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Geology topics

Tricia A. Miller

Publications and source records attributed to Tricia A. Miller.

At least 19 recordsLinked to original sources
USGS2024

Efficacy of non-lead ammunition distribution programs to offset fatalities of golden eagles in southeast Wyoming

Golden eagles ( Aquila chrysaetos ) face many anthropogenic risks including illegal shooting, electrocution, collision with wind turbines and vehicles, and lead poisoning. Minimizing or offsetting eagle deaths resulting from human-caused sources is often viewed as an important management objective. Despite understanding the leading anthropogenic sources of eagle fatalities, existing scientific research supports few practical solutions to mitigate these causes of death. We implemented a non-lead ammunition distribution program in southeast Wyoming, USA, and evaluated its effectiveness as a compensatory mitigation action to offset incidental take (i.e., fatalities) of golden eagles at wind energy facilities. In 2020 and 2022, we distributed non-lead ammunition to 699 hunters with big-game tags specific to our >400,000-ha study area. These hunters harvested 296 pronghorn ( Antilocapra americana ), 14 deer ( Odocoileus spp.), and 33 elk ( Cervus canadensis ) in the study area, which accounted for 6.9% and 6.5% of the harvest in these hunt units in 2020 and 2022, respectively. We used road surveys in 2020 to estimate a density of 0.036 (95% CI = 0.018–0.058) golden eagles/km 2 during the big game hunting season in our study area. Model output suggests that our non-lead ammunition distribution program offset the fatality of 3.84 (95% CI = 1.06–23.72) eagles over the course of these 2 hunting seasons. Our work illustrates the potential usefulness of non-lead ammunition distribution programs as an action to mitigate eagle fatalities caused by wind facilities or other anthropogenic causes of death.

Wyoming↗
USGS2024

Key breeding habitats of threatened golden eagles across Eastern Canada identified using a multi-level, multi-scale habitat selection approach

Context In a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions. Objectives The objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles ( Aquila chrysaetos ). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection). Methods Using tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km 2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection. Results Eagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles. Conclusion Here we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.

Landscape Ecology↗
USGS2023

Variation in flight characteristics associated with entry by eagles into rotor-swept zones of wind turbines

Automated curtailment of wind turbines can reduce fatality rates of wildlife, but the resulting increased number of curtailments can reduce power generation. Tailoring curtailment criteria for each individual turbine could reduce unnecessary curtailment, yet it is unknown whether the risk to wildlife varies among turbines. We demonstrate turbine-specific variation in the speed, altitude, approach angle, and distance metrics associated with entry by eagles into rotor-swept zones. Our results thus illustrate the potential value of turbine-specific curtailment criteria to reduce fatality rates of wildlife at wind energy facilities.

Ibis↗
USGS2022

A review of supervised learning methods for classifying animal behavioural states from environmental features

Accurately predicting behavioural modes of animals in response to environmental features is important for ecology and conservation. Supervised learning (SL) methods are increasingly common in animal movement ecology for classifying behavioural modes. However, few examples exist of applying SL to classify polytomous animal behaviour from environmental features especially in the context of millions of animal observations. We review SL methods (weighted k -nearest neighbours; neural nets; random forests; and boosted classification trees with XGBoost) for classifying polytomous animal behaviour from environmental predictors. We also describe tuning parameter selection and assessment strategies, approaches for visualizing relationships between predictors and class outputs, and computational considerations. We demonstrate these methods by predicting three categories of risk to bald eagles from colliding with wind turbines using, as predictors, 12 environmental state features associated with 1.7 million GPS telemetry data points from 57 eagles. Of the SL methods we considered, XGBoost yielded the most accurate model with 86.2% classification accuracy and pairwise-averaged area under the ROC curve of 90.6. Computational time of XGBoost scaled better to large data than any other SL method. We also show how SHAP values integrated in the R package ( xgboost ) facilitate investigation of variable relationships and importance. For big data applications, XGBoost appears to provide superior classification accuracy and computational efficiency. Our results suggest XGBoost should be considered as an early modelling option in situations where the intent is to classify millions of animal behaviour observations from environmental predictors and to understand relationships between those predictors and movement behaviours. We also offer a tutorial to assist researchers in implementing this method.

Methods in Ecology and Evolution↗
USGS2022

Interannual consistency of migration phenology is season- and breeding region-specific in North American Golden Eagles

Interannual consistency (an indicator of the strength of adjustments) in migration phenology of Golden Eagles ( Aquila chrysaetos ) in North America is most strongly associated with the breeding region, the season, and with late-season temperature on breeding and wintering grounds. Consistency was greatest in boreal spring migration and the breeding regions of eastern Canada. Using multi-year GPS tracks of 83 adults breeding in 3 spatially distant regions (Alaska, northeast Canada, and southeast Canada), we quantified the interannual consistency of migration phenology and wintering latitude within and among individuals tracked across multiple years and the repeatability ( r ) by breeding regions and seasons. By comparing regions and seasons, we found that consistency was highest ( r > 0.85) for boreal spring migration in eastern Canada while Alaska had the lowest value ( r < 0.15). Because seasonal consistency of migration phenology was only detected in eastern Canada, we conclude that seasonal features are not a primary constraint. While regional differences in consistency were not related to differences in migratory distances, they could be the result of genetic or habitat differences. We also found that temperatures warmer than the decadal average at the region of departure delayed the start of boreal spring migration by ~10 days and advanced boreal autumn migration by ~20 days. These results suggest that warmer temperatures would reduce residence time on breeding grounds, contrary to expectations and trends found in other studies. Wide variations in migratory strategies across a species distribution can add to the list of challenges for conservation but may give migrants the capacity to acclimate to environmental changes.

Ornithology↗
USGS2022

Flight characteristics forecast entry by eagles into rotor-swept zones of wind turbines

Operators of wind power facilities can mitigate wildlife mortality by slowing or stopping wind turbines (hereafter ‘curtail’) when birds are at an increased risk of collision. Some facility operators curtail when individual birds have flight characteristics (e.g. altitude, distance or relative bearing of a bird's flight path) that exceed some threshold value, but thresholds currently in use have not been empirically evaluated. Overly restrictive thresholds can cause turbine curtailment for birds that never enter rotor-swept zones, thereby resulting in excess power loss. We evaluated the probability that birds, specifically eagles, entered the rotor-swept zone (hereafter ‘entry probability’) in response to their flight characteristics. We used an automated monitoring system to classify individuals as eagles or non-eagles and record flight paths of purported eagles at a wind facility in Wyoming, USA. We used logistic regression with occupancy dynamics and a distance-dependent colonization process to model entry probability. As a result, this model allowed entry probability to decrease with horizontal distance to the nearest turbine. The probability of entry varied with distance to the nearest turbine and approached zero when that distance was more than 202 m. Entry probability peaked when eagles flew 89 m above ground, corresponding to hub heights of turbines (80 m), and decreased to near-zero at altitudes of 189 m or more. Entry probabilities were greatest when flight paths were near the rotor-swept zone and when eagles flew slowly toward the nearest turbine. Compass bearing of a flight path was not associated with entry probability. Our model accurately forecasted entry probability in Wyoming (area under the curve (AUC) = 0.96) and was transferable to another facility in California, USA (AUC = 0.97); therefore, our results may be applicable across a variety of settings. Curtailment criteria can be based on flight path characteristics to forecast entry into rotor-swept zones. The use of distance and altitude thresholds when making curtailment decisions is justified. However, this analysis suggests alteration of the time to collision threshold, with curtailment initiated at greater distances as the speed of the bird decreases. Our novel modelling method and our results can inform curtailment criteria in any situation where curtailment decisions are made in real-time.

Wyoming↗
USGS2022

Classifying behavior from short-interval biologging data: An example with GPS tracking of birds

Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets. We apply a framework for using K -means clustering to classify bird behavior using points from short time interval GPS tracks. K -means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K -means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free-flying bald eagles ( Haliaeetus leucocephalus ) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior. After filtering for data quality, the K -means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight. The K -means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.

Ecology and Evolution↗
USGS2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles ( Aquila chrysaetos ) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale ( &lt; "> < 50 m) and wind conditions are known at facility scale ( &lt; "> < 3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio &gt; "> > 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

Wyoming↗
USGS2022

A flexible movement model for partially migrating species

We propose a flexible model for a partially migrating species, which we demonstrate using yearly paths for golden eagles ( Aquila chrysaetos ). Our model relies on a smoothly time-varying potential surface defined by a number of attractors. We compare our proposed approach using varying coefficients to a latent-state model, which we define differently for migrating, dispersing, and local individuals. While latent-state models are more common in the existing animal movement literature, varying coefficient models have various benefits including the ability to fit a wide range of movement strategies without the need for major model adjustments. We compare simulations from the models for three individuals to illustrate the ability of our model to better describe movement behavior for specific movement strategies. We also demonstrate the flexibility of our model by fitting several individuals whose movement behavior is less stereotypical.

Spatial Statistics↗
USGS2022

Demographic implications of lead poisoning for eagles across North America

Lead poisoning occurs worldwide in populations of predatory birds, but exposure rates and population impacts are known only from regional studies. We evaluated the lead exposure of 1210 bald and golden eagles from 38 US states across North America, including 620 live eagles. We detected unexpectedly high frequencies of lead poisoning of eagles, both chronic (46 to 47% of bald and golden eagles, as measured in bone) and acute (27 to 33% of bald eagles and 7 to 35% of golden eagles, as measured in liver, blood, and feathers). Frequency of lead poisoning was influenced by age and, for bald eagles, by region and season. Continent-wide demographic modeling suggests that poisoning at this level suppresses population growth rates for bald eagles by 3.8% (95% confidence interval: 2.5%, 5.4%) and for golden eagles by 0.8% (0.7%, 0.9%). Lead poisoning is an underappreciated but important constraint on continent-wide populations of these iconic protected species.

Science↗
USGS2022

Influence of anthropogenic subsidies on movements of common ravens

Anthropogenic subsidies can benefit populations of generalist predators such as common ravens (ravens; Corvus corax ), which in turn may depress populations of many types of species at lower-trophic levels, including desert tortoises (Gopherus agassizii ) or greater sage-grouse (Centrocercus urophasianus). Management of subsidized ravens often has targeted local breeding populations that are presumed to affect species of concern and ignored “urban” populations of ravens. However, little is known about how ravens move, especially in response to the presence of anthropogenic subsidies. Therefore, subsidized ravens from distant populations that are not managed may influence local prey. To better understand this issue, we deployed global positioning system – global system for mobile communications transmitters to track movements of 19 ravens from September to December 2020 relative to 2 land cover types that provide subsidies: developed areas and cultivated crops. On average, ravens moved 41.5 km (±30.5) per day, although daily movement distances ranged from 0.13– 206.1 km. Raven movement among cover types during the non-breeding season varied widely, with 100% of individuals each using land cover types that provide subsidy and other types at least once in the season. On 100% of days ravens used areas that did not provide subsidy, on 86.7% of days they used developed areas, and on 20.5% of days they used cultivated crops. Although on some days a raven would stay exclusively in areas that did not provide subsidy, there were no days in which a single raven ever stayed exclusively in developed or cultivated crops. Ravens moved shorter distances on days when they used subsidies more frequently. Further, time spent in developed areas and cultivated crops increased when ravens roosted closer to them, although this effect was greater for developed areas than for cultivated crops. Individual ravens were not associated exclusively with either of the subsidy-providing landscapes we considered, but instead all birds used both subsidized and other landscapes. Our research suggests that management of ravens during the non-breeding season and possibly during the breeding season, intended to reduce risk of predation on desert tortoises, will be most effective if conducted on a broad scale because of distances the birds travel and the lack of separation between putative “urban” and “natural” populations of ravens.

California↗
USGS2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k -fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

Ecography↗
USGS2021

Eagles enter rotor-swept zones of wind turbines at rates that vary per turbine

There is increasing pressure on wind energy facilities to manage or mitigate for wildlife collisions. However, little information exists regarding spatial and temporal variation in collision rates, meaning that mitigation is most often a blanket prescription. To address this knowledge gap, we evaluated variation among turbines and months in an aspect of collision risk—probability of entry by an eagle into a rotor-swept zone (hereafter, “probability of entry”). We examined 10,222 eagle flight paths identified and recorded by an automated bird monitoring system at a wind energy facility in Wyoming, USA. Probabilities of entry per turbine–month combination were 4.03 times greater in some months than others, ranging 0.15 to 0.62. The overall probability of entry for the riskiest turbine (i.e., the one with the greatest probability of entry) was 2.39 times greater than the least-risky turbine. Our methodology describes large variation across turbines and months in the probability of entry. If subsequently combined with information on other sources of variation (i.e., weather, topography), this approach can identify risky versus safe situations for eagles under which cost of management, curtailment prescriptions, and collision risk can be simultaneously minimized.

Wyoming↗
USGS2021

Golden Eagle

The Golden Eagle inhabits a wide range of latitudes and habitats throughout the Palearctic and into northern Africa, where it is largely resident. In North America, its breeding distribution includes most of Canada and Alaska, as well as the western half of the United States and northern and western Mexico. Most eagles that nest in northern Canada and interior and northern Alaska migrate thousands of kilometers to wintering grounds. Southern eagles tend to be resident year-round, but some make northward, latitudinal, or altitudinal migrations when not on territory. During the non-breeding season, Golden Eagle occurs in Mexico, every U.S. state, and in the southern parts of Canada. It is most common in western North America, especially near open spaces that provide hunting habitat with ample prey, near cliffs or trees that supply nesting sites, and topography that creates updrafts essential for flight. Recent research has shown that the Golden Eagle is more common than once thought in eastern North America as well as in forested areas continent-wide, and that young individuals may summer in large numbers in the vast and productive wetlands of northernmost North America.

Book chapter↗
USGS2021

Red-footed Falcon Falco vespertinus

Red-footed falcons (Falco vespertinus) are a small, long-distance and obligate migrant falcon that breeds at the forest-steppe interface in Eurasia and winters in Southern Africa. Research carried out with geolocators and satellite transmitters show that during the southbound migration Central Asian birds migrate through the Caucasus and the Middle East, while those from Eastern Europe cross the Eastern Mediterranean. Once in Africa, they cross the Sahara, the Sahel, where they perform short stopovers, and the Congo rainforest or the Rift valley, heading towards the wintering grounds. Northbound migration involves stopovers in West Africa and long crossings over the Mediterranean Sea. Both visual observations and tracking studies show that at least some populations of the species perform a clockwise loop migration that involves extensive longitudinal movements and. for some sub-populations, crossing the Central Mediterranean only during spring. Pre-breeding migration has a longer duration than post-breeding migration, probably because stopovers are longer and wind assistance lower. Red-footed falcons also face substantial anthropogenic threats along migration, particularly the risk of shooting at migratory bottlenecks.

Book chapter↗
USGS2020

Illegal killing of nongame wildlife and recreational shooting in conservation areas

Illegal killing of nongame wildlife is a global yet poorly documented problem. The prevalence and ecological consequences of illegal killing are often underestimated or completely unknown. We review the practice of legal recreational shooting and present data gathered from telemetry, surveys, and observations on its association with illegal killing of wildlife (birds and snakes) within conservation areas in Idaho, USA. In total, 33% of telemetered long‐billed curlews ( Numenius americanus ) and 59% of other bird carcasses found with known cause of death (or 32% of total) were illegally shot. Analysis of spatial distributions of illegal and legal shooting is consistent with birds being shot illegally in the course of otherwise legal recreational shooting, but snakes being intentionally sought out and targeted elsewhere, in locations where they congregate. Preliminary public surveys indicate that most recreational shooters find abhorrent the practice of illegal killing of wildlife. Viewed through this lens, our data may imply only a small fraction of recreational shooters is responsible for this activity. This study highlights a poorly known conservation problem that could have broad implications for some species and populations of wildlife.

Idaho↗
USGS2020

Use of upland and riparian areas by wintering bald eagles and implications for wind energy

Weather can shape movements of animals and alter their exposure to anthropogenic threats. Bald eagles ( Haliaeetus leucocephalus ) are increasingly at risk from collision with turbines used in onshore wind energy generation. In the midwestern United States, development of this energy source typically occurs in upland areas that bald eagles use only intermittently. Our objective was to determine the factors that cause wintering bald eagles to occupy riparian areas and riskier, upland areas. We tracked 20 bald eagles using telemetry in the Upper Midwest (MN, IA, MO, WI, IL, USA) during winter 2014–2015 and 2015–2016 and evaluated habitat use by eagles in response to variation in weather and time of year. Eagles used riparian areas more when wind speed and atmospheric pressure were low. Exclusive use of uplands was more frequent during weather systems with low pressure and high humidity and after long periods of cold weather. There was a non‐linear response to time of year (measured by days before migration) in the frequency of exclusive use of uplands or riparian areas. Probability of exclusive use of either landscape was generally constant within 95 days prior to migration. The probability of use of riparian areas, however, was markedly less during dates >100 days before migration. Our results suggest that eagles are most likely to be exposed to wind energy developments located in upland areas during low pressure systems, after long periods of cold weather, and several months before the onset of spring migration. This information helps to better understand the factors influencing bald eagle habitat use in winter and will be useful to managers and developers wishing to establish effective strategies to avoid, minimize, and mitigate take, and to survey for mortalities at wind energy developments.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗