Climate change is rapidly driving global biodiversity declines. How wetland macroinvertebrate assemblages are responding is unclear, a concern given their vital function in these ecosystems. Using a data set from 769 minimally impacted depressional wetlands across the globe (467 temporary and 302 permanent), we evaluated how temperature and precipitation (average, range, variability) affects the richness and beta diversity of 144 macroinvertebrate families. To test the effects of climatic predictors on macroinvertebrate diversity, we fitted generalized additive mixed-effects models (GAMM) for family richness and generalized dissimilarity models (GDMs) for total beta diversity. We found non-linear relationships between family richness, beta diversity, and climate. Maximum temperature was the main climatic driver of wetland macroinvertebrate richness and beta diversity, but precipitation seasonality was also important. Assemblage responses to climatic variables also depended on wetland water permanency. Permanent wetlands from warmer regions had higher family richness than temporary wetlands. Interestingly, wetlands in cooler and dry-warm regions had the lowest taxonomic richness, but both kinds of wetlands supported unique assemblages. Our study suggests that climate change will have multiple effects on wetlands and their macroinvertebrate diversity, mostly via increases in maximum temperature, but also through changes in patterns of precipitation. The most vulnerable wetlands to climate change are likely those located in warm-dry regions, where entire macroinvertebrate assemblages would be extirpated. Montane and high-latitude wetlands (i.e., cooler regions) are also vulnerable to climate change, but we do not expect entire extirpations at the family level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.153052","usgsCitation":"Epele, L., Grech, M.G., Williams-Subiza, E.A., Stenert, C., McLean, K., Greig, H., Maltchik, L., Pires, M.M., Bird, M.S., Boissezon, A., Boix, D., Demierre, E., García, P., Gascón, S., Jeffries, M., Kneitel, J.M., Loskutov, O., Manzo, L.M., Mataloni, G., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E.E., Wu, H., Wissinger, S., and Batzer, D., 2022, Perils of life on the edge: Climatic threats to global diversity patterns of wetland macroinvertebrates: Science of the Total Environment, v. 820, 153052, 10 p., https://doi.org/10.1016/j.scitotenv.2022.153052.","productDescription":"153052, 10 p.","ipdsId":"IP-127993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":449106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nrl.northumbria.ac.uk/id/eprint/48317/1/STOTEN_Epele_Manuscript.pdf","text":"External Repository"},{"id":396114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"820","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Epele, Luis B.","contributorId":279551,"corporation":false,"usgs":false,"family":"Epele","given":"Luis B.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grech, Marta G.","contributorId":279552,"corporation":false,"usgs":false,"family":"Grech","given":"Marta","email":"","middleInitial":"G.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, 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M.","contributorId":279567,"corporation":false,"usgs":false,"family":"Manzo","given":"Luz","email":"","middleInitial":"M.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835136,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mataloni, Gabriela 0000-0002-6852-6143","orcid":"https://orcid.org/0000-0002-6852-6143","contributorId":279568,"corporation":false,"usgs":false,"family":"Mataloni","given":"Gabriela","email":"","affiliations":[{"id":57288,"text":"Instituto de Investigación e Ingeniería Ambiental -IIIA, UNSAM, CONICET, Campus Miguelete, 1650-San Martín, Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":835137,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Mlambo, Musa C.","contributorId":279569,"corporation":false,"usgs":false,"family":"Mlambo","given":"Musa","email":"","middleInitial":"C.","affiliations":[{"id":57289,"text":"Department of Freshwater Invertebrates, Albany Museum, and Department of Zoology and Entomology, Rhodes University, Makhanda (Grahamstown) 6139, South Africa.","active":true,"usgs":false}],"preferred":false,"id":835138,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Oertli, Beat 0000-0002-8372-9045","orcid":"https://orcid.org/0000-0002-8372-9045","contributorId":279570,"corporation":false,"usgs":false,"family":"Oertli","given":"Beat","email":"","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835139,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Sala, Jordi","contributorId":279571,"corporation":false,"usgs":false,"family":"Sala","given":"Jordi","email":"","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835140,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Scheibler, Erica E. 0000-0001-6802-8702","orcid":"https://orcid.org/0000-0001-6802-8702","contributorId":279572,"corporation":false,"usgs":false,"family":"Scheibler","given":"Erica","email":"","middleInitial":"E.","affiliations":[{"id":57290,"text":"Entomology Laboratory, IADIZA CCT Mendoza CONICET, Av. Adrián Ruiz Leal s/n, Parque General San Martín, 5500, Mendoza, Argentina","active":true,"usgs":false}],"preferred":false,"id":835141,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wu, Haitao","contributorId":279573,"corporation":false,"usgs":false,"family":"Wu","given":"Haitao","email":"","affiliations":[{"id":57291,"text":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, 130012, China","active":true,"usgs":false}],"preferred":false,"id":835142,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Wissinger, Scott A","contributorId":279574,"corporation":false,"usgs":false,"family":"Wissinger","given":"Scott A","affiliations":[{"id":57292,"text":"Biology and Environmental Science Departments, Allegheny College, Meadville, PA 16335, USA","active":true,"usgs":false}],"preferred":false,"id":835143,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Batzer, Darold P.","contributorId":279575,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold P.","affiliations":[{"id":57293,"text":"Department of Entomology, University of Georgia, Athens, GA, USA","active":true,"usgs":false}],"preferred":false,"id":835144,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70230062,"text":"70230062 - 2022 - Soil moisture response to seasonal drought conditions and post-thinning forest structure","interactions":[],"lastModifiedDate":"2022-08-01T16:55:02.409081","indexId":"70230062","displayToPublicDate":"2022-01-19T06:17:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Soil moisture response to seasonal drought conditions and post-thinning forest structure","docAbstract":"Prolonged drought conditions in semi-arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below-average total precipitation. We used the data to assess the effects of fore-summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine-scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand-level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought.
Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear (Ursus arctos) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km2 during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22185","usgsCitation":"Schmidt, J., Wilson, T.L., Thompson, W., and Mangipane, B., 2022, Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration: Journal of Wildlife Management, v. 86, no. 3, e22185, 15 p., https://doi.org/10.1002/jwmg.22185.","productDescription":"e22185, 15 p.","ipdsId":"IP-130320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.33642551397307,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 61.82002560280111\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Joshua H.","contributorId":349537,"corporation":false,"usgs":false,"family":"Schmidt","given":"Joshua H.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, William L.","contributorId":349538,"corporation":false,"usgs":false,"family":"Thompson","given":"William L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924369,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck A.","contributorId":349540,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924370,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227434,"text":"dr1148 - 2022 - Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary","interactions":[],"lastModifiedDate":"2022-01-19T12:22:31.582508","indexId":"dr1148","displayToPublicDate":"2022-01-18T14:45:43","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1148","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data Summary","title":"Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary","docAbstract":"We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the San Antonio Dam near Upland, California, in 2021. Four vireo surveys were conducted between April 16 and July 15, 2021, and three flycatcher surveys were conducted between May 27 and July 15, 2021.
We detected one transient vireo and one transient flycatcher. No territorial vireos or flycatchers were observed. The vireo was found in riparian scrub habitat dominated by native mule fat (Baccharis salicifolia), whereas the flycatcher was using habitat dominated by non-native tamarisk (Tamarix ramosissima).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1148","programNote":"Ecosystems Mission Area-Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2022, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary: U.S. Geological Survey Data Report 1148, 8 p., https://doi.org/10.3133/dr1148.","productDescription":"vii, 8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-135056","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":394401,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1148/images"},{"id":394400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1148/dr1148.xml"},{"id":394398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1148/covrthb.jpg"},{"id":394399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1148/dr1148.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Los Angeles County, San Bernardino County","otherGeospatial":"San Antonio Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.76039123535155,\n 34.12942636161218\n ],\n [\n -117.56744384765625,\n 34.12942636161218\n ],\n [\n -117.56744384765625,\n 34.231673921638475\n ],\n [\n -117.76039123535155,\n 34.231673921638475\n ],\n [\n -117.76039123535155,\n 34.12942636161218\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Pennsylvania leads the Nation in the number of individuals that use groundwater for private domestic water supply; more than 3 million rural and suburban Pennsylvania residents rely on private domestic supplies for drinking water. These supplies are not regulated nor routinely monitored; thus relevant groundwater-quality information is not widely available. The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PaDEP) Safe Drinking Water Bureau, established a statewide, fixed-station ambient groundwater quality network in 2015. The goals for the Pennsylvania Groundwater Monitoring Network (GWMN) include characterizing ambient groundwater quality conditions in rural areas of the State and documenting potential changes in conditions over time. Seventeen wells were selected for monitoring at 6-month intervals beginning in 2015. Since then, several wells have been added to the GWMN, bringing the total number of wells sampled in the fall of 2019 to 28. Routinely monitored constituents included physical characteristics and chemical concentrations in filtered and unfiltered samples (major and trace elements, nutrients, and organic compounds). Samples for volatile organic compounds (VOCs), radionuclides, and dissolved hydrocarbon gases were collected during the first sampling event at each well.
To offer insights on the quality of groundwater used for domestic supply in Pennsylvania, summary statistics for the 221 GWMN samples collected during 2015–19 are compared to U.S. Environmental Protection Agency (EPA) drinking-water standards, which are applicable to public water supplies. Results show that samples across the GWMN generally meet drinking-water standards for inorganic and organic constituents; however, a percentage of samples had concentrations that exceeded maximum contaminant level (MCL) thresholds for nitrate (3 percent) and secondary maximum contaminant level (SMCL) thresholds for iron (32 percent), manganese (36 percent), and aluminum (5 percent). Radon-222 activities, which were sampled only during the initial visit to a well, exceeded the lower proposed drinking water standard of 300 picocuries per liter (pCi/L) in 64 percent of wells in the GWMN; additionally, 7 percent of wells exceeded the higher proposed standard of 4,000 pCi/L. There were no exceedances for VOCs, but one well had a tribromomethane detection. Three wells had detectable concentrations of methane, with one sample exceeding the Pennsylvania action level of 7 milligrams per liter (mg/L).
The pH and dissolved oxygen concentrations varied widely across the GWMN and were correlated with dissolved metal concentrations and other chemical characteristics of groundwater samples. Considering all samples collected for the study, the pH ranged from 4.2 to 8.3; 42 percent of pH values were either above or below the SMCL range of 6.5–8.5. The highest pH values resulted from contamination of loose grout used in the construction of one well and decreased to levels consistent with other wells in the vicinity after repeated sampling rounds. Dissolved oxygen (DO), which ranged from 0 to 13.9 mg/L, influences the mobility and prevalence of constituents with variable oxidation state, including iron, manganese, and nitrogen species. Samples with acidic pH (less than 6.5) and (or) low DO had the highest concentrations of manganese and iron, whereas those with neutral to alkaline pH values had the highest concentrations of calcium, magnesium, sodium, and other major ions. Analysis of major ions indicates that calcium/bicarbonate water types are the most common, with a few characterized as calcium/chloride or sodium/chloride, and most others as mixed water types including calcium-magnesium/bicarbonate, sodium-magnesium/bicarbonate, and sodium/bicarbonate-chloride.
Nonparametric statistical methods were used to evaluate the data for spatial and temporal trends. A principal components analysis (PCA) model developed with ranked data values for the entire network resulted in three components, (1) dissolved solids, (2) redox, and (3) sodium-chloride, which explained 74.5 percent of variance in the dataset. On the basis of individual contributions to the PCA, certain wells were identified through hierarchical cluster analysis that shared relevant water-quality characteristics. The spatial distribution of sampling locations and the temporal trends of constituent concentrations indicate that hydrogeologic setting and topographic position as defined in the PCA model are important factors affecting the spatial and temporal patterns of groundwater quality in the GWMN.
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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
This paper analyzes the relationship between actual reservoir conditions and predicted measures of performance of carbon dioxide enhanced oil recovery (CO2–EOR) programs. It then shows how CO2–EOR operators might maximize the value of their projects by approaching implementation using a “flexible selective” pattern development strategy, where the CO2–EOR program patterns are selectively developed based on site-specific reservoir properties. It also analyzes performance measures and economic consequences of utilizing a continuous CO2 injection strategy intended to maximize CO2 retention for a defined time period. “Net CO2 utilization,” calculated as difference between the volumes of CO2 injected and CO2 recovered in the production stream divided by the oil produced, is a standard measure of CO2–EOR carbon utilization, but it can be a misleading predictor of the actual CO2 retained in the reservoir. Asset value can be added to a CO2–EOR project by recognizing effects of variations in reservoir parameter values and basing incremental development decisions on those data. For policy analysts, the consequences of ignoring geologic variability within a reservoir that is a candidate for CO2–EOR will likely be to substantially overestimate predicted adoption of CO2–EOR in response to economic incentives. This result holds true whether the CO2–EOR program objective is to maximize net value by maximizing oil production or maximize CO2 storage with oil recovery.
Effectively managing take of wildlife resulting from human activities poses a major challenge for applied conservation. Demographic data essential to decisions regarding take are often expensive to collect and are either not available or based on limited studies for many species. Therefore, modeling approaches that efficiently integrate available information are important to improving the scientific basis for sustainable take thresholds. We used the prescribed take level (PTL) framework to estimate allowable take for bald eagles (Haliaeetus leucocephalus) in the conterminous United States. We developed an integrated population model (IPM) that incorporates multiple sources of information and then use the model output as the scientific basis for components of the PTL framework. Our IPM is structured to identify key parameters needed for the PTL and to quantify uncertainties in those parameters at the scale at which the United States Fish and Wildlife Service manages take. Our IPM indicated that mean survival of birds >1 year old was high and precise (0.91, 95% CI = 0.90–0.92), whereas mean survival of first-year eagles was lower and more variable (0.69, 95% CI = 0.62–0.78). We assumed that density dependence influenced recruitment by affecting the probability of breeding, which was highly imprecise and estimated to have declined from approximately 0.988 (95% CI = 0.985–0.993) to 0.66 (95% CI = 0.34–0.99) between 1994 and 2018. We sampled values from the posterior distributions of the IPM for use in the PTL and estimated that allowable take (e.g., permitted take for energy development, incidental collisions with human made structures, or removal of nests for development) ranged from approximately 12,000 to 20,000 individual eagles depending on risk tolerance and form of density dependence at the scale of the conterminous United States excluding the Southwest. Model-based thresholds for allowable take can be inaccurate if the assumptions of the underlying framework are not met, if the influence of permitted take is under-estimated, or if undetected population declines occur from other sources. Continued monitoring and use of the IPM and PTL frameworks to identify key uncertainties in bald eagle population dynamics and management of allowable take can mitigate this potential bias, especially where improved information could reduce the risk of permitting non-sustainable take.
Of all terrestrial biomes, grasslands are losing the most biodiversity the most rapidly, so there is a critical need to document and learn from large-scale restoration successes.
In the Loess Canyons ecoregion of the Great Plains, USA, an association of private ranchers and natural resource agencies has led a multi-decadal, ecoregion-scale initiative to combat the loss of grasslands to woody plant encroachment by restoring large-scale fire regimes. Here, we use 14 years of fire treatment history with 6 years of grassland bird monitoring and remotely sensed tree cover data across 136,767 ha of privately owned grassland to quantify outcomes of large-scale grassland restoration efforts.
Grassland bird richness increased across 65% (90,032 ha) of the Loess Canyons, and woody plant cover decreased up to 55% across 25% (7408 ha) of all fire-treated areas.
This was accomplished with extreme fire treatments that killed mature trees, were large (mean annual area burned was 3100 ha), spatially clustered and straddled boundaries between invasive woodlands and remaining grasslands – not heavily infested woodlands.
Findings from this study provide the first evidence of human management reversing the impacts of woody encroachment on grassland birds at an ecoregion scale.
Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.
BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s
Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid.
As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.
","language":"English","publisher":"BioMed Central Ltd.","doi":"10.1186/s40317-022-00273-3","usgsCitation":"Nebiolo, K., and Castro-Santos, T.R., 2022, BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data: Animal Biotelemetry, v. 10, p. 1-16, https://doi.org/10.1186/s40317-022-00273-3.","productDescription":"2, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-122256","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-022-00273-3","text":"Publisher Index Page"},{"id":394441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nebiolo, Kevin","contributorId":271123,"corporation":false,"usgs":false,"family":"Nebiolo","given":"Kevin","email":"","affiliations":[{"id":56294,"text":"Kleinschmidt Associates, Essex, CT","active":true,"usgs":false}],"preferred":false,"id":830917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":830918,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227445,"text":"70227445 - 2022 - Northern Cascadia Margin gas hydrates — Regional geophysical surveying, IODP drilling leg 311, and cabled observatory monitoring","interactions":[],"lastModifiedDate":"2022-01-17T16:45:44.547375","indexId":"70227445","displayToPublicDate":"2022-01-17T10:31:53","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Northern Cascadia Margin gas hydrates — Regional geophysical surveying, IODP drilling leg 311, and cabled observatory monitoring","docAbstract":"This article reviews extensive geophysical survey data, ocean drilling results and long-term seafloor monitoring that constrain the distribution and concentration of gas hydrates within the accretionary prism of the northern Cascadia subduction margin, located offshore Vancouver Island in Canada. Seismic surveys and geologic studies conducted since the 1980s have mapped the bottom simulating reflector (BSR), detected gas hydrate occurrence and estimated gas hydrate and free gas concentrations. Additional constraints were obtained from seafloor-towed, controlled-source electromagnetic surveying. A component of these studies has been the examination of low-temperature seafloor vents and seeps that emit gas and fluids into the ocean. These features are identified seismically as chimney-like zones of reduced acoustic reflectivity within the sediment stratigraphy, functioning as conduits for gas and fluid migration from below the BSR to the seafloor. Gas hydrates have been recovered from the seafloor and from sediment cores at vent sites, mostly in massive (nodular) form and as a vein-like fracture filling. The Ocean Networks Canada cabled NEPTUNE observatory has gathered extensive continuous, long-term observations on gas hydrate dynamics at the seafloor and in boreholes at two nodes on the continental slope featuring high gas hydrate concentrations. Measurements taken at the observatory include a time-series of gas bubble emission rates, changes in the near-seafloor electromagnetic structure and seafloor compliance linked to gas hydrate formation and dissociation. Two Integrated Ocean Drilling Program (IODP) expeditions collected cores, measured downhole properties and deployed downhole instruments within the central accretionary prism. At IODP Site U1364, pore pressures are being monitored above and below the base of the gas hydrate stability zone at a slope setting using an “Advanced Circulation Obviation Retrofit Kit” (A-CORK). Downhole pore pressures, temperatures and electrical resistivities also are being monitored at IODP Site U1416 using the “Simple Cabled Instrument for Measuring Parameters In Situ” (SCIMPI) tool at a vent site from near-seafloor to just above the base of the gas hydrate stability zone.
A wealth of information has been accumulated regarding the occurrence of gas hydrates in nature, leading to significant advancements in our understanding of the geologic controls on their occurrence in both the terrestrial and marine settings of the Arctic. Gas hydrate accumulations discovered in the Alaska North Slope have been the focus of several important geoscience and production testing research programs. The Mount Elbert Gas Hydrate Stratigraphic Test Well of 2007 yielded one of the most complete geologic datasets on Arctic gas hydrate systems and important reservoir engineering data. The 2011/2012 field test of the Iġnik Sikumi gas hydrate production test well provided important insight into gas hydrate production technologies, yielding additional information on the petrophysical properties of gas hydrate reservoir systems. The Hydrate-01 Stratigraphic Test Well, drilled late in 2018, confirmed the geologic conditions at an Alaska North Slope drill site that was selected for an extended gas hydrate production test. In 2018, the US Geological Survey used information derived from previous scientific drilling programs to assess the volume of undiscovered, technically recoverable gas resources at a mean estimate of about 54 trillion cubic feet (~1.5 trillion cubic meters) within the gas hydrates in the North Slope of Alaska. This assessment has shown that the amount of gas stored as gas hydrates in this area is equal to about half of the known volume of conventional natural gas resources in the region.
With improved analytical techniques, environmental monitoring studies are increasingly able to report the occurrence of tens or hundreds of chemicals per site, making it difficult to identify the most relevant chemicals from a biological standpoint. For this study, organic chemical occurrence was examined, individually and as mixtures, in the context of potential biological effects. Sediment was collected at 71 Great Lakes tributary sites and analyzed for 87 chemicals. Multiple risk-based lines of evidence were used to prioritize chemicals and locations, including comparing sediment concentrations and estimated porewater concentrations to established whole-organism benchmarks (i.e., sediment and water quality criteria and screening values) and to high-throughput toxicity screening data from the U.S. Environmental Protection Agency's ToxCast database, estimating additive effects of chemical mixtures on common ToxCast endpoints, and estimating toxic equivalencies for mixtures of alkylphenols and polycyclic aromatic hydrocarbons (PAHs). This multiple-lines-of-evidence approach enabled the screening of more chemicals, mitigated the uncertainties of individual approaches, and strengthened common conclusions. Collectively, at least one benchmark/screening value was exceeded for 54 of the 87 chemicals, with exceedances observed at all 71 of the monitoring sites. Chemicals with the greatest potential for biological effects, both individually and as mixture components, were bisphenol A, 4-nonylphenol, indole, carbazole, and several polycyclic aromatic hydrocarbons (PAHs). Potential adverse outcomes based on ToxCast gene targets and putative adverse outcome pathways relevant to individual chemicals and chemical mixtures included tumors, skewed sex ratios, reproductive dysfunction, hepatic steatosis, and early mortality, among others. Results provide a screening level prioritization of chemicals with the greatest potential for adverse biological effects and an indication of sites where they are most likely to occur.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5286","usgsCitation":"Baldwin, A.K., Corsi, S., Stefaniak, O.M., Loken, L.C., Villeneuve, D.L., Ankley, G., Blackwell, B., Lenaker, P.L., Nott, M.A., and Mills, M.A., 2022, Risk-based prioritization of organic chemicals and locations of ecological concern in sediment from Great Lakes tributaries: Environmental Toxicology and Chemistry, v. 41, no. 4, p. 1016-1041, https://doi.org/10.1002/etc.5286.","productDescription":"26 p.","startPage":"1016","endPage":"1041","ipdsId":"IP-129929","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449145,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/etc.5286","text":"External 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0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830961,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Villeneuve, Daniel L. 0000-0003-2801-0203","orcid":"https://orcid.org/0000-0003-2801-0203","contributorId":197436,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":830962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ankley, Gerald T.","contributorId":177970,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, 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0000-0003-3968-7586","orcid":"https://orcid.org/0000-0003-3968-7586","contributorId":221766,"corporation":false,"usgs":true,"family":"Nott","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830966,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":830967,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70262054,"text":"70262054 - 2022 - A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams","interactions":[],"lastModifiedDate":"2025-01-13T14:43:45.646463","indexId":"70262054","displayToPublicDate":"2022-01-15T07:41:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams","docAbstract":"Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80–94%. Accounting for previously unmapped dams resulted in a 62–90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.113952","usgsCitation":"Buchanan, B., Sethi, S., Cuppett, S., Lung, M., Jackman, G., Zarri, L., Duvall, E., Dietrich, J., Sullivan, P., Dominitz, A., Archibald, J., Flecker, A., and Rahm, B., 2022, A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams: Journal of Environmental Management, v. 302, no. Part A, 113952, 11 p., https://doi.org/10.1016/j.jenvman.2021.113952.","productDescription":"113952, 11 p.","ipdsId":"IP-129279","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467204,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2021.113952","text":"Publisher Index Page"},{"id":465979,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Foundry Brook, Lattintown Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.85258131006508,\n 41.29588488429172\n ],\n [\n -73.85258131006508,\n 41.72638789605105\n ],\n [\n -74.0515960683781,\n 41.72638789605105\n ],\n [\n -74.0515960683781,\n 41.29588488429172\n ],\n [\n 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University","active":true,"usgs":false}],"preferred":false,"id":922908,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dominitz, Alon","contributorId":348057,"corporation":false,"usgs":false,"family":"Dominitz","given":"Alon","affiliations":[{"id":56930,"text":"New York DEC","active":true,"usgs":false}],"preferred":false,"id":922909,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Archibald, Josephine","contributorId":348060,"corporation":false,"usgs":false,"family":"Archibald","given":"Josephine","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":922910,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Flecker, Alexander","contributorId":348061,"corporation":false,"usgs":false,"family":"Flecker","given":"Alexander","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922911,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rahm, Brian","contributorId":348062,"corporation":false,"usgs":false,"family":"Rahm","given":"Brian","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922912,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70250299,"text":"70250299 - 2022 - A comparison of orbital-resolution, Late Pleistocene Alkenone and foraminiferal assemblage-based sea surface temperature reconstructions from the Southwest Pacific","interactions":[],"lastModifiedDate":"2023-12-01T12:50:53.526701","indexId":"70250299","displayToPublicDate":"2022-01-15T06:49:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of orbital-resolution, Late Pleistocene Alkenone and foraminiferal assemblage-based sea surface temperature reconstructions from the Southwest Pacific","docAbstract":"Global and regional reconstructions of past climate conditions often incorporate sea surface temperature (SST) estimates from multiple proxies because not every paleotemperature proxy is applicable in all geographic locations. This practice of assimilating estimates from different proxies in global or regional temperature syntheses makes the implicit assumption that estimates derived from different proxies can be meaningfully intercompared. However, evidence to support the validity of this assumption is limited. Using paleotemperature data from sediments collected from ODP Site 1125 in the Southwest Pacific, we conduct a ∼1 Myr, orbital-scale SST proxy comparison of a recently published alkenone-derived SST record with a previously published foraminiferal assemblage-based SST record. These alkenone and foraminiferal assemblage SST datasets show strong structural similarity and yield remarkably similar estimates for basic climate metrics, including mean, median, standard deviation, and range. Statistical analysis indicates that the correlation between the two SST records is highly significant. In the spectral domain, the records share the same dominant 100 kyr beat, are coherent and in phase with each other at this frequency, and have the same coherence and phase relationship with benthic foraminiferal δ18O. Results from this work demonstrate that these two proxies would yield very similar estimates for the paleoclimate metrics most commonly used in empirical paleoclimate reconstructions that seek to document the evolution of climate over this interval. However, significant disparities between SST estimates derived from the two proxies exist for some time periods, particularly during glacial and interglacial extrema. This comparison suggests that treating estimates from these proxies as equivalent in studies that focus on short time windows (e.g. a few thousand to tens-of-thousands of years), particularly in investigations that seek to characterize glacial or interglacial extrema, could be potentially problematic. However, the sensitive location of Site 1125, just north of the Subtropical Front, likely accentuates the difference between temperature estimates from these proxies, which may be attenuated in other oceanographic settings. We attribute the discrepancies between the two SST records to two main causes: seasonal leakages of cold water across the Subtropical Front during glacial extrema and differential seasonality of maximum alkenone and foraminiferal production.
Since the mid-2000s, recruitment of Walleye Sander vitreus in some northern Wisconsin lakes has declined, potentially because of climate-induced changes in lake environments. Yellow Perch Perca flavescens is also an ecologically and culturally important fish species in this region, but mechanisms driving Yellow Perch recruitment are unclear because of a lack of targeted sampling. Previous studies have suggested that recruitment of these two species may be regulated by similar factors, and observed declines in Walleye recruitment may be cause for concern about Yellow Perch recruitment. Our objectives were to determine if abiotic factors related to recruitment success were similar between Walleye and Yellow Perch populations in northern Wisconsin lakes and if the probability of successful Walleye recruitment was related to estimates of juvenile Yellow Perch abundance before Walleye recruitment declines were observed. We addressed these objectives using historical data from Wisconsin lakes. Random forest analysis incorporating lake-specific averages of predictor variables indicated that winter conditions (duration or severity), growing degree days, variation in spring temperatures, peak summer temperature, and Secchi depth were important predictors of recruitment success for both species. Logistic regression indicated that before Walleye recruitment declines were observed on some lakes (2000–2006), Walleye recruitment success was related to relative abundance of juvenile Yellow Perch in mini-fyke-net sampling. Our results indicate that landscape-level patterns in recruitment success for the two species are likely similar and additional research to understand Yellow Perch recruitment trends is warranted. Better information on Yellow Perch recruitment could contribute to a better understanding of Walleye recruitment trends as declines in Yellow Perch could influence prey availability and survival of age-0 Walleye. Furthermore, potential declines in Yellow Perch could lead to changes in the numbers and size of Yellow Perch caught by anglers, which may have implications for harvest management.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10729","usgsCitation":"Brandt, E., Feiner, Z., Latzka, A., and Isermann, D.A., 2022, Similar environmental conditions are associated with Walleye and Yellow Perch recruitment success in Wisconsin lakes: North American Journal of Fisheries Management, v. 42, no. 3, p. 630-641, https://doi.org/10.1002/nafm.10729.","productDescription":"12 p.","startPage":"630","endPage":"641","ipdsId":"IP-127934","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.3619142125417,\n 46.69624201075456\n ],\n [\n -92.3619142125417,\n 45.451292765361956\n ],\n [\n -89.83163049007825,\n 45.451292765361956\n ],\n [\n -89.83163049007825,\n 46.69624201075456\n ],\n [\n -92.3619142125417,\n 46.69624201075456\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandt, Ethan J.","contributorId":348096,"corporation":false,"usgs":false,"family":"Brandt","given":"Ethan J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":922936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feiner, Zachary S.","contributorId":348097,"corporation":false,"usgs":false,"family":"Feiner","given":"Zachary S.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":922937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latzka, Alexander W.","contributorId":348099,"corporation":false,"usgs":false,"family":"Latzka","given":"Alexander W.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":922938,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922935,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227372,"text":"ofr20211120 - 2022 - Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","interactions":[],"lastModifiedDate":"2026-03-25T17:51:49.580485","indexId":"ofr20211120","displayToPublicDate":"2022-01-14T14:40:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1120","displayTitle":"Implementation Plan of the National Cooperative Geologic Mapping Program Strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","title":"Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","docAbstract":"The U.S. Geological Survey (USGS) National Cooperative Geologic Mapping Program (NCGMP) has published a strategic plan entitled “Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge”. This plan provides the following vision, mission, and goals for the program for the years 2020–30:
To achieve the goal outlined in the strategic plan, the NCGMP has developed an Implementation Plan. This Implementation Plan will guide annual reviews of the FEDMAP component (that is, the component of the USGS NCGMP that funds geologic mapping by USGS geologists) of the NCGMP projects described in the plan and the development of the annual FEDMAP prospectus, which will ensure the application of the NCGMP strategy.
This publication is part of the Implementation Plan of the NCGMP strategy and addresses the following three major topics:
The regions used in this chapter correspond with physiographic divisions of the United States as defined by Fenneman. Physiographic divisions are delineated on the basis of topography, and to a lesser extent, the geologic structure and history. The physiographic divisions are subdivided into physiographic provinces, and the physiographic provinces are subdivided into physiographic sections. Fenneman’s physiographic divisions of the United States provide a robust and useful spatial organization for delineating large geographic regions of the United States for various scientific and industrial applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211120","usgsCitation":"Swezey, C.S., Blome, C.D., Kincare, K.A., Lundstrom, S.C., Stone, B.D., Sweetkind, D.S., Berg, R.C., Brown, S.E., and Yellich, J.A., 2022, Implementation plan of the National Cooperative Geologic Mapping Program strategy—Great Lakes (Central Lowland and Superior Upland Physiographic Provinces): U.S. Geological Survey Open-File Report 2021–1120, 24 p., https://doi.org/10.3133/ofr20211120.","productDescription":"iv, 24 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128891","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501534,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112124.htm","linkFileType":{"id":5,"text":"html"}},{"id":394416,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211120/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":394244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1120/coverthb.jpg"},{"id":394245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.pdf","text":"Report","size":"3.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1120"},{"id":394246,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1120/images/"},{"id":394247,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1120/ofr20211120.XML"}],"country":"Canada, United States","otherGeospatial":"Great Lakes (Central Lowland and Superior Upland Physiographic Provinces)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 34.016241889667015\n ],\n [\n -75.234375,\n 34.016241889667015\n ],\n [\n -75.234375,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 50.51342652633956\n ],\n [\n -98.701171875,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
The U.S. Geological Survey (USGS) 21st-century science strategy 2020–30 promotes a bureau-wide strategy to develop and deliver an integrated, predictive science capability that works at the scales and timelines needed to inform societally relevant resource management and protection and public safety and environmental health decisions (U.S. Geological Survey, 2021). This is the overarching goal of the USGS Earth Monitoring, Analysis, and Prediction (EarthMAP) vision, which consists of three components: (1) integrated data and information, (2) integrated predictive science, and (3) actionable information—all designed and delivered to respond to user needs. To launch this vision and help shape the design and implementation of integrated predictive science, the USGS Regional Offices each developed a set of use cases (hereafter Use Cases)—short descriptions of potential science applications that could clearly address high priority decision-making needs of our stakeholders and that align with an integrated science focus. Use Cases are not actionable science planning documents, nor stand-alone scholarly works, but should be considered as innovative, next-generation science ideas that can be considered as potential components of science plans still under development. The goal of Use Case development was to (1) identify and characterize existing USGS scientific capacities and expertise that can support science goals and products, (2) identify opportunities to leverage current capacities for next-generation science, and (3) foster engagement across the entire Bureau to further refine the USGS strategy for EarthMAP and integrated predictive science.
The Use Case development effort documented in this report was coordinated by the Use Case Development Team (UCDT), consisting of representatives from each region. The UCDT undertook five tasks: (1) develop a unified approach to engage bureau scientists consistently across all regions in aspirational thinking about what can be accomplished; (2) work with the regions and their Science Centers to generate an initial set of Use Cases, authored directly by scientists; (3) characterize, summarize, and document the initial set of Use Case submissions from authors to illuminate bureau-level demand for integrated science; (4) compare existing and needed capacities from the Use Case descriptions with preliminary results of the EarthMAP Capacity Assessment (Keisman and others, 2021); and (5) describe lessons learned from the Use Case development process and provide recommendations to inform future efforts to generate integrated science activities. This report outlines the approach the UCDT developed to solicit Use Cases from the regions and summarizes the high-level qualitative findings from this first-round effort.
The UCDT received 36 Use Cases from the regions and identified potential points of convergence and commonalities considered useful in making connections among the participating scientists. The Southwest (SW) Region and the Rocky Mountain (RM) Region asked scientists to give special consideration to Use Cases with applicability to the Colorado River Basin, and seven of the Use Cases specifically named that geographic area as a focus. Coastal hazards and coastal resilience were identified in Use Cases from the Alaska (AK), Northeast (NE), and Southeast (SE) Regions. Aspects of wildfire and post-wildfire response were part of Uses Cases from AK, RM, and SW Regions. The greatest convergence of Use Case themes was related to conservation of public lands and waters, which is a powerful linkage lending strength to future collaborative efforts.
The most common type of stakeholder decisions that would be informed by the Use Case science applications were related to adaptation, mitigation, and response (for example, how to increase the resilience of coastal communities to climate-related stressors and how to prevent or respond to harmful algal blooms). Other common types of decisions included water and land management decisions (including operational water management decisions such as reservoir operations and land use planning in the sagebrush biome), decisions about how to manage and conserve habitats and species, and risk management decisions (such as managing the post-wildfire flood risks). These decision types are not exclusive because many Use Cases cross categories.
Use Case authors identified existing and needed science and technology capabilities required for Use Case implementation, which were then aligned to capabilities assessed in the EarthMAP Capacity Assessment (Keisman and others, 2021). Strong alignment was found for data and information integration approaches, modeling and prediction approaches, and capabilities related to delivery of actionable information. A majority of Use Cases indicated insufficient current capacity for needed data collection methods, data integration, and modeling and prediction approaches, whereas only 25 percent indicated insufficient capacity for actionable information delivery. Overall, many Use Case capacity demand gaps could potentially be met by existing bureau-wide capacity. In addition, nearly half of the Use Cases could potentially be implemented within 3 years if funding, capabilities, and personnel impediments were removed and science priorities were realigned.
Several challenges emerged during the Use Case development process. The first challenge was developing an approach that was flexible enough to accommodate regional differences in planning and implementation, while also ensuring enough guidance to promote meaningful summary analyses. The UCDT encountered a strong demand for continuous communication and education to improve overall understanding of the integrated predictive science strategy. Another challenge was managing expectations about EarthMAP activities as a design effort that was not aligned to an immediate funding opportunity. Connecting the Use Cases to stakeholder needs without the opportunity for direct stakeholder engagement was also challenging. The last notable challenge was in obtaining consistent interpretation and characterization of the qualitative data housed in the narrative descriptions of Use Cases, written in different styles.
Overall, the 36 Use Cases can serve as components of a road map for advancing integrated monitoring and predictive science throughout the USGS by revealing opportunities to (1) encourage cross-region initiatives that address shared interests in common themes by integrating similar Use Cases and through direct involvement of stakeholders in identifying needs and designing effective responses, (2) leverage the Use Cases to target investments that are aligned with the Bureau and Department of the Interior (DOI) priorities, (3) connect Use Cases and the results of the companion EarthMAP Capacity Assessment (Keisman and others, 2021) to identify potential priorities for capacity building investments, and (4) raise awareness of common integrated and interdisciplinary science interests within and across the regions through Use Case and Capacity Assessment summary outreach activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211108","programNote":"Science Synthesis, Analysis and Research Program","usgsCitation":"Wilson, T.S., Wiltermuth, M.T., Jenni, K.E., Horton, R.J., Hunt, R.J., Williams, D.M., Nolan, V.P., Aumen, N.G., Brown, D.S., Blasch, K.W., and Murdoch, P.S., 2022, Use case development for earth monitoring, analysis, and prediction (EarthMAP)—A road map for future integrated predictive science at the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2021–1108, 137 p., https://doi.org/10.3133/ofr20211108.","productDescription":"vii, 132 p.","numberOfPages":"132","onlineOnly":"Y","ipdsId":"IP-129972","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":394402,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1108/covrthb.jpg"},{"id":394403,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1108/ofr20211108.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394404,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1108/ofr20211108.xml"},{"id":394405,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1108/images"}],"contact":"Director,
U.S. Geological Survey
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Reston, VA 20192
Repeated sampling of spatially distributed river chemistry can be used to assess the location, scale, and persistence of carbon and nutrient contributions to watershed exports. Here, we provide a comprehensive set of water chemistry measurements and ecohydrological metrics describing the biogeochemical conditions of permafrost-affected Arctic watersheds. These data were collected in watershed-wide synoptic campaigns in six stream networks across northern Alaska. Three watersheds are associated with the Arctic Long-Term Ecological Research site at Toolik Field Station (TFS), which were sampled seasonally each June and August from 2016 to 2018. Three watersheds were associated with the National Park Service (NPS) of Alaska and the U.S. Geological Survey (USGS) and were sampled annually from 2015 to 2019. Extensive water chemistry characterization included carbon species, dissolved nutrients, and major ions. The objective of the sampling designs and data acquisition was to characterize terrestrial–aquatic linkages and processing of material in stream networks. The data allow estimation of novel ecohydrological metrics that describe the dominant location, scale, and overall persistence of ecosystem processes in continuous permafrost. These metrics are (1) subcatchment leverage, (2) variance collapse, and (3) spatial persistence. Raw data are available at the National Park Service Integrated Resource Management Applications portal (O'Donnell et al., 2021, https://doi.org/10.5066/P9SBK2DZ) and within the Environmental Data Initiative (Abbott, 2021, https://doi.org/10.6073/pasta/258a44fb9055163dd4dd4371b9dce945).
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-14-95-2022","usgsCitation":"Shogren, A., Zarnetske, J.P., Abbott, B., Bratsman, S.P., Brown, B.C., Carey, M.P., Fulweiber, R., Greaves, H., Haines, E., Iannucci, F., Koch, J.C., Medvedeff, A., O’Donnell, J.A., Patch, L., Poulin, B., Williamson, T.J., and Bowden, W.B., 2022, Multi-year, spatially extensive, watershed-scale synoptic stream chemistry and water quality conditions for six permafrost-underlain Arctic watersheds: Earth System Science Data, v. 14, p. 95-116, https://doi.org/10.5194/essd-14-95-2022.","productDescription":"22 p.","startPage":"95","endPage":"116","ipdsId":"IP-127222","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":491327,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBK2DZ","text":"USGS data release","linkHelpText":"Stream and River Chemistry in Watersheds of Northwestern Alaska, 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jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855633,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Medvedeff, Alex","contributorId":298453,"corporation":false,"usgs":false,"family":"Medvedeff","given":"Alex","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":855634,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"O’Donnell, Jonathan A. 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":191423,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":855635,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Patch, Leika","contributorId":298455,"corporation":false,"usgs":false,"family":"Patch","given":"Leika","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":855636,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":855637,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Williamson, Tanner J.","contributorId":223165,"corporation":false,"usgs":false,"family":"Williamson","given":"Tanner","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":855638,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Bowden, William B.","contributorId":169388,"corporation":false,"usgs":false,"family":"Bowden","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":6735,"text":"University of Vermont, Rubenstein School of Environment and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":855639,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70227766,"text":"70227766 - 2022 - Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty","interactions":[],"lastModifiedDate":"2023-06-09T13:51:25.428617","indexId":"70227766","displayToPublicDate":"2022-01-14T06:44:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty","docAbstract":"Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle (Glyptemys insculpta) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λmean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.
We present a new field measurement and numerical interpretation method (combined termed ‘test’) to parameterize the diffusion of trichloroethene (TCE) and its biodegradation products (DPs) from the matrix of sedimentary rock. The method uses a dual-packer system to interrogate a low-permeability section of the rock matrix adjacent to a previously contaminated borehole and uses the borehole monitoring history to establish the pre-test condition. TCE and its DPs are removed from the groundwater between the packers at the onset of the testing. The parameters estimated by fitting a radial diffusion model to the concentration history and borehole concentration data, also termed back-diffusion, are the tortuosity factor and sorption coefficients of TCE and DPs in the rock matrix and the TCE and DP biodegradation rate coefficients in the borehole. We demonstrate the equipment design and the interpretive method using a borehole accessing the grey mudstone at a TCE contaminated site in the Newark Basin. In this test, both nonreactive (bromide) and reactive (trichlorofluoroethene) tracers are used to constrain the estimated parameters; however, the bromide tracer was not needed to estimate the parameters in this test. The parameters estimated from the field test are consistent with values measured independently in laboratory experiments using field samples of similar lithology. From the interpretation, we compute the TCE and DP concentration distributions in the rock matrix prior to the test to illustrate how the results can be used to enhance understanding of contaminant distribution in the rock matrix.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwmr.12495","usgsCitation":"Allen-King, R.M., Kiekhaefer, R.L., Goode, D.J., Hsieh, P.A., Lorah, M.M., and Imbrigiotta, T.E., 2022, A borehole test for chlorinated solvent diffusion and degradation rates in sedimentary rock: Groundwater Monitoring and Remediation, v. 42, no. 2, p. 23-34, https://doi.org/10.1111/gwmr.12495.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-122580","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394804,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99I50JE","text":"USGS data release","linkHelpText":"A finite-difference algorithm used to simulate radial diffusion, adsorption, and reactions of chlorinated ethenes in porous media"}],"volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen-King, Richelle M. 0000-0001-5559-1213","orcid":"https://orcid.org/0000-0001-5559-1213","contributorId":272047,"corporation":false,"usgs":false,"family":"Allen-King","given":"Richelle","email":"","middleInitial":"M.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiekhaefer, Rebecca L.","contributorId":272048,"corporation":false,"usgs":false,"family":"Kiekhaefer","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true}],"preferred":true,"id":831402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229185,"text":"70229185 - 2022 - The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-03-03T15:34:56.28404","indexId":"70229185","displayToPublicDate":"2022-01-12T09:27:17","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","docAbstract":"Depressional wetlands are extremely sensitive to changes in temperature and precipitation, so understanding how wetland inundation dynamics respond to changes in climate is essential for describing potential effects on wildlife breeding habitat. Millions of depressional basins make up the largest wetland complex in North America known as the Prairie Pothole Region (PPR). The wetland ecosystems that have formed in these basins provide important migratory-bird breeding habitat. The southeast portion of the U.S. PPR in Minnesota and Iowa has faced some of the greatest challenges in wetland conservation. Many existing prairie-pothole wetlands are small (<1 ha) and shallow (<2 m) and are typically not inundated with surface water year-round. Our goal with this project is to increase the efficacy of mapping tools used by management agencies to predict future changes in water levels in the PPR. We accomplish this goal by improving the link between existing data (about wetland water characteristics) and existing tools (mapping products). Our results successfully validated (2009-2021) the current mapping tool (a wetland hydrology model) used by the U.S. Fish and Wildlife Service (USFWS) to manage 22 wetlands in Minnesota. We were able to hindcast wetland water levels to 1984 and assess the accuracy of a satellite-derived surface water product and forecast water levels through 2099 using a suite of modeled climate data. This newly refined link between monitoring data and remote sensing tools will increase understanding and prediction for other wetlands beyond our study sites and through the Minnesota and Iowa portions of the PPR. Through conference presentations, publications, and development of an interactive climate change dashboard we are now working with managers to determine how we can help incorporate these predicted changes to waterfowl breeding habitat into their future management, acquisition, and restoration strategy.
Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves (Canis lupus). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.
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