I am a Research Statistician Emerita with the USGS Forest and Rangeland Ecosystem Science Center in Corvallis, OR.
I have a courtesy faculty appointment in the Department of Statistics at Oregon State University (OSU). Before coming to the USGS, I spent more than 20 years as a consulting statistician at OSU.
Education and Certifications
M.S., Statistics, Oregon State University, Corvallis, OR (1988)
M.S., Theoretical Ecology, University of Oregon, Corvallis, OR (1984)
B.A., Biology, Whitman College, Walla Walla, WA (1978)
Science and Products
Wind Energy and Wildlife Team (FRESC)
A Generalized Estimator for Estimating Bird and Bat Mortality at Renewable Energy Facilities - GenEst
Statistical Tools for Wind and Solar Energy Development and Operations
Wind Energy and Wildlife Team (FRESC)
Effects of Wind and Solar Energy Development on Wildlife
Applied Statistical Methods and Tools
If you are unable to access or download a product, email fresc_outreach@usgs.gov a request, including the full citation, or call (541) 750-1030.
San Gorgonio Pass Wind Resource Area Repower Data (2018-2019)
If you are unable to access or download a product, email fresc_outreach@usgs.gov a request, including the full citation, or call (541) 750-1030.
GenEst statistical models—A generalized estimator of mortality
Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines
Modeling the spatial distribution of carcasses of eagles killed by wind turbines
A review of supervised learning methods for classifying animal behavioural states from environmental features
Classifying behavior from short-interval biologging data: An example with GPS tracking of birds
Relative energy production determines effect of repowering on wildlife mortality at wind energy facilities
Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators
Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol
Some approaches to accounting for incidental carcass discoveries in non-monitored years using the Evidence of Absence model
Wind energy: An ecological challenge
Estimating population size with imperfect detection using a parametric bootstrap
Impacts to wildlife of wind energy siting and operation in the United States
Wildlife mortality at wind facilities: How we know what we know how we might mislead ourselves, and how we set our future course
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
If you are unable to access or download a product, email fresc_outreach@usgs.gov a request, including the full citation, or call (541) 750-1030.
Density-weighted proportion (dwp) tool suite
GenEst - A Generalized Estimator of Mortality
GenEst, a generalized estimator of wildlife mortality at renewable energy facilities.
Generalized Mortality Estimator (GenEst) - R code & GUI
Evidence of Absence (EoA) Software and User's Guide
Software to Estimate Bird and Bat Fatality at Wind Farms
Fatality Estimator Software
Tool to Evaluate Wildlife Fatalities at Wind-Power Facilities
To request an interview, contact fresc_outreach@usgs.gov or call (541) 750-1030.
Science and Products
- Science
Wind Energy and Wildlife Team (FRESC)
FRESC's Wind Energy and Wildlife Team is lead by Manuela Huso. She and her team are involved in design and analysis of post-construction fatality monitoring studies as well as deterrent and curtailment studies at several wind-power generation facilities.A Generalized Estimator for Estimating Bird and Bat Mortality at Renewable Energy Facilities - GenEst
GenEst - One estimator for accurate bird and bat fatality estimatesStatistical Tools for Wind and Solar Energy Development and Operations
Solar and wind power development is increasing exponentially in the United States. However, these energy sources may affect wildlife, either directly from collisions with the turbine blades or photovoltaic arrays or indirectly from loss of habitat and migration routes. An important component to understanding the effects of these renewable energy projects on wildlife is accurate and precise...Wind Energy and Wildlife Team (FRESC)
FRESC's Wind Energy and Wildlife Team is lead by Manuela Huso. She and her team are involved in design and analysis of post-construction fatality monitoring studies as well as deterrent and curtailment studies at several wind-power generation facilities.Effects of Wind and Solar Energy Development on Wildlife
This research theme informs adaptive management and siting decsions in relation to bats at wind and solar power-generation facilities.Applied Statistical Methods and Tools
This research theme addresses several issues important in deriving accurate and precise estimates of fatality at wind and solar power-generation facilities. - Data
If you are unable to access or download a product, email fresc_outreach@usgs.gov a request, including the full citation, or call (541) 750-1030.
San Gorgonio Pass Wind Resource Area Repower Data (2018-2019)
Variation in avian and bat mortality as a function of turbine size was investigated in the San Gorgonio Pass Wind Resource Area near Palm Springs, CA. Five sites were monitored for carcasses by dog-handler teams every 3 days from May 2018 to April 2019. The data consist of six tables used for analyses on mortality rates including: specifications on selected wind turbines (including energy capacity - Multimedia
- Publications
If you are unable to access or download a product, email fresc_outreach@usgs.gov a request, including the full citation, or call (541) 750-1030.
GenEst statistical models—A generalized estimator of mortality
IntroductionGenEst (a generalized estimator of mortality) is a suite of statistical models and software tools for generalized mortality estimation. It was specifically designed for estimating the number of bird and bat fatalities at solar and wind power facilities, but both the software (Dalthorp and others, 2018) and the underlying statistical models are general enough to be useful in various sitAuthorsDaniel Dalthorp, Lisa Madsen, Manuela M. Huso, Paul A. Rabie, Robert Wolpert, Jared Studyvin, Juniper Simonis, Jeffrey MintzFilter Total Items: 36Reanalysis 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 inAuthorsManuela Huso, Daniel DalthorpModeling 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 meaAuthorsManuela Huso, Daniel Dalthorp, Jeffrey Michael Mintz, Torgeir Nygård, Roel MayA 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 millionsAuthorsSilas Bergen, Manuela Huso, Adam E. Duerr, Missy A Braham, Sara Schmuecker, Tricia A. Miller, Todd E. KatznerClassifying 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-meaAuthorsSilas Bergen, Manuela Huso, A. Duerr, Missy A Braham, Todd E. Katzner, Sara Schmuecker, Tricia A. MillerRelative 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 oAuthorsManuela Huso, Tara Conkling, Daniel Dalthorp, Melanie J Davis, Heath Smith, Amy Fesnock-Parker, Todd E. KatznerPerformance 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 bAuthorsPaul Rabie, Daniel Riser-Espinoza, Jared Studyvin, Daniel Dalthorp, Manuela HusoComparing 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 distaAuthorsJoseph Maurer, Manuela Huso, Daniel Dalthorp, Lisa Madsen, Claudio FuentesSome approaches to accounting for incidental carcass discoveries in non-monitored years using the Evidence of Absence model
Executive SummaryWe 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 deteAuthorsDaniel Dalthorp, Paul Rabie, Manuela Huso, Andrew TredennickWind energy: An ecological challenge
No abstract available.AuthorsTodd E. Katzner, David M. Nelson, Jay E. Diffendorfer, Adam E. Duerr, Caitlin J. Campbell, Douglas Leslie, Hanna B. Vander Zanden, Julie L. Yee, Maitreyi Sur, Manuela M. Huso, Melissa A. Braham, Michael L. Morrison, Scott R. Loss, Sharon Poessel, Tara Conkling, Tricia A. MillerEstimating 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. WAuthorsLisa Madsen, Daniel Dalthorp, Manuela Huso, Andy AdermanImpacts to wildlife of wind energy siting and operation in the United States
Electricity from wind energy is a major contributor to the strategy to reduce greenhouse gas emissions from fossil fuel use and thus reduce the negative impacts of climate change. Wind energy, like all power sources, can have adverse impacts on wildlife. After nearly 25 years of focused research, these impacts are much better understood, although uncertainty remains. In this report, we summarize pAuthorsTaber Allison, James E. Diffendorfer, Erin Baerwald, Julie Beston, David Drake, Amanda Hale, Cris Hein, Manuela M. Huso, Scott Loss, Jeffrey E. Lovich, Dale Strickland, Kate Williams, Virginia WinderWildlife mortality at wind facilities: How we know what we know how we might mislead ourselves, and how we set our future course
To accurately estimate per turbine – or per megawatt – annual wildlife mortality at wind facilities, the raw counts of carcasses found must be adjusted for four major sources of imperfect detection: (1) fatalities that occur outside the monitoring period; (2) carcasses that land outside the monitored area; (3) carcasses that are removed by scavengers or deteriorate beyond recognition prior to deteAuthorsManuela M. HusoNon-USGS Publications**
Kibler, K.M., Skaugset, A.E., Ganio, L.M., Huso, M.M., 2013, Effect of contemporary forest harvesting practices on headwater stream temperatures- Initial response of the Hinkle Creek catchment, Pacific Northwest, USA: Forest Ecology and Management, v. 310, p. 680-691, https://doi.org/10.1016/j.foreco.2013.09.009Arnett, E.B., Huso, M.M., Schirmacher, M.R., Hayes, J.P., 2011, Altering turbine speed reduces bat mortality at wind-energy facilities: Frontiers in Ecology and the Environment, v. 9, no. 4, p. 209-214.Huso, M.M., 2010, An estimator of wildlife fatality from observed carcasses: Environmetrics, p. 1-19, https://doi.org/10.1002/env.1052.Betts, M.G., Ganio, L., Huso, M.M., Som, N., Huettmann, F., Bowman, J., Wintle, B.A., 2009, Comment on ‘‘Methods to account for spatial autocorrelation in the analysis of species distributional data- a review’’: Ecography, v. 32, p. 374-378.Boland, J.L., Hayes, J.P., Smith, W.P., Huso, M.M., 2009, Selection of day-roosts by Keen's myotis (Myotis keenii) at multiple spatial scales: Journal of Mammalogy, v. 90, no. 1, p. 222-234.Shaw, D.C., Huso, M.M., Bruner, H., 2008, Basal area growth impacts of dwarf mistletoe on western hemlock in an old-growth forest: Canadian Journal of Forest Research, v. 38, p. 576-583.Waldien, D.L., Hayes, J.P., Huso, M.M., 2006, Use of downed wood by Townsend's Chipmunks (Tamias Townsendii) in western Oregon: Journal of Mammalogy, v. 87, no. 3, p. 454-460, https://doi.org/10.1644/05-MAMM-A-136R1.1Kelsey, R.G., Hennon, P.E., Huso, M.M., Karchesy, J.J., 2005, Changes in heartwood chemistry of dead yellow-cedar trees that remain standing for 80 years or more in southeast Alaska: Journal of Chemical Ecology, v. 31, no. 11, p. 2653-2669.Krankina, O.N., Houghton, R.A., Harmon, M.E., Hogg, E.H., Butman, D., Yatskov, M., Huso, M.M., Treyfeld, R.F., Razuvaev, V.N., Spycher, G., 2005, Effects of climate, disturbance and species on forest biomass across Russia: Canadian Journal of Forest Research, v. 35, no. 9, p. 2821-2293, https://doi.org/10.1139/x05-151.Grotta, A.T., Gartner, B.L., Radosevich, S.R., Huso, M.M., 2005, Influence of red alder competition on cambial phenology and latewood formation in Douglas-fir: International Association of Wood Anatomists Journal, v. 26, no. 3, p. 309-324.HyperLinkHayes, J.P., Weikel, J., Huso, M.M., Erickson, J., 2003, Response of Birds to Thinning Young Douglas-fir Forests: U.S. Geological Survey Fact Sheet 033-03 p. 2, https://doi.org/10.3133/fs03303Hayes, J.P., Weikel, J., Huso, M.M., 2003, Response of birds to thinning young Douglas-fir forests: Ecological Applications, v. 13, no. 5, p. 1222-1232, https://doi.org/10.1890/02-5068Clausnitzer, D., Huso, M.M., Pyke, D.A., Belnap, J., Graham, T.B., Sanford, R.L., Phillips, S.L., 2003, Interactions of cattle grazing and climate change- hierarchical data analysis In Allsopp, N., Walker, N., eds., VIIth International Rangeland Conference Proceedings, 26 July-1 August 2003,: Durban, South Africa, The Congress, p. 1062-1064McEvoy, P.B., Rudd, N.T., Cox, C.S., Huso, M.M., 2002, Disturbance, competition, and herbivory effects on ragwort Senecio jacobaea populations: Ecological Monographs, v. 63, no. 1, p. 55-75.Smith, J.E., Molina, R., Huso, M.M., Luoma, D.L., McKay, D., Castellano, M.A., Lebel, T., Valachovic, Y., 2002, Species richness, abundance and composition of hypogeous and epigeous ectomycorrhizal fungal sporocarps in young, rotation-age and old-growth stands of Douglas-fir (Psedotsuga menziesii) in the Cascade Range of Oregon: Canadian Journal of Botany, v. 80, p. 186-204, https://doi.org/10.1139/B02-003.Smith, J.E., Molina, R., Huso, M.M., Larsen, M.J., 2000, Occurrence of Piloderma fallax in young, rotation-age, and old-growth stands of Douglas-fir (Pseudotsuga menziesii) in the Cascade Range of Oregon, U.S.A.: Canadian Journal of Botany, v. 78, p. 995-1001, https://doi.org/10.1139/b00-085.Csuti, B., Polansky, S., Williams, P.H., Pressey, R.L., Camm, J.D., Kershaw, M., Kiester, A.R., Downs, B., Hamilton, R., Huso, M.M., Sahr, K., 1997, A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon: Biological Conservation, v. 80, p. 83-97.Csuti, B., Kimerling, A.J., O'Neil, T.A., Shaughnessy, M.M., Gaines, E.P., Huso, M.M., 1997, Atlas of Oregon Wildlife- distribution, habitat, and natural history: Corvallis, OR, Oregon State University Press, p. 498.Arthur, J.L., Hachey, M., Sahr, K., Huso, M.M., Kiester, A.R., 1997, Finding all optimal solutions to the reserve site selection problem- formulation and computational analysis: Environmental and Ecological Statistics, v. 4, no. 2, p. 153-165, https://doi.org/10.1023/A:1018570311399.Reams, G.A., Huso, M.M., Vong, R.J., McCollum, J.M., 1997, Kriging direct and indirect estimates of sulfate deposition- a comparison: USDA Forest Service, Southern Research Station Research Paper SRS-7, p. 8, https://doi.org/10.2737/SRS-RP-7Reams, G.A., Huso, M.M., 1990, Stand history - an alternative explanation of red spruce radial growth reduction: Canadian Journal of Forest Research, v. 20, p. 250-253.McEvoy, P.B., Rudd, N.T., Cox, C.S., Huso, M.M., 1993, Disturbance, competition, and herbivory effects on ragwort Senecio jacobaea populations: Ecological Monographs, v. 63, no. 1, p. 55-75.**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
- Software
If you are unable to access or download a product, email fresc_outreach@usgs.gov a request, including the full citation, or call (541) 750-1030.
Density-weighted proportion (dwp) tool suite
Estimating the number of birds or bats killed at a wind power facility involves adjusting observed carcass counts to account for imperfect detection. To account for carcasses that lie outside the searched area, the count is adjusted by dividing by the estimated proportion of carcasses lying within the searched area or the density-weighted proportion (dwp). The dwp package provides tools for estimaGenEst - A Generalized Estimator of Mortality
GenEst, a generalized estimator of wildlife mortality at renewable energy facilities.
Generalized Mortality Estimator (GenEst) - R code & GUI
GenEst is a tool for estimating mortalities from efficiency, persistence, and carcass data.Evidence of Absence (EoA) Software and User's Guide
Software to Estimate Bird and Bat Fatality at Wind Farms
Fatality Estimator Software
Tool to Evaluate Wildlife Fatalities at Wind-Power Facilities
- News
To request an interview, contact fresc_outreach@usgs.gov or call (541) 750-1030.