Synergistic interactions between geologic structures and topography have long been recognized to reflect numerous Earth processes and rock properties over time. It was not until the advent of plate tectonics in the midtwentieth century that researchers began to view the nature of the northern Cordillera orogen as a quilt of foreign pieces of crust or “suspect terranes”. The Alaska Range shows complexity in topographic, geometric, and exhumational age asymmetry along and across the strike of the Denali fault zone attributable to several factors. Although direct exposures of the Denali fault zone in bedrock are exceptionally rare, regional to outcrop scale observations show the common internal structure consisting of some degree of strain localization in one or more, and presumably relatively weak, fault cores and an associated, commonly hydrothermally altered, damage zone.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Atlas of structural geological and geomorphological interpretation of remote sensing images","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781119813392.ch13","usgsCitation":"Caine, J., and Benowitz, J.A., 2022, Tectonics, fault zones, and topography in the Alaska-Canada Cordillera with a focus on the Alaska Range and Denali fault zone, chap. 13 of Atlas of structural geological and geomorphological interpretation of remote sensing images, p. 135-145, https://doi.org/10.1002/9781119813392.ch13.","productDescription":"11 p.","startPage":"135","endPage":"145","ipdsId":"IP-129709","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":409692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -141,\n 64.23134398587158\n ],\n [\n -156.36743326683552,\n 64.23134398587158\n ],\n [\n -156.36743326683552,\n 60.48117613047023\n ],\n [\n -141,\n 60.48117613047023\n ],\n [\n -141,\n 64.23134398587158\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":857582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benowitz, Jeff A. 0000-0003-2294-9172","orcid":"https://orcid.org/0000-0003-2294-9172","contributorId":229570,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":41671,"text":"Geophysical Institute and Geochronology Laboratory, University of Alaska–Fairbanks","active":true,"usgs":false}],"preferred":false,"id":857583,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241136,"text":"70241136 - 2022 - Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","interactions":[],"lastModifiedDate":"2023-03-13T12:07:56.782111","indexId":"70241136","displayToPublicDate":"2022-10-21T07:05:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","docAbstract":"Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.
Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely.
We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation and reproductive initiation of the Brewer's sparrow (Spizella breweri) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019.
We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance.
If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines.
Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.
Biological communities in freshwater streams are often impaired by multiple stressors (e.g., flow or water quality) originating from anthropogenic activities such as urbanization, agriculture, or energy extraction. Restoration efforts in the Chesapeake Bay watershed, USA seek to improve biological conditions in 10% of freshwater tributaries and to protect the biological integrity of existing healthy watersheds. To achieve these goals, resource managers need to better understand which stressors are most likely driving biological impairment. Our study addressed this knowledge gap through two approaches: 1) reviewing and synthesizing published multi-stressor studies, and 2) examining 303(d) listed impairments linked to biological impairment as identified by jurisdiction regulatory agencies (the states within the watershed and the District of Columbia). Results identified geomorphology (i.e., physical habitat), salinity, and toxic contaminants as important for explaining variability in benthic community metrics in the literature review. Geomorphology (i.e., physical habitat and sediment), salinity, and nutrients were the most reported stressors in the jurisdictional impairment analysis. Salinity is likely a major stressor in urban and mining settings, whereas geomorphology was commonly reported in agricultural settings. Toxic contaminants, such as pesticides, were rarely measured; more research is needed to quantify the extent of their effects in the region. Flow alteration was also highlighted as an important urban stressor in the literature review but was rarely measured in the literature or reported by jurisdictions as a cause of impairment. These results can be used to prioritize stressor monitoring by managers, and to improve stressor identification methods for identifying causes of biological impairment.
Uncovering the historical and contemporary processes shaping rare species with complex distributions is of growing importance due to threats such as habitat destruction and climate change. Species restricted to specialized, patchy habitat may persist by virtue of life history characteristics facilitating ongoing gene flow and dispersal, but they could also reflect the remnants of formerly widespread, suitable habitat that existed during past climate regimes. If formerly widespread species did not rely upon traits facilitating high dispersibility to persist, contemporary populations could be at high risk of extirpation or extinction. Fortunately, genomic investigations provide an opportunity to illuminate such alternative scenarios while simultaneously offering guidance for future management interventions. Herein, we test the role of these mechanisms in shaping patterns of genomic diversity and differentiation across a highly restricted and rare ecosystem: desert hanging gardens. We focus on Carex specuicola (Cyperaceae), a hanging garden obligate narrowly distributed in the Four Corners region of the southwestern United States that is listed as Threatened under the United States Endangered Species Act. Population structure and diversity analyses reveal that hanging garden populations are shaped by strong genetic drift, but that individuals in gardens are occasionally more closely related to individuals at other gardens than to individuals within the same garden. Similarly, gardens separated by long geographic distances may contain individuals that are more closely related compared to individuals in gardens separated by short geographic distances. Demographic modeling supports historical gene flow between some contemporary garden pairs, which is corroborated by low estimates of inbreeding coefficients and recent divergence times. As such, multiple lines of evidence support dispersal and gene flow across C. specuicola populations at both small and large spatial scales, indicating that even if C. specuicola was formerly more widespread, it may be well suited to persist in hanging gardens so long as suitable habitat remains available. Analyses like those demonstrated herein may be broadly applicable for understanding the short- and long-term evolutionary processes influencing rare species, and especially those having complex distributions across heterogeneous landscapes.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2022.941002","usgsCitation":"Chapin, K.J., Jones, M.R., Winkler, D.E., Rink, G., and Massatti, R., 2022, Evolutionary dynamics inform management interventions of a hanging garden obligate, Carex specuicola: Frontiers in Conservation Science, v. 3, 941002, 15 p., https://doi.org/10.3389/fcosc.2022.941002.","productDescription":"941002, 15 p.","ipdsId":"IP-141134","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446227,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2022.941002","text":"Publisher Index Page"},{"id":435666,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LLZ1XD","text":"USGS data release","linkHelpText":"Carex specuicola genomic data for the southern Colorado Plateau Desert"},{"id":408036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"southern Colorado Plateau Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.478515625,\n 35.88905007936091\n ],\n [\n -109.072265625,\n 35.88905007936091\n ],\n [\n -109.072265625,\n 37.75334401310656\n ],\n [\n -110.478515625,\n 37.75334401310656\n ],\n [\n -110.478515625,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2022-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Chapin, Kenneth James 0000-0002-8382-4050","orcid":"https://orcid.org/0000-0002-8382-4050","contributorId":297377,"corporation":false,"usgs":true,"family":"Chapin","given":"Kenneth","email":"","middleInitial":"James","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Matthew R","contributorId":297378,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"R","affiliations":[{"id":64389,"text":"formerly: USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rink, Glenn","contributorId":297379,"corporation":false,"usgs":false,"family":"Rink","given":"Glenn","affiliations":[{"id":64390,"text":"Deaver Herbarium, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853973,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238634,"text":"70238634 - 2022 - Regional models do not outperform continental models for invasive species","interactions":[],"lastModifiedDate":"2022-12-02T13:01:29.078063","indexId":"70238634","displayToPublicDate":"2022-10-04T07:00:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"Regional models do not outperform continental models for invasive species","docAbstract":"Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents.
Location: Continental United States.
Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions.
Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response.
Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.
","language":"English","publisher":"NeoBiota","doi":"10.3897/neobiota.77.86364","usgsCitation":"Jarnevich, C.S., Sofaer, H., Engelstad, P., and Belamaric, P., 2022, Regional models do not outperform continental models for invasive species: NeoBiota, v. 77, https://doi.org/10.3897/neobiota.77.86364.","productDescription":"22 p.","startPage":"1-22","ipdsId":"IP-137001","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446233,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.77.86364","text":"Publisher Index Page"},{"id":435667,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90AL0PN","text":"USGS data release","linkHelpText":"Data to create and evaluate distribution models for invasive species for different geographic extents"},{"id":409981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","noUsgsAuthors":false,"publicationDate":"2022-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":858156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sofaer, Helen R. 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":858157,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Peder","contributorId":238758,"corporation":false,"usgs":false,"family":"Engelstad","given":"Peder","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":858158,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belamaric, Pairsa 0000-0001-7529-0370","orcid":"https://orcid.org/0000-0001-7529-0370","contributorId":299593,"corporation":false,"usgs":false,"family":"Belamaric","given":"Pairsa","affiliations":[{"id":64897,"text":"Student Contractor to the USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":858159,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238435,"text":"70238435 - 2022 - One hundred years of cobalt production in the Democratic Republic of the Congo","interactions":[],"lastModifiedDate":"2022-11-23T12:42:47.997751","indexId":"70238435","displayToPublicDate":"2022-10-03T06:40:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3266,"text":"Resources Policy","active":true,"publicationSubtype":{"id":10}},"title":"One hundred years of cobalt production in the Democratic Republic of the Congo","docAbstract":"Cobalt is an indispensable element for the manufacture of strategic technologies including advanced batteries, jet engines, rare-earth permanent magnets, petroleum catalysts, and tool parts that enable construction, manufacturing, and mining. Cobalt routinely scores high in mineral supply risk assessments due to the concentration of cobalt mine production in the Democratic Republic of the Congo (DRC). This stands in contrast to the fact that DRC cobalt mine production had a 20% compound annual growth rate from 1995 through 2020—in large part due to investments by Chinese firms beginning in the mid-2000s. Given this continuous growth, one may ask why this supply is perceived to be so risky. This analysis illuminates the causes of historic disruptions to DRC cobalt mine and refinery production by analyzing country-level production, historical reports, and cobalt prices back to 1924. The results indicate that the main causes of supply disruptions were damage to transportation routes, underinvestment in maintaining nationalized mining assets, and the disintegration of the DRC economy during the early 1990s. On the other hand, cobalt mine production increased 50% from 1977 to 1979 despite two secessionist conflicts in DRC's cobalt producing region and increased seven-fold from 1996 to 2003 despite two African wars over the DRC and its resources. These results indicate that—barring another economic disintegration or mining industry nationalization—DRC mine production will likely continue to be the dominant supplier of the world's growing demand for cobalt in lithium-ion batteries. These results also indicate that sustained development of transportation (and other) infrastructure in Africa, as well as support for good governance in the DRC may prove key to the continued stability of DRC cobalt mine supplies.
Peatlands play a disproportionate role in the global carbon cycle. However, many peatlands have been ditched to lower the water table and converted into agriculture, which contributes to anthropogenic greenhouse gas emissions. Hydrologic restoration of drained peatlands could offset greenhouse gas emissions from these actions, but field examples that consider various greenhouse gases are still rare. Here, we examined emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from soils in drained shrub bogs in North Carolina, USA, before and after hydrologic restoration. We used static chamber methods and a before-and-after, control-impact (BACI) experimental design. We found that hydrologic manipulation (akin to restoration) increased water table levels by 65%, even with the impact of two hurricanes before and one after hydrologic manipulation. Increased water table levels led to a 58% decrease in CO2 fluxes, and an increase in CH4 (251%) and N2O fluxes (85%). Water table depth and soil temperature explained 43% of variation in CO2, while water table depth explained 25% and 18% of variation in CH4 and N2O fluxes, respectively. Despite the increases in CH4 and N2O, the higher magnitude of fluxes and large decline in CO2 lead to an overall lowering of greenhouse gas emissions after hydrologic restoration. Our results suggest that raising the water table in this shrub bog peatland decreased overall greenhouse gas emissions, illustrating that hydrologic restoration of peatlands can be a valuable climate mitigation practice.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01605-y","usgsCitation":"Armstrong, L., Peralta, A., Krauss, K., Cormier, N., Moss, R., Soderholm, E., McCall, A., Pickens, C., and Ardon, M., 2022, Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US: Wetlands, v. 42, 81, 10 p., https://doi.org/10.1007/s13157-022-01605-y.","productDescription":"81, 10 p.","ipdsId":"IP-135745","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Pocosin Lakes National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.61453247070312,\n 35.60930140634475\n ],\n [\n -76.16958618164062,\n 35.60930140634475\n ],\n [\n -76.16958618164062,\n 35.862343734896484\n ],\n [\n -76.61453247070312,\n 35.862343734896484\n ],\n [\n -76.61453247070312,\n 35.60930140634475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Armstrong, Luise","contributorId":298009,"corporation":false,"usgs":false,"family":"Armstrong","given":"Luise","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":854835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peralta, Ariane","contributorId":298010,"corporation":false,"usgs":false,"family":"Peralta","given":"Ariane","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":854836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cormier, N. 0000-0003-2453-9900","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":221147,"corporation":false,"usgs":false,"family":"Cormier","given":"N.","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":854838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moss, Rebecca 0000-0002-7599-9758 mossr@usgs.gov","orcid":"https://orcid.org/0000-0002-7599-9758","contributorId":169722,"corporation":false,"usgs":true,"family":"Moss","given":"Rebecca","email":"mossr@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854839,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Soderholm, Eric","contributorId":298011,"corporation":false,"usgs":false,"family":"Soderholm","given":"Eric","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854840,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCall, Aaron","contributorId":298012,"corporation":false,"usgs":false,"family":"McCall","given":"Aaron","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854841,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pickens, Christine","contributorId":298013,"corporation":false,"usgs":false,"family":"Pickens","given":"Christine","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854842,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ardon, Marcelo","contributorId":298014,"corporation":false,"usgs":false,"family":"Ardon","given":"Marcelo","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":854843,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237772,"text":"70237772 - 2022 - Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations","interactions":[],"lastModifiedDate":"2022-10-24T15:20:21.435018","indexId":"70237772","displayToPublicDate":"2022-09-26T10:08:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10071,"text":"Environmental Science and Technology Water","active":true,"publicationSubtype":{"id":10}},"title":"Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations","docAbstract":"In the United States (US), private-supply tapwater (TW) is rarely monitored. This data gap undermines individual/community risk-management decision-making, leading to an increased probability of unrecognized contaminant exposures in rural and remote locations that rely on private wells. We assessed point-of-use (POU) TW in three northern plains Tribal Nations, where ongoing TW arsenic (As) interventions include expansion of small community water systems and POU adsorptive-media treatment for Strong Heart Water Study participants. Samples from 34 private-well and 22 public-supply sites were analyzed for 476 organics, 34 inorganics, and 3 in vitro bioactivities. 63 organics and 30 inorganics were detected. Arsenic, uranium (U), and lead (Pb) were detected in 54%, 43%, and 20% of samples, respectively. Concentrations equivalent to public-supply maximum contaminant level(s) (MCL) were exceeded only in untreated private-well samples (As 47%, U 3%). Precautionary health-based screening levels were exceeded frequently, due to inorganics in private supplies and chlorine-based disinfection byproducts in public supplies. The results indicate that simultaneous exposures to co-occurring TW contaminants are common, warranting consideration of expanded source, point-of-entry, or POU treatment(s). This study illustrates the importance of increased monitoring of private-well TW, employing a broad, environmentally informative analytical scope, to reduce the risks of unrecognized contaminant exposures.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.2c00293","usgsCitation":"Bradley, P., Romanok, K., Smalling, K., Focazio, M.J., Charboneau, R., George, C.M., Navas-Acien, A., O’Leary, M., Red Cloud, R., Zacher, T., Breitmeyer, S.E., Cardon, M.C., Cuny, C.K., Ducheneaux, G., Enright, K., Evans, N., Gray, J., Harvey, D.E., Hladik, M.L., Kanagy, L.K., Loftin, K.A., McCleskey, R., Medlock-Kakaley, E., Meppelink, S.M., Valder, J., and Weis, C.P., 2022, Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations: Environmental Science and Technology Water, v. 2, no. 10, p. 1772-1788, https://doi.org/10.1021/acsestwater.2c00293.","productDescription":"17 p.","startPage":"1772","endPage":"1788","ipdsId":"IP-117867","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":446328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Surveys were conducted within the Ta‘ū Unit and Tutuila Unit, each on separate islands of American Samoa. We detected a total of 14 species during surveys and there were sufficient detections of seven species to allow for density estimation and abundance within each unit. We assessed differences in density between surveys with a two-sample z-test and found significant declines of Blue-crowned Lorikeets (Vini australis) in the Ta‘ū Unit, and of Samoan Starlings (Aplonis atrifusca) in the Tutuila Unit. Density estimates of the Crimson-crowned Fruit Dove (Ptilinopus porphyraceus), Pacific Kingfisher (Todiramphus sacer), Polynesian Wattled Honeyeater (Foulehaio carunculatus), and Samoan Starling (in the Ta‘ū Unit) were also lower in 2018 than 2011, but differences were inconclusive because of relatively large variance estimates. Densities of the Polynesian Starling (Aplonis tabuensis) and Pacific Imperial Pigeon (Ducula pacifica) in the Ta‘ū Unit were higher in 2018 than 2011, but differences were similarly inconclusive. Lower 2018 densities could be due to Tropical Cyclone Gita that struck the islands just four months before the surveys. We provide indices of relative occurrence and abundance for the remaining seven species detected, which include the Many-colored Fruit Dove (Ptilinopus perousii) and the rarely detected Spotless Crake (Zapornia tabuensis)—both of which are species of concern in American Samoa.
","language":"English","publisher":"BioOne","doi":"10.2984/76.2.4","usgsCitation":"Judge, S., Camp, R.J., Vaivai, V., and Hart, P.J., 2022, Status of landbirds in the National Park of American Samoa: Pacific Science, v. 76, no. 2, p. 139-156, https://doi.org/10.2984/76.2.4.","productDescription":"18 p.","startPage":"139","endPage":"156","ipdsId":"IP-131689","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":446365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2984/76.2.4","text":"Publisher Index Page"},{"id":407958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"American Samoa","otherGeospatial":"National Park of American Samoa, Ofu-Olosega, Ta'u, Tutuila","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.7392120361328,\n -14.317282180862385\n ],\n [\n -170.6403350830078,\n -14.317282180862385\n ],\n [\n -170.6403350830078,\n -14.231439639624147\n ],\n [\n -170.7392120361328,\n -14.231439639624147\n ],\n [\n -170.7392120361328,\n -14.317282180862385\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.6831512451172,\n -14.191834591858717\n ],\n [\n -169.60693359375,\n -14.191834591858717\n ],\n [\n -169.60693359375,\n -14.15055809981021\n ],\n [\n -169.6831512451172,\n -14.15055809981021\n ],\n [\n -169.6831512451172,\n -14.191834591858717\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.49363708496094,\n -14.275695888737538\n ],\n [\n -169.4194793701172,\n -14.275695888737538\n ],\n [\n -169.4194793701172,\n -14.207810571387945\n ],\n [\n -169.49363708496094,\n -14.207810571387945\n ],\n [\n -169.49363708496094,\n -14.275695888737538\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Judge, Seth 0000-0003-3832-3246","orcid":"https://orcid.org/0000-0003-3832-3246","contributorId":189965,"corporation":false,"usgs":false,"family":"Judge","given":"Seth","email":"","affiliations":[],"preferred":false,"id":853715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":853716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaivai, Visa","contributorId":254982,"corporation":false,"usgs":false,"family":"Vaivai","given":"Visa","affiliations":[{"id":51382,"text":"National Park Service, I&M","active":true,"usgs":false}],"preferred":false,"id":853717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":853718,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237705,"text":"70237705 - 2022 - Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development","interactions":[],"lastModifiedDate":"2022-10-31T14:56:21.921244","indexId":"70237705","displayToPublicDate":"2022-09-21T08:23:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development","docAbstract":"Wildlife avoid human disturbances, including roads and development. Avoidance and displacement of wildlife into less suitable habitat due to human development can affect their energy expenditures and fitness. The heart rate and oxygen uptake of large mammals varies with both natural aspects of their habitat (terrain, climate, predators, etc.) and anthropogenic influence (noise, light, fragmentation, etc.). Although incorporating physiological analyses of energetics can inform the impacts of both development and conservation, management decisions rarely incorporate individuals’ energetic requirements when deciding on locations for potential development.
We aimed to estimate the change in expected energy expenditure, numerically and spatially, for mule deer to traverse a landscape with varying levels of oil and gas development through time.
Using calculations of energy expenditure of mule deer (Odocoileus hemionus) by weight, in relation to physical terrain components, plus avoidance factors for anthropogenic disturbance, we developed a spatiotemporal model of the minimum energy required for mule deer to traverse a landscape. We compared expected energy expenditure across 12 study sites with increasing levels of oil and gas development and over time in our study area, on the northern Colorado Plateau of Utah.
We found that energy expenditure can be increased by development, regardless of terrain, through increased travel distance associated with avoidance behavior. Maximum median energy expenditure to traverse a 1400 ha sample area rose from 1135 to 1935 kilocalories, a 70% increase in energy required of a mule deer. There was a significant relationship between energy expenditure and the size of oil and gas development (p < 0.001), its compactness (p < 0.05), and its ‘thinness’ (p < 0.001), but not terrain ruggedness (p = 0.25).
As the energy costs of movement correlate across multiple species of large mammals, our analysis of the energetic cost, for mule deer, associated with development can serve as a quantitative representative of the impacts of oil and gas development for multiple mammals—including threatened or endangered species. Our bioenergetic cost-distance model provides a means of delineating impediments to efficient movement and can be used to quantify the expected energetic costs of proposed future developments. As wildlife are exposed to increasing anthropogenic stressors which reduce fitness, it is important to make strategic siting decisions to reduce energetic costs imposed by human activities.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01521-w","usgsCitation":"Chambers, S.N., Villarreal, M.L., Duane, O.J., Munson, S.M., Stuber, E.F., Tyree, G., Waller, E.K., and Duniway, M.C., 2022, Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development: Landscape Ecology, v. 37, p. 2947-2961, https://doi.org/10.1007/s10980-022-01521-w.","productDescription":"15 p.","startPage":"2947","endPage":"2961","ipdsId":"IP-138879","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":505527,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.usu.edu/wild_facpub/3277","text":"External Repository"},{"id":435685,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99JGAYG","text":"USGS data release","linkHelpText":"Maps of mule deer avoidance areas based on density of oil and gas developments, Book Cliffs, Utah"},{"id":408538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"northern Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.753173828125,\n 38.77978137804918\n ],\n [\n -109.072265625,\n 38.77978137804918\n ],\n [\n -109.072265625,\n 40.49709237269567\n ],\n [\n -110.753173828125,\n 40.49709237269567\n ],\n [\n -110.753173828125,\n 38.77978137804918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Samuel Norton 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Center","active":true,"usgs":true}],"preferred":true,"id":855079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tyree, Gayle L","contributorId":298085,"corporation":false,"usgs":false,"family":"Tyree","given":"Gayle L","affiliations":[{"id":64492,"text":"Plant and Environmental Sciences Department, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":855080,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waller, Eric K","contributorId":298087,"corporation":false,"usgs":false,"family":"Waller","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":64493,"text":"Independent USGS contractor","active":true,"usgs":false}],"preferred":false,"id":855081,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":855082,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236827,"text":"sir20225087 - 2022 - Sixty years of channel adjustments to dams in the two segments of the Missouri National Recreational River, South Dakota and Nebraska","interactions":[],"lastModifiedDate":"2026-04-27T18:42:06.38109","indexId":"sir20225087","displayToPublicDate":"2022-09-20T06:44:21","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5087","displayTitle":"Sixty Years of Channel Adjustments to Dams in the Two Segments of the Missouri National Recreational River, South Dakota and Nebraska","title":"Sixty years of channel adjustments to dams in the two segments of the Missouri National Recreational River, South Dakota and Nebraska","docAbstract":"The Missouri National Recreational River (MNRR) consists of two Missouri River segments managed by the National Park Service on the border of South Dakota and Nebraska. Both river segments are unchannelized and maintain much of their pre-dam channel form, but upstream dams have caused reductions in peak flow magnitudes and sediment supply. The 39-mile segment is located between Fort Randall and Gavins Point Dams, transitioning from a riverine process domain to a distributary delta process domain in the headwaters of Lewis and Clark Lake. The 59-mile segment, an entirely riverine process domain, is downstream from Gavins Point Dam, the most downstream main channel dam on the Missouri River, and upstream from a highly altered navigation channel extending more than 1,000 kilometers downstream to St. Louis, Missouri. The National Park Service seeks to preserve the outstandingly remarkable natural, cultural, and recreational values of the MNRR. There is a particular need to understand bank-erosion processes to guide management decisions related to bank-erosion controls.
Changes in channel shape, as measured in topographic cross sections surveyed every 5–10 years since the mid-20th century, document bed incision (bed-elevation lowering) in riverine process domains, a mix of aggradation and incision in the delta, and aggradation in Lewis and Clark Lake. Channel incision is greatest in the 59-mile segment, where mean thalweg (deepest point in a cross section) incision is 3.5 meters, and net incision in the thalweg greater than 5 meters was observed at a cross section 93 kilometers downstream from Gavins Point Dam. Analysis of topographic cross sections also indicates that rates of bed-elevation change since 1960 were lowest in the 39-mile river segment and in Lewis and Clark Lake. Rates of bed-elevation change were higher in the delta and 59-mile segments but lower in cross sections near Gavins Point Dam where the channel is confined by bank revetment on both banks and the bed has coarsened substantially since completion of the dam. Several large floods in recent decades, including a post-dam record flood event in 2011, scoured the bed and deposited large high-elevation sandbars in both river segments, especially in the 59-mile segment. Analysis of topographic cross-sections indicates the 2011 flood event caused substantial erosion and deposition, low magnitude net incision in the river segments and delta, and considerable sediment aggradation in the lake. Surveys taken after the 2011 flood in the 59-mile segment indicate a trend of sediment rearrangement and channel recovery following large floods, with the highest parts of the bed, sandbars, eroding and lowering while sediment was deposited on the deepest parts of the channel, which increased in elevation.
Inundation modeling results indicate that the narrower valley in the 39-mile segment results in a higher percentage of the flood plain being inundated by flooding relative to the 59-mile segment, which has a much wider valley. Likewise, bed incision in the 59-mile segment has increased channel capacity and resulted in a modern channel corridor inset into a higher flood-plain surface. The inset flood plain was inundated by the 2011 flood, but the pre-dam flood plain is rarely inundated. Analysis of channel boundaries over time indicates that pre-dam channel-migration rates were as much as five times larger than modern channel-migration rates in the 59-mile segment. Bank erosion in the 59-mile segment has primarily been into post-1894 channel deposits; bank-erosion rates are comparably very low in the 39-mile segment. Analysis of channel-migration zones indicates that most erosion is isolated to local hot spots and is used to establish predictions for 10 and 20 years into the future based on past movement rates in both MNRR segments. Long-term bed-elevation and planform trends indicate that rates of adjustment in the 59-mile segment are slowing and may be approaching a new equilibrium, but recent large floods and spatial variability contribute to considerable uncertainty. Additional monitoring of channel morphology would be needed to confirm trends observed in this analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225087","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Elliott, C.M., and Jacobson, R.B., 2022, Sixty years of channel adjustments to dams in the two segments of the Missouri National Recreational River, South Dakota and Nebraska: U.S. Geological Survey Scientific Investigations Report 2022–5087, 75 p., https://doi.org/10.3133/sir20225087.","productDescription":"Report: ix, 75 p.; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-128033","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":406981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5087/coverthb.jpg"},{"id":406982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5087/sir20225087.pdf","text":"Report","size":"27.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5087"},{"id":406983,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5087/sir20225087.XML"},{"id":503554,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113523.htm","linkFileType":{"id":5,"text":"html"}},{"id":407044,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225087/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406985,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RZPNJR","text":"USGS data release","linkHelpText":"Channel geometry, banklines and floodplain inundation over a range of discharges in two segments of the Missouri National Recreational River, South Dakota and Nebraska, 1955–2018"},{"id":406984,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5087/images"}],"country":"United States","state":"Nebraska, South Dakota","otherGeospatial":"Missouri National Recreational River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 42.49640294093705\n ],\n [\n -96.5313720703125,\n 42.49640294093705\n ],\n [\n -96.5313720703125,\n 43.1450861841603\n ],\n [\n -98.61328125,\n 43.1450861841603\n ],\n [\n -98.61328125,\n 42.49640294093705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
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Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland and wetland ecosystems to compensate for unavoidable environmental damage from anthropogenic disturbances such as energy development and road construction. Energy-extraction and -generation facilities continue to proliferate across the natural landscapes of the United States, yet mitigation tools to ameliorate the negative behavioral effects on wildlife from these types of facilities are rarely implemented. Scientists from the U.S. Geological Survey conducted a 10-year before-after-control-impact (commonly referred to as BACI) study that evaluated the displacement effects of wind facilities on breeding grassland birds. The study determined behavioral avoidance for 7 of 9 species. This research is notable because of its design, geographical scope, and duration, which allowed for the determination of immediate, short-term effects; delayed or sustained effects; and discrete distances at which effects occurred. In addition, the U.S. Fish and Wildlife Service and Ducks Unlimited conducted a 3-year concurrent-year paired-reference study to determine behavioral avoidance for five species of dabbling ducks. By quantifying displacement rate from these two studies, U.S. Geological Survey and U.S. Fish and Wildlife Service scientists developed the Avian-Impact Offset Method (AIOM) to quantify and compensate for loss in value of breeding habitat. The AIOM converts the biological value (that is, number of bird pairs) lost by way of avoidance and estimates the site-specific number of hectares of grasslands and number of wetlands needed to compensate for displaced pairs of grassland birds and waterfowl. By converting biological value to traditional units of measure in which land is described and purchased or sold, the AIOM lends itself readily to the delivery of offsetting measures such as easement protections and restoration projects. The AIOM tool is applicable to wind, solar, oil, gas, and transportation infrastructure.
This tutorial was designed to increase awareness of the AIOM and to promote its proper application. The tutorial is divided into four sections, each of which explains a discrete topic concerning aspects of behavioral displacement. The first section provides geographical and biological context, and the second section describes the field and statistical methods and results. The third section provides step-by-step instructions for applying the AIOM to several scenarios involving grassland birds or waterfowl at wind or oil facilities. The fourth section describes decision-support tools created to implement the AIOM. The appendices provide the actual field protocols constituting the methods for the research, provide detailed results by species and wind facility for that research, and provide detailed instructions for downloading and applying the decision-support tools.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221049","collaboration":"Prepared in collaboration with the U.S. Fish and Wildlife Service","usgsCitation":"Shaffer, J.A., Loesch, C.R., and Buhl, D.A., 2022, Understanding the Avian-Impact Offset Method—A tutorial: U.S. Geological Survey Open-File Report 2022–1049, 227 p., https://doi.org/10.3133/ofr20221049.","productDescription":"Report: v, 227 p.; 2 Data Releases","numberOfPages":"238","onlineOnly":"Y","ipdsId":"IP-134338","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":406394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1049/coverthb.jpg"},{"id":406396,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J6QUF6","text":"USGS data release","linkHelpText":"North American Breeding Bird Survey dataset 1966–2019"},{"id":406395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1049/ofr20221049.pdf","text":"Report","size":"95.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1049"},{"id":406397,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43SDG","text":"USGS data release","linkHelpText":"Effects of wind-energy facilities on breeding grassland bird distributions"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 52.10650519075632\n ],\n [\n -113.73046875,\n 52.16045455774706\n ],\n [\n -114.78515624999999,\n 51.17934297928927\n ],\n [\n -112.8515625,\n 48.922499263758255\n ],\n [\n -111.796875,\n 47.45780853075031\n ],\n [\n -107.40234375,\n 47.040182144806664\n ],\n [\n -103.798828125,\n 47.81315451752768\n ],\n [\n -102.39257812499999,\n 47.338822694822\n ],\n [\n -100.72265625,\n 45.82879925192134\n ],\n [\n -100.37109375,\n 44.5278427984555\n ],\n [\n -99.49218749999999,\n 43.32517767999296\n ],\n [\n -96.064453125,\n 41.96765920367816\n ],\n [\n -94.39453125,\n 42.87596410238256\n ],\n [\n -95.09765625,\n 45.644768217751924\n ],\n [\n -95.97656249999999,\n 49.03786794532644\n ],\n [\n -98.7890625,\n 50.45750402042058\n ],\n [\n -112.236328125,\n 52.10650519075632\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
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The actual state of ecological systems is rarely known with certainty, but management actions must often be taken regardless of imperfect measurement (partial observability). Because of the difficulties in accounting for partial observability, it is usually treated in an ad hoc fashion, or simply ignored altogether. Yet incorporating partial observability into decision processes lends a realism that has the potential to improve ecological outcomes significantly. We review frameworks for dealing with partial observability, focusing specifically on dynamic ecological systems with Markovian transitions, i.e., transitions among system states that are influenced by the current system state and management action over time. Fully observable states are represented in an observable Markov decision process (MDP), whereas obscure or hidden states are represented in a partially observable process (POMDP). POMDPs can be seen as a natural extension of observable MDPs. Management under partial observability generalizes the situation for complete observability, by recognizing uncertainty about the system's state and incorporating sequential observations associated with, but not the same as, the states themselves. Decisions that otherwise would depend on the actual state must be based instead on state probability distributions (“belief states”). Partial observability requires adaptation of the entire decision process, including the use of belief states and Bayesian updates, valuation that includes expectations over observations, and optimal strategy that identifies actions for belief states over a continuous belief space. We compare MDPs and POMDPs and highlight POMDP applications to some common ecological problems. We clarify the structure and operations, approaches for finding solutions, and analytic challenges of POMDPs for practicing ecologists. Both observable and partially observable MDPs can use an inductive approach to identify optimal strategies and values, with a considerable increase in mathematical complexity with POMDPs. Better understanding of POMDPs can help decision makers manage imperfectly measured ecological systems more effectively.
The expansion of human infrastructure has contributed to novel risks and disturbance regimes in most ecosystems, leading to considerable uncertainty about how species will respond to altered landscapes. A recent assessment revealed that whooping cranes (Grus americana), an endangered migratory waterbird species, avoid wind-energy infrastructure during migration. However, uncertainties regarding collective impacts of other types of human infrastructure, such as power lines on migration, variable drought conditions, and continued construction of wind energy infrastructure may compromise ongoing recovery efforts for whooping cranes. Droughts are increasing in frequency and severity throughout the whooping crane migration corridor, and the impacts of drought on stopover habitat use are largely unknown. Moreover, decision-based analyses are increasingly advocated to guide recovery planning for endangered species, yet applications remain rare. Using GPS locations from 57 whooping cranes from 2010 through 2016 in the United States Great Plains, we assessed habitat selection and avoidance of potential disturbances during migration relative to drought conditions, and we used these results in an optimization analysis to select potential sites for new wind energy developments that minimize relative habitat loss for whooping cranes and maximize wind energy potential. Drought occurrence and severity varied spatially and temporally across the migration corridor during our study period. Whooping cranes rarely used areas <5 km from human settlements and wind energy infrastructure under both drought and non-drought conditions, and <2 km from power lines during non-drought conditions, with the lowest likelihood of use near wind energy infrastructure. Whooping cranes differed in their selection of wetland and cropland land cover types depending on drought or non-drought conditions. We identified scenarios for wind energy expansion across the migration corridor and in select states, which are robust to uncertain drought conditions, where future loss of highly selected stopover habitats could be minimized under a common strategy. Our approach was to estimate functional habitat loss while integrating current disturbances, potential future disturbances, and uncertainty in drought conditions. Therefore, dynamic models describing potential costs associated with risk-averse behaviors resulting from future developments can inform proactive conservation before population impacts occur.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.931260","usgsCitation":"Ellis, K.S., Pearse, A.T., Brandt, D.A., Bidwell, M., Harrell, W.C., Butler, M.J., and Post van der Burg, M., 2022, Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes: Frontiers in Ecology and Evolution, v. 10, 931260, 17 p., https://doi.org/10.3389/fevo.2022.931260.","productDescription":"931260, 17 p.","ipdsId":"IP-138784","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446538,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.931260","text":"Publisher Index Page"},{"id":435701,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P902I4WO","text":"USGS data release","linkHelpText":"Whooping crane migration habitat selection disturbance data and maps"},{"id":406216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Montana, Nebraska, North Dakota, South Dakota, Oklahoma, Texas","otherGeospatial":"Great Plains","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"editors":[{"text":"Hamilton, Diana","contributorId":296218,"corporation":false,"usgs":false,"family":"Hamilton","given":"Diana","email":"","affiliations":[{"id":12803,"text":"Mount Allison University","active":true,"usgs":false}],"preferred":false,"id":850888,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Ellis, Kristen S. 0000-0003-2759-3670","orcid":"https://orcid.org/0000-0003-2759-3670","contributorId":251877,"corporation":false,"usgs":true,"family":"Ellis","given":"Kristen","email":"","middleInitial":"S.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bidwell, Mark T.","contributorId":139204,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark T.","affiliations":[{"id":12696,"text":"Environmental Canada","active":true,"usgs":false}],"preferred":false,"id":850805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harrell, Wade C.","contributorId":147143,"corporation":false,"usgs":false,"family":"Harrell","given":"Wade","email":"","middleInitial":"C.","affiliations":[{"id":16793,"text":"USFWS, Ecological Services, Austwell, TX","active":true,"usgs":false}],"preferred":false,"id":850806,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butler, Matthew J.","contributorId":296149,"corporation":false,"usgs":false,"family":"Butler","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":850807,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Post van der Burg, Max 0000-0002-3943-4194","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":216013,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850808,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239848,"text":"70239848 - 2022 - Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i","interactions":[],"lastModifiedDate":"2023-01-23T14:59:19.873812","indexId":"70239848","displayToPublicDate":"2022-09-02T08:46:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5898,"text":"Data in Brief","onlineIssn":"2352-3409","active":true,"publicationSubtype":{"id":10}},"title":"Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i","docAbstract":"Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dib.2022.108572","usgsCitation":"Fortini, L., Kaiser, L.R., Xue, L., and Wang, Y., 2022, Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i: Data in Brief, v. 45, 108572, 11 p., https://doi.org/10.1016/j.dib.2022.108572.","productDescription":"108572, 11 p.","ipdsId":"IP-138113","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":446553,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dib.2022.108572","text":"Publisher Index Page"},{"id":435702,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MF7SG","text":"USGS data release","linkHelpText":"Hawaiian Islands bioclimatic variables for baseline and future climate 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\"}}]}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":862133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Lauren R.","contributorId":200422,"corporation":false,"usgs":false,"family":"Kaiser","given":"Lauren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":862134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xue, Lulin","contributorId":301129,"corporation":false,"usgs":false,"family":"Xue","given":"Lulin","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":862135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Yaping","contributorId":191943,"corporation":false,"usgs":false,"family":"Wang","given":"Yaping","email":"","affiliations":[],"preferred":false,"id":862136,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237260,"text":"70237260 - 2022 - Little bugs, big data, and Colorado River adaptive management: Preliminary findings from the ongoing bug flow experiment at Glen Canyon Dam","interactions":[],"lastModifiedDate":"2025-03-14T15:11:55.173887","indexId":"70237260","displayToPublicDate":"2022-09-01T09:22:40","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":8569,"text":"Boatman's Quarterly Review","active":true,"publicationSubtype":{"id":30}},"title":"Little bugs, big data, and Colorado River adaptive management: Preliminary findings from the ongoing bug flow experiment at Glen Canyon Dam","docAbstract":"The undammed Colorado River in Grand Canyon was characterized by spring snow-melt floods that sometimes exceeded 100,000 cubic feet per second (cfs). These were followed by occasional flash floods during summer monsoons, then by low flows from fall through early spring (Figure 1; Topping and others, 2003). This seasonally variable flow regime carried huge loads of sediment and was an important driver of natural processes that sustained the Colorado River ecosystem. For instance, high turbidity associated with this flow regime likely restricted algal growth to the river’s edge or shallow cobble habitats, similar to other desert rivers. Aquatic invertebrate assemblages were probably diverse and adapted to these variable conditions (Vinson, 2001; Haden and others, 2003). Native fishes were likely opportunistic feeders, consuming ants, seeds, and other terrestrial resources during times of flooding and switching to aquatic-derived resources like algae and aquatic invertebrates at other times (Minckley, 1991; Behn and Baxter, 2019). Regulation of the Colorado River by Glen Canyon Dam in 1963 eliminated the annual snowmelt floods, it sharply increased base flows by more than 50 percent, and dramatically increased within-day fluctuations in discharge for hydropower production (the ‘daily tides’ of the river, Figure 1 and 2; Topping and others, 2003). Glen Canyon Dam also changed other aspects of the river’s physical template, particularly temperature, sediment, and nutrient regimes. These changes to the physical template of the river led to fundamental changes in the natural processes that the sustain Colorado River ecosystem. For example, algae are common throughout the river during periods of clear water and represent the foundation of aquatic food webs (Stevens and others, 1997; Cross and others 2013). Many types of aquatic insects have disappeared or become rare, particularly sensitive groups such as mayflies, stoneflies, and caddisflies (Kennedy and others, 2016). Because aquatic insect assemblages in the Colorado River in Grand Canyon are neither diverse nor productive, food webs are simplified and inherently unstable, limiting populations of hungry fish (Cross and others 2013; Korman and others 2021).
","language":"English","publisher":"Grand Canyon River Guides Association","usgsCitation":"Kennedy, T., Metcalfe, A., Deemer, B., Ford, M., Szydlo, C.M., Yackulic, C., and Muehlbauer, J., 2022, Little bugs, big data, and Colorado River adaptive management: Preliminary findings from the ongoing bug flow experiment at Glen Canyon Dam: Boatman's Quarterly Review, v. 35, no. 3, p. 26-31.","productDescription":"6 p.","startPage":"26","endPage":"31","ipdsId":"IP-143763","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":483348,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcrg.org/bqr"},{"id":407960,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.48968696594237,\n 36.93095788125762\n ],\n [\n -111.47878646850586,\n 36.93095788125762\n ],\n [\n -111.47878646850586,\n 36.94021961852396\n ],\n [\n -111.48968696594237,\n 36.94021961852396\n ],\n [\n -111.48968696594237,\n 36.93095788125762\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Metcalfe, Anya 0000-0002-6286-4889","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":221738,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, Morgan 0000-0001-5104-9566","orcid":"https://orcid.org/0000-0001-5104-9566","contributorId":221740,"corporation":false,"usgs":true,"family":"Ford","given":"Morgan","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853871,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Szydlo, Cheyenne Maxime 0000-0003-4818-2395","orcid":"https://orcid.org/0000-0003-4818-2395","contributorId":297340,"corporation":false,"usgs":true,"family":"Szydlo","given":"Cheyenne","email":"","middleInitial":"Maxime","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853872,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853873,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Muehlbauer, Jeffrey 0000-0003-1808-580X","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":221739,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853874,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70235898,"text":"70235898 - 2022 - Collateral damage: Anticoagulant rodenticides pose threats to California condors","interactions":[],"lastModifiedDate":"2022-08-25T15:53:53.204495","indexId":"70235898","displayToPublicDate":"2022-08-18T10:41:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Collateral damage: Anticoagulant rodenticides pose threats to California condors","docAbstract":"Anticoagulant rodenticides (ARs) are widespread environmental contaminants that pose risks to scavenging birds because they routinely occur within their prey and can cause secondary poisoning. However, little is known about AR exposure in one of the rarest avian scavengers in the world, the California condor (Gymnogyps californianus). We assessed AR exposure in California condors and surrogate turkey vultures (Cathartes aura) to gauge potential hazard to a proposed future condor flock by determining how application rate and environmental factors influence exposure. Additionally, we examined whether ARs might be correlated with prolonged blood clotting time and potential mortality in condors. Only second-generation ARs (SGARs) were detected, and exposure was detected in all condor flocks. Liver AR residues were detected in 42% of the condors (27 of 65) and 93% of the turkey vultures (66 of 71). Although concentrations were generally low (<10 ng/g ww), 48% of the California condors and 64% of the turkey vultures exposed to ARs exceeded the 5% probability of exhibiting signs of toxicosis (>20 ng/g ww), and 10% and 13% exceeded the 20% probability of exhibiting signs toxicosis (>80 ng/g ww). There was evidence of prolonged blood clotting time in 16% of the free-flying condors. For condors, there was a relationship between the interaction of AR exposure index (legal use across regions where condors existed) and precipitation, and the probability of detecting ARs in liver. Exposure to ARs may complicate recovery efforts of condor populations within their current range and in the soon to be established northern California experimental population. Continued monitoring of AR exposure using plasma blood clotting assays and residue analysis would allow for an improved understanding of their hazard to condors, particularly if paired with recent movement data that could elucidate exposure sources on the landscape occupied by this endangered species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2022.119925","usgsCitation":"Herring, G., Eagles-Smith, C., Wolstenholme, R., Welch, A., West, C., and Rattner, B.A., 2022, Collateral damage: Anticoagulant rodenticides pose threats to California condors: Environmental Pollution, v. 311, 119925, 9 p., https://doi.org/10.1016/j.envpol.2022.119925.","productDescription":"119925, 9 p.","ipdsId":"IP-139709","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446732,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2022.119925","text":"Publisher Index Page"},{"id":435724,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NHPLHX","text":"USGS data release","linkHelpText":"Anticoagulant rodenticide concentrations in blood and tissue of California condors and turkey vultures (ver. 2.0, May 2023)"},{"id":405589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pinnacles National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.2506103515625,\n 36.39696752441776\n ],\n [\n -121.10229492187501,\n 36.39696752441776\n ],\n [\n -121.10229492187501,\n 36.56370306576917\n ],\n [\n -121.2506103515625,\n 36.56370306576917\n ],\n [\n -121.2506103515625,\n 36.39696752441776\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"311","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Herring, Garth 0000-0003-1106-4731 gherring@usgs.gov","orcid":"https://orcid.org/0000-0003-1106-4731","contributorId":4403,"corporation":false,"usgs":true,"family":"Herring","given":"Garth","email":"gherring@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":849634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":849635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolstenholme, Rachel","contributorId":295522,"corporation":false,"usgs":false,"family":"Wolstenholme","given":"Rachel","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":849636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Welch, Alacia","contributorId":206083,"corporation":false,"usgs":false,"family":"Welch","given":"Alacia","email":"","affiliations":[{"id":37236,"text":"Pinnacles National Park","active":true,"usgs":false}],"preferred":false,"id":849637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"West, Chris","contributorId":295524,"corporation":false,"usgs":false,"family":"West","given":"Chris","email":"","affiliations":[{"id":38097,"text":"Yurok Tribe","active":true,"usgs":false}],"preferred":false,"id":849638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849639,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262064,"text":"70262064 - 2022 - Seasonal activity patterns of bats in high-elevation conifer sky islands","interactions":[],"lastModifiedDate":"2025-01-10T15:45:58.85035","indexId":"70262064","displayToPublicDate":"2022-08-18T09:40:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":629,"text":"Acta Chiropterologica","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal activity patterns of bats in high-elevation conifer sky islands","docAbstract":"In the southern Appalachian Mountains of the southeastern USA, bat communities in high-elevation habitats tend to be relatively under-surveyed. High-elevation habitats may provide important habitat to certain species (i.e., migratory tree bats), and may serve as climate refugia during droughts or high temperatures. We conducted an opportunistic acoustic survey of bat communities in ten survey areas in high elevation (1,585–1,920 m a.s.l.) montane Picea rubens (red spruce)-Abies fraseri (Fraser fir) forest in the southern Appalachian Mountains of western North Carolina. In each survey area, we randomly placed three full spectrum acoustic detectors (N = 30) during three seasons (spring, summer and fall) in 2015. We deployed each detector for two five-day periods during each season (n = 900 survey nights). Although we detected seven bat species/groups during the surveys, 73% of echolocation files were attributed to Lasiurus cinereus (hoary bat) and Lasionycteris noctivagans (silver-haired bat). Generally rare in the Appalachians and typically present only at low densities in the summer at mid- and low-elevations, both species were detected at all sites during all seasons. Overall, mean nightly activity of bats was higher in the summer than the spring or fall. We observed 3.7–5 times greater activity of L. cinereus in spruce-fir forests during the summer compared to spring and fall, whereas L. noctivagans had 1.3–5 times more activity in the summer compared to other seasons. After accounting for precipitation events, our finite mixture models showed that season, temperature, elevation, and canopy height influenced L. cinereus activity, whereas season and temperature affected L. noctivagans activity. Our observations suggest that high-elevation spruce-fir forests are providing summer foraging and possibly day-roosting habitat of tree bats not previously documented this far south in North America.
","language":"English","publisher":"Museum and Institute of Zoology at the Polish Academy of Sciences","doi":"10.3161/15081109acc2022.24.1.007","usgsCitation":"Diggins, C., and Ford, W., 2022, Seasonal activity patterns of bats in high-elevation conifer sky islands: Acta Chiropterologica, v. 24, no. 1, p. 91-101, https://doi.org/10.3161/15081109acc2022.24.1.007.","productDescription":"11 p.","startPage":"91","endPage":"101","ipdsId":"IP-121634","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zotero.org/groups/5435545/items/TUMBJJ9Q","text":"External Repository"},{"id":465988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.01925096181063,\n 36.1284749704932\n ],\n [\n -83.75535293883529,\n 36.1284749704932\n ],\n [\n -83.75535293883529,\n 35.0263131090354\n ],\n [\n -82.01925096181063,\n 35.0263131090354\n ],\n [\n -82.01925096181063,\n 36.1284749704932\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diggins, Corinne A.","contributorId":270602,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":922940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":922939,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248068,"text":"70248068 - 2022 - Application of tail transmitters for tracking feral horses as an alternative to radio collars","interactions":[],"lastModifiedDate":"2023-09-05T14:42:56.079147","indexId":"70248068","displayToPublicDate":"2022-08-18T09:33:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Application of tail transmitters for tracking feral horses as an alternative to radio collars","docAbstract":"Radio collars have been used to examine the spatial ecology of all North American ungulates, but are rarely used on feral horses due to concerns that they may cause injury. Due to public concerns for animal welfare, an alternative to radio collars for tracking feral horses, particularly stallions, over the short term would be useful. We developed a method of attaching a global positioning system (GPS) transmitter to feral horse tails, and provide step by step instructions so that others may apply this method. We braided the tail and affixed a transmitter tag to the braid with epoxy, cable ties, and an attachment cord run through the braid. Between 2016 and 2017 we fitted 114 VHF or VHF-GPS tags in the tails of free-roaming feral horses in western Utah. From when tags were fitted to September 2020 tag retention time ranged from <1 to 36 months (n = 111, mean = 8.50 ± SD 6.39 months). We found that our braided, tail-mounted transmitter tags can provide a viable alternative to radio collars for meeting shorter-term data collection needs once data transmission difficulties are overcome.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1338","usgsCitation":"King, S.R., and Schoenecker, K., 2022, Application of tail transmitters for tracking feral horses as an alternative to radio collars: Wildlife Society Bulletin, v. 46, no. 4, e1338, 9 p., https://doi.org/10.1002/wsb.1338.","productDescription":"e1338, 9 p.","ipdsId":"IP-129913","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":420478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Conger Herd Management Area, Frisco Herd Management Area, Sulphur Springs Herd Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.03358980597041,\n 41.99245468276973\n ],\n [\n -114.05551299770433,\n 37.00574853871275\n ],\n [\n -113.3320476704814,\n 37.01450169249067\n ],\n [\n -112.29069606311519,\n 38.28160056526153\n ],\n [\n -112.04954095404076,\n 39.914801662668765\n ],\n [\n -114.03358980597041,\n 41.99245468276973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"King, Sarah R. B. 0000-0002-9316-7488","orcid":"https://orcid.org/0000-0002-9316-7488","contributorId":280063,"corporation":false,"usgs":false,"family":"King","given":"Sarah","email":"","middleInitial":"R. B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":881741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881742,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70235724,"text":"sir20225043 - 2022 - Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island","interactions":[],"lastModifiedDate":"2026-04-09T17:35:58.64895","indexId":"sir20225043","displayToPublicDate":"2022-08-17T19:45:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5043","displayTitle":"Water-Quality Conditions and Constituent Loads, Water Years 2013–19, and Water-Quality Trends, Water Years 1983–2019, in the Scituate Reservoir Drainage Area, Rhode Island","title":"Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island","docAbstract":"The Scituate Reservoir is the primary source of drinking water for more than 60 percent of the population of Rhode Island. From October 1, 1982, to September 30, 2019, water years (WYs) 1983–2019 (a water year is the period between October 1 and September 30 and is designated by the year in which it ends), the Providence Water Supply Board maintained a fixed-frequency sampling program at 37 stations to monitor water quality in tributaries to the Scituate Reservoir. The U.S. Geological Survey (USGS), in cooperation with the Providence Water Supply Board, has measured streamflow at selected streamgages in the Scituate Reservoir drainage area since WY 1994, monitored water quality at selected stations since WY 2009, and conducted targeted base-flow and stormflow sampling at five stations in WYs 2016–19. Daily loads and yields of constituents (chloride, nitrite, nitrate, total coliform bacteria, Escherichia coli, and orthophosphate) were determined for sampled days during WYs 2013–19, and trends were examined for the entire period of record, predominantly WYs 1983–2019. USGS water-quality data were used to determine annual loads and yields of chloride and sodium for WYs 2013–19 at 14 stations, and nutrients and suspended sediment for WYs 2016–19 at 5 stations.
Tributaries in the Scituate Reservoir drainage area for WYs 2013–19 were slightly acidic (pH values less than 7.0 standard units) and often below the recommended pH range of 6.5 to 8.5 standard units, as described by the U.S. Environmental Protection Agency (EPA) in the secondary drinking-water regulations. Most measurements of water color in the tributaries were greater than the EPA secondary drinking-water regulation of 15 platinum-cobalt units. Chloride concentrations in Providence Water Supply Board samples rarely exceeded the EPA secondary drinking-water regulation for chloride (250 milligrams per liter); however, chloride concentrations estimated from continuous measurements of specific conductance exceeded the EPA criterion continuous concentration recommended for freshwater (230 milligrams per liter) for short periods ranging from 10 minutes to 26 hours at two streamgages.
Positive trends in pH, color, alkalinity, and chloride at more than half of the monitoring stations were identified for WYs 1983–2019. Fewer than half of the stations had significant trends in turbidity values, and significant trends varied in direction (positive or negative trends). Trend tests were not performed on total coliform bacteria, Escherichia coli, and nitrate concentrations because of analytical method changes that coincide with abrupt shifts in the magnitude and distribution of concentration data.
The median of daily loads and yields of chloride, nitrite, nitrate, orthophosphate, and bacteria determined for each Providence Water Supply Board sample in WYs 2013–19 varied across the 37 monitoring stations, but yields were generally greater at stations in the Moswansicut and Regulating Reservoir subbasins. Average daily yields of chloride and sodium estimated from continuous records of specific-conductance and streamflow data at 14 stations ranged from 42 to 310 kilograms per square mile per day and 28 to 180 kilograms per square mile per day, respectively. The mean annual yields of total phosphorus, total nitrogen, and suspended sediment determined for five stations ranged from 16 to 78 kilograms per square mile, from 370 to 2,100 kilograms per square mile, and from 5,000 to 13,000 kilograms per square mile, respectively. More than half of the nutrient and suspended sediment loads occurred during stormflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225043","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Spaetzel, A.B., and Smith, K.P., 2022, Water-quality conditions and constituent loads, water years 2013–19, and water-quality trends, water years 1983–2019, in the Scituate Reservoir drainage area, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2022–5043, 102 p., https://doi.org/10.3133/sir20225043.","productDescription":"Report: xiv, 102 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128796","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":405192,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5043/images/"},{"id":405190,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98XCK0R","text":"USGS data release","linkHelpText":"Water-quality, streamflow, and quality-control data supporting estimation of nutrient and sediment loads in the Scituate Reservoir drainage area, Rhode Island, water years 2016–19"},{"id":502398,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113397.htm","linkFileType":{"id":5,"text":"html"}},{"id":405191,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5043/sir20225043.XML"},{"id":405188,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5043/sir20225043.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5043"},{"id":405187,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5043/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir drainage area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.79840087890625,\n 41.724180549563606\n ],\n [\n -71.52786254882812,\n 41.724180549563606\n ],\n [\n -71.52786254882812,\n 41.96459591213679\n ],\n [\n -71.79840087890625,\n 41.96459591213679\n ],\n [\n -71.79840087890625,\n 41.724180549563606\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Power line corridors have been repeatedly assessed as habitat for wild bees; however, few studies have examined them as bee habitat relative to nearby crop fields and surrounding landscape context.
We surveyed bee communities in power line corridors near to and isolated from lowbush blueberry fields in two landscape contexts in Maine, U.S.A. We examined the influences of blooming plant abundance and diversity and bee life-history traits including sociality, nesting preference, and body size.
We surveyed wild bees and blooming plants in power line corridors from 2013 to 2015. We calculated landscape composition surrounding sites at multiple scales and gathered bee trait information from the literature. We assessed differences in bee communities owing to landscape context with generalized linear models.
We collected 125 wild bee species and observed a rare plant-pollinator relationship within power line corridors. We found greater bee abundance and species richness throughout a complex, resource-rich landscape, while mass-flowering lowbush blueberry fields enhanced bee species richness only in a simple, resource-poor landscape. Landscape composition and blooming plant diversity varied with landscape context, though only landscape composition influenced bee communities. Solitary and ground-nesting species were more sensitive to landscape context than social or cavity-nesting species.
Power line corridors provide crucial refugia for crop pollinating wild bees in agricultural landscapes with resource-poor natural habitat, while bees may selectively forage in power line corridors within agricultural landscapes containing resource-rich natural habitat. We found high-quality forage within corridors; quantifying nesting resources could clarify corridor use by wild bees.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01495-9","usgsCitation":"Du Clos, B., Drummond, F.A., and Loftin, C., 2022, Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context: Landscape Ecology, v. 37, p. 2619-2634, https://doi.org/10.1007/s10980-022-01495-9.","productDescription":"16 p.","startPage":"2619","endPage":"2634","ipdsId":"IP-124416","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":428535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Du Clos, Brianne","contributorId":336548,"corporation":false,"usgs":false,"family":"Du Clos","given":"Brianne","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drummond, Francis A.","contributorId":336549,"corporation":false,"usgs":false,"family":"Drummond","given":"Francis","email":"","middleInitial":"A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":900271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":900269,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241508,"text":"70241508 - 2022 - Optimizing survey design for shasta salamanders (Hydromantes spp.) to estimate occurrence in little-studied portions of their range","interactions":[],"lastModifiedDate":"2023-03-22T11:41:45.905962","indexId":"70241508","displayToPublicDate":"2022-08-09T06:40:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing survey design for shasta salamanders (Hydromantes spp.) to estimate occurrence in little-studied portions of their range","docAbstract":"Shasta salamanders (collectively, Hydromantes samweli, H. shastae, and H. wintu; hereafter, Shasta salamander) are endemic to northern California in the general vicinity of Shasta Lake reservoir. Although generally associated with limestone, they have repeatedly been found in association with other habitats, calling into question the distribution of the species complex. Further limiting our knowledge of the species' distributions is that they are only active or available for sampling on the soil surface for a small portion of the year, and detection probabilities for the species have never been estimated. We developed and implemented a survey protocol designed to estimate detection, availability, and occurrence probabilities from December 2019 through March 2020. We provide inference on Shasta salamander occurrence in portions of their range that have received little survey effort. We found that Shasta salamander occurrence was positively associated with the percent cover of embedded rock, and the species' availability (i.e., probability of being active on the soil surface during sampling) was positively related to relative humidity. The probability of occurrence of Shasta salamanders in our study area was low, and our winter-to-spring survey protocol was effective for estimating detection, availability, and occurrence probabilities in the study area and at specific sites. We suggest that conducting replicate surveys that quantify animal availability and detection probabilities will facilitate a better understanding of the habitat associations of Shasta salamanders and other rare species that might often be unavailable for detection.