based on mitochondrial DNA and karyotype data, it is likely that golden eagle populations from Japan and the Korean Peninsula together form a common conservation unit. These results provide an important framework for conservation actions for Japanese golden eagle populations in zoos, and in situ reintroduction and translocation programs. Zoo Biol 17:111–121, 1998. © 1998 Wiley-Liss, Inc.