Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.
Harvest sustainability is a primary goal of wildlife management and conservation, and in a changing world it is increasingly important to consider environmental drivers of population dynamics alongside harvest in cohesive management plans. This is particularly pertinent for harvested species that are acutely experiencing effects of climate change. The Pacific walrus (Odobenus rosmarus divergens), a critical traditional subsistence resource for indigenous communities, is simultaneously subject to rapid habitat loss associated with diminishing sea ice and an increasing anthropogenic footprint in the Arctic. We developed a theta-logistic population modeling-management framework to evaluate various harvest scenarios combined with four potential climate/disturbance scenarios (ranging from optimistic–pessimistic) which simulates Pacific walrus population dynamics to the end of the 21st century. We considered two types of harvest strategies: (1) adaptive harvest scenarios wherein harvest is calculated as a percentage of the population and annual harvests are updated at set intervals as the population is reassessed, and (2) non-adaptive harvest scenarios wherein annual harvest remains constant. All climate/disturbance scenarios indicated declines of varying severity in Pacific walrus abundance to the end of the 21st century, even in the absence of harvest. However, we found that an adaptive annual harvest of 1.23% of the independent-aged female subset of the population (e.g., 1,280 independent-aged females harvested in 2020, representing contemporary harvest levels) met our criterion for sustainability (>70% probability of maintaining population abundance above maximum net productivity level) under all climate/disturbance scenarios, accepting a medium risk tolerance level of 25%. This suggests that the present rate of Pacific walrus harvest is sustainable and will continue to be—provided the harvest adapts to match changes in population dynamics. Our simulations suggest that a sustainable non-adaptive harvest is also possible, but only at low levels if the population declines as expected. Applying a constant annual harvest of 1,280 independent-aged females (equivalent to contemporary harvest levels of 1.23) exceeded our criterion for sustainability and resulted in a >5% chance of quasi-extinction by the end of the 21st century under three of the four climate/disturbance scenarios we evaluated. Our results highlight the importance of adaptive co-management strategies, and we suggest such modeling frameworks are useful for managing for harvest sustainability in a changing climate.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2024.05.29.596516","usgsCitation":"Johnson, D., Eisaguirre, J.M., Taylor, R.L., Andersen, E., and Garlich-Miller, J., 2024, Assessing the sustainability of Pacific walrus harvest in a changing environment: BioRxiv, https://doi.org/10.1101/2024.05.29.596516.","productDescription":"37 p.","ipdsId":"IP-166369","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":467001,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.05.29.596516","text":"External Repository"},{"id":464799,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Devin","contributorId":346945,"corporation":false,"usgs":false,"family":"Johnson","given":"Devin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":920335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eisaguirre, Joseph Michael 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":301980,"corporation":false,"usgs":true,"family":"Eisaguirre","given":"Joseph","email":"","middleInitial":"Michael","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":920336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":920337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andersen, Erik","contributorId":346947,"corporation":false,"usgs":false,"family":"Andersen","given":"Erik","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":920338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garlich-Miller, Joel","contributorId":346948,"corporation":false,"usgs":false,"family":"Garlich-Miller","given":"Joel","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":920339,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256580,"text":"70256580 - 2024 - Treed Gaussian processes for animal movement modeling","interactions":[],"lastModifiedDate":"2024-08-08T11:10:37.521981","indexId":"70256580","displayToPublicDate":"2024-06-02T06:08:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Treed Gaussian processes for animal movement modeling","docAbstract":"Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.
Invasive carp tracking, monitoring, and contracted removal will continue throughout the Upper IWW system as part of an adaptive management effort to mitigate, control, and contain invasive carp. To help facilitate these actions, there is a need to compile and analyze data from the multitude of partner agencies that are collecting invasive carp-related data throughout the Illinois River system. These data are often in disparate formats, and integrating these data into a common format allows both researchers and managers to assess invasive carp monitoring, control, and removal efforts at several scales is necessary. Ensuring the interoperability of these data sets allows for their use in various analyses and modeling efforts. Implementing an interoperable data management framework also provides mechanisms for end users to find and use existing data. Integrating data for use in modeling and analysis furthers the MRWG partnership’s collective understanding of invasive carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts; assessing invasive carp abundance to support modeling efforts; informing deployment of control actions). An effective data management strategy will streamline the database update process, providing partners with timely, well-organized, uniformly formatted data and analyses in support of informed decision-making processes.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Monitoring and response plan","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Invasive Carp Regional Coordinating Committee","usgsCitation":"Brey, M.K., and Fritts, A.K., 2024, USGS invasive carp database management & integration support, 4 p.","productDescription":"4 p.","startPage":"90","endPage":"93","ipdsId":"IP-151293","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":432420,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html","linkFileType":{"id":5,"text":"html"}},{"id":432447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.48380903104598,\n 41.68958409016773\n ],\n [\n -87.64143789713586,\n 41.8332919304695\n ],\n [\n -89.48044133484973,\n 41.38806459671454\n ],\n [\n -90.75022942279477,\n 39.83926695317629\n ],\n [\n -90.66265783052245,\n 39.082040884611246\n ],\n [\n -90.47000032752403,\n 38.809609526616384\n ],\n [\n -90.39118589447908,\n 38.89826454066113\n ],\n [\n -90.39118589447908,\n 39.86615823720618\n ],\n [\n -89.32281246875988,\n 40.847090394251495\n ],\n [\n -89.200212239579,\n 41.184074184856854\n ],\n [\n -88.24568188381326,\n 41.32233092104789\n ],\n [\n -87.80782392245264,\n 41.46029495699605\n ],\n [\n -87.48380903104598,\n 41.68958409016773\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":909372,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257695,"text":"70257695 - 2024 - Climate change","interactions":[],"lastModifiedDate":"2024-08-23T14:57:43.631661","indexId":"70257695","displayToPublicDate":"2024-06-01T09:52:42","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Climate change","docAbstract":"Amphibian ecology and distribution are strongly correlated with climate. Regional patterns of amphibian biodiversity are intimately linked to temperature, evapotranspiration rate, and clines in humidity. While amphibians are and will continue to be adversely affected by recent and projected changes in climate, research suggests that adaptation may happen more slowly than the expected rate of environmental shifts. Here, we review conservation-relevant aspects of both realised and potential impacts of climate change, and outline options for amphibian conservation planning and management. Recent advances in our understanding of climate change impacts on amphibians have primarily stemmed from ecological modelling and direct assessment of climatic tolerances and dispersal capacities through physiological assays, landscape genetics, and dispersal tracking. Anthropogenic climate change has already altered amphibian assemblages and their impacts on ecosystem functioning and services. Because of known and hypothesised ecological tolerances, many amphibians might have reached or exceeded most limits in their ability to adapt to or tolerate further climate change, however the uncertainties are substantial. Implementation of conservation planning and action can help to forestall severe impacts of environmental shifts. Scientific research and science-based decision-making and policy development have already lagged; the current pace of conservation planning and action may not allow for effective identification of threats and mitigation. An increased response rate could help to avert further loss of amphibian biodiversity and decay of ecosystem services. The lack of basic field research in natural habitats continues to be an underlying challenge. We suggest priority areas of research to include the development of biologically realistic predictive models of amphibian response to climate change, field verification of model estimates and key parameters, population monitoring across multiple sites and taxa, and a combination of efforts within and across ecosystems to understand how impacts of climate change can be better mitigated.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Amphibian conservation action plan: A status review and roadmap for global amphibian conservation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"IUCN","doi":"10.2305/QWVH2717","usgsCitation":"Bickford, D., Wogan, G.O., Olson, D., Seshadri, K., Urban, M.C., Carnaval, A., Measey, J., Rowley, J.J., Rovito, S., von May, R., and Walls, S., 2024, Climate change, chap. 3 of Amphibian conservation action plan: A status review and roadmap for global amphibian conservation, p. 52-86, https://doi.org/10.2305/QWVH2717.","productDescription":"35 p.","startPage":"52","endPage":"86","ipdsId":"IP-143427","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":489110,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10261/388430","text":"External Repository"},{"id":433098,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-08","publicationStatus":"PW","contributors":{"editors":[{"text":"Wren, Sally","contributorId":343610,"corporation":false,"usgs":false,"family":"Wren","given":"Sally","email":"","affiliations":[],"preferred":false,"id":911521,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Borzee, Arnael","contributorId":343611,"corporation":false,"usgs":false,"family":"Borzee","given":"Arnael","email":"","affiliations":[],"preferred":false,"id":911522,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Marcec-Greaves, Ruth","contributorId":343612,"corporation":false,"usgs":false,"family":"Marcec-Greaves","given":"Ruth","email":"","affiliations":[],"preferred":false,"id":911523,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Angulo, Ariadne","contributorId":62528,"corporation":false,"usgs":true,"family":"Angulo","given":"Ariadne","email":"","affiliations":[],"preferred":false,"id":911524,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Bickford, David 0000-0003-1478-3387","orcid":"https://orcid.org/0000-0003-1478-3387","contributorId":329545,"corporation":false,"usgs":false,"family":"Bickford","given":"David","email":"","affiliations":[{"id":78638,"text":"Natural Sciences Division, University of La Verne, California, USA","active":true,"usgs":false}],"preferred":false,"id":911450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wogan, Guinevere O.U.","contributorId":149463,"corporation":false,"usgs":false,"family":"Wogan","given":"Guinevere","email":"","middleInitial":"O.U.","affiliations":[{"id":17743,"text":"Museum of Vertebrate Zoology, UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":911451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olson, Deanna H.","contributorId":338541,"corporation":false,"usgs":false,"family":"Olson","given":"Deanna H.","affiliations":[{"id":81141,"text":"US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Corvallis, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":911452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seshadri, K.S.","contributorId":343579,"corporation":false,"usgs":false,"family":"Seshadri","given":"K.S.","email":"","affiliations":[{"id":27952,"text":"Indian Institute of Science","active":true,"usgs":false}],"preferred":false,"id":911453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Urban, Mark C.","contributorId":202396,"corporation":false,"usgs":false,"family":"Urban","given":"Mark","email":"","middleInitial":"C.","affiliations":[{"id":36410,"text":"Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA","active":true,"usgs":false}],"preferred":false,"id":911454,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carnaval, Ana","contributorId":343580,"corporation":false,"usgs":false,"family":"Carnaval","given":"Ana","email":"","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":911455,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Measey, John","contributorId":303671,"corporation":false,"usgs":false,"family":"Measey","given":"John","affiliations":[{"id":65872,"text":"Stellenbosch University; Stellenbosch, South Africa","active":true,"usgs":false}],"preferred":false,"id":911456,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rowley, Jodi J.L.","contributorId":343583,"corporation":false,"usgs":false,"family":"Rowley","given":"Jodi","email":"","middleInitial":"J.L.","affiliations":[{"id":82114,"text":"Australian Museum Research Institute","active":true,"usgs":false}],"preferred":false,"id":911457,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rovito, Sean","contributorId":343586,"corporation":false,"usgs":false,"family":"Rovito","given":"Sean","email":"","affiliations":[{"id":82115,"text":"Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional","active":true,"usgs":false}],"preferred":false,"id":911458,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"von May, Rudolf","contributorId":343587,"corporation":false,"usgs":false,"family":"von May","given":"Rudolf","email":"","affiliations":[{"id":40193,"text":"California State University Channel Islands","active":true,"usgs":false}],"preferred":false,"id":911459,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Walls, Susan 0000-0001-7391-9155","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":215987,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":911460,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70256149,"text":"70256149 - 2024 - Post-wildfire curve number estimates for the southern Rocky Mountains in Colorado, USA","interactions":[],"lastModifiedDate":"2026-02-03T15:43:38.894615","indexId":"70256149","displayToPublicDate":"2024-06-01T09:32:17","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":91,"text":"Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"ERDC-TR-24-12","title":"Post-wildfire curve number estimates for the southern Rocky Mountains in Colorado, USA","docAbstract":"The curve number method first developed by the US Department of Agriculture Soil Conservation Service (now the Natural Resources Conservation Service) is often used for post-wildfire runoff assessments. These assessments are critical for land and emergency managers making decisions on life and property risks following a wildfire event. Three approaches (i.e., historical event observations, linear regression model, and regression tree model) were used to help estimate a post-wildfire curve number from watershed and wildfire parameters. For the first method, we used runoff events from 102 burned watersheds in Colorado, southern Wyoming, northern New Mexico, and eastern Utah to quantify changes in curve number values from pre- to post-wildfire conditions. The curve number changes from the measured runoff events vary substantially between positive and negative values. The measured curve number changes were then associated with watershed characteristics (e.g., slope, elevation, northness, and eastness) and land cover type to develop prediction models that provide estimates of post-wildfire curve number changes. Finally, we used a regression tree method to demonstrate that accurate predications can be developed using the measured curve number changes from our study domain. These models can be used for future post-wildfire assessments within the region.
","language":"English","publisher":"US Army Engineer Research and Development Center","doi":"10.21079/11681/48652","usgsCitation":"Giovando, J., Reis, W., Shillito, R., Shaloka, E., Chow, C., Kohn, M.S., and Memarsadeghi, N., 2024, Post-wildfire curve number estimates for the southern Rocky Mountains in Colorado, USA: Technical Report ERDC-TR-24-12, ix, 84 p., https://doi.org/10.21079/11681/48652.","productDescription":"ix, 84 p.","ipdsId":"IP-162032","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":499444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"southern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.95412182470984,\n 34.62746181535826\n ],\n [\n -103.83257544602158,\n 34.78603007078627\n ],\n [\n -104.19360390493368,\n 40.626407642715634\n ],\n [\n -104.90056848621875,\n 42.22354390303653\n ],\n [\n -114.15700156995486,\n 41.76504082769486\n ],\n [\n -113.95988170488984,\n 40.27157097499236\n ],\n [\n -109.46888006608783,\n 37.34012253873847\n ],\n [\n -108.99864474095172,\n 37.28173602808141\n ],\n [\n -108.95412182470984,\n 34.62746181535826\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Giovando, Jeremy","contributorId":352388,"corporation":false,"usgs":false,"family":"Giovando","given":"Jeremy","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":906925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reis, Wyatt","contributorId":340330,"corporation":false,"usgs":false,"family":"Reis","given":"Wyatt","affiliations":[{"id":40033,"text":"US Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":906926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shillito, Rose","contributorId":340331,"corporation":false,"usgs":false,"family":"Shillito","given":"Rose","affiliations":[{"id":40033,"text":"US Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":906927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shaloka, Elizabeth","contributorId":340332,"corporation":false,"usgs":false,"family":"Shaloka","given":"Elizabeth","affiliations":[{"id":81580,"text":"US Army Corps of Engineers Philadelphia District","active":true,"usgs":false}],"preferred":false,"id":906928,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chow, Christina","contributorId":340333,"corporation":false,"usgs":false,"family":"Chow","given":"Christina","affiliations":[{"id":40033,"text":"US Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":906929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906930,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Memarsadeghi, Natalie","contributorId":340336,"corporation":false,"usgs":false,"family":"Memarsadeghi","given":"Natalie","affiliations":[{"id":81581,"text":"Coastal and Hydraulics Laboratory US Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":906931,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254688,"text":"70254688 - 2024 - Applying local and global sensitivity analysis to inform bigheaded carp management","interactions":[],"lastModifiedDate":"2024-12-09T15:34:40.676932","indexId":"70254688","displayToPublicDate":"2024-06-01T08:40:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17797,"text":"Journal of Fish and Wildlife Managment","active":true,"publicationSubtype":{"id":10}},"title":"Applying local and global sensitivity analysis to inform bigheaded carp management","docAbstract":"Natural resource managers commonly use population-level models to aid in understanding the status of target populations or the potential implications of management actions. Sensitivity analyses, specifically, local sensitivity analysis (LSA) and global sensitivity analysis (GSA), exist as tools to improve understanding of these models, the importance of specific parameters to model outcomes, and the assumptions inherent to population models. However, applied ecology and closely related fields such as fisheries and wildlife management rarely use LSA or GSA. Here, we apply LSA and GSA to the Spatially Explicit Invasive Carp Population model, a population-level model used to simulate and compare management actions on the invasive silver carp Hypophthalmichthys molitrix population in the Illinois River. The spatial structure within the model arises from the navigation “pools” that exist between navigation lock-and-dam in the river and are also the focus of fisheries management in the river. We completed LSA and GSA for two reasons: (1) to compare the importance of model input parameters on simulated model populations and (2) to demonstrate the use of LSA and GSA for population-level models. In our system, the most important sensitivity values generally were detected in Alton Pool. This pool is the farthest downstream and includes a direct connection with the Mississippi River. These results indicate that the population dynamics in Alton Pool are an important driver of the population dynamics throughout the Illinois River. Management outcomes in this system will, therefore, be strongly dependent on the dynamics of Alton Pool.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-23-032","usgsCitation":"Erickson, R.A., Marcek, B.J., Thompson, H.M., Schoenung, B., Dettmers, J.M., and Fienen, M., 2024, Applying local and global sensitivity analysis to inform bigheaded carp management: Journal of Fish and Wildlife Managment, v. 15, no. 1, p. 112-126, https://doi.org/10.3996/JFWM-23-032.","productDescription":"15 p.","startPage":"112","endPage":"126","ipdsId":"IP-154554","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":439466,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-032","text":"Publisher Index Page"},{"id":429634,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.47067866744594,\n 41.63003798665733\n ],\n [\n -87.75301635955124,\n 42.20775931304047\n ],\n [\n -88.30886513743135,\n 41.64322208666994\n ],\n [\n -89.39408212461949,\n 41.46495404872903\n ],\n [\n -89.60582738039393,\n 41.213228798132434\n ],\n [\n -90.21464189799754,\n 40.425353097617545\n ],\n [\n -90.69107472176378,\n 39.906198151706576\n ],\n [\n -90.68227876321413,\n 38.89721553509264\n ],\n [\n -90.21464451351676,\n 38.745987538956484\n ],\n [\n -90.02936726731902,\n 38.890348012783875\n ],\n [\n -90.3822696414528,\n 39.15767727296338\n ],\n [\n -90.4087454076512,\n 39.79105558376847\n ],\n [\n -89.05881037240643,\n 41.06705323545242\n ],\n [\n -87.90299672085834,\n 41.39879783610135\n ],\n [\n -87.47067866744594,\n 41.63003798665733\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":902289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcek, Benjamin J.","contributorId":337263,"corporation":false,"usgs":false,"family":"Marcek","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":81001,"text":"U.S. Fish and Wildlife Service, Carterville Fish and Wildlife Conservation Office","active":true,"usgs":false}],"preferred":false,"id":902290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Hannah Mann 0000-0001-8316-3232","orcid":"https://orcid.org/0000-0001-8316-3232","contributorId":316560,"corporation":false,"usgs":true,"family":"Thompson","given":"Hannah","email":"","middleInitial":"Mann","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":902291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schoenung, Brian","contributorId":337264,"corporation":false,"usgs":false,"family":"Schoenung","given":"Brian","email":"","affiliations":[{"id":33955,"text":"Illinois Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":902292,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dettmers, John M.","contributorId":191256,"corporation":false,"usgs":false,"family":"Dettmers","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":902293,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902294,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255676,"text":"70255676 - 2024 - Surficial geology and Quaternary fault map of the Las Vegas Valley, Clark County, Nevada","interactions":[],"lastModifiedDate":"2024-06-28T13:28:34.382957","indexId":"70255676","displayToPublicDate":"2024-06-01T08:22:22","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":17988,"text":"Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"193","title":"Surficial geology and Quaternary fault map of the Las Vegas Valley, Clark County, Nevada","docAbstract":"The Surficial Geology and Quaternary Fault Map of the Las Vegas Valley, Clark County, Nevada is a 1:50,000-scale compilation of published 1:24,000-scale geologic maps integrated with new field and desktop mapping. This geologic map compilation and GIS database are part of a broader study on the Quaternary faults in the Las Vegas Valley, the results of which are summarized in this report. The map compilation utilized a variety of digital base maps to evaluate the characteristics of surficial deposits and Quaternary fault scarps including lidar data in undeveloped parts of the valley and orthorectified historical aerial photos in the urbanized center of the valley. The map distinguishes twenty surficial map units including alluvial-fan and ephemeral-wash deposits, groundwater discharge deposits of the Las Vegas Formation, and alluvium deposits of Las Vegas Wash. Quaternary faults include the Quaternary Las Vegas Valley fault system in the center of the valley and the Frenchman Mountain fault system along the eastern margin. All Quaternary fault traces in the valley were evaluated, and the previously published fault mapping was modified as needed. The new mapping and uniform correlation of surficial deposits displaced by Quaternary faults yields a consistent valley-wide characterization of the recency of fault activity. The map and GIS database include a Quaternary geochronology compilation of 119 previously published surficial deposit ages of various types, 35 new luminescence ages, and 3 new radiocarbon ages. The new age data further bracket the ages of the Las Vegas basin Quaternary stratigraphy and provide new constraints on the timing of Quaternary fault activity.
Previous work found that numerous genes positively selected within the hoary bat (Lasiurus cinereus) lineage are physically clustered in regions of conserved synteny. Here I further validate and expand on those finding utilizing an updated L. cinereus genome assembly and additional bat species as well as other tetrapod outgroups.
A chromosome-level assembly was generated by chromatin-contact mapping and made available by DNAZoo (www.dnazoo.org). The genomic organization of orthologous genes was extracted from annotation data for multiple additional bat species as well as other tetrapod clades for which chromosome-level assemblies were available from the National Center for Biotechnology Information (NCBI). Tests of branch-specific positive selection were performed for L. cinereus using PAML as well as with the HyPhy package for comparison.
Twelve genes exhibiting significant diversifying selection in the L. cinereus lineage were clustered within a 12-Mb genomic window; one of these (Trpc4) also exhibited diversifying selection in bats generally. Ten of the 12 genes are landmarks of two distinct blocks of ancient synteny that are not linked in other tetrapod clades. Bats are further distinguished by frequent structural rearrangements within these synteny blocks, which are rarely observed in other Tetrapoda. Patterns of gene order and orientation among bat taxa are incompatible with phylogeny as presently understood, implying parallel evolution or subsequent reversals. Inferences of positive selection were found to be robust to alternative phylogenetic topologies as well as a strong shift in background nucleotide composition in some taxa.
This study confirms and further localizes a genomic hotspot of protein-coding divergence in the hoary bat, one that also exhibits an increased tempo of structural change in bats compared with other mammals. Most genes in the two synteny blocks have elevated expression in brain tissue in humans and model organisms, and genetic studies implicate the selected genes in cranial and neurological development, among other functions.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.17482","usgsCitation":"Cornman, R.S., 2024, A genomic hotspot of diversifying selection and structural change in the hoary bat (Lasiurus cinereus): PeerJ, v. 12, e17482, 32 p., https://doi.org/10.7717/peerj.17482.","productDescription":"e17482, 32 p.","ipdsId":"IP-159824","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439470,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.17482","text":"Publisher Index Page"},{"id":434952,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YKF5BJ","text":"USGS data release","linkHelpText":"DNA sequences used to analyze evolutionary rates of genes in bats"},{"id":429444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2024-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":901903,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254586,"text":"70254586 - 2024 - Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology","interactions":[],"lastModifiedDate":"2024-06-04T11:50:56.137709","indexId":"70254586","displayToPublicDate":"2024-05-31T06:49:52","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology","docAbstract":"Fire-induced geomorphic changes, such as enhanced erosion and debris-flow activity, are expected to increase with climate change owing to increases in fire activity and rainfall intensification. In this Review, we summarize how landscape attributes, rainfall and burn severity influence post-fire geomorphic responses over a range of temporal and spatial scales. Sub-hourly rainfall intensity and burn severity control the magnitude of many post-fire geomorphic process rates through their influence on ground cover and rainfall-runoff partitioning. Post-fire debris flows (PFDFs) make a substantial contribution to the post-fire sediment cascade, transporting sediment from hillslopes to channels, adjacent floodplains and alluvial fans. By the late twenty-first century, PFDF activity is estimated to increase in 68% of areas in which PFDFs have occurred in the past and decrease in only 2% of locations. Once altered by fire, geomorphic state variables — such as infiltration capacity, canopy cover, ground cover and sediment availability — can recover to their pre-fire value or be shifted to a new value. Improved understanding of the factors that influence these post-fire trajectories could support targeted management and intervention strategies. Additionally, monitoring that extends beyond the first 1–3 years after fire and deeper integration of ecohydrological processes into geomorphic models are needed to improve forecasts of post-fire geomorphic responses.
","language":"English","publisher":"Nature","doi":"10.1038/s43017-024-00557-7","usgsCitation":"McGuire, L., Ebel, B., Rengers, F.K., Vieira, D., and Nyman, P., 2024, Fire effects on geomorphic processes: Nature Reviews Earth and Environment, v. 5, p. 486-503, https://doi.org/10.1038/s43017-024-00557-7.","productDescription":"18 p.","startPage":"486","endPage":"503","ipdsId":"IP-154984","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":430449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2024-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McGuire, Luke","contributorId":197027,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","affiliations":[],"preferred":false,"id":904796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":904797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":904798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vieira, Diana","contributorId":339659,"corporation":false,"usgs":false,"family":"Vieira","given":"Diana","email":"","affiliations":[{"id":81374,"text":"European Commission - Joint Research Center","active":true,"usgs":false}],"preferred":false,"id":904799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nyman, Petter","contributorId":187489,"corporation":false,"usgs":false,"family":"Nyman","given":"Petter","email":"","affiliations":[],"preferred":false,"id":904800,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269003,"text":"70269003 - 2024 - Effects of telemetry collars on two free-roaming feral equid species","interactions":[],"lastModifiedDate":"2025-07-14T14:23:23.292109","indexId":"70269003","displayToPublicDate":"2024-05-30T09:16:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Effects of telemetry collars on two free-roaming feral equid species","docAbstract":"There are two species of free-roaming feral equids in North America: horses (Equus caballus) and donkeys or “burros” (E. asinus). Both species were introduced as domestic animals to North America in the early 1500s and currently inhabit rangelands across the western United States, Canada, and all continents except Antarctica. Despite their global distribution, little is known about their fine scale spatial ecology. Contemporary research tools to assess space use include global positioning system (GPS) tracking collars, but older models were problematic due to stiff collar belting causing poor fit. We tested modern designs of GPS collars on n = 105 horses and n = 60 burros for 4 years in five populations (3 horse, 2 burro) across the western United States, to assess whether collars posed welfare risks to horses or burros. We found no difference in survival of collared versus uncollared mares and jennies, and no difference in survival of their foals. In 4036 of 4307 observations for horses (93.7%) and 2115 of 2258 observations for burros (93.6%), collars were observed symmetrical, maintaining proper fit on the neck. Fur effects from collars (sweaty neck, indented fur, broken fur) were seen in 3% of horse observations and 25% of burro observations. Superficial effects (chafes and marks on skin surface) were seen in 2% of horse observations and 11% of burro observations; no severe effects from collars were seen. Body condition was not affected by collars; mean body condition of collared horses was 4.70 ± 0.54 (mean ± s.d) and 4.71 ± 0.65 for collared burros. Behavior results indicated minimal effects; collared horses stood slightly more than uncollared, and collared burros stood and foraged more in one population, but not in the other. For 6.3% of observations of horses and 6.4% of observations of burros, we found an effect of time wearing a collar on the cumulative sum of fur effects which increased over time (burros: rs = 0.87, P = <0.0001; horses: rs = 0.31, P = 0.002). Burros also showed an increase over time in the number of superficial effects, but horses did not. Collars occasionally moved into the wrong position, shifting forward over the ears; we observed this on 19 horses and 1 burro. Of those, most collars went over the ears in summer (n = 12). All collars were equipped with a remote release mechanism as well as a timed-release mechanism for redundancy, thus removed when observed in wrong position to avoid rubbing or discomfort. Our finding of no consequential physical effects in 98% of horse observations, and 89% of burro observations suggests the consequences of collars on free-roaming equid welfare and survival is biologically insignificant, although collars should be monitored regularly and continue to be equipped with a remote release mechanism to remove a collar if needed. With frequent welfare-driven, visual monitoring, collaring of free-roaming equids can be a safe and useful tool to increase our understanding of their spatial ecology, demography, habitat use, behavior, and interactions with other wildlife.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0303312","usgsCitation":"Schoenecker, K., King, S., Hennig, J.D., Cole, M., Scasta, J., and Beck, J., 2024, Effects of telemetry collars on two free-roaming feral equid species: PLoS ONE, v. 19, no. 5, e0303312, 25 p., https://doi.org/10.1371/journal.pone.0303312.","productDescription":"e0303312, 25 p.","ipdsId":"IP-153889","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":492801,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZSTSU","text":"USGS data release","linkHelpText":"Data Describing Effects of Telemetry Collars on Horses and Burros Between 2016 and 2020"},{"id":492486,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0303312","text":"Publisher Index Page"},{"id":492200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.06222303379008,\n 41.072757505190026\n ],\n [\n -107.36220051733886,\n 41.115245664294946\n ],\n [\n -107.40026474431968,\n 42.06499507596479\n ],\n [\n -110.54265920350724,\n 42.13895203896956\n ],\n [\n -113.98376776007635,\n 39.63181029788828\n ],\n [\n -113.82085343427966,\n 33.356188610084644\n ],\n [\n -109.78674675994066,\n 33.290579751792464\n ],\n [\n -109.06222303379008,\n 41.072757505190026\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-05-30","publicationStatus":"PW","contributors":{"authors":[{"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":942884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sarah","contributorId":357927,"corporation":false,"usgs":false,"family":"King","given":"Sarah","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":942885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hennig, Jacob D.","contributorId":177569,"corporation":false,"usgs":false,"family":"Hennig","given":"Jacob","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":942886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Mary C.","contributorId":357931,"corporation":false,"usgs":false,"family":"Cole","given":"Mary C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":942887,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scasta, J. Derek","contributorId":357933,"corporation":false,"usgs":false,"family":"Scasta","given":"J. Derek","affiliations":[{"id":85567,"text":"University of WY","active":true,"usgs":false}],"preferred":false,"id":942888,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beck, Jeffrey L.","contributorId":357934,"corporation":false,"usgs":false,"family":"Beck","given":"Jeffrey L.","affiliations":[{"id":85567,"text":"University of WY","active":true,"usgs":false}],"preferred":false,"id":942889,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261201,"text":"70261201 - 2024 - Human activities shape global patterns of decomposition rates in rivers","interactions":[],"lastModifiedDate":"2024-11-29T15:16:46.375837","indexId":"70261201","displayToPublicDate":"2024-05-30T09:03:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Human activities shape global patterns of decomposition rates in rivers","docAbstract":"Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.
","language":"English","publisher":"AAAS","doi":"10.1126/science.adn1262","usgsCitation":"Tiegs, S., Capps, K., Costello, D.M., Schmidt, J.P., Patrick, C., Follstad Shah, J., LeRoy, C.J., Acuna, V., Albarino, R., Allen, D., Alonso, C., Andino, P., Arango, C., Aroviita, J., Barbosa, M., Barmuta, L.A., Baxter, C., Bellinger, B., Boyero, L., Bragina, L., Brown, L.E., Bruder, A., Bruesewitz, D., Burdon, F., Callisto, M., Camacho, A.G., Canhoto, C., Castillo, M., Chauvet, E., Clapcott, J., Colas, F., Colon-Gaud, C., Cornut, J., Crespo-Perez, V., Cross, W., Culp, J.M., Danger, M., Dangles, O., de Eyto, E., Derry, A., Diaz-Villanueva, V., Douglas, M., Elosegi, A., Encalada, A., Entrekin, S., Espinosa, R., Ferreira, V., Ferriol, C., Flanagan, K., Flecker, A., Fleituch, T., Frainer, A., Friberg, N., Frost, P., Garcia, E.A., Garcia-Lago, L., Garcia Soto, P., Gessner, M., Ghate, S., Giling, D., Gilmer, A., Goncalves, J., Gonzales, R., Graca, M., Grace, M., Griffiths, N.A., Grossart, H., Guerold, 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backpack-harness effects on greater sage-grouse survival and device retention in the Great Basin","docAbstract":"Wildlife tracking studies have become ubiquitous in ecology and now provide previously unobtainable data regarding individual movement, vital rates, and population demographics. However, tracking devices can potentially reduce survival of study subjects, generating biases in the vital rates they seek to measure. Previous studies have found that greater sage-grouse (Centrocercus urophasianus) fitted with Global Positioning System (GPS) tracking devices may experience reduced survival, relative to those tracked with traditional radio transmitters, and have documented skin abrasions and lacerations associated with typical backpack-style GPS harnesses. We implemented an experimental study comparing survival and harness retention between 2 different backpack-style GPS transmitter harnesses. We captured female sage-grouse at 3 study sites in the northwest Great Basin of Oregon, Nevada, and California during 2019–2021. We fit each individual, following previously published recommendations, with either a standard backpack harness or a modified harness hypothesized to reduce skin abrasion and laceration. We used known-fate models in Program MARK to model variation in survival and harness retention separately as a function of harness type, year, age, a linear effect of time, and the ratio of the device to individual body mass. Neither survival nor retention varied systematically by harness type, however retention decreased as a function of body mass ratio. We echo previous recommendations for standardized harness attachment protocols and studies designed to isolate and test potential mechanisms by which tracking devices and attachment methods might affect survival and well-being of sage-grouse and other tracked species.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1523","usgsCitation":"Lundblad, C.G., Anthony, C.R., Dungannon, T., Haab, K.A., Schuyler, E., Sink, C.E., Dugger, K., and Hagen, C., 2024, Evaluating satellite-transmitter backpack-harness effects on greater sage-grouse survival and device retention in the Great Basin: The Wildlife Society Bulletin, v. 48, no. 2, e1523, 15 p., https://doi.org/10.1002/wsb.1523.","productDescription":"e1523, 15 p.","ipdsId":"IP-154308","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439479,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1523","text":"Publisher Index Page"},{"id":430129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California Nevada, Oregon","otherGeospatial":"Great 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University","active":true,"usgs":false}],"preferred":false,"id":903643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haab, Kimberly A.","contributorId":338863,"corporation":false,"usgs":false,"family":"Haab","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schuyler, Elizabeth M.","contributorId":338867,"corporation":false,"usgs":false,"family":"Schuyler","given":"Elizabeth M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sink, Chelsea E.","contributorId":338870,"corporation":false,"usgs":false,"family":"Sink","given":"Chelsea","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hagen, Christian A.","contributorId":338874,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903648,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254782,"text":"70254782 - 2024 - Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed","interactions":[],"lastModifiedDate":"2024-06-18T14:02:16.226227","indexId":"70254782","displayToPublicDate":"2024-05-29T08:30:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed","title":"Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed","docAbstract":"The purpose of this study was to evaluate the performance of HF183 Bacteroides for estimating pathogen exposures during recreational water activities. We compared the use of Bacteroides-based exposure assessment to exposure assessment that relied on pathogen measurements. We considered two types of recreational water sites: those impacted by combined sewer overflows (CSOs) and those not impacted by CSOs. Samples from CSO-impacted and non-CSO-impacted urban creeks were analysed by quantitative polymerase chain reaction (qPCR) for HF183 Bacteroides and eight human gastrointestinal pathogens. Exposure assessment was conducted two ways for each type of site (CSO-impacted vs. non-CSO impacted): 1) by estimating pathogen concentrations from HF183 Bacteroides concentrations using published ratios of HF183 and pathogens in sewage and 2) by estimating pathogen concentrations from qPCR measurements. QMRA (quantitative microbial risk assessment) was then conducted for swimming, wading, and fishing exposures. Overall, mean risk estimates varied from 0.27 to 53 illnesses per 1,000 recreators depending on exposure assessment, site, activity, and norovirus dose-response model. HF183-based exposure assessment identified CSO-impacted sites as higher risk, and the recommended HF183 risk-based threshold of 525 genomic copies per 100 mL was generally protective of public health at the CSO-impacted sites but was not as protective at the non-CSO-impacted sites. In the context of our urban watershed, HF183-based exposure assessment over- and under-estimated risk relative to exposure assessment based on pathogen measurements and the etiology of predicted pathogen-specific illnesses differed significantly. Across all sites, the HF183 model overestimated risk for norovirus, adenovirus, and Campylobacter jejuni, and it underestimated risk for E. coli and Cryptosporidium. To our knowledge, this study is the first to directly compare health risk estimates using HF183 and empirical pathogen measurements from the same waterways. Our work highlights the importance of site-specific hazard identification and exposure assessment to decide whether HF183 is applicable for monitoring risk.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.121852","usgsCitation":"Skiendzielewski, K., Burch, T., Stokdyk, J.P., McGinnis, S., McLoughlin, S., Firnstahl, A.D., Spencer, S., Borchardt, M.A., and Murphy, H., 2024, Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed: Water Research, v. 259, 121852, 16 p., https://doi.org/10.1016/j.watres.2024.121852.","productDescription":"121852, 16 p.","ipdsId":"IP-159977","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439482,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2024.121852","text":"Publisher Index Page"},{"id":429633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","city":"Philadelphia","otherGeospatial":"Cobbs Creek, Tacony Creek, Wissahickon Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.85988405796229,\n 40.143466276240844\n ],\n [\n -75.36578555034887,\n 40.16571325279898\n ],\n [\n -75.46114396657245,\n 39.85973677036142\n ],\n [\n -75.14696307943568,\n 39.87128371570064\n ],\n [\n -75.07770275607366,\n 39.95290291070239\n ],\n [\n -74.85988405796229,\n 40.143466276240844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skiendzielewski, K","contributorId":299292,"corporation":false,"usgs":false,"family":"Skiendzielewski","given":"K","email":"","affiliations":[],"preferred":false,"id":902522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":902523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGinnis, Shannon","contributorId":203051,"corporation":false,"usgs":false,"family":"McGinnis","given":"Shannon","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":902525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLoughlin, S","contributorId":299293,"corporation":false,"usgs":false,"family":"McLoughlin","given":"S","affiliations":[],"preferred":false,"id":902526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spencer, Sandy","contributorId":302981,"corporation":false,"usgs":false,"family":"Spencer","given":"Sandy","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902528,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":902529,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murphy, Heather","contributorId":203052,"corporation":false,"usgs":false,"family":"Murphy","given":"Heather","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":902530,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70254636,"text":"70254636 - 2024 - A phylogeographical study of the discontinuously distributed Harlequin Duck (Histrionicus histrionicus)","interactions":[],"lastModifiedDate":"2024-10-07T16:09:44.357781","indexId":"70254636","displayToPublicDate":"2024-05-29T06:47:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A phylogeographical study of the discontinuously distributed Harlequin Duck (Histrionicus histrionicus)","title":"A phylogeographical study of the discontinuously distributed Harlequin Duck (Histrionicus histrionicus)","docAbstract":"Species distributions are often indicative of historical biogeographical events and contemporary spatial biodiversity patterns. The Harlequin Duck Histrionicus histrionicus is a sea duck of conservation concern that has a disjunct distribution, with discrete portions of its range associated with northern Pacific and Atlantic Ocean basins. Movement data indicate migratory connectivity within regions of each ocean basin but not cross-continent dispersal, suggesting that genetic structuring could exist at multiple spatial scales. Little is known regarding the impacts of past vicariance events on the species phylogeographical structure and historical demography, or rates of gene flow at different spatial scales. We used data from microsatellite loci and mitochondrial DNA (mtDNA) sequences to quantify levels of genetic diversity within, and the extent of spatial genetic differentiation among locations sampled at multiple spatial scales across the species range. Samples were collected at nonbreeding locations, which represent groupings appropriate for characterizing genetically differentiated subgroups at regional and continental scales. Collectively, genetic data and coalescence modelling suggested that individuals colonized regions currently occupied within both ocean basins in the Holocene from a single refuge in the Atlantic. Further, it seems likely there was secondary contact with lineages derived from populations in Asia, based on the shallow species-wide mtDNA phylogeny and high incidence of recently derived private mtDNA haplotypes. Estimates of inter-location variance in microsatellite allele and mtDNA haplotype frequency were moderate and significant between western (Pacific – North America) and eastern (Atlantic – North America, Greenland and Iceland) ocean basins and among sampling groups within each ocean basin. Genetic differentiation among sampling groups was particularly evident at the species distributional margins in the Atlantic (Iceland) and the Pacific (Shemya Island) Ocean basins. Coalescent modelling results suggest that contemporary spatial genetic patterns in the species arose through the combined influences of secondary contact, shared ancestry and gene flow after the last glacial maxima.
Soil erosion rates after wildfire are strongly controlled by intrinsic properties such as topography, weather, climate, soil, and vegetation. These landscape and hydroclimatic properties are important in determining post-fire erosion rates; however, their influence on post-fire erosion and their interaction with the intensity of a wildfire remains uncertain. A key limitation in resolving this uncertainty is the lack of conceptual models and frameworks for organising data related to the geomorphic sensitivity of landscapes to wildfire. Our aim is to develop a framework for consolidating understanding of post-fire erosion in the context of hydroclimatic conditions which contribute to system states, for example soil and vegetation properties, and wildfire regime. The framework is developed around a simple conceptual model where the change in erosion due to wildfire is a product of change in runoff generation and sediment supply, which is strongly related to landscape net primary productivity (NPP). We hypothesised that geomorphic sensitivity to wildfire should vary as a unimodal humped relationship across a gradient of NPP, peaking at an intermediate level. To develop this framework and to test the hypothesis, we first review intrinsic soil and vegetation properties related to the supply and transport of sediment from burned and unburned hillslopes. Net primary productivity is systematically related to these intrinsic properties because it integrates many processes involved in soil and vegetation development. Empirical data indicate a trend in the change in surface runoff generation with NPP after wildfire, peaking at an NPP of approximately 15 Mg C ha−1 y−1. A simple model of fuel availability and soil heating are correlated with a similar “humped” trend in sediment supply. These results are consistent with our conceptual model, which indicates that sediment supply and runoff contribute towards a distinct peak in wildfire effects on erosion at an intermediate level of NPP. We propose that landscapes of intermediate NPP typically have the highest quantity of fuel available to burn, which cause large changes to the soil surface properties. Landscapes at intermediate NPP also tend to produce intrinsic soil and vegetation properties that promote erosion after wildfire. The interplay between these short and long-term landscape characteristics is strongest at intermediate levels of NPP. Our proposed biogeographic model of geomorphic sensitivity to wildfire was supported by erosion data from burned hillslope and zero-order catchments studies from a range fire-prone landscapes in Australia and North America. Our proposed conceptual model will help identify areas most vulnerable to post-fire erosion changes.
Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.
Explosive volcanic eruptions produce hazardous atmospheric plumes composed of tephra particles, hot gas and entrained air. Such eruptions are generally driven by magmatic fragmentation or steam expansion. However, an eruption mechanism outside this phreatic–magmatic spectrum was suggested by a sequence of 12 explosive eruptions in May 2018 at Kīlauea, Hawaii, that occurred during the early stages of caldera collapse and produced atmospheric plumes reaching 8 km above the vent. Here we use seismic inversions for reservoir pressure as a source condition for three-dimensional simulations of transient multiphase eruptive plume ascent through a conduit and stratified atmosphere. We compare the simulations with conduit ascent times inferred from seismic and infrasound data, and with plume heights from radar data. We find that the plumes are consistent with eruptions caused by a stomp-rocket mechanism involving the abrupt subsidence of reservoir roof rock that increased pressure in the underlying magma reservoir. In our model, the reservoir was overlain by a pocket of accumulated high-temperature magmatic gas and lithic debris, which were driven through a conduit approximately 600 m long to erupt particles at rates of around 3,000 m3 s−1. Our results reveal a distinct collapse-driven type of eruption and provide a framework for integrating diverse geophysical and atmospheric data with simulations to gain a better understanding of unsteady explosive eruptions.
Spatial modeling of wildlife diseases can be used to describe patterns of disease risk, understand biological mechanisms of disease occurrence, and for spatial prediction. Risk of wildlife disease occurrence in relation to environmental variables is often modeled and predicted using Markov chain Monte Carlo (MCMC) methods, which are unsuitable for large datasets and those covering large spatial extents. Integrated nested Laplace approximation (INLA) and INLA using the stochastic partial differential equation (INLA-SPDE) approach have become popular alternatives to MCMC for Bayesian inference because of their fast computational time and ability to process large datasets. Studies investigating risk of disease occurrence in wildlife, to our knowledge, have not yet compared Bayesian hierarchical spatial models over large spatial extents using real world data. Using chronic wasting disease (CWD) surveillance data from white-tailed deer (Odocoileus virginianus) collected in Pennsylvania, United States, as a case study, we first demonstrate how parameter estimates compare among MCMC, INLA, and INLA-SPDE modeling frameworks. We then model CWD (detected/non-detected) using INLA-SPDE over a much larger spatial extent than has been conducted previously for this disease to determine how surveillance type (e.g., hunter harvest, roadkill, or all surveillance) influences model parameters and predicted risk of CWD occurrence at locations not sampled. Fixed effects considered in the models included deer age and sex, elevation, slope, distance to streams, percent clay, and proportion of two habitat classes (forest and open) known to influence deer movements. We found INLA to produce comparable estimates to MCMC and permit modeling large datasets covering expansive spatial extents much faster and more efficiently than MCMC. We identified potential biases in surveillance types, indicating the value of including all surveillance in models rather than only a single type. Comparing modeling tools available for mapping diseases of wildlife in relation to ecological variables at large spatial extents will guide future modeling efforts for CWD and other wildlife diseases. Understanding spatial patterns of CWD using different surveillance types can help improve understanding of CWD disease outbreaks, assist with control of CWD through geographical targeting, and inform future CWD surveillance efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110756","usgsCitation":"Bondo, K.J., Rosenberry, C., Stainbrook, D., and Walter, W., 2024, Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types: Ecological Modeling, v. 493, 110756, 16 p., https://doi.org/10.1016/j.ecolmodel.2024.110756.","productDescription":"110756, 16 p.","ipdsId":"IP-163658","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bondo, Kristin J.","contributorId":343150,"corporation":false,"usgs":false,"family":"Bondo","given":"Kristin","email":"","middleInitial":"J.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":910646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Christopher S.","contributorId":343151,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stainbrook, David","contributorId":343152,"corporation":false,"usgs":false,"family":"Stainbrook","given":"David","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910649,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261997,"text":"70261997 - 2024 - Development and evaluation of public-supply community water service area boundaries for the conterminous United States","interactions":[],"lastModifiedDate":"2025-01-08T15:23:27.857215","indexId":"70261997","displayToPublicDate":"2024-05-26T09:18:16","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of public-supply community water service area boundaries for the conterminous United States","docAbstract":"The water service area dataset, derived from the National Boundary Dataset for public-supply water systems in the United States, offers a detailed resolution surpassing county-level assessments, emphasizing water-centric land use. Crucial for linking populations and infrastructure to system withdrawals, it supports the creation of a national public-supply water-use model, enhancing accuracy in estimating water use and distinguishing between publicly supplied and self-supplied domestic water use. Integrating tabular water system data strengthens the national water-use model by enabling tracking of withdrawal locations, source water, and water quality. Evaluated against U.S. Census-derived population datasets, 16 state-provided water service area datasets, and two national land use datasets, the study covers 22,849 community water systems, excluding most small systems serving fewer than 1000 people. Robust correlations between water service areas (WSAs) and satellite-sourced urban and exurban land use types facilitate tracking changes over time. A comparison of state and national datasets for population and WSAs reveals discrepancies ranging from 5% to 73% in state-level populations and 0% to 167% in state-level WSAs. Significant differences can be attributed to the exclusion of sizable incorporated and unincorporated areas in the state-based datasets. Additional comparisons of major metropolitan areas exhibit differences ranging from 2% to 56%.
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Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922613,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255052,"text":"70255052 - 2024 - Behavioral trade-offs and multitasking by elk in relation to predation risk from Mexican gray wolves","interactions":[],"lastModifiedDate":"2024-06-14T11:18:17.724666","indexId":"70255052","displayToPublicDate":"2024-05-26T06:09:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral trade-offs and multitasking by elk in relation to predation risk from Mexican gray wolves","docAbstract":"Predator non-consumptive effects (NCE) can alter prey foraging time and habitat use, potentially reducing fitness. Prey can mitigate NCEs by increasing vigilance, chewing-vigilance synchronization, and spatiotemporal avoidance of predators. We quantified the relationship between Mexican wolf (Canis lupus baileyi) predation risk and elk (Cervus canadensis) behavior. We conducted behavioral observations on adult female elk and developed predation risk indices using GPS collar data from Mexican wolves, locations of elk killed by wolves, and landscape covariates. We compared a priori models to determine the best predictors of adult female behavior and multitasking. Metrics that quantified both spatial and temporal predation risk were the most predictive. Vigilance was positively associated with increased predation risk. The effect of predation risk on foraging and resting differed across diurnal periods. During midday when wolf activity was lower, the probability of foraging increased while resting decreased in high-risk areas. During crepuscular periods when elk and wolves were most active, increased predation risk was associated with increased vigilance and slight decreases in foraging. Our results suggest elk are temporally avoiding predation risk from Mexican wolves by trading resting for foraging, a trade-off often not evaluated in behavioral studies. Probability of multitasking depended on canopy openness and an interaction between maternal period and predation risk; multitasking decreased prior to parturition and increased post parturition in high-risk areas. Openness was inversely related to multitasking. These results suggest adult female elk are altering the type of vigilance used depending on resource availability/quality, current energetic needs, and predation risk. Our results highlight potentially important, but often-excluded behaviors and trade-offs prey species may use to reduce the indirect effects of predation and contribute additional context to our understanding of predator–prey dynamics.
Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken (Tympanuchus pallidicinctus), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.
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Rossi, Beauprez, G., Kuklinski, K.E., Martin, R., Koslovsky, M.D., Rintz, T.B., and Hooten, M., 2024, Melded integrated population models: Journal of Agricultural, Biological and Environmental Statistics, v. 5, 31 p., https://doi.org/10.1007/s13253-024-00620-2.","productDescription":"31 p.","ipdsId":"IP-157570","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432158,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.41733481268557,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 32.25672574231251\n ],\n [\n -98.89034146341777,\n 38.10618949808156\n ],\n [\n -104.41733481268557,\n 38.10618949808156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Ee, Justin J.","contributorId":341159,"corporation":false,"usgs":false,"family":"Van Ee","given":"Justin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908023,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagen, Christian A.","contributorId":341160,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[],"preferred":false,"id":908024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavlacky, David C. 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The scenarios included historical and future drought conditions, and a land-cover condition where shrubland and forest within the cloud zone were converted to grassland. For the historical drought condition, island-wide mean annual recharge estimates range from a decrease of 30 percent (239 million gallons per day [Mgal/d]) for Kauaʻi to a decrease of 39 percent (2,706 Mgal/d) for the Island of Hawaiʻi, relative to the reference condition consisting of 1978–2007 rainfall and 2020 land cover. For the future drought condition, estimates of island-wide mean annual recharge range from a decrease of 40 percent (477 Mgal/d) on Maui to a decrease of 51 percent (116 Mgal/day) on Molokaʻi. Complete conversion of all shrubland and forest within the cloud zone to grassland for each drought condition produces estimated land-cover-related decreases in island-wide mean annual recharge (in addition to the drought-related decreases) of 11–12 Mgal/d on Oʻahu, 119–135 Mgal/day on Maui, and 689–849 Mgal/d on the Island of Hawaiʻi. The spatial distributions of increases in conditions indicative of moisture stress and potential wildfire hazard were quantified using the relative frequency of soil moisture less than a selected threshold value (monthly mean soil moisture less than 0.074, expressed as a fraction of available water capacity), evapotranspiration less than a selected threshold value (monthly evapotranspiration less than 0.96 inches), and climatic water deficit greater than a selected threshold value (monthly climatic water deficit greater than 0.77, expressed as fraction of potential evapotranspiration). For the historical drought condition, the greatest increases in the relative frequency for the moisture-stress indicators occur across parts of east and southwest Kauaʻi; central, east, and west Oʻahu; central Molokaʻi; central Maui and low- to mid-altitude parts of West Maui volcano; and the northwestern and southern parts of the Island of Hawaiʻi. For the future drought condition, the greatest increases in the relative frequency of the moisture-stress indicators occur across parts of west Kauaʻi; central and west Oʻahu and Molokaʻi; a band of mid-altitude area on the southern slope of West Maui volcano and across the southwestern slope of Haleakalā; and mid-altitude areas of the northwestern and southern parts of the Island of Hawaiʻi. Complete conversion of all shrubland and forest within the cloud zone to grassland for each drought condition results in land-cover-related increases in the relative frequency of moisture-stress indicators around Kaʻala in the Waiʻanae Range and the southeastern part of the Koʻolau Range on Oʻahu, the southern part of West Maui volcano and the southwestern slope of Haleakalā on Maui, and the upland regions of the western and southern parts of the Island of Hawaiʻi.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235141","collaboration":"Prepared in cooperation with the Pacific Islands Climate Adaptation Science Center","usgsCitation":"Mair, A., Oki, D.S., Kāne, H.L., Johnson, A.G., and Rotzoll, K., 2024, Effects of drought and cloud-water interception on groundwater recharge and wildfire hazard for recent and future climate conditions, Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2023–5141, 98 p., https://doi.org/10.3133/sir20235141","productDescription":"Report: viii, 98 p.; 2 Data Releases","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-139810","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":429177,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235130","text":"Scientific Investigations Report 2023-5130","linkHelpText":"- Estimated Groundwater Recharge for Mid-Century and End-of-Century Climate Projections, Kaua‘i, O‘ahu, Moloka‘i, Lāna‘i, Maui, and the Island of Hawai‘i"},{"id":429174,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGHWS4","text":"USGS Data Release","description":"Mair, A., 2024, Frequency characteristics of soil moisture, evapotranspiration, and climatic water deficit for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of rainfall and land-cover conditions: U.S. Geological Survey data release, https://doi.org/10.5066/P9HGHWS4.","linkHelpText":"Frequency characteristics of soil moisture, evapotranspiration, and climatic water deficit for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of rainfall and land-cover conditions"},{"id":429173,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DDP1C6","text":"USGS Data Release","description":"Mair, A., 2024, Mean annual groundwater recharge rates for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of drought and land-cover conditions: U.S. Geological Survey data release, https://doi.org/10.5066/P9DDP1C6.","linkHelpText":"Mean annual groundwater recharge rates for Kauaʻi, Oʻahu, Molokaʻi, Maui, and the Island of Hawaiʻi, for a set of drought and land-cover conditions"},{"id":429175,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5141/sir20235141.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499403,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117007.htm","text":"Island of Hawaii","linkFileType":{"id":5,"text":"html"}},{"id":499402,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117006.htm","text":"Islands of Kauai, Oahu, Molokai, and 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.
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