Construction activities may affect adjacent water systems by introducing increased levels of suspended solids into the water body and may subsequently affect the survival and growth of freshwater mussels. We tested three sediment types from sites in Missouri, including Spring River sediment (SRS), Osage River bank clay soil (ORC), and quarried limestone from Columbia (LMT). We prepared series of suspensions of each sediment with total suspended solids concentrations ranging from 0 to 5000 mg/L. Juveniles from three mussel species, Fatmucket (Lampsilis siliquoidea), Arkansas Brokenray (Lampsilis reeveiana), and Washboard (Megalonaias nervosa) were exposed to these suspensions in both acute (96-h) and chronic (28-d) tests. No clear impact on survival was observed from the acute or chronic exposures, but chronic test showed that juvenile mussels' growth was strongly affected. Interestingly, growth was enhanced at lower levels of SRS and ORC (≤500 mg/L, p < 0.05), and the juvenile mussels exposed to 500 mg/L SRS exhibited approximately 60 % more dry weight than those reared in the control. LMT did not enhance growth. Growth was slowed by high concentrations (>1000 mg/L) of all three sediments, implying that high suspended solids levels could reduce survival in the long term. Our findings may help to inform regulations and guidelines for construction activities to minimize adverse effects on juvenile mussels.
The purpose of this review is to better understand the full life cycle and influence of ammonia from an aquatic biology perspective. While ammonia has toxic properties in water and air, it also plays a central role in the biogeochemical nitrogen (N) cycle and regulates mechanisms of normal and abnormal fish physiology. Additionally, as the second most synthesized chemical on Earth, ammonia contributes economic value to many sectors, particularly fertilizers, energy storage, explosives, refrigerants, and plastics. But, with so many uses, industrial N2-fixation effectively doubles natural reactive N concentrations in the environment. The consequence is global, with excess fixed nitrogen driving degradation of soils, water, and air; intensifying eutrophication, biodiversity loss, and climate change; and creating health risks for humans, wildlife, and fisheries. Thus, the need for ammonia research in aquatic systems is growing. In response, we prepared this review to better understand the complexities and connectedness of environmental ammonia. Even the term “ammonia” has multiple meanings. So, we have clarified the nomenclature, identified units of measurement, and summarized methods to measure ammonia in water. We then discuss ammonia in the context of the N-cycle, review its role in fish physiology and mechanisms of toxicity, and integrate the effects of human N-fixation, which continuously expands ammonia's sources and uses. Ammonia is being developed as a carbon-free energy carrier with potential to increase reactive nitrogen in the environment. With this in mind, we review the global impacts of excess reactive nitrogen and consider the current monitoring and regulatory frameworks for ammonia. The presented synthesis illustrates the complex and interactive dynamics of ammonia as a plant nutrient, energy molecule, feedstock, waste product, contaminant, N-cycle participant, regulator of animal physiology, toxicant, and agent of environmental change. Few molecules are as influential as ammonia in the management and resilience of Earth's resources.
Grasslands are among the most impacted ecosystems globally. In the midcontinent of North America, a > 80% loss of grasslands has made their conservation a major priority for resource managers. Grassland ecosystems evolved under periodic disturbances; consequently, grassland management often involves regular actions such as grazing, haying, or burning to maintain ecosystem integrity. The timing of such practices has direct implications on grassland ecology, agricultural economics, and survival and fecundity of grassland nesting birds (hereafter grassland birds). We conducted a meta-analysis on the nesting phenology of grassland birds throughout North America, focusing on nest-survival literature. We constructed a well-fitting model to predict median date of expected nest departure (hereafter fledge date) for grassland birds across the midcontinent. Predictions from our model demonstrate considerable spatial variation in median nesting phenology that is predictable using a spatially explicit spring phenology index. Median fledge dates were 8 or 13 days earlier in years of extreme weather conditions (dry or wet, respectively) than in years of average conditions. Species that generally nest in taller vegetation tended to have later median nest phenologies than those using shorter vegetation. Our results incorporate the most rigorous information available in the literature on nesting phenology of 36 grassland bird species and improve information available to managers about nesting phenology of grassland birds in the midcontinent of North America. Our predictions approximate the day when one-half of the nesting efforts would be complete for a given area within the midcontinent grasslands and can inform management and conservation decisions about the timing of management actions in grassland ecosystems.
Wildlife diseases are a major global threat to biodiversity. Boreal toads (Anaxyrus [Bufo] boreas) are a state-endangered species in the southern Rocky Mountains of Colorado and New Mexico, and a species of concern in Wyoming, largely due to lethal skin infections caused by the amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd). We performed conservation and landscape genomic analyses using single nucleotide polymorphisms from double-digest, restriction site-associated DNA sequencing in combination with the development of the first boreal toad (and first North American toad) reference genome to investigate population structure, genomic diversity, landscape connectivity and adaptive divergence. Genomic diversity (π = 0.00034–0.00040) and effective population sizes (Ne = 8.9–38.4) were low, likely due to post-Pleistocene founder effects and Bd-related population crashes over the last three decades. Population structure was also low, likely due to formerly high connectivity among a higher density of geographically proximate populations. Boreal toad gene flow was facilitated by low precipitation, cold minimum temperatures, less tree canopy, low heat load and less urbanization. We found >8X more putatively adaptive loci related to Bd intensity than to all other environmental factors combined, and evidence for genes under selection related to immune response, heart development and regulation and skin function. These data suggest boreal toads in habitats with Bd have experienced stronger selection pressure from disease than from other, broad-scale environmental variations. These findings can be used by managers to conserve and recover the species through actions including reintroduction and supplementation of populations that have declined due to Bd.
We present the validation methodology and results of Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS). The DSWx-HLS product is the first of the DSWx suite, comprised of products each which map water from Earth Observation optical and SAR satellites. We detail the generation of high-resolution (3 m) validation datasets from a globally-stratified sample of dry, moderate, and wet sites. We provide the precise accounting of the classification metrics used to verify the Observational Products for End-users from Remote Sensing Analysis (OPERA) project requirements. We also report broader classification metrics across the validation datasets considered. OPERA performs validation in the public domain to ensure that the validation activities are transparent and reproducible. The resulting validation datasets and provisional OPERA products are publicly available; the software used for validation is also open-source.
","conferenceTitle":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 16-21, 2023","conferenceLocation":"Pasadena, CA","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS52108.2023.10283397","usgsCitation":"Arena, N., Bato, G., Bekaert, D., Bonnema, M., Chan, S., Chapman, B., Jones, J., Handwerger, A., Lewandowski, A., Marshak, C., Sangha, S., and Venkataramani, K., 2023, Opera Dynamic Surface Water extents for Harmonized Landsat Sentinel-2 (DSWX-HLS) validation activities, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, July 16-21, 2023, p. 2723-2726, https://doi.org/10.1109/IGARSS52108.2023.10283397.","productDescription":"4 p.","startPage":"2723","endPage":"2726","ipdsId":"IP-153954","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":428363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arena, Nicholas","contributorId":336167,"corporation":false,"usgs":false,"family":"Arena","given":"Nicholas","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bato, Grace","contributorId":336168,"corporation":false,"usgs":false,"family":"Bato","given":"Grace","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekaert, David","contributorId":336169,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew","contributorId":336170,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chan, Steven","contributorId":336171,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Bruce","contributorId":336172,"corporation":false,"usgs":false,"family":"Chapman","given":"Bruce","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":900093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Handwerger, Alexander L.","contributorId":336174,"corporation":false,"usgs":false,"family":"Handwerger","given":"Alexander L.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900094,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lewandowski, Alex","contributorId":336176,"corporation":false,"usgs":false,"family":"Lewandowski","given":"Alex","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900095,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marshak, Charlie","contributorId":336178,"corporation":false,"usgs":false,"family":"Marshak","given":"Charlie","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900096,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sangha, Simran","contributorId":336183,"corporation":false,"usgs":false,"family":"Sangha","given":"Simran","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900097,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Venkataramani, Karthik","contributorId":336185,"corporation":false,"usgs":false,"family":"Venkataramani","given":"Karthik","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900098,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70256488,"text":"70256488 - 2023 - Change-point models for identifying behavioral transitions in wild animals","interactions":[],"lastModifiedDate":"2024-08-07T15:40:00.947336","indexId":"70256488","displayToPublicDate":"2023-10-20T10:36:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Change-point models for identifying behavioral transitions in wild animals","docAbstract":"Animal behavior can be difficult, time-consuming, and costly to observe in the field directly. Innovative modeling methods, such as hidden Markov models (HMMs), allow researchers to infer unobserved animal behaviors from movement data, and implementations often assume that transitions between states occur multiple times. However, some behavioral shifts of interest, such as parturition, migration initiation, and juvenile dispersal, may only occur once during an observation period, and HMMs may not be the best approach to identify these changes. We present two change-point models for identifying single transitions in movement behavior: a location-based change-point model and a movement metric-based change-point model. We first conducted a simulation study to determine the ability of these models to detect a behavioral transition given different amounts of data and the degree of behavioral shifts. We then applied our models to two ungulate species in central Pennsylvania that were fitted with global positioning system collars and vaginal implant transmitters to test hypotheses related to parturition behavior. We fit these models in a Bayesian framework and directly compared the ability of each model to describe the parturition behavior across species. Our simulation study demonstrated that successful change point estimation using either model was possible given at least 12 h of post-change observations and 15 min fix interval. However, our models received mixed support among deer and elk in Pennsylvania due to behavioral variation between species and among individuals. Our results demonstrate that when the behavior follows the dynamics proposed by the two models, researchers can identify the timing of a behavioral change. Although we refer to detecting parturition events, our results can be applied to any behavior that results in a single change in time.
No abstract available.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2023.1227227","usgsCitation":"Kurz, D.J., Middleton, A., Chapman, M.S., Huber, B.R., Mcinturff, M.C., Sorgen, J., Van Houtan, K.S., Wilkinson, C.E., Withey, L., and Brashares, J., 2023, Including Rural America in academic conservation science: Frontiers in Conservation Science, v. 4, 1227227, 6 p., https://doi.org/10.3389/fcosc.2023.1227227.","productDescription":"1227227, 6 p.","ipdsId":"IP-147164","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":441821,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2023.1227227","text":"Publisher Index Page"},{"id":430141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2023-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kurz, David J.","contributorId":339166,"corporation":false,"usgs":false,"family":"Kurz","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":903826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Arthur","contributorId":39274,"corporation":false,"usgs":true,"family":"Middleton","given":"Arthur","affiliations":[],"preferred":false,"id":903827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapman, Melissa S.","contributorId":339171,"corporation":false,"usgs":false,"family":"Chapman","given":"Melissa","email":"","middleInitial":"S.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":903828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huber, Bruce R.","contributorId":339175,"corporation":false,"usgs":false,"family":"Huber","given":"Bruce","email":"","middleInitial":"R.","affiliations":[{"id":81248,"text":"Notre Dame Law School","active":true,"usgs":false}],"preferred":false,"id":903829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mcinturff, Michael C 0000-0002-4858-1292","orcid":"https://orcid.org/0000-0002-4858-1292","contributorId":337290,"corporation":false,"usgs":true,"family":"Mcinturff","given":"Michael","email":"","middleInitial":"C","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sorgen, Jeremy","contributorId":339176,"corporation":false,"usgs":false,"family":"Sorgen","given":"Jeremy","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":903831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Houtan, Kyle S.","contributorId":339177,"corporation":false,"usgs":false,"family":"Van Houtan","given":"Kyle","email":"","middleInitial":"S.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":903832,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wilkinson, Christine E.","contributorId":339178,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":903833,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Withey, Lauren","contributorId":339179,"corporation":false,"usgs":false,"family":"Withey","given":"Lauren","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":903834,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brashares, Justin S.","contributorId":339180,"corporation":false,"usgs":false,"family":"Brashares","given":"Justin S.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":903835,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70247132,"text":"70247132 - 2023 - Monitoring long-term changes of urban surface temperature using time-series land cover and remote sensing data across 50 major cities in the United States","interactions":[],"lastModifiedDate":"2024-05-28T14:42:03.934403","indexId":"70247132","displayToPublicDate":"2023-10-20T09:35:45","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Monitoring long-term changes of urban surface temperature using time-series land cover and remote sensing data across 50 major cities in the United States","docAbstract":"The increase of developed land changes the Earth’s ecosystems and, in doing so, impacts the natural environment and further affects the services it provides to humans. Urban growth and associated land cover transitions alter the thermal and physical properties of the land surface, resulting in surface temperature change in urban areas. In this study, we integrated both land cover and surface temperature information to characterize surface temperature spatiotemporal variations using the recently available time series of Landsat land surface temperature and annual land change products. We analyzed over thirty-year trends of land surface temperature (LST) in urban and surrounding non-urban lands. We found that the transitions of different land cover types to urban affected urban LST trends differently, and further impacted the temporal trend of urban heat island intensity.
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Product","interactions":[],"lastModifiedDate":"2024-05-28T14:05:25.727146","indexId":"70246672","displayToPublicDate":"2023-10-20T09:04:28","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping the Surface Urban Heat Island effect using the Landsat Surface Temperature Product","docAbstract":"Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Urban areas usually exhibit relatively warmer air and surface temperatures than surrounding non-urban lands, a phenomenon recognized as Surface Urban Heat Island (SUHI). As urban areas continue to develop and the climate continues to warm, it has become increasingly important to quantify and map the SUHI effect and learn how to mitigate it. To help meet the expanding need of analysis ready data for SUHI based studies, a methodology was developed to evaluate Land Surface Temperature (LST) using the Landsat Collection 1 Provisional Surface Temperature Science Product. The Landsat derived LST products were processed for 50 major cities throughout the Conterminous U.S. The SUHI product package includes per-pixel annual surface temperature, annual intensity, annual hotspot, and hotspot probability bands from 1985 to 2020.
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Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":877863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":877864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":877865,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259714,"text":"70259714 - 2023 - An integrated framework for examining groundwater vulnerability in the Mekong River Delta region","interactions":[],"lastModifiedDate":"2024-10-19T13:06:25.936468","indexId":"70259714","displayToPublicDate":"2023-10-20T08:04:22","publicationYear":"2023","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":"An integrated framework for examining groundwater vulnerability in the Mekong River Delta region","docAbstract":"The Mekong River provides water, food security, and many other valuable benefits to the more than 60 million Southeast Asian residents living within its basin. However, the Mekong River Basin is increasingly stressed by changes in climate, land cover, and infrastructure. These changes can affect water quantity and quality and exacerbate related hazards such as land subsidence and saltwater intrusion, resulting in multiple compounding risks for neighboring communities. In this study, we demonstrate the connection between climate change, groundwater availability, and social vulnerability by linking the results of a numerical groundwater model to land cover and socioeconomic data at the Cambodia-Vietnam border in the Mekong River Delta region. We simulated changes in groundwater availability across 20 years and identified areas of potential water stress based on domestic and agriculture-related freshwater demands. We then assessed adaptive capacity to understand how communities may be able to respond to this stress to better understand the growing risk of groundwater scarcity driven by climate change and overextraction. This study offers a novel approach for assessing risk of groundwater scarcity by linking the effects of climate change to the socioeconomic context in which they occur. Increasing our understanding of how changes in groundwater availability may affect local populations can help water managers better plan for the future, leading to more resilient communities.
The study of nocturnally active bats is difficult even for those species that seasonally congregate. This challenge is particularly acute for ‘ōpe‘ape‘a (Hawaiian hoary bat; Lasiurus semotus) because of its solitary foliage-roosting behavior. Yet surveys are essential for conservation and management of this endangered species and only land mammal endemic to the Hawaiian Islands. We surveyed for ‘ōpe‘ape‘a at 23 sites and a range of elevations (33–2,341 m) on Hawai‘i Island from May 2018 to August 2021. We captured 138 unique bats (37 female, 101 male) over 224 mist-netting events. We averaged 16 net-hours per bat capture, with peak captures 30–90 min after sunset. We marked all captured individuals in this study with identifying forearm bands and recaptures represented 7% of total captures (10 of 148). We developed generalized linear mixed models to examine the relationship of nightly bat captures by sex to elevation and time-of-year while accounting for variable sampling effort and repeated sampling in this study. Both males and females were captured at low and high elevations with peak capture rates occurring at approximately 930 m. The capture rate for females was highest during the reproductive season (May to September), whereas it was highest for males during the non-reproductive season (October to April). This study informs future fieldwork with a description of ‘ōpe‘ape‘a capture on Hawai‘i Island by sex, elevation, time-of-year and time-of-night, radio transmitter retention, and recapture frequency.
","language":"English","publisher":"University of Hawai'i Press","doi":"10.2984/77.1.1","usgsCitation":"Hoeh, J.P., Aguirre, A.A., Calderon, F.A., Casler, S.P., Ciarrachi, S.G., Courtot, K., Montoya-Aiona, K., Pinzari, C., and Gorresen, P., 2023, Seasonal and elevational differences by sex in capture rate of ʻōpeʻapeʻa (Lasiurus semotus) on Hawai‘i Island: Pacific Science, v. 77, no. 1, p. 1-26, https://doi.org/10.2984/77.1.1.","productDescription":"26 p.","startPage":"1","endPage":"26","ipdsId":"IP-145607","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":422009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai‘i","otherGeospatial":"Hawai‘i Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.96842132885067,\n 19.33138539404075\n ],\n [\n -154.7897923836328,\n 19.485585611959735\n ],\n [\n -154.79887521135575,\n 19.551218990406824\n ],\n [\n -154.91089675327194,\n 19.591156680319756\n ],\n [\n -154.9684212594931,\n 19.645342013414833\n ],\n [\n -154.96236604101108,\n 19.696658591411236\n ],\n [\n -154.99264213342082,\n 19.756507141135103\n ],\n [\n -155.0774151921682,\n 19.756507141135103\n ],\n [\n -155.0774151921682,\n 19.864747424330687\n ],\n [\n -155.19851956180733,\n 19.98998602165949\n ],\n [\n -155.41045220867588,\n 20.095223300447913\n ],\n [\n -155.56183267072475,\n 20.137866947170835\n ],\n [\n -155.6011915908574,\n 20.146394280939575\n ],\n [\n -155.78890336379806,\n 20.262887779216527\n ],\n [\n -155.87670403178655,\n 20.28276819224483\n ],\n [\n -155.90698012419628,\n 20.254366822413203\n ],\n [\n -155.9160629519192,\n 20.16344755159399\n ],\n [\n -155.89789729647333,\n 20.095223300447913\n ],\n [\n -155.84037272089478,\n 20.026969315439658\n ],\n [\n -155.85551076709956,\n 19.9871407929613\n ],\n [\n -155.91303534267828,\n 19.92453277061513\n ],\n [\n -155.94331143508802,\n 19.867594857310905\n ],\n [\n -155.99175318294365,\n 19.861899940222415\n ],\n [\n -156.03111210307634,\n 19.81348489371841\n ],\n [\n -156.070471023209,\n 19.79069623879731\n ],\n [\n -156.0734986324501,\n 19.71946068185811\n ],\n [\n -155.98569796446162,\n 19.588304345937644\n ],\n [\n -155.96147709053392,\n 19.46845951057206\n ],\n [\n -155.91000776811617,\n 19.34850848155353\n ],\n [\n -155.925902725301,\n 19.21704010756863\n ],\n [\n -155.94293303161635,\n 19.105507378038368\n ],\n [\n -155.88814437651806,\n 19.00626880146993\n ],\n [\n -155.81565527064834,\n 18.990692600887243\n ],\n [\n -155.72554212736438,\n 18.938198274908366\n ],\n [\n -155.69548950343082,\n 18.8925651150143\n ],\n [\n -155.6244303900727,\n 18.91275839274777\n ],\n [\n -155.55569553714562,\n 19.002395865451618\n ],\n [\n -155.52134277651828,\n 19.078471509831623\n ],\n [\n -155.48905706234626,\n 19.119349850631657\n ],\n [\n -155.44276140564918,\n 19.13124152882338\n ],\n [\n -155.3530735113342,\n 19.191279451469995\n ],\n [\n -155.265880055271,\n 19.23839493374041\n ],\n [\n -155.17278493527743,\n 19.23992550373882\n ],\n [\n -154.96842132885067,\n 19.33138539404075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"77","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoeh, Julia P. 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Marcos 0000-0002-0707-9212","orcid":"https://orcid.org/0000-0002-0707-9212","contributorId":196628,"corporation":false,"usgs":false,"family":"Gorresen","given":"P. Marcos","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":886520,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70251165,"text":"70251165 - 2023 - Comparing NISAR (using Sentinel-1), USDA/NASS CDL, and ground truth crop/non-crop areas in an urban agricultural region","interactions":[],"lastModifiedDate":"2024-01-25T13:03:36.65225","indexId":"70251165","displayToPublicDate":"2023-10-20T06:59:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Comparing NISAR (using Sentinel-1), USDA/NASS CDL, and ground truth crop/non-crop areas in an urban agricultural region","docAbstract":"No abstract available.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwmr.12621","usgsCitation":"Rosenberry, D.O., 2023, In my experience: Groundwater-surface-water interactions reflect the path of least resistance: Groundwater Monitoring and Remediation, v. 43, no. 4, p. 119-121, https://doi.org/10.1111/gwmr.12621.","productDescription":"3 p.","startPage":"119","endPage":"121","ipdsId":"IP-153839","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499266,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwmr.12621","text":"Publisher Index Page"},{"id":422449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":887768,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70249728,"text":"70249728 - 2023 - Bayesian weighting of climate models based on climate sensitivity","interactions":[],"lastModifiedDate":"2023-10-26T11:54:44.46868","indexId":"70249728","displayToPublicDate":"2023-10-20T06:53:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian weighting of climate models based on climate sensitivity","docAbstract":"Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.
In 2016, the National Oceanic and Atmospheric Administration deployed the first iteration of an operational National Water Model (NWM) to forecast the water cycle in the continental United States. With many versions, an hourly, multi-decadal historic simulation is made available to the public. In all released to date, the files containing simulated streamflow contain a snapshot of model conditions across the entire domain for a single timestep which makes accessing time series a technical and resource-intensive challenge. In the most recent release, extracting a complete streamflow time series for a single location requires managing 367,920 files (~16.2 TB). In this work we describe a reproducible process for restructuring a sequential set of NWM steamflow files for efficient time series access and provide restructured datasets for versions 1.2 (1993–2018), 2.0 (1993–2020), and 2.1 (1979–2022). These datasets have been made accessible via an OPeNDAP enabled THREDDS data server for public use and a brief analysis highlights the latest version of the model should not be assumed best for all locations. Lastly we describe an R package that expedites data retrieval with examples for multiple use-cases.
Characterizing the crustal structure of Indonesia is important to gain a better understanding of its geodynamic evolution and improve seismic hazard assessments in the area. However, a unified crustal model of the entire Indonesian region and its surroundings is lacking. We present new maps of crustal thickness and bulk Vp/Vs ratio in Indonesia and the surrounding area that are obtained using P-wave receiver functions at 36 seismic stations from several permanent regional networks. The measured crustal thickness varies from ∼24 km to ∼38 km. The thickest crust, ∼38 km, is beneath Flores Island, southern Maluku, and neighboring northernmost Australia, whereas the thinnest crust, ∼24 km, is found under eastern Malaysia. Thus, crustal thickness varies by ∼14 km (from ∼24 km to ∼38 km) despite the small changes in elevation at the measurement points. The Vp/Vs ratios are 1.79
Collaborative governance structures are increasingly common among natural resource managers. While studies have assessed the conditions under which collaborative action occurs, little emphasis has been placed on the role leadership may play in joint-jurisdictional systems. Management of species under the Endangered Species Act offers an opportunity to assess the collaboration of federal, state, and tribal resource agencies. The Gulf of Maine Distinct Population Segment of Atlantic salmon (Salmo salar) was managed under a structure called the Atlantic Salmon Recovery Framework (ASRF) from 2011 to 2019. Using the ASRF as a case study, we examined the influence of leadership approaches on perceived program efficacy, member buy-in, and experience through semi-structured interviews. Participant reflections revealed three major leadership themes that participants found inadequate: (1) shared goals, (2) transparency, and (3) trust. Collaborative approaches that foster these leadership conditions may increase adaptive capacity and the likelihood of sustained success in this, and other, environmental governance structures.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.119203","usgsCitation":"Flye, M.E., Sponarski, C.C., McGreavy, B., and Zydlewski, J.D., 2023, Leading the charge: A qualitative case-study of leadership conditions in collaborative environmental governance structures: Journal of Environmental Management, v. 348, 119203, 9 p., https://doi.org/10.1016/j.jenvman.2023.119203.","productDescription":"119203, 9 p.","ipdsId":"IP-119349","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":486919,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2023.119203","text":"Publisher Index Page"},{"id":433513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"348","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flye, Melissa. E.","contributorId":275664,"corporation":false,"usgs":false,"family":"Flye","given":"Melissa.","email":"","middleInitial":"E.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sponarski, Carly. C.","contributorId":342620,"corporation":false,"usgs":false,"family":"Sponarski","given":"Carly.","email":"","middleInitial":"C.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGreavy, Bridie","contributorId":275231,"corporation":false,"usgs":false,"family":"McGreavy","given":"Bridie","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910227,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":910224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250885,"text":"70250885 - 2023 - Comparing methods to estimate feral burro abundance","interactions":[],"lastModifiedDate":"2024-01-10T16:06:43.747101","indexId":"70250885","displayToPublicDate":"2023-10-19T09:56:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Comparing methods to estimate feral burro abundance","docAbstract":"Obtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter-based, simultaneous double-observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio-collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark-recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter-based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio-collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model-generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio-collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark-recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR-based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio-collars and mark-recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1495","usgsCitation":"Hennig, J., and Schoenecker, K., 2023, Comparing methods to estimate feral burro abundance: Wildlife Society Bulletin, v. 47, no. 4, e1495, 15 p., https://doi.org/10.1002/wsb.1495.","productDescription":"e1495, 15 p.","ipdsId":"IP-143673","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":441836,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1495","text":"Publisher Index Page"},{"id":435146,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZS081M","text":"USGS data release","linkHelpText":"Feral burro detections from aerial infrared surveys collected in Sinbad Herd Management Area, Utah, USA, from 2015-2016"},{"id":424281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Sinbad Herd Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.64724760767687,\n 38.90843748458232\n ],\n [\n -110.80027463938974,\n 38.90843748458232\n ],\n [\n -110.80027463938974,\n 38.836410812983246\n ],\n [\n -110.64724760767687,\n 38.836410812983246\n ],\n [\n -110.64724760767687,\n 38.90843748458232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hennig, Jacob D.","contributorId":333094,"corporation":false,"usgs":false,"family":"Hennig","given":"Jacob D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":891913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":891914,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249642,"text":"70249642 - 2023 - Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles","interactions":[],"lastModifiedDate":"2023-10-21T13:43:50.552769","indexId":"70249642","displayToPublicDate":"2023-10-19T08:39:55","publicationYear":"2023","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":"Alternative measures of trait–niche relationships: A test on dispersal traits in saproxylic beetles","docAbstract":"Functional trait approaches are common in ecology, but a lack of clear hypotheses on how traits relate to environmental gradients (i.e., trait–niche relationships) often makes uncovering mechanisms difficult. Furthermore, measures of community functional structure differ in their implications, yet inferences are seldom compared among metrics. Community-weighted mean trait values (CWMs), a common measure, are largely driven by the most common species and thus do not reflect community-wide trait–niche relationships per se. Alternatively, trait–niche relationships can be estimated across a larger group of species using hierarchical joint species distribution models (JSDMs), quantified by a parameter Γ. We investigated how inferences about trait–niche relationships are affected by the choice of metric. Using deadwood-dependent (saproxylic) beetles in fragmented Finnish forests, we followed a protocol for investigating trait–niche relationships by (1) identifying environmental filters (climate, forest age, and deadwood volume), (2) relating these to an ecological function (dispersal ability), and (3) identifying traits related to this function (wing morphology). We tested 18 hypothesized dispersal relationships using both CWM and Γ estimates across these environmental gradients. CWMs were more likely than Γ to show support for trait–niche relationships. Up to 13% of species' realized niches were explained by dispersal traits, but the directions of effects were consistent with fewer than 11%–39% of our 18 trait–niche hypotheses (depending on the metric used). This highlights the difficulty in connecting morphological traits and ecological functions in insects, despite the clear conceptual link between landscape connectivity and flight-related traits. Caution is thus warranted in hypothesis development, particularly where apparent trait–function links are less clear. Inferences differ when CWMs versus Γ estimates are used, necessitating the choice of a metric that reflects study questions. CWMs help explain the effects of environmental gradients on community trait composition, whereas the effects of traits on species' niches are better estimated using hierarchical JSDMs.
Sustained-yield hunting of introduced ungulates in the Hawaiian Islands often conflicts with the conservation of native species, but there is little reliable data to guide effective management. European mouflon sheep (Ovis musimon; mouflon) and axis deer (Axis axis; deer) were introduced on the island of Lāna‘i to provide additional hunting opportunities. Managers will require better information regarding the ecological associations of introduced ungulate species, relative to the habitats occupied, to resolve longstanding conflicts between native species conservation and sustained-yield hunting on islands. To address this information need, we modeled sheep and deer ecological associations, habitat-use, and suitability using data obtained from an intensive aerial survey completed in 2013 and temporally matching environmental data. In habitat suitability models evaluated by Receiver Operating Characteristic (ROC) metrics, predictor importance in a generalized linear model (GLM) of deer decreased in the following order: afternoon cloud cover, topographic slope, mean annual precipitation (MAP), elevation, normalized difference vegetation index (NDVI), and bare soil index. In a random GLM model of mouflon, predictor importance decreased in the following order: afternoon cloud cover, deer habitat suitability, NDVI, bare soil index, topographic slope, elevation, and MAP. Mouflon were restricted to lower elevation arid slopes, whereas deer were more broadly distributed throughout upland environments of the island. The presence of deer was also an important predictor for mouflon distribution, although mouflon was not an important predictor of deer, suggesting asymmetrical competition. Removal of the more abundant deer population may lead to an increase in abundance and distribution of mouflon without containment. This work represents the first habitat suitability analysis for all nonnative ungulates on any entire Hawaiian island. Our results are applicable to other islands where conflicts may arise with introduced ungulates, sustained-yield hunting, and native species conservation.
Large-scale dam removals provide opportunities to restore river function in the long-term and are massive disturbances to riverine ecosystems in the short-term. The removal of two dams on the Elwha River (WA, USA) between 2011 and 2014 was the largest dam removal project to be completed by that time and has since resulted in major changes to channel dynamics, river substrates, in-stream communities, and the size and shape of the river delta. To assess ecosystem function across the restored Elwha watershed, we compared leaf litter decomposition at twenty sites: 1) four tributary sites not influenced by restoration activities; 2) four river sites downstream of the upper dam (Glines Canyon Dam); 3) four river sites within the footprint of the former Aldwell Reservoir upstream of the lower dam (Elwha Dam); 4) four river sites downstream of the lower dam; and 5) four lentic sites in the newly developing Elwha delta. Three major findings emerged: 1) decomposition rates differed among sections of the Elwha watershed, with slowest decomposition rates at the delta sites and fastest decomposition rates just downstream of the upper dam; 2) aquatic macroinvertebrate communities establishing in leaf litterbags differed significantly among sections of the Elwha watershed; and 3) aquatic fungal communities growing on leaf litter differed significantly among sections. Aquatic macroinvertebrate and fungal diversity were sensitive to differences in canopy cover, water chemistry, and river bottom sediments across sites, with a stronger relationship to elevation for aquatic macroinvertebrates. As the Elwha River undergoes recovery following the massive sediment flows associated with dam removal, we expect to see changes in leaf litter processing dynamics and shifts in litter-dependent decomposer communities (both fungal and invertebrate) involved in this key ecosystem process.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1231689","usgsCitation":"LeRoy, C.J., Morley, S.A., Duda, J.J., Zinck, A.A., Lamoureux, P.J., Pennell, C., Bailey, A., Oswell, C., Silva, M., Kamakawiwo’ole, B.K., Hartford, S., Van Der Hout, J., Peters, R., Mahan, R., Stapleton, J., Johnson, R.C., and Foley, M.M., 2023, Leaf litter decomposition and detrital communities following the removal of two large dams on the Elwha River (Washington, USA): Frontiers of Ecology and Evolution, v. 11, 1231689, 17 p., https://doi.org/10.3389/fevo.2023.1231689.","productDescription":"1231689, 17 p.","ipdsId":"IP-154059","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":441841,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/fevo.2023.1231689","text":"Publisher Index Page"},{"id":422096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.70384847912715,\n 48.18248970246785\n ],\n [\n -123.69747256054883,\n 47.89956988612926\n ],\n [\n -123.36728352594747,\n 47.903844451880445\n ],\n [\n -123.3573654303813,\n 48.18248970246785\n ],\n [\n -123.70384847912715,\n 48.18248970246785\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"LeRoy, Carri J.","contributorId":331098,"corporation":false,"usgs":false,"family":"LeRoy","given":"Carri","email":"","middleInitial":"J.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morley, Sarah A.","contributorId":148956,"corporation":false,"usgs":false,"family":"Morley","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":17601,"text":"NOAA Fisheries, Northwest Fisheries Science Center, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":886743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zinck, Alex A.","contributorId":331099,"corporation":false,"usgs":false,"family":"Zinck","given":"Alex","email":"","middleInitial":"A.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamoureux, Paris J.","contributorId":331100,"corporation":false,"usgs":false,"family":"Lamoureux","given":"Paris","email":"","middleInitial":"J.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pennell, Cameron","contributorId":331101,"corporation":false,"usgs":false,"family":"Pennell","given":"Cameron","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bailey, Ali","contributorId":331102,"corporation":false,"usgs":false,"family":"Bailey","given":"Ali","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886748,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oswell, Caitlyn","contributorId":331103,"corporation":false,"usgs":false,"family":"Oswell","given":"Caitlyn","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886749,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Silva, Mary","contributorId":331104,"corporation":false,"usgs":false,"family":"Silva","given":"Mary","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886750,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kamakawiwo’ole, Brandy K.","contributorId":331105,"corporation":false,"usgs":false,"family":"Kamakawiwo’ole","given":"Brandy","email":"","middleInitial":"K.","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886751,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hartford, Sorrel","contributorId":331106,"corporation":false,"usgs":false,"family":"Hartford","given":"Sorrel","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886752,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Van Der Hout, Jacqueline","contributorId":331107,"corporation":false,"usgs":false,"family":"Van Der Hout","given":"Jacqueline","email":"","affiliations":[{"id":79118,"text":"Environmental Studies Program, The Evergreen State College, Olympia, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886753,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peters, Roger","contributorId":219502,"corporation":false,"usgs":false,"family":"Peters","given":"Roger","affiliations":[],"preferred":false,"id":886754,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mahan, Rebecca","contributorId":331108,"corporation":false,"usgs":false,"family":"Mahan","given":"Rebecca","email":"","affiliations":[{"id":79121,"text":"Clallam County Department of Community Development, Port Angeles, WA, USA","active":true,"usgs":false}],"preferred":false,"id":886755,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Stapleton, Justin","contributorId":241974,"corporation":false,"usgs":false,"family":"Stapleton","given":"Justin","email":"","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":886756,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886757,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Foley, Melissa M.","contributorId":316727,"corporation":false,"usgs":false,"family":"Foley","given":"Melissa","email":"","middleInitial":"M.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":886758,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70252521,"text":"70252521 - 2023 - Use of physical blockers to control invasive red swamp crayfish in burrows","interactions":[],"lastModifiedDate":"2024-03-27T12:06:47.68054","indexId":"70252521","displayToPublicDate":"2023-10-19T07:05:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Use of physical blockers to control invasive red swamp crayfish in burrows","docAbstract":"The red swamp crayfish Procambarus clarkii is native to the southeast United States\nbut has successfully invaded nearly every continent around the world. Although physical,\nbiological, and chemical controls are employed to reduce or eliminate populations\nin open-water systems, terrestrial burrows provide a potential refuge from aquatic\ncontrol treatments. We conducted burrow trials to test whether two physical blocker\ntreatments would kill P. clarkii in their burrows. Bentonite clay (a sealing agent) and\nexpanding foam (an insulating sealant) were each applied to 37 crayfish burrows,\nand 36 burrows served as treatment controls (i.e., 110 total burrows). Burrows were\nexcavated 48 hr after the application of the physical blockers to assess the status of\ncrayfish in treated and control burrows. There was 74% mortality of crayfish in\noccupied burrows treated with bentonite clay, 62% in burrows treated with expanding\nfoam, and 6% mortality in control burrows. We believe bentonite clay should continue\nto be field-tested; however, because expanding foam is toxic to aquatic organisms and\nis expected to persist in the environment, we do not believe it is a suitable physical\nblocker for the control of invasive crayfish in burrows. Bentonite clay applications\nlikely will not need permits, will mitigate damage to banks and levees caused by\nburrowing crayfish, and can be used with other control agents such as pesticides.\nHowever, the use of physical blockers may be limited at field sites that have burrows\nwith complex morphologies. We believe the use of bentonite clay to control invasive\ncrayfish in terrestrial burrows will provide resource managers with an effective tool\nfor their integrative pest management programs.","language":"English","publisher":"Reabic","doi":"10.3391/mbi.2023.14.4.09","usgsCitation":"Bates, B.L., Allert, A., Wildhaber, M.L., and Stoeckel, J., 2023, Use of physical blockers to control invasive red swamp crayfish in burrows: Management of Biological Invasions, v. 14, no. 4, p. 709-729, https://doi.org/10.3391/mbi.2023.14.4.09.","productDescription":"21 p.","startPage":"709","endPage":"729","ipdsId":"IP-153209","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":441843,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.4.09","text":"Publisher Index Page"},{"id":435147,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96V08D0","text":"USGS data release","linkHelpText":"Crayfish morphometric measurements, burrow attributes and occupancy in response to physical burrow barriers"},{"id":427139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bates, Benjamin Lee 0000-0001-5142-8881","orcid":"https://orcid.org/0000-0001-5142-8881","contributorId":330857,"corporation":false,"usgs":true,"family":"Bates","given":"Benjamin","email":"","middleInitial":"Lee","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allert, Ann 0000-0001-7063-8016 aallert@usgs.gov","orcid":"https://orcid.org/0000-0001-7063-8016","contributorId":178200,"corporation":false,"usgs":true,"family":"Allert","given":"Ann","email":"aallert@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoeckel, Jim","contributorId":299806,"corporation":false,"usgs":false,"family":"Stoeckel","given":"Jim","email":"","affiliations":[],"preferred":false,"id":897401,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249598,"text":"fs20233043 - 2023 - Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center","interactions":[],"lastModifiedDate":"2026-02-09T17:47:45.10106","indexId":"fs20233043","displayToPublicDate":"2023-10-18T16:01:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3043","displayTitle":"Hydrologic Investigations of Green Infrastructure by the Central Midwest Water Science Center","title":"Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center","docAbstract":"The water management system within developed communities includes stormwater, wastewater, and drinking-water sources and sinks. Each water management system component provides critical services that support public health in these areas. Stormwater can be quite variable and difficult to manage in developed communities because the amount of stormwater that must be routed through a developed area depends on changing land cover and variable precipitation. In addition to flooding concerns, stormwater also is a major cause of water contamination in developed communities because it carries contaminants such as trash, bacteria, heavy metals, and sediments to local waterways. Historically, communities have managed stormwater with gray infrastructure such as street gutters, culverts, sewer systems, and tunnels. Although these structures efficiently capture and route stormwater to a local waterway or treatment plant, they do not filter any contaminants. Furthermore, many older communities have combined storm sewer and sanitary sewer systems. These combined systems result in an excessive amount of wastewater to be treated before being released into receiving water or the untreated waters are released directly to receiving waters during storms.
Many communities are now incorporating green infrastructure stormwater mitigating solutions—pervious surfaces (allows water through), grassed swales, bioretention basins, and rain gardens—into their stormwater-management systems. Green infrastructure can absorb and filter stormwater where it falls by taking advantage of natural soil and plant storage and filtration capabilities. Thus, green infrastructure projects can potentially reduce the amount of stormwater and the concentration and transport of contaminants. Increasing green infrastructure in a developed community may reduce the requirements for new storm sewer infrastructure, improve the water quality of nearby waterways, and enhance aesthetics.
The U.S. Geological Survey has partnered with several cooperators to quantify the effects of green infrastructure projects in several developed communities throughout the central Midwest. As part of these green infrastructure projects, the U.S. Geological Survey Central Midwest Water Science Center and cooperators installed, calibrated, and monitored equipment to measure hydrologic responses (including flooding and water movement) and selected water-quality constituents in developed communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233043","usgsCitation":"Atkinson, A.A., Heimann, D.C., and Bailey, C.R., 2023, Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center: U.S. Geological Survey Fact Sheet 2023–3043, 4 p., https://doi.org/10.3133/fs20233043.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147766","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499695,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115581.htm","linkFileType":{"id":5,"text":"html"}},{"id":499694,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115580.htm","linkFileType":{"id":5,"text":"html"}},{"id":421980,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3043/fs20233043.pdf","text":"Report","size":"2.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3043"},{"id":421979,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3043/images"},{"id":421978,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3043/fs20233043.XML","text":"XML"},{"id":421976,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3043/coverthb.jpg"},{"id":421981,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233043/full","text":"HTML","linkFileType":{"id":5,"text":"html"}}],"contact":"Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-23-3261-2023","usgsCitation":"Patton, A., Luna, L., Roering, J.J., Jacobs, A., Korup, O., and Mirus, B., 2023, Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA: Natural Hazards and Earth System Sciences, v. 23, no. 10, p. 3261-3284, https://doi.org/10.5194/nhess-23-3261-2023.","productDescription":"24 p.","startPage":"3261","endPage":"3284","ipdsId":"IP-148647","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":441845,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-23-3261-2023","text":"Publisher Index Page"},{"id":422429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Sitka","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -135.5083890264902,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 57.18995904083906\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Annette","contributorId":303028,"corporation":false,"usgs":false,"family":"Patton","given":"Annette","email":"","affiliations":[{"id":65615,"text":"Sitka Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":866314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luna, Lisa","contributorId":303029,"corporation":false,"usgs":false,"family":"Luna","given":"Lisa","email":"","affiliations":[{"id":52955,"text":"University of Potsdam","active":true,"usgs":false}],"preferred":false,"id":866315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roering, Josh J.","contributorId":303030,"corporation":false,"usgs":false,"family":"Roering","given":"Josh","email":"","middleInitial":"J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":866316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobs, Aaron","contributorId":204855,"corporation":false,"usgs":false,"family":"Jacobs","given":"Aaron","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":866317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Korup, Oliver","contributorId":218071,"corporation":false,"usgs":false,"family":"Korup","given":"Oliver","email":"","affiliations":[{"id":39735,"text":"Institute of Earth and Environmental Science, University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":866318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866319,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256600,"text":"70256600 - 2023 - Assessing potential spawning locations of Silver Chub in Lake Erie","interactions":[],"lastModifiedDate":"2024-08-23T16:05:33.325572","indexId":"70256600","displayToPublicDate":"2023-10-18T10:57:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Assessing potential spawning locations of Silver Chub in Lake Erie","docAbstract":"Silver Chub Macrhybopsis storeriana, a predominately riverine species throughout its native range, exists within Lake Erie as the only known lake population. Its population declined in the 1950s and never fully recovered. Canada has listed Silver Chub in the Great Lakes–St. Lawrence River as endangered and has initiated a recovery plan that recognized the identification of spawning areas as a critical component to inform Silver Chub's recovery potential.
We investigated potential spawning locations of Silver Chub using capture records, otolith microchemistry, and daily age analysis. Lapillus otolith Sr:Ca ratios from 27 age-0 Silver Chub were used to identify potential spawning areas. Daily ages estimated from lapilli were used to calculate hatch dates, which then were compared with capture data of adults and river flows to further inform potential spawning areas.
The Detroit River (and its nearshore area) was all but ruled out as a potential spawning location. The Maumee, Portage, and Sandusky rivers or their nearshore areas were all possible spawning locations. Projected hatch dates spanned the end of May through the end of June and occurred across a wide range of flows, although some peaks in hatch dates corresponded to flow peaks, indicating recruitment is potentially enhanced by high flows.
Silver Chub spawning period and hypothesized spawning rivers or lacustuaries overlap those of invasive Grass Carp Ctenopharyngodon idella, creating a need to jointly consider Grass Carp control efforts with conservation of Silver Chub when assessing management alternatives. Further research on spawning guild and the use of rivers themselves or nearshore areas influenced by rivers as spawning areas are required to maximize potential for conservation and recovery of Silver Chub.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10870","usgsCitation":"McKenna, J.R., Bowen, A., Farver, J.R., Long, J.M., Miner, J.G., Stott, N.D., and Kocovsky, P.M., 2023, Assessing potential spawning locations of Silver Chub in Lake Erie: North American Journal of Fisheries Management, v. 43, no. 5, p. 1166-1179, https://doi.org/10.1002/nafm.10870.","productDescription":"14 p.","startPage":"1166","endPage":"1179","ipdsId":"IP-139223","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":441847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10870","text":"Publisher Index Page"},{"id":433107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n 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]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Jorden R.","contributorId":341316,"corporation":false,"usgs":false,"family":"McKenna","given":"Jorden","email":"","middleInitial":"R.","affiliations":[{"id":81722,"text":"Lake Erie Biological Station","active":true,"usgs":false}],"preferred":false,"id":908227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowen, Anjanette","contributorId":341317,"corporation":false,"usgs":false,"family":"Bowen","given":"Anjanette","affiliations":[{"id":81723,"text":"US Fish and Wildlife Service Alpena Fish and Wildlife Conservation Office","active":true,"usgs":false}],"preferred":false,"id":908228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farver, John R.","contributorId":341318,"corporation":false,"usgs":false,"family":"Farver","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":908229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908230,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miner, Jeffrey G.","contributorId":341319,"corporation":false,"usgs":false,"family":"Miner","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":908231,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stott, Nathan D.","contributorId":341320,"corporation":false,"usgs":false,"family":"Stott","given":"Nathan","email":"","middleInitial":"D.","affiliations":[{"id":13587,"text":"Bowling Green State University","active":true,"usgs":false}],"preferred":false,"id":908232,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":908233,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}