Geology topics

Manuela Huso

Publications and source records attributed to Manuela Huso.

12 recordsLinked to original sources

USGS2025

Ultrasonic deterrents provide no additional benefit over curtailment in reducing bat fatalities at an Ohio wind energy facility

Wind energy is important for achieving net-zero greenhouse gas emissions but also contributes to global bat mortality. Current strategies to minimize bat mortality due to collision with wind-turbine blades fall broadly into two categories: curtailment (limiting turbine operation during high-risk periods) and deterrence (discouraging bat activity near turbines). Recently, there has been interest in combining these strategies to achieve greater reductions in bat fatalities than either strategy might achieve in isolation. To investigate the effectiveness of combining curtailment with ultrasonic deterrent minimization strategies, we deployed six ultrasonic deterrents at nacelle height on 16 experimental turbines at Avangrid Renewables’ Blue Creek Wind Energy Facility. We rotated between four conditions (normal operations, curtailment only, deterrent only, curtailment and deterrent) randomly assigned to four wind turbines each night between 15 June and 3 October 2017. We found that bat mortality at wind turbines was independent of wind speed. The effectiveness of ultrasonic acoustic deterrents varied between high-frequency-calling species (eastern red bats) and low-frequency-calling species (hoary bats, silver-haired bats, and big brown bats). When deterrents were active, mortality was twice as high for eastern red bats compared to the control. Conversely, deterrents had a weak dampening effect on bat mortality for low-frequency species. We found no additive effects on mortality reduction for turbines operating both curtailment and deterrents compared to either approach in isolation. Our findings suggest that ultrasonic acoustic deterrents may not be effective for both high and low frequency echolocating bats. The increase in fatalities of eastern red bats is alarming and underscores the importance of considering site- and species-specific effects of minimization solutions.

Ohio↗

USGS2024

A decade of curtailment studies demonstrates a consistent and effective strategy to reduce bat fatalities at wind turbines in North America

There is a rapid, global push for wind energy installation. However, large numbers of bats are killed by turbines each year, raising concerns about the impacts of wind energy expansion on bat populations. Preventing turbine blades from spinning at low wind speeds, referred to as curtailment, is a method to reduce bat fatalities, but drawing consistent inference across studies has been challenging. We compiled publicly available studies that evaluated curtailment at six wind energy facilities in North America across 10 years. We used meta-regression of 29 implemented treatments to determine fatality reduction efficacy as well as sources of variation influencing efficacy. We also estimated species-specific fatality reduction for three species that comprise most fatalities in North America: hoary bat ( Lasiurus cinereus ), eastern red bat ( Lasiurus borealis ) and silver-haired bat ( Lasionycteris noctivagans ). We found that curtailment reduced total bat fatalities by 33% with every 1.0 ms −1 increase in curtailment wind speed. Estimates of the efficacy for the three target species were similar (hoary bats: 28% per ms −1 , 95% CI: 0.4%–48%, eastern red bats: 32% per ms −1 , 95% CI: 13%–47% and silver-haired bats: 32% per ms −1 , 95% CI: 3%–53%). Across multiple facilities and years, a 5.0 ms −1 cut-in speed was estimated to reduce total bat fatalities by an average of 62% (95% CI: 54%–69%). Mortality reductions at individual facilities in any given year were estimated to fall between 33%–79% (95% prediction interval). Inter-annual differences rather than inter-site or turbine characteristics accounted for most of the variation in efficacy rates. Species-specific average mortality reduction at 5.0 ms −1 curtailment wind speed was 48% (95% CI: 24%–64%) for hoary bats, 61% (95% CI: 42%–74%) for eastern red bats and 52% (95% CI: 30%–66%) for silver-haired bats. Practical implication . curtailment reduced bat mortality at wind turbines in this North American study. Efficacy increased proportionally as curtailment speed is raised, and patterns and rates of efficacy were similar across species. This indicates that curtailment is an effective strategy to reduce bat fatalities at wind energy facilities, but exploration of further refinements could both minimize bat mortality and maximize energy production.

Ecological Solutions and Evidence↗

USGS2024

Accounting for the fraction of carcasses outside the searched area in the estimation of bird and bat fatalities at wind energy facilities

Accurate estimation of bird and bat mortality at wind energy facilities requires accounting for carcasses that lie outside the search plots because they lie beyond the search radius or in areas within the search radius that remain unsearched due to sub-optimal search conditions such as thick vegetation, rough or dangerous ground, water, or restricted access to the land. However, carcass density is not constant around a turbine and the fraction of carcasses within the unsearched area can vary greatly depending on where the area lies relative to the turbine. The density-weighted proportion approach takes into account the changing density of carcasses around turbines to estimate the fraction of carcasses lying in unsearched areas (dwp). It involves tallying the carcasses found in concentric rings centered at the turbine, fitting a curve to the carcass densities in the rings, and dividing the integral of the curve over the area searched by the integral over the total area. Accounting for unsearched area presents special difficulties such as extrapolation beyond the search radius, spatial prediction, and model selection, which are frequently ignored or under-appreciated, potentially resulting in substantial estimation errors. A powerful new R software package (dwp) is available to perform the calculations, given the distances at which carcasses were found from turbines and a map of the searched area used to discern the fraction of the ground searched at each distance. If all ground within a given search radius has been searched, the map is simply the search radius. For more complicated search plots, other kinds of maps may be used: R polygons for plots that can be readily delineated into searched and not-searched areas (for example, searches restricted to access roads and turbine pads), GIS shape files for complicated search patterns (for example, non-uniform vegetation or ground texture resulting in spatially varying search conditions), or raster files for complicated search patterns coupled with carcass spatial distribution that depends on both distance and direction from turbines. This study discusses estimation and interpretation of dwp in the context of several realistic examples; provides guidance for use of the dwp software for doing the analyses; and addresses questions of extrapolation, spatial prediction, and model selection.

Techniques and Methods↗

USGS2023

Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines

Unintended consequences of renewable energy development include collision-caused deaths of birds and bats. Energy companies may risk prosecution if protected species are among the casualties. Shutting down turbines during high collision-risk conditions could reduce mortality rates, and several companies are developing systems to identify such conditions. A recent peer-reviewed article published in the Journal of Applied Ecology reported a remarkable ‘82% (75%–89%) reduction in the fatality rate’ of eagles at a wind energy facility due to a device marketed as Identiflight®—remarkable because of the impressive effect size and the extremely high level of precision. We show that reported results stem from four major errors, which, when corrected, give an unremarkable estimate of 50% (−159%, 89%) reduction (or possible increase) in the fatality rate. The errors include the following: (i) Ignoring annual variation . They compare the average number of eagle fatalities over 4 years before activation of Identiflight® to the number in a single year after, ignoring annual variation in fatalities. (ii) Unfounded causal inference . Lack of replication (one treatment year at one site) is ignored, leading to unwarranted causal inference. (iii) Inflated effect size . Effect size is inflated by assuming (without providing evidence) that the difference in fatality relative to the mean at a neighbouring site would be exactly repeated at the treatment site. Furthermore, the observed difference in fatalities at the control site depends strongly on the arbitrarily chosen date distinguishing the ‘Before’ and ‘After’ periods, yielding unreliable results. (iv) Inconsistency of data . It is unclear why 7 of 42 reported eagle fatalities were not included in the data analyzed, potentially further inflating the estimated effect size. Synthesis and applications . The recent claim, published in the Journal of Applied Ecology, that ‘Eagle fatalities are reduced by automated curtailment of wind turbines’ is not supported by the data but stems from errors that led to strongly overstated effect size and precision, and unfounded inference. In theory, automated curtailment has obvious potential for reducing eagle fatalities, but several more years of data at several locations and appropriate statistical analyses will be required to evaluate its effectiveness and to inform management prescriptions involving this technology.

Journal of Applied Ecology↗

USGS2023

Modeling the spatial distribution of carcasses of eagles killed by wind turbines

Currently, the US Fish and Wildlife Service makes eagle permitting and management decisions nationwide based on a limited understanding of the impacts of wind power generation on eagles, and the factors that influence risk at a given facility. Accurate estimates of eagle mortality at wind power facilities form the basis for comparing the magnitudes of mortality rates in different areas and for measuring the benefits of proposed methods of minimizing the collision-caused impacts to eagle populations. Simple counts of observed eagle carcasses at wind facilities are almost certainly underestimates of the true mortality because fatalities can be removed by scavengers, be missed by searchers, or fall outside searched areas. For the latter, models of relative carcass density as a function of distance from the turbine can be fit to observed carcass locations and used to estimate the proportion of carcasses expected to land within an area of any configuration beneath a turbine. In the USA, however, it has been difficult to estimate these models for large birds such as Bald Eagles ( Haliaeetus leucocephalus ) and Golden Eagles ( Aquila chrysaetos ) due to inadequate numbers of dead eagles found at any single facility. In this case, analysis of a surrogate species might be useful to inform carcass distributions. We chose to model the carcass distribution of White-tailed Eagles ( Haliaeetus albicilla ) in Norway as an informative surrogate for Bald Eagles and Golden Eagles in the USA. Our three best-fitting parametric models were very consistent in estimating that 50% (95% CI: 40–60%) of White-tailed Eagle carcasses land within approximately 42 m of the turbines that had 70-m hubs and approximately 40-m blades. Although our models were fit to data from White-tailed Eagles and not Bald or Golden Eagles, applying these models when calculating mortality impacts of wind developments on both eagle species will likely improve the accuracy of post-construction mortality estimates, particularly at sites where substantial areas may be unsearchable. Accurate post-construction mortality estimates can inform pre-construction fatality prediction models. Resource managers can determine whether their conditions are sufficiently similar to those we modeled to warrant the use of these models for Bald and Golden Eagle carcass distributions.

Journal of Raptor Research↗

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

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↗

USGS2021

Relative energy production determines effect of repowering on wildlife mortality at wind energy facilities

Reduction in wildlife mortality is often cited as a potential advantage to repowering wind facilities, that is, replacing smaller, lower capacity, closely spaced turbines, with larger, higher capacity ones, more widely spaced. Wildlife mortality rates, however, are affected by more than just size and spacing of turbines, varying with turbine operation, seasonal and daily weather and habitat, all of which can confound our ability to accurately measure the effect of repowering on wildlife mortality rates. We investigated the effect of repowering on wildlife mortality rates in a study conducted near Palm Springs, CA. We controlled for confounding effects of weather and habitat by measuring turbine‐caused wildlife mortality rates over a range of turbine sizes and spacing, all within the same time period, habitat and local weather conditions. We controlled for differences in turbine operation by standardizing mortality rate per unit energy produced. We found that avian and bat mortality rate was constant per unit of energy produced, across all sizes and spacings of turbines. Synthesis and applications . In the context of repowering a wind facility, our results suggest that the relative amount of energy produced, rather than simply the size, spacing or nameplate capacity of the replacement turbines, determines the relative rate of mortality prior to and after repowering. Consequently, in a given location, newer turbines would be expected to be less harmful to wildlife only if they produced less energy than the older models they replace. The implications are far‐reaching as 18% of US and 8% of world‐wide wind power capacity will likely be considered for repowering within ~5 years.

California↗

USGS2021

Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators

The impacts of wind power development on bat and bird populations are commonly assessed by estimating the number of fatalities at wind power facilities through post-construction monitoring (PCM) studies. Standard methodology involves periodic carcass searches on plots beneath turbines (Strickland et al. 2011, US Fish and Wildlife Service 2012). The resulting counts are adjusted to compensate for bias due to imperfect carcass detection by searchers, removal of carcasses by scavengers or other processes (Korner-Nievergelt et al. 2011), and carcasses that may have fallen outside of searched areas. To account for the bias in counts due to imperfect detection and carcass removal, investigators typically conduct bias trial experiments to inform models of carcass detection probability. Many different estimators have been proposed that combine information about the bias trial experiments to estimate a detection probability for carcasses (g) and ultimately obtain an estimate of total mortality (M). The two estimators that have seen the most widespread use in North America recently are the Huso (Huso 2011, Huso et al. 2012) and Shoenfeld (Shoenfeld 2004; also called the Erickson estimator) estimators. GenEst (Dalthorp et al. 2018a, 2018b, 2018c) is the newest statistical estimator to become available and was designed to improve upon the Huso and Shoenfeld estimators by generalizing the key assumptions in both, and to improve comparability among new PCM studies. In addition to relaxing some of the assumptions inherent to the Huso and Shoenfeld estimators, GenEst uses a parametric bootstrap applied to a novel approach to variance estimation (Madsen et al. 2019). The current study was undertaken to document the performance of GenEst relative to the Huso and Shoenfeld estimators. We took a simulation approach to the study because simulation data provides the basis to compare mortality estimators under conditions where the “truth” is known. The estimators were compared on three metrics: 1) bias—the tendency of an estimator to over- or under-estimate actual mortality, 2) precision—the ability of an estimator to constrain an estimate to a narrow range (measured here as the width of a 90% confidence interval [CI] around the point estimate divided by the true, known mortality), and 3) CI coverage—the probability a CI with a specified level of confidence actually includes the true level of mortality. Although our simulations were conceived and designed—and are discussed—with respect to wind power facilities, it is important to note that the estimators and results discussed here are relevant to any post-construction fatality monitoring study that may occur (such as at solar facilities) where detection is imperfect. Although our study treats the problem of mortality estimation when detection is imperfect, it is also important to note that all of the estimators considered here are Horvitz-Thompson (Horvitz and Thompson 1952) style estimators, that is, none are designed to estimate the mortality of rare species as might be necessary under an Incidental Take Permit. The Evidence of Absence estimator (Dalthorp et al. 2017) is still the most appropriate statistical tool for rare event estimation. The simulations cover a broad range of conditions that may occur in field studies and complete results are presented without commentary in the appendix. The main body of this report does not provide a comprehensive treatment of our results; rather, we try to identify some of the more important differences among the estimators and some conditions under which reliable mortality estimates are especially challenging.

AWWI Technical Report↗

USGS2020

Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol

Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance, and ideally also for variation with direction (anisotropy). We compare five methods of accounting for carcasses that land outside the searched area (ratio, weighted distribution, non-parametric, and two generalized linear models ( glm )) by simulating spatial arrival patterns and the detection process to mimic observations which result from surveying only, or primarily, roads and pads (R&P) and applying the five methods. Simulations vary R&P configurations, spatial carcass distributions (isotropic and anisotropic), and per turbine fatality rates. Our results suggest that the ratio method is less accurate with higher variation relative to the other four methods which all perform similarly under isotropy. All methods were biased under anisotropy; however, including direction covariates in the glm method substantially reduced bias. In addition to comparing methods of accounting for unsearched areas, we suggest a semiparametric bootstrap to produce confidence-based bounds for the proportion of carcasses that land in the searched area.

Environmental and Ecological Statistics↗

USGS2020

Some approaches to accounting for incidental carcass discoveries in non-monitored years using the Evidence of Absence model

Executive Summary We evaluate three approaches to accounting for incidental carcasses when estimating an upper bound on total mortality (𝑀) as 𝑀 ∗ using the Evidence of Absence model (EoA; Dalthorp and others, 2017) to assess compliance with an Incidental Take Permit (ITP) (Dalthorp & Huso, 2015) under a monitoring protocol that includes formal, dedicated carcass surveys that achieve an overall detection probability of 𝑔 𝑠 =0.15 in the first year, followed by 4 years with no formal monitoring but with carcasses potentially discovered incidentally by operations and maintenance crews in their normal course of activity or otherwise discovered outside the formal searches. We refer to carcasses discovered incidentally as “incidentals” and define 𝑥 𝑖 as the count of incidentals. Similarly, we define 𝑥 𝑠 as the number of carcasses found during the formal searches conducted the first year.

Open-File Report↗

USGS2019

Estimating population size with imperfect detection using a parametric bootstrap

We develop a novel method of estimating population size from imperfectly detected counts of individuals and a separate estimate of detection probability. Observed counts are separated into classes within which detection probability is assumed constant. Within a detection class, counts are modeled as a single binomial observation X with success probability p where the goal is to estimate index N . We use a Horvitz–Thompson‐like estimator for N and account for uncertainty in both sample data and estimated success probability via a parametric bootstrap. Unlike capture–recapture methods, our model does not require repeated sampling of the population. Our method is able to achieve good results, even with small X . We show in a factorial simulation study that the median of the bootstrapped sample has small bias relative to N and that coverage probabilities of confidence intervals for N are near nominal under a wide array of scenarios. Our methodology begins to break down when P ( X =0)>0.1 but is still capable of obtaining reasonable confidence coverage. We illustrate the proposed technique by estimating (1) the size of a moose population in Alaska and (2) the number of bat fatalities at a wind power facility, both from samples with imperfect detection probabilities, estimated independently.

Environmetrics↗