We present new 3-D P wave and S wave velocity models of the upper 20 km of the Mount St. Helens (MSH) region. These were obtained using local-source arrival time tomography from earthquakes and explosions recorded at 70 broadband stations deployed as part of the imaging Magma Under St. Helens (iMUSH) project and augmented by several data sets. Principal features of our models include (1) low P wave and S wave velocities along the St. Helens seismic zone to depths of at least 20 km corresponding to high conductivity imaged by iMUSH magnetotelluric studies. This delineates a zone of weakness that magma can exploit at the location of MSH; (2) a 5- to 7-km diameter, 6–15 km deep, 3–6% negative P wave and S wave velocity anomaly beneath MSH, consistent with previous estimates of the source region for recent eruptions. We interpret this as a magma storage region containing up to 15–20 km3 of partial melt, which is about 5 times more than the largest documented eruption at MSH; (3) a broad region of low P wave velocity below 10-km depth extending between Mount Adams and Mount Rainier along and to the east of the main Cascade arc, which is likely due to high-temperature arc crust and possible presence of fluids or melt; (4) several anomalies associated with surface-mapped features, including high-velocity igneous units such as the Spud Mountain and Spirit Lake plutons and low velocities in the Chehalis sedimentary basin and the Indian Heaven volcanic field. Our results place further constraints on the geometry of these features at depth.
Upon reproductive failure, many bird species make a secondary attempt at nesting (hereafter, “renesting”). Renesting may be an effective strategy to maximize current and lifetime reproductive success, but individuals face uncertainty in the probability of success because reproductive attempts initiated later in the breeding season often have reduced nest, pre-fledging, and post-fledging brood survival. We evaluated renesting propensity, renesting intervals, and renest reproductive success of Piping Plovers (Charadrius melodus) by following 1,922 nests and 1,785 unique breeding adults from 2014 to 2016 in the Northern Great Plains of the United States. The apparent renesting rate for individuals was 25% for reproductive attempts that failed in the nest stage (egg laying and incubation) and only 1.2% for reproductive attempts when broods were lost. Renesting propensity declined if reproductive attempts failed during the brood-rearing stage, nests were depredated, reproductive failure occurred later in the breeding season, or individuals had previously renested that year. Additionally, plovers that nested on reservoirs were less likely to renest compared to other habitats. Renesting intervals declined when individuals had not already renested, were after-second-year adults without known prior breeding experience, and moved short distances between nest attempts. Renesting intervals also decreased if the attempt failed later in the season. Overall, reproductive success and daily nest survival were lower for renests than first nests throughout the breeding season. Furthermore, renests on reservoirs had reduced apparent reproductive success and daily nest survival unless the predicted amount of habitat on reservoirs increased within the breeding season. Our results provide important demographic measures for this threatened species and suggest that predation- and water-management strategies that maximize success of early nests would be more likely to increase productivity. Altogether, renesting appears to be an unproductive reproductive strategy to replace lost reproductive attempts for Piping Plovers breeding in the Northern Great Plains.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duz066","usgsCitation":"Swift, R.J., Anteau, M.J., Ring, M., Toy, D.L., and Sherfy, M.H., 2020, Low renesting propensity and reproductive success make renesting unproductive for the threatened Piping Plover (Charadrius melodus): The Condor, v. 2, no. 122, duz066, 18 p., https://doi.org/10.1093/condor/duz066.","productDescription":"duz066, 18 p.","ipdsId":"IP-108250","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457680,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz066","text":"Publisher Index Page"},{"id":437105,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VAS8P7","text":"USGS data release","linkHelpText":"Renesting propensity, intervals, and reproductive success data for the Northern Great Plains Piping Plover, a threatened shorebird species 2014-2016"},{"id":375685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.39208984375,\n 48.21003212234042\n ],\n [\n -103.71093749999999,\n 48.10743118848039\n ],\n [\n -102.89794921875,\n 48.3416461723746\n ],\n [\n -101.97509765625,\n 47.90161354142077\n ],\n [\n -101.4697265625,\n 47.82790816919329\n ],\n [\n -100.78857421875,\n 47.76886840424207\n ],\n [\n -100.52490234375,\n 47.010225655683485\n ],\n [\n -100.43701171875,\n 46.558860303117164\n ],\n [\n -100.39306640625,\n 46.10370875598026\n ],\n [\n -100.21728515624999,\n 45.81348649679973\n ],\n [\n -100.39306640625,\n 44.731125592643274\n ],\n [\n -100.37109375,\n 44.449467536006935\n ],\n [\n -99.6240234375,\n 44.449467536006935\n ],\n [\n -99.03076171875,\n 45.120052841530544\n ],\n [\n -98.96484375,\n 46.08847179577592\n ],\n [\n -98.94287109375,\n 46.99524110694593\n ],\n [\n -99.29443359375,\n 47.54687159892238\n ],\n [\n -99.95361328125,\n 48.22467264956519\n ],\n [\n -101.1181640625,\n 48.647427805533546\n ],\n [\n -102.41455078125,\n 48.647427805533546\n ],\n [\n -103.18359375,\n 48.90805939965008\n ],\n [\n -104.4580078125,\n 48.951366470947725\n ],\n [\n -104.65576171875,\n 48.472921272487824\n ],\n [\n -104.39208984375,\n 48.21003212234042\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"122","noUsgsAuthors":false,"publicationDate":"2020-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Swift, Rose J. 0000-0001-7044-6196","orcid":"https://orcid.org/0000-0001-7044-6196","contributorId":212082,"corporation":false,"usgs":true,"family":"Swift","given":"Rose","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ring, Megan M. 0000-0001-8331-8492","orcid":"https://orcid.org/0000-0001-8331-8492","contributorId":225026,"corporation":false,"usgs":true,"family":"Ring","given":"Megan M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toy, Dustin L. 0000-0001-5390-5784 dtoy@usgs.gov","orcid":"https://orcid.org/0000-0001-5390-5784","contributorId":5150,"corporation":false,"usgs":true,"family":"Toy","given":"Dustin","email":"dtoy@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherfy, Mark H. 0000-0003-3016-4105 msherfy@usgs.gov","orcid":"https://orcid.org/0000-0003-3016-4105","contributorId":125,"corporation":false,"usgs":true,"family":"Sherfy","given":"Mark","email":"msherfy@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791041,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211506,"text":"70211506 - 2020 - American eels produce and release bile acids that vary across life stage","interactions":[],"lastModifiedDate":"2020-07-29T14:41:29.589294","indexId":"70211506","displayToPublicDate":"2020-02-18T09:37:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"title":"American eels produce and release bile acids that vary across life stage","docAbstract":"The American eel (Anguilla rostrata ) is an imperilled fish hypothesized to use conspecific cues, in part, to coordinate long‐distance migration during their multistage life history. Here, holding water and tissue from multiple American eel life stages was collected and analysed for the presence, profile and concentration of bile acids. Distinct bile acid profiles were identified in glass, elver, yellow eel and silver eel holding waters using ultraperformance liquid chromatography high‐resolution mass spectrometry and principal component analysis. Taurochenodeoxycholic acid, taurodeoxycholic acid, cholic acid, deoxycholic acid, taurolithocholic acid and taurocholic acid were detected in whole tissue of American glass eels and elvers, and in liver, intestine and gallbladder samples of late‐stage yellow eels. Bile acids were not a major component of silver eel washings or tissue. This study is novel because little was previously known about bile acids produced and emitted into the environment by American eels. Future behavioural studies could evaluate whether any bile acids produced by American eels influence conspecific migratory behaviour.
What factors drive population variability through space and time? Here we assess patterns of abundance of seven species of gall wasps in three genera occurring on the leaves of valley oaks (Quercus lobata ) at 10 sites throughout this species' statewide range in California, from 2000 to 2006. Our primary goals were to understand the factors driving variability in gall abundance and to assess the extent of spatial synchrony in gall wasp communities at both large and small geographic scales. On the large, statewide scale, there was significant site‐to‐site variation in gall abundance, driven in all cases primarily by differences in mean maximum seasonal temperatures, and lesser year‐to‐year variation. In contrast, on the small, local scale, differences were more pronounced from year to year than from tree to tree, and were to some extent correlated with differences in acorn production, suggesting an interaction with the reproductive effort of hosts. Significant spatial synchrony was detected, particularly at the statewide scale, but in no case did synchrony decline significantly with distance, despite sites being up to 741 km apart. Variation in spatial synchrony was correlated with a number of exogenous factors, including seasonal weather conditions, the acorn crop at the statewide scale and soil phosphorus availability at the local scale; however, most variation in spatial synchrony in our analyses remained unexplained.
","language":"English","publisher":"Wiley","doi":"10.1002/1438-390X.12040","usgsCitation":"Barringer, B., Koenig, W.D., Pearse, I.S., and Knops, J., 2020, Population ecology and spatial synchrony in abundance within and among populations of valley oak (Quercus lobata) leaf gall wasps: Population Ecology, v. 62, no. 2, p. 220-232, https://doi.org/10.1002/1438-390X.12040.","productDescription":"13 p.","startPage":"220","endPage":"232","ipdsId":"IP-106434","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437109,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O5F8DU","text":"USGS data release","linkHelpText":"Data on gall wasp abundance and biomass on valley oak and blue oak trees in California"},{"id":376494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"62","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Barringer, Brian","contributorId":229458,"corporation":false,"usgs":false,"family":"Barringer","given":"Brian","email":"","affiliations":[{"id":41651,"text":"U Wisconsin, Stevens Point","active":true,"usgs":false}],"preferred":false,"id":793237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koenig, Walter D.","contributorId":46255,"corporation":false,"usgs":false,"family":"Koenig","given":"Walter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":793238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science 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Nebraska","active":true,"usgs":false}],"preferred":false,"id":793240,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215437,"text":"70215437 - 2020 - Phase equilibrium of a high-SiO2, andesite at fO2 = RRO: Implications for Augustine volcano and other high-fO2 arc andesites","interactions":[],"lastModifiedDate":"2020-10-20T14:38:11.855094","indexId":"70215437","displayToPublicDate":"2020-02-17T09:34:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1336,"text":"Contributions to Mineralogy and Petrology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Phase equilibrium of a high-SiO2, andesite at fO2 = RRO: Implications for Augustine volcano and other high-fO2 arc andesites","title":"Phase equilibrium of a high-SiO2, andesite at fO2 = RRO: Implications for Augustine volcano and other high-fO2 arc andesites","docAbstract":"Understanding the impact of magmatic plumbing systems on explosive volcanic activity is important for hazard management. This study describes phase equilibria experiments using a high-silica andesite (HSA; SiO2 = 62.5 wt%) from the 2006 eruption of Augustine Volcano, Alaska. Experiments were conducted under H2O saturated conditions,
Avian research may require investigators to capture birds for many reasons, including to collect measurements and attach leg bands or transmitters. The effectiveness and efficiency of capture techniques varies by species, habitat, and time of year. Rails are particularly challenging to capture because of their secretive behavior and the dense vegetation they inhabit. As such, basic natural history questions for many rail species remain unanswered. We paired audio lures with modified noose carpets to capture and study 69 Yuma Ridgway's rails (Rallus obsoletus yumanensis) in the southwestern United States during 2016–2018. We compared results with other more commonly used capture methods, and our results show that noose carpets paired with audio lures can be an effective tool to capture rails, thereby facilitating studies of their ecology and life history. Noose carpets are easy to use, cheap to build and maintain, and effective over a wide range of conditions. This method could be used to capture rails other than the Yuma Ridgway's rail by adjusting the noose size, noose line weight, and audio lures to match the target species. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.
The topic of carbon sequestration in coastal salt marshes can serve as the basis of an investigation story line with plenty of authentic relevance and drama! Consider establishing the context with students as an introduction to this lesson. Many resources for teaching about carbon uptake and sequestration in coastal wetlands can be found at the Bringing Wetlands to Market website. Some of the elements of the story are:
• To understand and address adverse impacts of climate change, action can be taken at all levels, from local to regional to global
• Climate change is linked to high levels of CO₂ in the atmosphere that can be reduced through lowering emissions or removing CO₂
• Salt marsh ecosystems are especially efficient at removing CO₂ and storing it for many years, but only if the marsh is ecologically healthy
• Students can learn about some ways to serve as stewards of local salt marshes or wetlands at the Bringing Wetlands to Market curriculum.
","language":"English","publisher":"Waquoit Bay National Estuarine Research Reserve","collaboration":"Waquoit Bay National Estuarine Research Reserve","usgsCitation":"Gonneea Eagle, M., 2020, Getting to the core of the matter, 4 p.","productDescription":"4 p.","ipdsId":"IP-112966","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372632,"type":{"id":15,"text":"Index Page"},"url":"https://waquoitbayreserve.org/wp-content/uploads/BWM2-Teaching-Notes-Getting-to-Core-final-2020-for-website.pdf"},{"id":372646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783134,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70209602,"text":"70209602 - 2020 - Improving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response","interactions":[],"lastModifiedDate":"2020-04-15T11:41:55.853997","indexId":"70209602","displayToPublicDate":"2020-02-17T06:39:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"Improving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response","docAbstract":"Disaster response operations require fast and coordinated actions based on the real-time disaster situation information. Although Volunteered Geographic Information (VGI) or crowdsourced geospatial data applications have demonstrated to be valuable tools for gathering real-time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of compatibility and interoperability. VGI based on Geospatial Semantic Web (GSW) technologies has the potential to overcome the incompatibility and heterogeneity problems. However, GSW-based VGI often has poor performance due to complex geometric computation. The objective of this research is to explore how to use optimization techniques to improve performance of an interoperable geographic situation-awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client-side optimization techniques for improving performance of an IGSAS prototype for flooding disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on-the-fly spatial indexing, tile-based rendering, efficient algorithms for spatial join, and caching, especially for those spatial-join geospatial queries that involve a large number of spatial features and heavy geometric computation.","language":"English","publisher":"Wiley","doi":"10.1111/tgis.12614","collaboration":"","usgsCitation":"Zhang, C., Zhao, T., Usery, E., Varanka, D.E., and Li, W., 2020, Improving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response: Transactions in GIS, v. 24, no. 2, p. 508-525, https://doi.org/10.1111/tgis.12614.","productDescription":"18 p.","startPage":"508","endPage":"525","ipdsId":"IP-099887","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":373999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Chuanrong 0000-0002-9165-5584","orcid":"https://orcid.org/0000-0002-9165-5584","contributorId":224119,"corporation":false,"usgs":false,"family":"Zhang","given":"Chuanrong","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":787094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Tian 0000-0001-6456-9763","orcid":"https://orcid.org/0000-0001-6456-9763","contributorId":224120,"corporation":false,"usgs":false,"family":"Zhao","given":"Tian","email":"","affiliations":[{"id":40828,"text":"University of Wisconsin - Milwaukee","active":true,"usgs":false}],"preferred":false,"id":787095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":787096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":787097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Weidong 0000-0002-4558-3292","orcid":"https://orcid.org/0000-0002-4558-3292","contributorId":224121,"corporation":false,"usgs":false,"family":"Li","given":"Weidong","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":787098,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219489,"text":"70219489 - 2020 - Intraspecific variation in surface water uptake in a perennial desert shrub","interactions":[],"lastModifiedDate":"2021-04-09T11:48:26.84806","indexId":"70219489","displayToPublicDate":"2020-02-16T06:45:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Intraspecific variation in surface water uptake in a perennial desert shrub","docAbstract":"There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.
","language":"English","publisher":"Wiley","doi":"10.1016/j.ecolind.2020.106172","usgsCitation":"Rappaport, D.I., Royle, J.A., and Morton, D.C., 2020, Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes: Ecological Indicators, v. 113, 106172, 9 p., https://doi.org/10.1016/j.ecolind.2020.106172.","productDescription":"106172, 9 p.","ipdsId":"IP-113750","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.106172","text":"Publisher Index Page"},{"id":375463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Mato Grosso","city":"Feliz Natal, Nova Ubirita","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -59.64477539062499,\n -13.667338259654947\n ],\n [\n -53.2177734375,\n -13.667338259654947\n ],\n [\n -53.2177734375,\n -10.055402736564224\n ],\n [\n -59.64477539062499,\n -10.055402736564224\n ],\n [\n -59.64477539062499,\n -13.667338259654947\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rappaport, Danielle I.","contributorId":225138,"corporation":false,"usgs":false,"family":"Rappaport","given":"Danielle","email":"","middleInitial":"I.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":790497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":790498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morton, Douglas C.","contributorId":225139,"corporation":false,"usgs":false,"family":"Morton","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":790499,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208700,"text":"70208700 - 2020 - Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms","interactions":[],"lastModifiedDate":"2020-03-11T15:59:43","indexId":"70208700","displayToPublicDate":"2020-02-15T08:52:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms","docAbstract":"Recent experimental studies have detected the presence of anoxic microzones in hyporheic sediments. These microzones are small‐scale anoxic pores, embedded within oxygen‐rich porous media and can act as anaerobic reaction sites producing reduction compounds such as nitrous oxide, a potent greenhouse gas. Microbes are a key control on nutrient transformation in hyporheic sediment, but their associated biomass growth is also capable of altering hydraulic flux, leading to potential bioclogging. Here, we developed one of the first computational modeling approaches that combined hydraulics and microbial conditions to explore the continuous evolution of microzones in stream sediments. The model assessed stream and sediment conditions with different hydraulic flux (0.1–1.0 m/day Darcy flux), nutrient concentrations (O2 = 8 mg/L, OrgC = 20 mg/L, NO−3 = 1.5–3 mg/L, and NH3 = 0.5–1 mg/L), and biomass scenarios (with and without). The model domain is a pore network model with random sized pore‐throat radii creating heterogeneous and anisotropic flow that is representative of a natural streambed composed of medium sand with a hydraulic conductivity of 0.8 m/day. Results from 30 day simulations indicate that hyporheic microzone formation will occur and microzone distributions are not simply controlled by residence time alone, rather by the complex interactions of hydraulic flux, nutrient concentrations, and biomass, with bioclogging having strong feedbacks on both hydraulics and nutrients. Under all conditions with biomass growth, anoxic microzones were unstable, perishing a few days after formation, because bioclogging primarily occurs near the influent (downwelling) area of the hyporheic zone. In turn, this bioclogging shifts transport conditions from advection‐dominated to diffusion‐dominated transport, removing all oxic regions in the hyporheic zone. Overall, results from the modeling show that anoxic microzones are likely to form under many hyporheic zone conditions, and be dynamic through space and time as they are dependent on both hydraulic flux and nutrient transport.
","language":"English","publisher":"Wiley","doi":"10.1029/2019WR025971","usgsCitation":"Chowdhury, S.R., Zarnetske, J., Phanikumar, M., Briggs, M.A., Day-Lewis, F.D., and Singha, K., 2020, Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms: Water Resources Research, v. 56, no. 3, e2019WR025971, 15 p., https://doi.org/10.1029/2019WR025971.","productDescription":"e2019WR025971, 15 p.","ipdsId":"IP-113836","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Chowdhury, S. R.","contributorId":222748,"corporation":false,"usgs":false,"family":"Chowdhury","given":"S.","email":"","middleInitial":"R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnetske, J.","contributorId":222749,"corporation":false,"usgs":false,"family":"Zarnetske","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phanikumar, M.S.","contributorId":222750,"corporation":false,"usgs":false,"family":"Phanikumar","given":"M.S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":783078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Singha, K.","contributorId":201025,"corporation":false,"usgs":false,"family":"Singha","given":"K.","email":"","affiliations":[],"preferred":false,"id":783079,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208575,"text":"70208575 - 2020 - Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi?","interactions":[],"lastModifiedDate":"2020-04-06T21:58:32.077746","indexId":"70208575","displayToPublicDate":"2020-02-15T06:15:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Does Lake Erie Still Have Sufficient Oxythermal Habitat for Cisco Coregonus artedi?","title":"Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi?","docAbstract":"In Lake Erie, cisco Coregonus artedi once supported one of the most valuable freshwater fisheries on earth, yet overfishing caused their eventual extirpation from the lake. With warming lake temperatures, some have questioned whether Lake Erie still contains suitable oxythermal conditions for cisco. Using published oxythermal thresholds for cisco and oxythermal profiles from Lake Erie, we sought to answer two questions critical to cisco restoration science. First, is cisco habitat still available during the most restrictive periods? Second, what is the distribution of cisco habitat during these times? Beta regression was used to determine that cisco habitat was most limited during the month of August, and that August of 2010 was the most restrictive period in the time series. We then used Empirical Bayesian Kriging (EBK) to map the spatial extent of cisco habitat during these times. EBK maps revealed large areas of summer refugia for cisco in Lake Erie, even during the least favorable periods. Most of the Central and East Basins contain suitable habitat during the average August, yet during August of 2010, suitable conditions were limited to the eastern edge of the Central Basin and the deep waters of the East Basin. These findings align well with historical accounts of cisco landings. While suitable oxythermal habitat still exists for cisco in Lake Erie, future restoration efforts, if attempted, will partially depend on: 1) better management of nutrient inputs, 2) the realization of future climate scenarios, and 3) the ability of cisco to adapt to a changing lake.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.01.019","usgsCitation":"Schmitt, J., Vandergoot, C.S., O’Malley, B.P., and Kraus, R., 2020, Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi?: Journal of Great Lakes Research, v. 46, no. 2, p. 330-338, https://doi.org/10.1016/j.jglr.2020.01.019.","productDescription":"9 p.","startPage":"330","endPage":"338","ipdsId":"IP-112702","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":372406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Erie ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.21044921875,\n 42.13082130188811\n ],\n [\n -83.507080078125,\n 41.68932225997044\n ],\n [\n -82.4853515625,\n 41.36031866306708\n ],\n [\n -81.968994140625,\n 41.48389104267175\n ],\n [\n -81.650390625,\n 41.48389104267175\n ],\n [\n -81.419677734375,\n 41.68111756290652\n ],\n [\n -80.540771484375,\n 41.94314874732696\n ],\n [\n -79.27734374999999,\n 42.374778361114195\n ],\n [\n -78.826904296875,\n 42.827638636242284\n ],\n [\n -78.837890625,\n 42.90011265525328\n ],\n [\n -79.1015625,\n 42.91620643817353\n ],\n [\n -79.541015625,\n 42.924251753870685\n ],\n [\n -80.013427734375,\n 42.827638636242284\n ],\n [\n -80.299072265625,\n 42.80346172417078\n ],\n [\n -80.562744140625,\n 42.62587560259137\n ],\n [\n -80.91430664062499,\n 42.67435857693381\n ],\n [\n -81.2109375,\n 42.69858589169842\n ],\n [\n -81.45263671875,\n 42.69051116998238\n ],\n [\n -81.82617187499999,\n 42.431565872579185\n ],\n [\n -82.0458984375,\n 42.342305278572816\n ],\n [\n -82.518310546875,\n 42.09007006868398\n ],\n [\n -82.891845703125,\n 42.01665183556825\n ],\n [\n -83.21044921875,\n 42.13082130188811\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schmitt, Joseph","contributorId":222565,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":782571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergoot, Christoper S.","contributorId":222566,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christoper","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":782572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Malley, Brian P. bomalley@usgs.gov","contributorId":5615,"corporation":false,"usgs":true,"family":"O’Malley","given":"Brian","email":"bomalley@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":782573,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":782574,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209565,"text":"70209565 - 2020 - Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model","interactions":[],"lastModifiedDate":"2020-04-14T11:20:35.897788","indexId":"70209565","displayToPublicDate":"2020-02-15T06:14:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model","docAbstract":"The Delmarva Peninsula in the eastern United States is dominated by thousands of small, forested depressional wetlands that are highly sensitive to climate change and climate variability but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art deep semantic segmentation network to map forested wetland inundation in the Delmarva region by integrating leaf-off Worldview-3 (WV3) multispectral data with fine resolution light detection and ranging (lidar) intensity and topographic data, including digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation maps generated from lidar intensity were used for model calibration and validation. The wetland inundation map results were also validated by field polygons and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% against field data and high overlap with lidar mapped inundation. The integration of topographic metrics in deep learning model can improve classification accuracy in depressional wetlands. This study highlights the great potential of deep learning models to map wetland inundation through use of high resolution optical and lidar remote sensing datasets.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040644","collaboration":"","usgsCitation":"Du, L., McCarty, G.W., Zhang, X., Lang, M.W., Vanderhoof, M.K., Lin, X., Huang, C., Lee, S., and Zou, Z., 2020, Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model: Remote Sensing, v. 12, no. 4, 644, 19 p., https://doi.org/10.3390/rs12040644.","productDescription":"644, 19 p.","ipdsId":"IP-114826","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040644","text":"Publisher Index Page"},{"id":373937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.26434326171875,\n 38.46649284538942\n ],\n [\n -75.71502685546875,\n 38.46649284538942\n ],\n [\n -75.71502685546875,\n 39.08530414503412\n ],\n [\n -76.26434326171875,\n 39.08530414503412\n ],\n [\n -76.26434326171875,\n 38.46649284538942\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Du, Ling","contributorId":224056,"corporation":false,"usgs":false,"family":"Du","given":"Ling","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":786898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarty, Greg W.","contributorId":131149,"corporation":false,"usgs":false,"family":"McCarty","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":786899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Xinhow","contributorId":177143,"corporation":false,"usgs":false,"family":"Zhang","given":"Xinhow","email":"","affiliations":[],"preferred":false,"id":786900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lang, Megan W.","contributorId":131150,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":7264,"text":"USDA Forest Service, Northern Research Station, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":786901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":786902,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lin, Xian-Dan","contributorId":171991,"corporation":false,"usgs":false,"family":"Lin","given":"Xian-Dan","email":"","affiliations":[],"preferred":false,"id":786903,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":786904,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":786905,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zou, Zhenhua","contributorId":224062,"corporation":false,"usgs":false,"family":"Zou","given":"Zhenhua","email":"","affiliations":[],"preferred":false,"id":786946,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228549,"text":"70228549 - 2020 - The economics of territory selection","interactions":[],"lastModifiedDate":"2022-02-14T22:38:36.179198","indexId":"70228549","displayToPublicDate":"2020-02-14T16:34:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"The economics of territory selection","docAbstract":"Territorial behavior is a fundamental and conspicuous behavior within numerous species, but the mechanisms driving territory selection remain uncertain. Theory and empirical precedent indicate that many animals select territories economically to satisfy resource requirements for survival and reproduction, based on benefits of food resources and costs of competition and travel. Costs of competition may vary by competitive ability, and costs of predation risk may also drive territory selection. Habitat structure, resource requirements, conspecific density, and predator distribution and abundance are likely to further influence territorial behavior. We developed a mechanistic, spatially-explicit, individual-based model to better understand how and why animals select particular territories. The model was based on optimal selection of individual patches for inclusion in a territory according to their net value, i.e., benefits (food resources) minus costs (travel, competition, predation risk). Simulations produced predictions for what may be observed empirically if such optimization drives placement and characteristics of territories. Simulations consisted of sequential, iterative selection of territories by simulated animals that interacted to defend and maintain territories. Results explain why certain patterns in space use are commonly observed, and when and why these patterns will differ from the norm. For example, more clumped or abundant food resources are predicted to result, on average, in smaller territories with more overlap. Strongly different resource requirements for individuals or groups in a population will directly affect space use and are predicted to cause different responses under identical conditions. Territories are predicted to decrease in size with increasing population density, which can enable a population’s density of territories to change at faster rates than their spatial distribution. Due to competition, less competitive territory-holders are generally predicted to have larger territories in order to accumulate sufficient resources, which could produce an ideal despotic distribution of territories. Interestingly, territory size and overlap are predicted to show a parabolic response to increases in predator densities, and territories are predicted to be larger where predators are more clumped in distribution. Our model’s predictions are consistent with many empirical observations, providing support for optimal patch selection as a mechanism for the economical territories of animals commonly observed in nature.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2020.109329","usgsCitation":"Mitchell, M.S., and Sells, S., 2020, The economics of territory selection: Ecological Modelling, v. 338, 109329, 15 p., https://doi.org/10.1016/j.ecolmodel.2020.109329.","productDescription":"109329, 15 p.","ipdsId":"IP-117338","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"338","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sells, Sarah N.","contributorId":276102,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah N.","affiliations":[{"id":50219,"text":"um","active":true,"usgs":false}],"preferred":false,"id":834548,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208051,"text":"sir20205006 - 2020 - Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18","interactions":[],"lastModifiedDate":"2022-04-25T20:56:36.421159","indexId":"sir20205006","displayToPublicDate":"2020-02-14T15:35:24","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5006","displayTitle":"Potential Groundwater Recharge Rates for Two Subsurface-Drained Agricultural Fields, Southeastern Minnesota, 2016–18","title":"Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18","docAbstract":"Subsurface drainage is used to efficiently drain saturated soils to support productive agriculture in poorly drained terrains. Although subsurface drainage alters the water balance for agricultural fields, its effect on groundwater resources and groundwater recharge is poorly understood. In Minnesota, subsurface drainage has begun to increase in southeastern Minnesota, even though this part of the State is underlain by permeable karstic bedrock aquifers with only a thin layer of glacial sediments separating these aquifers from land surface.
To gain a better understanding of groundwater recharge effects from subsurface drainage, the U.S. Geological Survey (USGS), in cooperation with the Legislative-Citizen Commission on Minnesota Resources, led a 2-year hydrologic study to investigate this connection for two agricultural fields in southeastern Minnesota with subsurface drainage. A total of three monitoring plots were used between the two agricultural fields: two monitoring plots that included an actively drained area with peripheral, undrained areas, and a third monitoring plot without any subsurface drainage. Multiple piezometer transects were set up across the three monitoring plots to characterize the unsaturated zone and shallow water-table flow using pressure transducers and soil moisture probes. From these piezometers, groundwater recharge rates were derived using two different methods: the RISE Water-Table Fluctuation (WTF) method and the DRAINMOD model. In addition to these two methods, the USGS Soil-Water-Balance (SWB) model was used to estimate potential recharge rates for three different monitoring plots.
In addition to deriving groundwater recharge rates, the hydrologic budget was analyzed to interpret the water-table surface elevation and soil volumetric water content time series. At one of the two drained plots, the transects exhibited varying water-table surface elevation patterns. Frequent backflow from the adjacent ditch caused subsurface drainage flow to slow down or stop drainage through the main collector drain and cause pipe pressurization, so the closest transect appeared to be mostly controlled by the drain pressurization, whereas the farthest transect was more efficiently drained. Both of the drained monitoring plots had an elevation gradient parallel to the pattern tiles, sloping downward towards the collector drain that aggregated the parallel lines into a single drain. Because the transects were set at different gradients in the field, some of the water-table surface elevation differences were also attributed to lateral flow towards the lowest parts of the field.
Three methods were used to derive potential groundwater recharge rates: the RISE WTF method, the USGS SWB model, and DRAINMOD-derived deep seepage rates. Potential groundwater recharge rates, using the RISE WTF method, across all piezometers were 1.55 and 1.94 inches per year, respectively, for water years 2017 and 2018. More differentiation of potential recharge rates between different piezometer types occurred for water year 2018. Although the difference was slightly more than 1 inch between the drained and nondrained piezometers for water year 2018, this difference was statistically significant based on a t-test with a p-value of 0.036 (α=0.05). When looking at recharge based on distance from the drain, the subsurface drain did not affect potential recharge, although other factors such as variability in screen depths, well construction, and specific yield variability cannot be eliminated. The SWB model was also used to estimate potential recharge rates for water years 2017–18, with rates between 2.44 and 5.92 inches per year for the two drained sites, generally higher than the RISE WTF estimates. DRAINMOD-derived potential recharge rates were generally the highest of the three methods, with potential recharge rates varying from 2.07 to 9.49 inches per year.
Overall, there was a lack of agreement between the three methods. These results were not remarkable, considering the fundamental differences in the methodology for each method. However, all methods did show a fundamental difference between piezometers within the drained area and piezometers outside the drained area, including the third undrained monitoring plot. The drained areas show a lower overall potential groundwater recharge compared to the nondrained areas for all three estimates. The difference for the 2018 recharge estimates was slightly higher than 1 inch for the RISE WTF method, the difference was almost double for the nine sites for the DRAINMOD model, and the difference between the drain and undrained plots was even more significant for the SWB model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205006","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., and Berg, A.M., 2020, Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5006, 57 p., https://doi.org/10.3133/sir20205006.","productDescription":"Report: ix, 54 p.; 5 Appendixes; 3 Data Releases; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112919","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":372354,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendixes.xlsx","text":"Appendix 1 and 2","size":"3.55 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5006 Appendixes"},{"id":372353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006.pdf","text":"Report","size":"4.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5006"},{"id":372352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5006/coverthb.jpg"},{"id":372355,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.1.csv","text":"Appendix 1.1","size":"1.55 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.1"},{"id":372356,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.2.csv","text":"Appendix 1.2","size":"1.66 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.2"},{"id":372357,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.1.csv","text":"Appendix 2.1","size":"13.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.1"},{"id":372358,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.2.csv","text":"Appendix 2.2","size":"13.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.2"},{"id":372359,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P987N30U","text":"USGS data release","linkHelpText":"DRAINMOD simulations for two agricultural drainage sites in western Fillmore County, southeastern Minnesota"},{"id":372360,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90N4AWG","text":"USGS data release","linkHelpText":"Soil-Water Balance model datasets used to estimate recharge for southeastern Minnesota, 2014–2018"},{"id":372361,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LMOPP","text":"USGS data release","linkHelpText":"Potential groundwater recharge estimates based on a groundwater rise analysis technique for two agricultural sites in southeastern Minnesota, 2016–2018"},{"id":372362,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS dataset","linkHelpText":"– USGS groundwater data for Minnesota in USGS water data for the Nation"},{"id":399628,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109687.htm"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.4167,\n 43.595\n ],\n [\n -92.45,\n 43.595\n ],\n [\n -92.45,\n 43.5444\n ],\n [\n -92.4167,\n 43.5444\n ],\n [\n -92.4167,\n 43.595\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
A rapid batch extraction method was evaluated to estimate potential for total dissolved solids (TDS) release by 65 samples of rock from coal and gas-bearing strata of the Appalachian Basin in eastern USA. Three different extractant solutions were considered: deionized water (DI), DI equilibrated with 10% CO2 atmosphere (DI + CO2), or 30% H2O2 under 10% CO2 (H2O2+CO2). In all extractions, 10 g of pulverized rock (<0.5-mm) were mixed with 20 mL of extractant solution and shaken for 4 h at 50 rpm and 20–22 °C. The 65 rock samples were classified as coal (n=3), overburden (n = 17), coal refuse that had weathered in the field (n = 14), unleached coal refuse that had oxidized during indoor storage (n = 20), gas-bearing shale (n = 10), and pyrite (n = 1). Extracts were analyzed for specific conductance (SC), TDS, pH, and major and trace elements, and subsequently speciated to determine ionic contributions to SC. The pH of extractant blanks decreased in the order DI (6.0), DI + CO2 (5.1), and H2O2+CO2 (2.6). The DI extractant was effective for mobilizing soluble SO4 and Cl salts. The DI + CO2 extractant increased weathering of carbonates and resulted in equivalent or greater TDS than the DI leach of the same material. The H2O2+CO2 extractant increased weathering of sulfides (and carbonates) and resulted in the greatest TDS production and lowest pH values. Of the 65 samples, 19 had leachate chemistry data from previous column experiments and 35 were paired to 10 field sites with leachate chemistry data. When accounting for the water-to-rock ratio, TDS from DI and DI + CO2 extractions were correlated to TDS from column experiments while TDS from H2O2+CO2 extractions was not. In contrast to column experiments, field SC was better correlated to SC measured from H2O2+CO2 extractions than DI extractions. The field SC and SC from H2O2+CO2 extractions were statistically indistinguishable for 7 of 9 paired data sets while SC from DI extractions underestimated field SC in 5 of 9 cases. Upscaling comparisons suggest that (1) weathering reactions in the field are more aggressive than DI water or synthetic rainwater extractants used in batch or column tests, and (2) a batch extraction method utilizing 30% H2O2 (which is mildly acidic without CO2 enrichment) could be effective for identifying rocks that will release high amounts of TDS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104540","usgsCitation":"Castillo-Meza, L.E., Cravotta, C., Tasker, T.L., Warner, N.R., Daniels, W.L., Orndorff, Z.W., Bergstresser, T., Douglass, A., Kimble, G., Streczywilk, J., Barton, C., Thompson, A., and Burgos, W.D., 2020, Batch extraction method to estimate total dissolved solids (TDS) release from coal refuse and overburden: Applied Geochemistry, v. 115, 104540, 16 p., https://doi.org/10.1016/j.apgeochem.2020.104540.","productDescription":"104540, 16 p.","ipdsId":"IP-106585","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":467297,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102448","text":"External Repository"},{"id":377359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Castillo-Meza, L. 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In lizards, competition and predation are major forces influencing distribution and abundance, but there is also increasing evidence pointing at the influence of habitat structure and prey abundance. Our work explored the latter further by quantifying the effects of vegetation and prey abundance on occupancy and abundance (i.e., estimated probability of detecting more than two individuals) of two sympatrically occurring species in the northern karst belt of Puerto Rico. We hypothesized that Anolis cristatellus would occur in trunk–ground substrates and Anolis krugi on grass–bush substrates according to their ecomorphological classification. We also hypothesized that prey abundance, a component of habitat quality, would have a positive and strong effect on occupancy and abundance. Anolis cristatellus exhibited high occupancy rates (>0.80), influenced by mid-story tree size. A. cristatellus abundance fluctuated over time, with highest probability of detecting two or more individuals in January–March and July–September when prey abundance transitioned from low to high levels. Occupancy of A. krugi was positively influenced by sapling density and prey abundance. Prey abundance exerted a stronger influence on occupancy, but its influence on abundance was negative and strong. Biological interactions and the type of understory substrates may explain the negative relationship. Our study supported predicted relationships between ecomorphology and habitat, but also showed that higher prey abundance may not always translate to higher local abundance. We shed light on these interactions, knowledge needed to advance anole conservation in the advent of land use and climate change.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/19-009","usgsCitation":"Vega-Castillo, S.J., Collazo, J.A., Puente-Rolón, A., and Cuevas, E., 2020, Influence of habitat structure and prey abundance on cccupancy and abundance of two anole ecomorphs, Anolis cristatellus and Anolis krugi, in secondary karst forests in northern Puerto Rico: Journal of Herpetology, v. 54, no. 1, p. 107-117, https://doi.org/10.1670/19-009.","productDescription":"11 p.","startPage":"107","endPage":"117","ipdsId":"IP-102474","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.8463134765625,\n 18.278910262696105\n ],\n [\n -66.5386962890625,\n 18.278910262696105\n ],\n [\n -66.5386962890625,\n 18.48742375381096\n ],\n [\n -66.8463134765625,\n 18.48742375381096\n ],\n [\n -66.8463134765625,\n 18.278910262696105\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vega-Castillo, S. J.","contributorId":274615,"corporation":false,"usgs":false,"family":"Vega-Castillo","given":"S.","email":"","middleInitial":"J.","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":833162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833163,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Puente-Rolón, A. R.","contributorId":274616,"corporation":false,"usgs":false,"family":"Puente-Rolón","given":"A. 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For the GoMAMN region of the Gulf of Mexico, the Landbird Working Group identified 19 species from 12 families as priorities for monitoring (Table 3.1). In addition, all species that stopover within the GoMAMN region during migration (i.e., passage migrants) are of concern, as are the habitats they use. The 19 priority species use a wide range of habitat types and include species that spend some (e.g., breeding, wintering, migration seasons) or all (e.g., residents) of their annual cycle in the GoMAMN region. The GoMAMN Landbird Working Group organized the priority landbirds into five groups based on a combination of habitat and season—forest breeding, forest wintering, grassland breeding, grassland wintering, and passage migrants—realizing that there would be overlap of habitats and seasons for some species. For example, Swainson's Warbler (Limnothlypis swainsonii) breeds in and migrates through forested habitat in the Gulf of Mexico region and Northern Bobwhite (Colinus virginianus) uses both prairie grasslands and evergreen forest (i.e., open pine savannas) (Table 3.1). For some species, such as Painted Bunting (Passerina ciris) and Common Ground-Dove (Columbina passerina), which often use scrub/shrub vegetation, the habitat-based designations above may be overly simplistic. Although it occurs along higher, drier fringes of palustrine and estuarine emergent marsh habitat, Sedge Wren (Cistothorus platensis) is included here as a landbird (rather than a marsh bird) because it is most commonly found during the winter along the Gulf coast in upland evergreen forest (i.e., wet pine savanna) habitat and grassland habitats. Selection of the five groups was predicated on the assumption that management efforts would be similar for species using these habitats in a given season, and that monitoring methods would be habitat and season specific.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Strategic Bird Monitoring Guidelines for the Northern Gulf of Mexico, Mississippi Agricultural and Forestry Experiment Station Research Bulletin 1228","language":"English","publisher":"Mississippi State University, Mississippi Agricultural and Forestry Experiment Station (MAFES)","usgsCitation":"Zenzal, T.J., Vermillion, W.G., Ferrato, J.R., Randall, L.A., Dobbs, R.C., and Baldwin, H., 2020, GoMAMN Strategic Bird Monitoring Guidelines: Landbirds, chap. 3 of Strategic Bird Monitoring Guidelines for the Northern Gulf of Mexico, Mississippi Agricultural and Forestry Experiment Station Research Bulletin 1228, p. 25-70.","productDescription":"46 p.","startPage":"25","endPage":"70","numberOfPages":"46","ipdsId":"IP-096258","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":381116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381107,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://gomamn.org/strategic-bird-monitoring-guidelines","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zenzal, Theodore J. 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We found 2/139 samples (1 coyote, 1 raccoon) had IAV antibodies and hemagglutination inhibition assays revealed the antibodies to the 2009/2010 H1N1 human pandemic virus or to the 2007 human seasonal H1N1 virus.
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0000-0002-8869-6633","orcid":"https://orcid.org/0000-0002-8869-6633","contributorId":214978,"corporation":false,"usgs":true,"family":"Nashold","given":"Sean","email":"","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":791184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, Jeffrey S. 0000-0001-5599-2826 jshall@usgs.gov","orcid":"https://orcid.org/0000-0001-5599-2826","contributorId":2254,"corporation":false,"usgs":true,"family":"Hall","given":"Jeffrey","email":"jshall@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":791185,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222501,"text":"70222501 - 2020 - Tissue distribution and immunomodulation in channel catfish (Ictalurus punctatus) following dietary exposure to polychlorinated biphenyl Aroclors and food deprivation","interactions":[],"lastModifiedDate":"2021-07-30T12:43:09.104431","indexId":"70222501","displayToPublicDate":"2020-02-14T07:41:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2041,"text":"International Journal of Environmental Research and Public Health","active":true,"publicationSubtype":{"id":10}},"title":"Tissue distribution and immunomodulation in channel catfish (Ictalurus punctatus) following dietary exposure to polychlorinated biphenyl Aroclors and food deprivation","docAbstract":"This study investigated collaborative groundwater‐flow modeling and scenario analysis in the Little Plover River basin, Wisconsin, USA where an unconfined aquifer supplies groundwater for agricultural irrigation, industrial processing, municipal water supply, and stream baseflow. We recruited stakeholders with diverse interests to identify, prioritize, and evaluate scenarios defined as management changes to the landscape. Using a groundwater flow model, we simulated the top 10 stakeholder‐ranked scenarios under historically informed dry, average, and wet weather conditions and evaluated the ability of scenarios to meet government‐defined stream flow performance measures. Results show that multiple changes to the landscape are necessary to maintain optimum stream flow, particularly during dry years. Yet, when landscape changes from three scenarios—transferring water from the local waste water treatment plant to basin headwaters, moving municipal wells further from the river and downstream, and converting 240 acre (97 ha) of irrigated land to unirrigated land—were simulated in combination, the probability of meeting or exceeding optimum flows rose to 75, 65, and 34% at upper, mid, and lower stream gages, respectively, in dry climate conditions. Discussions with stakeholders reveal that the collaborative model and scenario analysis process resulted in social learning that built upon the existing complex and dynamic institutional landscape. The approach provided a forum for solution‐based discussions, and the model served as an important mediation tool for the development and evaluation of community‐defined scenarios in a high conflict environment. Today, stakeholders continue to work collaboratively to overcome challenges and implement voluntary solutions in the basin.