In August 2020, the 3-m tall Pilchuck River Diversion Dam was removed from the Pilchuck River, allowing free fish passage to the upper third of the watershed for the first time in over a century. The narrow, 300-m long impoundment behind the dam was estimated to hold 4,000–7,500 m3 of sand and gravel, representing less than one year's typical bedload flux. Repeat cross section surveys, stage sensors, and time-lapse cameras were used to document the physical channel response over the first year following dam removal. A total of 7,400 m3 (effectively 100%) of impoundment sediment was eroded in the first year, with 25% accomplished by manual regrading during dam removal. Most river-caused erosion occurred during a sequence of modest flows in October 2020. Downstream deposition totaled 4,300 m3, predominately filling in the first 100 m downstream of the dam site. Deposition tapered below detectable levels within 350 m, and most downstream channel adjustments occurred before November 2020. Multiple high flows after December 2020 caused little upstream or downstream change. The physical river response to this dam removal then appears to have been largely accomplished within several months by modest flows, consistent with pre-removal modeling and observations from other regional dam removals. Efficient sediment evacuation was likely aided by the narrow and steep-walled impoundment geometry. Our observations support existing guidance that the physical river response to small dam removals is typically rapid and modest; the benefits of removal may then often be gained with minimal negative downstream geomorphic impacts.
","language":"English","publisher":"Northwest Scientific Association","doi":"10.3955/046.097.0113","usgsCitation":"Anderson, S.W., Shattuck, B., Shea, N., Seguin, C.M., Miles, J.J., Marks, D., and Coumou, N., 2024, River channel response to the removal of the Pilchuck River Diversion Dam, Washington State: Northwest Science, v. 97, no. 1-2, p. 134-145, https://doi.org/10.3955/046.097.0113.","productDescription":"12 p.","startPage":"134","endPage":"145","ipdsId":"IP-144271","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":427589,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Pilchuck River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.904,\n 48.02\n ],\n [\n -121.916,\n 48.02\n ],\n [\n -121.916,\n 48.016\n ],\n [\n -121.904,\n 48.016\n ],\n [\n -121.904,\n 48.02\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shattuck, Brett","contributorId":335415,"corporation":false,"usgs":false,"family":"Shattuck","given":"Brett","email":"","affiliations":[{"id":80397,"text":"Tulalip Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":898314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shea, Neil","contributorId":335416,"corporation":false,"usgs":false,"family":"Shea","given":"Neil","email":"","affiliations":[{"id":80397,"text":"Tulalip Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":898315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seguin, Catherine M.","contributorId":332787,"corporation":false,"usgs":false,"family":"Seguin","given":"Catherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":898316,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miles, Joe J. 0009-0003-4960-6783","orcid":"https://orcid.org/0009-0003-4960-6783","contributorId":337064,"corporation":false,"usgs":true,"family":"Miles","given":"Joe","email":"","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":901867,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marks, Derek","contributorId":225112,"corporation":false,"usgs":false,"family":"Marks","given":"Derek","email":"","affiliations":[],"preferred":false,"id":898318,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Coumou, Natasha","contributorId":335418,"corporation":false,"usgs":false,"family":"Coumou","given":"Natasha","email":"","affiliations":[{"id":80397,"text":"Tulalip Indian Tribe","active":true,"usgs":false}],"preferred":false,"id":898319,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255938,"text":"70255938 - 2024 - Demographics of a previously undocumented diamondback terrapin (Malaclemys terrapin) population","interactions":[],"lastModifiedDate":"2024-07-30T14:53:19.629068","indexId":"70255938","displayToPublicDate":"2024-06-18T10:33:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Demographics of a previously undocumented diamondback terrapin (Malaclemys terrapin) population","title":"Demographics of a previously undocumented diamondback terrapin (Malaclemys terrapin) population","docAbstract":"Coastal habitats are some of the most imperiled due to climate change and anthropogenic activities. As such, it is important to understand population dynamics of the species that may play a role in regulating coastal systems. Diamondback terrapins in Northwest Florida have been understudied, which has resulted in a gap in our knowledge for this region. To help fill this gap, we conducted a capture-mark-recapture study in St. Joseph Bay, Florida, from 2018 to 2021. Overall, we captured 518 individuals, including 146 recaptures, and we used several modeling frameworks to estimate apparent survival, recapture probability, population entrance, and population size. Our estimates of apparent survival were relatively low, especially for adult males (0.77) and adult females (0.83), but there is a considerable amount of uncertainty around our estimates. Our models indicated that the super-population consists of 1122 individuals (971–1327 95% CI), and the population is comprised of more adult males (753; 665–866 95% CI) than adult females (102; 85–130 95% CI) and juveniles (267; 221–331 95% CI). Estimates of population entrance varied by year throughout our study duration. This study is the first to document a Malaclemys terrapin population in this region of Florida, and we recommend long-term monitoring in order to gain inferences for the management of this declining coastal species.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-024-01380-5","usgsCitation":"Catizone, D.J., Thomas, T.M., Romagosa, C., and Lamont, M., 2024, Demographics of a previously undocumented diamondback terrapin (Malaclemys terrapin) population: Estuaries and Coasts, v. 47, p. 1684-1693, https://doi.org/10.1007/s12237-024-01380-5.","productDescription":"10 p.","startPage":"1684","endPage":"1693","ipdsId":"IP-151031","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":430976,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"St. Joseph Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.44660816786433,\n 29.900893125154155\n ],\n [\n -85.44660816786433,\n 29.665660146459913\n ],\n [\n -85.25729815600472,\n 29.665660146459913\n ],\n [\n -85.25729815600472,\n 29.900893125154155\n ],\n [\n -85.44660816786433,\n 29.900893125154155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2024-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Catizone, Daniel J. 0000-0002-7030-4208","orcid":"https://orcid.org/0000-0002-7030-4208","contributorId":248817,"corporation":false,"usgs":true,"family":"Catizone","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Travis M.","contributorId":289917,"corporation":false,"usgs":false,"family":"Thomas","given":"Travis","email":"","middleInitial":"M.","affiliations":[{"id":62286,"text":"Nature Coast Biological Station, Cedar Key, FL","active":true,"usgs":false}],"preferred":false,"id":906083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romagosa, Christina 0000-0003-1900-5648","orcid":"https://orcid.org/0000-0003-1900-5648","contributorId":299306,"corporation":false,"usgs":false,"family":"Romagosa","given":"Christina","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":906084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906085,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255886,"text":"70255886 - 2024 - Spatial patterns as long transients in submersed-floating plant competition with biocontrol","interactions":[],"lastModifiedDate":"2024-09-23T16:11:40.232139","indexId":"70255886","displayToPublicDate":"2024-06-18T06:44:25","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3592,"text":"Theoretical Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial patterns as long transients in submersed-floating plant competition with biocontrol","docAbstract":"A cellular automata model was developed and parameterized to test the effectiveness of application of biological control insects to water hyacinth (Pontederia crassipes), which is an invasive floating plant species in many parts of the world and outcompetes many submersed native aquatic species in southern Florida. In the model, P. crassipes was allowed to compete with Nuttall’s waterweed (Elodea nuttallii). In the absence of biocontrol acting on the P. crassipes, E. nuttallii excluded P. crassipes at low concentrations of the limiting nutrient (nitrogen), and the reverse occurred at high nutrient concentrations. At intermediate values, alternative stable states could occur; either P. crassipes alone or a mixture of the two species. When the biocontrol agent, the weevil Neochetina eichhorniae, was applied in the model, there was initially a rapid reduction of the P. crassipes, however, over time a regular striped pattern of moving spatially alternating stripes of P. crassipes and E. nuttallii emerged. -This pattern of moving stripes emerged and persisted over thousands of days but could quickly transform into an irregular pattern at some apparently random time, when either external stochasticity (added adult weevils) or only the weak internal stochasticity of weevil movements occurred. The cause of the end of the long transient can be traced to a single slightly irregular pixel within the striped pattern. Model parameters were varied to study effects of plant growth rate, nutrient concentration and nutrient diffusion rate on the dynamics of the system.
Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large-scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS-Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long-term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15197","usgsCitation":"Kincaid, D.W., Underwood, K.L., Hamshaw, S.D., Li, L., Seybold, E.C., Stewart, B., Rizzo, D.M., Ul Haq, I., and Perdrial, J.N., 2024, Solute export patterns across the contiguous USA: Hydrological Processes, v. 38, no. 6, e15197, 17 p., https://doi.org/10.1002/hyp.15197.","productDescription":"e15197, 17 p.","ipdsId":"IP-155135","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15197","text":"Publisher Index Page"},{"id":431219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"38","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kincaid, Dustin W. 0000-0003-1640-685X","orcid":"https://orcid.org/0000-0003-1640-685X","contributorId":340199,"corporation":false,"usgs":false,"family":"Kincaid","given":"Dustin","email":"","middleInitial":"W.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Underwood, Kristen L. 0000-0003-3008-3057","orcid":"https://orcid.org/0000-0003-3008-3057","contributorId":340200,"corporation":false,"usgs":false,"family":"Underwood","given":"Kristen","email":"","middleInitial":"L.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":906558,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, L.","contributorId":152225,"corporation":false,"usgs":false,"family":"Li","given":"L.","affiliations":[],"preferred":false,"id":906559,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seybold, Erin C. 0000-0002-0365-2333","orcid":"https://orcid.org/0000-0002-0365-2333","contributorId":340201,"corporation":false,"usgs":false,"family":"Seybold","given":"Erin","email":"","middleInitial":"C.","affiliations":[{"id":35641,"text":"Kansas Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906560,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stewart, Bryn 0000-0002-3199-0129","orcid":"https://orcid.org/0000-0002-3199-0129","contributorId":340202,"corporation":false,"usgs":false,"family":"Stewart","given":"Bryn","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":906561,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rizzo, Donna M.","contributorId":171679,"corporation":false,"usgs":false,"family":"Rizzo","given":"Donna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":906562,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ul Haq, Ijaz","contributorId":340203,"corporation":false,"usgs":false,"family":"Ul Haq","given":"Ijaz","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906563,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Perdrial, Julia N.","contributorId":177340,"corporation":false,"usgs":false,"family":"Perdrial","given":"Julia","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":906564,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255606,"text":"70255606 - 2024 - Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling","interactions":[],"lastModifiedDate":"2024-06-26T13:38:24.914144","indexId":"70255606","displayToPublicDate":"2024-06-17T08:36:13","publicationYear":"2024","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":"Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling","docAbstract":"Foragers on patchy landscapes must efficiently balance time between searching for and consuming resources to meet their daily energetic requirements. Spatial aggregation foraging behaviors may improve foraging efficiency by sharing information on locations of resource hotspots. Wading birds are an example of patch foragers that form colonial aggregations during the breeding season to obtain sufficient prey energy to sustain themselves and their offspring each day. Here, we describe a spatially-explicit simulation model of wading bird optimal foraging that represents information sharing through visual cues. The overall purpose of the model is to describe how wading bird daily foraging and reproductive success may change with alternative water control management practices that determine spatial availability of prey for wading birds on the landscape, throughout their breeding seasons. Wading birds are simulated as individuals that operate independently, sampling and selecting among patches based on a prey density tolerance threshold, but also use information from other birds to inform their selection decisions. Foraging success is evaluated against the fundamental objectives of (a) fulfilling daily energetic demands and (b) minimizing predation exposure, by tracking individual daily energetic intake and time spent foraging. In this way, the model approximates population level dynamics of wading bird aggregations that emerge through collective decision making of birds simulated at the lower individual level. Key results of this study suggest that aggregation behaviors may improve population-level foraging success rates, and the optimal settling threshold may modulate when resources become more scarce or difficult to find. Thus, the model addresses ecological theory on the advantages of foraging in groups versus independently. This technique is appropriate for evaluating wading bird populations that forage on patchy landscapes, such as seasonally-pulsed wetlands, wherever sufficient information is available to describe (1) foraging behavior (e.g., feeding rate, flight speeds, patch selection decisions), (2) key landscape characteristics, (3) spatial distributions of prey densities among foraging patches, and (4) changes in prey densities through time. The model was designed to predict qualitative, testable spatial patterns of wading bird foraging movements which can be compared with empirical observations and empirically-derived habitat suitability models. These techniques can also be applied to other bird species, such as shorebirds, or more generally to any species that transits between discrete foraging patches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110702","usgsCitation":"Yurek, S., DeAngelis, D.L., Lee, H.W., and Tennenbaum, S., 2024, Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling: Ecological Modelling, v. 493, 110702, 15 p., https://doi.org/10.1016/j.ecolmodel.2024.110702.","productDescription":"110702, 15 p.","ipdsId":"IP-153166","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110702","text":"Publisher Index Page"},{"id":430522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216738,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":904925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":904926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Hyo Won","contributorId":292184,"corporation":false,"usgs":false,"family":"Lee","given":"Hyo","email":"","middleInitial":"Won","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":904927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tennenbaum, Stephen","contributorId":292180,"corporation":false,"usgs":false,"family":"Tennenbaum","given":"Stephen","email":"","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":904928,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064B","publicationYear":"2024","noYear":false,"chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2024-06-17T22:21:15.873668","indexId":"sir20235064B","displayToPublicDate":"2024-06-17T07:11:12","publicationYear":"2024","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":"2023-5064","chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","docAbstract":"This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; 30 years 1991–2020) for trends, change points, and other statistical properties indicative of changing conditions. Median peak streamflows generally exhibit upward trends across the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 23-percent increase during the 30-year trend period to a 41-percent increase during the 100-year trend period. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. More than two-thirds of streamgages at the 100- and 75-year trend periods exhibit a trend and change point in median peak streamflow in the same direction. Temporally, clusters of change points are observed in the late 1960s through early 1980s for the 100- and 75-year trend periods and around 2006 for the 50- and 30-year trend periods. Trends in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, increased about the same amount as the 50-percent quantile peak streamflows, except at the 100-year trend period, where the 50-percent quantile peak flow increased more for almost all streamgages. The frequency of high flows has also increased in Illinois, with increases in peaks-over-threshold observed across much of the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with clusters of likely upward trends observed in northern and southern Illinois at the 50- and 30-year trend periods. As expected in response to increasing temperature, the modeled proportion of precipitation falling as snow has largely decreased in the study basins across the State, and modeled potential evapotranspiration has increased. Upward trends in modeled annual runoff, which in this report incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation.
The widespread upward trends in the magnitude of median peak streamflows and the frequency with which high flows occur across the State at the 100- and 75-year trend periods and in northern and southern Illinois at the 50- and 30-year trend periods appear to be driven largely by increases in precipitation based on spatial patterns of these changes and statistical relations between streamflow and climate metrics. Other effects not considered in this report, like urbanization, may be important drivers for certain streamgages in the State.
The prevalence of nonstationarity in peak streamflow in Illinois has important implications for peak-flow frequency analysis. Average annual precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Effect concentrations of ammonia, nickel, sodium chloride, and potassium chloride from short-term 7-day tests were compared to those from standard chronic 28-day toxicity tests with juvenile mussels (fatmucket, Lampsilis siliquoidea) to evaluate the sensitivities of the 7-day tests. The effect concentrations for nickel (59 µg Ni/L), chloride (316–519 mg Cl/L, a range from multiple tests), and potassium (15 mg K/L) obtained from the 7-day tests were within a range of effect concentrations for each corresponding chemical in the 28-day tests (41–91 µg Ni/L, 251–>676 mg Cl/L, 15–23 mg K/L), whereas the 7-day ammonia effect concentration (0.40 mg/L total ammonia nitrogen; TAN) was up to 3.3-fold greater than the 28-day effect concentrations (0.12–0.36 mg TAN/L) but with overlapped 95% confidence limits. These results indicate that the 7-day tests produced similar estimates compared to the 28-day tests. Further studies are needed to evaluate the 7-day test sensitivity using additional chemicals with different modes of toxic action. Environ Toxicol Chem 2024;00:1–6. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.
Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long-term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro-geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non-dendritic, trellis-like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro-geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long-term landscape evolution.
The eruption of Hunga volcano on 15 January 2022 produced a higher plume and faster-growing umbrella cloud than has ever been previously recorded. The plume height exceeded 58 km, and the umbrella grew to 450 km in diameter within 50 min. Assuming an umbrella thickness of 10 km, this growth rate implied an average volume injection rate into the umbrella of 330–500 km3 s−1. Conventional relationships between plume height, umbrella-growth rate, and mass eruption rate suggest that this period of activity should have injected a few to several cubic kilometers of rock particles (tephra) into the plume. Yet tephra fall deposits on neighboring islands are only a few centimeters thick and can be reproduced using ash transport simulations with only 0.1–0.2 km3 erupted volume (dense-rock equivalent). How could such a powerful eruption contain so little tephra? Here, we propose that seawater mixing at the vent boosted the plume height and umbrella growth rate. Using the one-dimensional (1-D) steady plume model Plumeria, we find that a plume fed by ~90% water vapor at a temperature of 100 °C (referred to here as steam) could have exceeded 50 km height while keeping the injection rate of solids low enough to be consistent with Hunga’s modest tephra-fall deposit volume. Steam is envisaged to rise from intense phreatomagmatic jets or pyroclastic density currents entering the ocean. Overall, the height and expansion rate of Hunga’s giant plume is consistent with the total mass of fall deposits plus underwater density current deposits, even though most of the erupted mass decoupled from the high plume. This example represents a class of high (> 10 km), ash-poor, steam-driven plumes, that also includes Kīlauea (2020) and Fukutoku-oka-no-ba (2021). Their height is driven by heat flux following well-established relations; however, most of the heat is contained in steam rather than particles. As a result, the heights of these water-rich plumes do not follow well-known relations with the mass eruption rate of tephra.
Wildlife populations near the periphery of a species’ range are vulnerable to changes in habitat conditions and climate. However, habitat management and maintenance can help with the persistence of these susceptible populations. Snowshoe hare (Lepus americanus) populations near the southern extent of their range are at risk of extirpation because of changing winter conditions, coupled with reduced early-successional habitat. Prescribed fire has been used to restore and maintain early-successional habitat in the southern range of snowshoe hares, but previous research suggests that burned areas might initially be unsuitable for hares. Therefore, more information is needed on the time required for hares to start using an area post-burning, and the long-term effects of prescribed fire on hare survival. We studied snowshoe hare habitat use and survival in northeastern Pennsylvania, where prescribed fire has been used for scrub oak barrens restoration. We used GPS locations from 71 hares and used resource selection functions to analyze hare selection for burned habitats of varying ages (0–12 years post-burn) and known-fate survival models to evaluate the effects of burning on survival. Hares started using burned areas ≥7 years post-burning but avoided areas burned 0–6 years prior. In addition, hare survival was positively associated with the amount of old burn habitat (≥7 years post-burn) used by an individual. Our results indicate that prescribed burning can be beneficial for hares, but that a time lag of ≥7 years is necessary for positive responses to occur. Planning burns within a mosaic of unburned areas could allow hares to persist during the 0–6 years post-burn when areas are not suitable for hares, which in turn could benefit the persistence of southern snowshoe hare populations. Collectively, our results highlight the importance of long-term demographic monitoring to understand wildlife population responses to management actions.
","language":"English","publisher":"Wiley","doi":"10.1111/acv.12959","usgsCitation":"Gigliotti, L., Boyd, E.S., and Diefenbach, D.R., 2024, Delayed positive responses of snowshoe hares to prescribed burning in a fire-adapted ecosystem: Animal Conservation, 9 p., https://doi.org/10.1111/acv.12959.","productDescription":"9 p.","ipdsId":"IP-154582","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":498269,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/acv.12959","text":"Publisher Index Page"},{"id":433624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"northeastern Pennsylvania","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-79.916171,39.720893],[-80.075947,39.72135],[-80.421388,39.721189],[-80.519342,39.721403],[-80.519423,39.806181],[-80.518891,39.890964],[-80.519248,39.936967],[-80.51896,40.078089],[-80.519039,40.342101],[-80.517991,40.367968],[-80.51769,40.462467],[-80.51899,40.473667],[-80.519002,40.877543],[-80.519891,40.906661],[-80.519091,40.921061],[-80.518928,41.070954],[-80.519144,41.171203],[-80.518693,41.248855],[-80.518993,41.268155],[-80.518794,41.305509],[-80.519129,41.312408],[-80.519345,41.340145],[-80.518993,41.435454],[-80.519339,41.539297],[-80.519425,41.977522],[-80.435451,42.005611],[-80.409776,42.011578],[-80.373066,42.024102],[-80.371869,42.023966],[-80.363251,42.027973],[-80.349169,42.030243],[-80.329976,42.036168],[-80.296758,42.049076],[-80.230486,42.077957],[-80.188085,42.094257],[-80.165884,42.105857],[-80.154084,42.114757],[-80.136213,42.149937],[-80.13043,42.156331],[-80.117368,42.166341],[-80.088512,42.173184],[-80.077388,42.171262],[-80.073381,42.168658],[-80.080028,42.163625],[-80.071981,42.155357],[-80.078781,42.151457],[-80.076281,42.147857],[-80.07198,42.146057],[-80.06108,42.144857],[-79.989186,42.177051],[-79.931324,42.206737],[-79.923924,42.207546],[-79.90105,42.216701],[-79.886187,42.224933],[-79.867979,42.230999],[-79.844661,42.235486],[-79.798447,42.255939],[-79.761951,42.26986],[-79.762152,42.243054],[-79.761759,42.162675],[-79.762122,42.131246],[-79.761709,42.11899],[-79.761798,42.019042],[-79.761374,41.999067],[-79.670128,41.999335],[-79.472472,41.998255],[-79.249772,41.998807],[-79.17857,41.999458],[-79.061265,41.999259],[-78.983065,41.998949],[-78.874759,41.997559],[-78.749754,41.998109],[-78.59665,41.999877],[-78.308128,41.999415],[-78.271204,41.998968],[-78.12473,42.000452],[-78.031177,41.999415],[-77.997508,41.998758],[-77.83203,41.998524],[-77.505308,42.00007],[-77.124693,41.999395],[-77.063676,42.000461],[-76.920784,42.001774],[-76.749675,42.001689],[-76.558118,42.000155],[-76.462155,41.998934],[-76.343722,41.998346],[-76.131201,41.998954],[-75.98025,41.999035],[-75.870677,41.998828],[-75.742217,41.997864],[-75.610316,41.99896],[-75.359579,41.999445],[-75.353504,41.99711],[-75.346568,41.995324],[-75.341125,41.992772],[-75.337602,41.9867],[-75.337791,41.984386],[-75.34246,41.974303],[-75.342204,41.972872],[-75.339488,41.970786],[-75.335771,41.970315],[-75.329318,41.968232],[-75.322384,41.961693],[-75.32004,41.960867],[-75.318168,41.954236],[-75.312817,41.950182],[-75.310358,41.949012],[-75.303966,41.948216],[-75.301664,41.94838],[-75.301233,41.9489],[-75.301593,41.952811],[-75.300409,41.953871],[-75.29858,41.954521],[-75.293713,41.954593],[-75.29143,41.952477],[-75.291762,41.947092],[-75.290966,41.945039],[-75.289383,41.942891],[-75.279094,41.938917],[-75.277243,41.933598],[-75.276501,41.926679],[-75.276552,41.922208],[-75.275368,41.919564],[-75.269736,41.911363],[-75.267562,41.907054],[-75.267773,41.901971],[-75.272778,41.897112],[-75.272581,41.893168],[-75.271292,41.88736],[-75.267789,41.885982],[-75.263005,41.885109],[-75.260623,41.883783],[-75.257564,41.877108],[-75.258439,41.875087],[-75.261488,41.873277],[-75.263815,41.870757],[-75.263673,41.868105],[-75.262802,41.866213],[-75.260527,41.8638],[-75.257825,41.862154],[-75.251197,41.86204],[-75.248045,41.8633],[-75.243345,41.866875],[-75.241134,41.867118],[-75.238743,41.865699],[-75.234565,41.861569],[-75.231612,41.859459],[-75.22572,41.857481],[-75.223734,41.857456],[-75.220125,41.860534],[-75.21497,41.867449],[-75.209741,41.86925],[-75.204002,41.869867],[-75.197836,41.868807],[-75.194382,41.867287],[-75.191441,41.865063],[-75.190203,41.862454],[-75.188888,41.861264],[-75.186993,41.860109],[-75.185254,41.85993],[-75.183937,41.860515],[-75.182271,41.862198],[-75.180497,41.86568],[-75.179134,41.869935],[-75.176633,41.872371],[-75.174574,41.87266],[-75.170565,41.871608],[-75.169142,41.87029],[-75.168053,41.867043],[-75.168733,41.859258],[-75.166217,41.853862],[-75.164168,41.851586],[-75.161541,41.849836],[-75.156512,41.848327],[-75.152898,41.848564],[-75.143824,41.851737],[-75.140241,41.852078],[-75.130983,41.845145],[-75.127913,41.844903],[-75.118789,41.845819],[-75.115598,41.844638],[-75.114399,41.843583],[-75.113369,41.840698],[-75.113441,41.836298],[-75.114998,41.8303],[-75.115147,41.827285],[-75.114837,41.82567],[-75.113334,41.822782],[-75.100024,41.818347],[-75.093537,41.813375],[-75.089484,41.811576],[-75.085789,41.811626],[-75.079818,41.814815],[-75.078063,41.815112],[-75.074409,41.815088],[-75.072172,41.813732],[-75.071751,41.811901],[-75.072168,41.808327],[-75.074412,41.802191],[-75.076889,41.798509],[-75.07827,41.797467],[-75.081415,41.796483],[-75.088328,41.797534],[-75.092876,41.796386],[-75.101463,41.787941],[-75.102329,41.786503],[-75.103548,41.782008],[-75.10464,41.774203],[-75.104334,41.772693],[-75.103492,41.771238],[-75.10099,41.769121],[-75.095451,41.768366],[-75.09281,41.768361],[-75.079478,41.771205],[-75.075942,41.771518],[-75.074231,41.770518],[-75.072664,41.768807],[-75.068567,41.767298],[-75.064901,41.766686],[-75.060759,41.764638],[-75.053431,41.752538],[-75.052808,41.744725],[-75.054818,41.735168],[-75.053527,41.72715],[-75.049699,41.715093],[-75.049862,41.713309],[-75.050689,41.711969],[-75.052226,41.711396],[-75.061174,41.712935],[-75.06663,41.712588],[-75.068642,41.710146],[-75.06883,41.708161],[-75.067278,41.705434],[-75.059829,41.699716],[-75.056745,41.695703],[-75.052736,41.688393],[-75.051234,41.682439],[-75.051285,41.679961],[-75.052653,41.678436],[-75.058765,41.674412],[-75.059332,41.67232],[-75.05843,41.669653],[-75.057251,41.668933],[-75.053991,41.668194],[-75.04992,41.662556],[-75.048683,41.656317],[-75.049281,41.641862],[-75.048658,41.633781],[-75.048199,41.632011],[-75.043562,41.62364],[-75.044224,41.617978],[-75.045508,41.616203],[-75.047298,41.615791],[-75.048385,41.615986],[-75.051856,41.618157],[-75.05385,41.618655],[-75.060098,41.617482],[-75.06156,41.616429],[-75.061675,41.615468],[-75.059956,41.612306],[-75.059725,41.610801],[-75.062716,41.609639],[-75.067795,41.610143],[-75.071667,41.609501],[-75.074626,41.607905],[-75.074613,41.605711],[-75.066955,41.599428],[-75.063677,41.594739],[-75.060012,41.590813],[-75.052858,41.587772],[-75.04676,41.583258],[-75.043879,41.575094],[-75.04049,41.569688],[-75.036989,41.567049],[-75.033162,41.565092],[-75.029211,41.564637],[-75.027343,41.563541],[-75.018524,41.551802],[-75.016328,41.546501],[-75.016144,41.544246],[-75.017626,41.542734],[-75.022828,41.541456],[-75.024798,41.539801],[-75.024757,41.535099],[-75.024206,41.534018],[-75.023018,41.533147],[-75.016616,41.53211],[-75.014919,41.531399],[-75.009552,41.528461],[-75.00385,41.524052],[-75.001297,41.52065],[-75.000911,41.519292],[-75.000935,41.517638],[-75.002592,41.51456],[-75.003706,41.511118],[-75.003694,41.509295],[-75.003151,41.508101],[-74.999612,41.5074],[-74.993893,41.508754],[-74.987645,41.508738],[-74.985653,41.507926],[-74.984372,41.506611],[-74.982385,41.500981],[-74.982168,41.498486],[-74.982463,41.496467],[-74.985247,41.489113],[-74.985595,41.485863],[-74.985004,41.483703],[-74.983341,41.480894],[-74.981652,41.479945],[-74.969887,41.477438],[-74.95826,41.476396],[-74.956411,41.476735],[-74.94808,41.480625],[-74.945634,41.483213],[-74.941798,41.483542],[-74.932585,41.482323],[-74.926835,41.478327],[-74.924092,41.477138],[-74.917282,41.477041],[-74.912517,41.475605],[-74.909181,41.472436],[-74.908133,41.468117],[-74.908103,41.464639],[-74.906887,41.461131],[-74.9042,41.459806],[-74.895069,41.45819],[-74.892114,41.456959],[-74.890358,41.455324],[-74.889116,41.452534],[-74.889075,41.451245],[-74.894931,41.446099],[-74.896399,41.442179],[-74.896025,41.439987],[-74.893913,41.43893],[-74.888691,41.438259],[-74.876721,41.440338],[-74.864688,41.443993],[-74.858578,41.444427],[-74.8542,41.443166],[-74.848602,41.440179],[-74.845572,41.437577],[-74.836915,41.431625],[-74.834635,41.430796],[-74.830671,41.430503],[-74.828592,41.430698],[-74.826031,41.431736],[-74.82288,41.436792],[-74.817995,41.440505],[-74.812123,41.442982],[-74.807582,41.442847],[-74.805655,41.442101],[-74.801225,41.4381],[-74.80037,41.43606],[-74.800095,41.432661],[-74.799546,41.43129],[-74.795396,41.42398],[-74.793856,41.422671],[-74.790417,41.42166],[-74.784339,41.422397],[-74.778029,41.425104],[-74.773239,41.426352],[-74.77065,41.42623],[-74.763701,41.423612],[-74.758587,41.423287],[-74.754359,41.425147],[-74.75068,41.427984],[-74.743821,41.430635],[-74.740932,41.43116],[-74.738455,41.430641],[-74.736688,41.429228],[-74.735519,41.427465],[-74.734893,41.425818],[-74.734731,41.422699],[-74.738684,41.413463],[-74.741086,41.411413],[-74.741717,41.40788],[-74.740963,41.40512],[-74.738554,41.401191],[-74.736103,41.398398],[-74.73364,41.396975],[-74.730384,41.39566],[-74.720891,41.39469],[-74.715979,41.392584],[-74.713411,41.389814],[-74.710391,41.382102],[-74.708458,41.378901],[-74.703282,41.375093],[-74.694968,41.370431],[-74.691129,41.367324],[-74.689516,41.363843],[-74.689767,41.361558],[-74.691076,41.36034],[-74.696398,41.357339],[-74.694914,41.357423],[-74.700595,41.354553],[-74.704429,41.354043],[-74.708514,41.352734],[-74.720923,41.347384],[-74.730373,41.345983],[-74.735622,41.346518],[-74.753239,41.346122],[-74.755971,41.344953],[-74.760325,41.340325],[-74.763499,41.331568],[-74.766714,41.328558],[-74.771588,41.325079],[-74.774887,41.324326],[-74.781584,41.324229],[-74.789095,41.323281],[-74.792116,41.322465],[-74.79504,41.320407],[-74.795822,41.318516],[-74.792377,41.314088],[-74.791991,41.311639],[-74.792558,41.310628],[-74.806858,41.303155],[-74.812033,41.298157],[-74.815703,41.296151],[-74.821884,41.293838],[-74.830057,41.2872],[-74.834067,41.281111],[-74.838366,41.277286],[-74.841137,41.27098],[-74.846319,41.263077],[-74.846506,41.261576],[-74.845031,41.258055],[-74.845883,41.254945],[-74.846932,41.253318],[-74.848987,41.251192],[-74.854669,41.25051],[-74.856003,41.250094],[-74.857151,41.248975],[-74.861678,41.241575],[-74.862049,41.237609],[-74.866182,41.232132],[-74.867405,41.22777],[-74.866839,41.226865],[-74.860837,41.222317],[-74.859323,41.220507],[-74.859632,41.219077],[-74.860398,41.217454],[-74.867287,41.208754],[-74.874034,41.198543],[-74.878275,41.190489],[-74.878492,41.187504],[-74.882139,41.180836],[-74.889424,41.1736],[-74.899701,41.166181],[-74.901172,41.16387],[-74.90178,41.161394],[-74.905256,41.155668],[-74.923169,41.138146],[-74.931141,41.133387],[-74.945067,41.129052],[-74.947714,41.126292],[-74.947334,41.124439],[-74.947912,41.12356],[-74.964294,41.114237],[-74.966298,41.113669],[-74.969312,41.113869],[-74.972917,41.113327],[-74.979873,41.110423],[-74.982212,41.108245],[-74.991718,41.092284],[-74.991815,41.089132],[-74.991013,41.088578],[-74.988263,41.088222],[-74.984782,41.088545],[-74.981314,41.08986],[-74.975298,41.094073],[-74.972036,41.095562],[-74.969434,41.096074],[-74.967464,41.095327],[-74.966759,41.093425],[-74.968389,41.087797],[-74.970987,41.085293],[-74.98259,41.079172],[-74.989332,41.078319],[-74.994847,41.076556],[-74.999617,41.073943],[-75.006376,41.067546],[-75.011133,41.067521],[-75.01257,41.066281],[-75.015271,41.061215],[-75.015867,41.05821],[-75.017239,41.055491],[-75.019186,41.052968],[-75.025702,41.046482],[-75.026376,41.04444],[-75.02543,41.04071],[-75.025777,41.039806],[-75.030701,41.038416],[-75.034496,41.036755],[-75.040668,41.031755],[-75.070532,41.01862],[-75.074999,41.01713],[-75.081101,41.016838],[-75.089787,41.014549],[-75.090312,41.013302],[-75.095556,41.008874],[-75.100682,41.006716],[-75.109114,41.004102],[-75.110595,41.002174],[-75.123423,40.996129],[-75.127196,40.993954],[-75.130575,40.991093],[-75.131619,40.9889],[-75.13153,40.984914],[-75.132106,40.982566],[-75.133086,40.980179],[-75.135521,40.976865],[-75.135526,40.973807],[-75.13378,40.970973],[-75.131364,40.969277],[-75.129074,40.968976],[-75.122603,40.970152],[-75.120514,40.968369],[-75.11977,40.96651],[-75.12065,40.964028],[-75.119893,40.961646],[-75.118904,40.956361],[-75.117764,40.953023],[-75.111683,40.948111],[-75.106153,40.939671],[-75.105524,40.936294],[-75.095526,40.924152],[-75.079279,40.91389],[-75.076956,40.90988],[-75.076092,40.907042],[-75.075188,40.900154],[-75.075957,40.895694],[-75.07534,40.894162],[-75.07392,40.892176],[-75.065438,40.885682],[-75.062149,40.882289],[-75.058655,40.877654],[-75.053664,40.87366],[-75.051508,40.870224],[-75.050839,40.868067],[-75.051029,40.865662],[-75.053294,40.8599],[-75.060491,40.85302],[-75.064328,40.848338],[-75.066014,40.847591],[-75.07083,40.847392],[-75.073544,40.84894],[-75.076684,40.849875],[-75.090962,40.849187],[-75.095784,40.847082],[-75.097221,40.844672],[-75.097586,40.843042],[-75.097572,40.840967],[-75.097006,40.839336],[-75.09494,40.837103],[-75.085517,40.830085],[-75.083822,40.827805],[-75.083929,40.824471],[-75.085387,40.821972],[-75.090518,40.815913],[-75.096147,40.812211],[-75.098279,40.810286],[-75.100277,40.807578],[-75.100739,40.805488],[-75.100165,40.803],[-75.100277,40.801176],[-75.1008,40.799797],[-75.108505,40.791094],[-75.111343,40.789896],[-75.116842,40.78935],[-75.123088,40.786746],[-75.125867,40.784026],[-75.131465,40.77595],[-75.133303,40.774124],[-75.1344,40.773765],[-75.139106,40.773606],[-75.149378,40.774786],[-75.16365,40.778386],[-75.169523,40.778473],[-75.171587,40.777745],[-75.173349,40.776129],[-75.17562,40.772923],[-75.176855,40.768721],[-75.177477,40.764225],[-75.17904,40.761897],[-75.183037,40.759344],[-75.191796,40.75583],[-75.196533,40.751631],[-75.196861,40.750097],[-75.196325,40.747137],[-75.195349,40.745473],[-75.18578,40.737266],[-75.182804,40.73365],[-75.182084,40.731522],[-75.1825,40.729922],[-75.186372,40.72397],[-75.189412,40.71797],[-75.192612,40.715874],[-75.19442,40.714018],[-75.19872,40.705298],[-75.20392,40.691498],[-75.20092,40.685498],[-75.19692,40.681299],[-75.19058,40.679379],[-75.184516,40.679971],[-75.180564,40.679363],[-75.177587,40.677731],[-75.176803,40.675715],[-75.177491,40.672595],[-75.182756,40.665971],[-75.18794,40.663811],[-75.190852,40.661939],[-75.196676,40.655123],[-75.200452,40.649219],[-75.200468,40.646899],[-75.193492,40.642275],[-75.192276,40.640803],[-75.191059,40.637971],[-75.188579,40.624628],[-75.189283,40.621492],[-75.190691,40.619956],[-75.197891,40.619332],[-75.200708,40.618356],[-75.201812,40.617188],[-75.201348,40.614628],[-75.198499,40.611492],[-75.195923,40.606788],[-75.192291,40.602676],[-75.190146,40.590359],[-75.190796,40.586838],[-75.194656,40.58194],[-75.195114,40.579689],[-75.194046,40.576256],[-75.192352,40.574257],[-75.186737,40.569406],[-75.183151,40.567354],[-75.175307,40.564996],[-75.168609,40.564111],[-75.162871,40.564096],[-75.158446,40.565286],[-75.147368,40.573152],[-75.141906,40.575273],[-75.136748,40.575731],[-75.117292,40.573211],[-75.110903,40.570671],[-75.100325,40.567811],[-75.0957,40.564401],[-75.078503,40.548296],[-75.068615,40.542223],[-75.067257,40.539584],[-75.066426,40.536619],[-75.06509,40.526148],[-75.065853,40.519495],[-75.066001,40.510716],[-75.065275,40.504682],[-75.062373,40.491689],[-75.061937,40.486362],[-75.062227,40.481391],[-75.064327,40.476795],[-75.067776,40.472827],[-75.06805,40.468578],[-75.067302,40.464954],[-75.070568,40.456348],[-75.070568,40.455165],[-75.067425,40.448323],[-75.062923,40.433407],[-75.061489,40.422848],[-75.058848,40.418065],[-75.056102,40.416066],[-75.046473,40.413792],[-75.043071,40.411603],[-75.041651,40.409894],[-75.036616,40.406796],[-75.028315,40.403883],[-75.024775,40.403455],[-75.017221,40.404638],[-75.003351,40.40785],[-74.998651,40.410093],[-74.996378,40.410528],[-74.988901,40.408773],[-74.985467,40.405935],[-74.982735,40.404432],[-74.969597,40.39977],[-74.965508,40.397337],[-74.963997,40.395246],[-74.953697,40.376081],[-74.948722,40.364768],[-74.946006,40.357306],[-74.945088,40.347332],[-74.943776,40.342564],[-74.939711,40.338006],[-74.933111,40.333106],[-74.92681,40.329406],[-74.91741,40.322406],[-74.90831,40.316907],[-74.90331,40.315607],[-74.896409,40.315107],[-74.891609,40.313007],[-74.887109,40.310307],[-74.880609,40.305607],[-74.868209,40.295207],[-74.860492,40.284584],[-74.856508,40.277407],[-74.853108,40.269707],[-74.846608,40.258808],[-74.842308,40.250508],[-74.836307,40.246208],[-74.823907,40.241508],[-74.819507,40.238508],[-74.795306,40.229408],[-74.781206,40.221508],[-74.77136,40.215399],[-74.770406,40.214508],[-74.766905,40.207709],[-74.760605,40.198909],[-74.756905,40.189409],[-74.755605,40.186709],[-74.754305,40.185209],[-74.751705,40.183309],[-74.744105,40.181009],[-74.737205,40.177609],[-74.733804,40.174509],[-74.722304,40.160609],[-74.721504,40.158409],[-74.721604,40.15381],[-74.722604,40.15001],[-74.724304,40.14701],[-74.725663,40.145495],[-74.740605,40.13521],[-74.742905,40.13441],[-74.745905,40.13421],[-74.755305,40.13471],[-74.758882,40.134036],[-74.762864,40.132541],[-74.769488,40.129145],[-74.782106,40.12081],[-74.785106,40.12031],[-74.788706,40.12041],[-74.800607,40.12281],[-74.812807,40.12691],[-74.816307,40.12761],[-74.819007,40.12751],[-74.822307,40.12671],[-74.825907,40.12391],[-74.828408,40.12031],[-74.832808,40.11171],[-74.835108,40.10391],[-74.838008,40.10091],[-74.843408,40.09771],[-74.851108,40.09491],[-74.854409,40.09311],[-74.856509,40.09131],[-74.858209,40.08881],[-74.859809,40.08491],[-74.860909,40.08371],[-74.863809,40.08221],[-74.880209,40.07881],[-74.88781,40.07581],[-74.909011,40.07021],[-74.911911,40.06991],[-74.920811,40.07111],[-74.925311,40.07071],[-74.932211,40.068411],[-74.944412,40.063211],[-74.974713,40.048711],[-74.983913,40.042711],[-74.989914,40.037311],[-75.007914,40.023111],[-75.011115,40.021311],[-75.015515,40.019511],[-75.039316,40.013012],[-75.047016,40.008912],[-75.051217,40.004512],[-75.059017,39.992512],[-75.072017,39.980612],[-75.088618,39.975212],[-75.093718,39.974412],[-75.108119,39.970312],[-75.11922,39.965412],[-75.12692,39.961112],[-75.13012,39.958712],[-75.13352,39.954412],[-75.13572,39.947112],[-75.13612,39.933912],[-75.13502,39.927312],[-75.13282,39.921612],[-75.13012,39.917013],[-75.12792,39.911813],[-75.13082,39.900213],[-75.13342,39.896213],[-75.140221,39.888213],[-75.145421,39.884213],[-75.150721,39.882713],[-75.183023,39.882013],[-75.189323,39.880713],[-75.195324,39.877013],[-75.210425,39.865913],[-75.221025,39.861113],[-75.235026,39.856613],[-75.243431,39.854597],[-75.271159,39.84944],[-75.293376,39.848782],[-75.309674,39.850179],[-75.323232,39.849812],[-75.330433,39.849012],[-75.341765,39.846082],[-75.3544,39.839917],[-75.371835,39.827612],[-75.390536,39.815312],[-75.403737,39.807512],[-75.415041,39.801786],[-75.428038,39.809212],[-75.45374,39.820312],[-75.463341,39.823812],[-75.481242,39.829112],[-75.498843,39.833312],[-75.518444,39.836311],[-75.539346,39.838211],[-75.570464,39.839007],[-75.579849,39.838526],[-75.593666,39.837455],[-75.617251,39.833999],[-75.634706,39.830164],[-75.641518,39.828363],[-75.662822,39.82115],[-75.685991,39.811054],[-75.701208,39.802606],[-75.716969,39.791998],[-75.727049,39.784126],[-75.736489,39.775759],[-75.744394,39.767855],[-75.753066,39.757631],[-75.760346,39.747231],[-75.766058,39.737811],[-75.773558,39.722411],[-75.788359,39.721811],[-75.998649,39.721576],[-76.013067,39.72192],[-76.233259,39.721305],[-76.715594,39.721103],[-76.8901,39.720401],[-76.936601,39.720701],[-76.990903,39.7198],[-77.058204,39.7202],[-77.534758,39.720134],[-77.724115,39.720894],[-77.874719,39.722219],[-78.330715,39.722689],[-78.337111,39.722461],[-78.438839,39.722481],[-78.461422,39.722869],[-78.537702,39.72249],[-78.546415,39.722869],[-78.575893,39.722561],[-78.723529,39.723043],[-79.045548,39.722883],[-79.548465,39.720778],[-79.610623,39.721245],[-79.763774,39.720776],[-79.916171,39.720893]]]},\"properties\":{\"name\":\"Pennsylvania\",\"nation\":\"USA \"}}]}","noUsgsAuthors":false,"publicationDate":"2024-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Laura C.","contributorId":204828,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":912757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyd, Emily S.","contributorId":342971,"corporation":false,"usgs":false,"family":"Boyd","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910551,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256001,"text":"70256001 - 2024 - Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions","interactions":[],"lastModifiedDate":"2024-07-12T11:58:11.727858","indexId":"70256001","displayToPublicDate":"2024-06-16T06:56:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions","docAbstract":"We examine how the choice of ground‐motion‐to‐intensity conversion equations (GMICEs) in earthquake early warning (EEW) systems affects resulting alert regions. We find that existing GMICEs can underestimate observed shaking at short rupture distances or overestimate the extent of low‐intensity shaking. Updated GMICEs that remove these biases would improve the accuracy of alert regions for the ShakeAlert EEW system for the West Coast of the United States. ShakeAlert uses ground‐motion prediction equations (GMPEs), which calculate spatial distributions of peak ground acceleration (PGA) and peak ground velocity (PGV) from earthquake source estimates, combined with GMICEs to translate GMPE output into modified Mercalli intensity (MMI). We find significant epistemic uncertainty in alert distances; near‐source MMI estimates from different GMICEs can differ by over 1 MMI unit, and MMI extents used for public EEW alerts can differ by hundreds of kilometers for larger magnitude earthquakes (M ∼6.5+). We use a catalog of “Did You Feel It?” shaking reports to evaluate how well GMICEs predict observed shaking. Our preferred GMICE is the one that computes MMI using PGV for high intensities and transitions to using PGA for nondamaging intensities. These results motivate updating GMICE relationships more generally, including in ShakeMap applications.
Understanding genetic structure and diversity among remnant populations of rare species can inform conservation and recovery actions. We used a population genetic framework to spatially delineate gene pools and estimate gene flow and effective population sizes for the endangered California Freshwater Shrimp Syncaris pacifica. Tissues of 101 individuals were collected from 11 sites in 5 watersheds, using non-lethal tissue sampling. Single Nucleotide Polymorphism markers were developed de novo using ddRAD-seq methods, resulting in 433 unlinked loci scored with high confidence and low missing data. We found evidence for strong genetic structure across the species range. Two hierarchical levels of significant differentiation were observed: (i) five clusters (regional gene pools, FST = 0.38–0.75) isolated by low gene flow were associated with watershed limits and (ii) modest local structure among tributaries within a watershed that are not connected through direct downstream flow (local gene pools, FST = 0.06–0.10). Sampling sites connected with direct upstream-to-downstream water flow were not differentiated. Our analyses suggest that regional watersheds are isolated from one another, with very limited (possibly no) gene flow over recent generations. This isolation is paired with small effective population sizes across regional gene pools (Ne = 62.4–147.1). Genetic diversity was variable across sites and watersheds (He = 0.09–0.22). Those with the highest diversity may have been refugia and are now potential sources of genetic diversity for other populations. These findings highlight which portions of the species range may be most vulnerable to future habitat fragmentation and provide management consideration for maintaining local effective population sizes and genetic connectivity.
Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.
Preventing the spread of aquatic invasive species is an important management action. Identifying the characteristics of lakes that are susceptible to invasion creates an opportunity for management groups to prioritize limited resources for high-risk areas. In this study, we leveraged big data from a popular fishing app and other publicly available sources of environmental and human-use exposure measurements to develop machine learning models to predict aquatic invasive species presence in 30,375 lakes in the upper Mississippi river basin of the United States. Our results predicted that an additional 665, 771, 544, 703, and 638 lakes in the basin are invaded or at high risk of invasion by Eurasian watermilfoil, curly-leaf pondweed, rusty crayfish, Chinese mystery snail, and dreissenid mussels, respectively. Lake invasions were predicted by a combination of environmental, human-use exposure, and community dynamics variables. Features that made a lake more attractive to recreationists were consistently important across our models including the presence of a boat ramp, larger lake size, and surrounding natural landscape. The importance of co-occurring invasive species in some models could reflect several scenarios including invasional meltdown, facilitation among species, similar pathways for introduction, or similar response to the environment. Our models predicted a higher proportion of invasions in less popular lakes compared to known invasions. The finding underscores the potential importance of less popular lakes in the invasion process and suggests that the detection of invasions may be lower in these lakes. These results serve as a valuable tool for data-driven management decisions and can provide actionable insights for effective aquatic invasive species management.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03367-6","usgsCitation":"Weir, J.L., Daniel, W., Hyder, K., Skov, C., and Venturelli, P.A., 2024, Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions: Biological Invasions, v. 26, p. 3163-3178, https://doi.org/10.1007/s10530-024-03367-6.","productDescription":"16 p.","startPage":"3163","endPage":"3178","ipdsId":"IP-162115","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":432144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Weir, Jessica L.","contributorId":330438,"corporation":false,"usgs":false,"family":"Weir","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":908933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daniel, Wesley 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219312,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":908934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyder, Kieran","contributorId":291284,"corporation":false,"usgs":false,"family":"Hyder","given":"Kieran","email":"","affiliations":[{"id":62658,"text":"The Centre for Environment, Fisheries and Aquaculture Science (Cefas) and Collaborative Centre for Sustainable Use of the Seas (CCSUS), School of Environmental Sciences, University of East Anglia, Norwich Research Park","active":true,"usgs":false}],"preferred":false,"id":908935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skov, Christian","contributorId":268055,"corporation":false,"usgs":false,"family":"Skov","given":"Christian","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":908936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Venturelli, Paul A.","contributorId":171477,"corporation":false,"usgs":false,"family":"Venturelli","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908937,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256119,"text":"70256119 - 2024 - Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","interactions":[],"lastModifiedDate":"2024-07-23T20:23:11.322287","indexId":"70256119","displayToPublicDate":"2024-06-15T09:11:46","publicationYear":"2024","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}},"title":"Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","docAbstract":"Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples. The variability in egg ages in virtual samples compare well to field samples. The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 kilometers upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102376","usgsCitation":"Soong, D., Jackson, P.R., Kocovsky, P.M., Morrison, L., Garcia, T., Santacruz, S., Chen, C., Zhu, Z., and Embke, H.S., 2024, Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model: Journal of Great Lakes Research, v. 50, no. 4, 102376, 14 p., https://doi.org/10.1016/j.jglr.2024.102376.","productDescription":"102376, 14 p.","ipdsId":"IP-157787","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439399,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102376","text":"Publisher Index Page"},{"id":431354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83,\n 41.5\n ],\n [\n -83.25,\n 41.5\n ],\n [\n -83.25,\n 41.25\n ],\n [\n -83,\n 41.25\n ],\n [\n -83,\n 41.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":906762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Lori","contributorId":340259,"corporation":false,"usgs":false,"family":"Morrison","given":"Lori","email":"","affiliations":[{"id":81526,"text":"Alaska Water Resources","active":true,"usgs":false}],"preferred":false,"id":906763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia, Tatiana","contributorId":340260,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":81527,"text":"AquaIntel Inc.","active":true,"usgs":false}],"preferred":false,"id":906764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santacruz, Santiago","contributorId":340261,"corporation":false,"usgs":false,"family":"Santacruz","given":"Santiago","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":906765,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Cindy","contributorId":340262,"corporation":false,"usgs":false,"family":"Chen","given":"Cindy","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":906766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhu, Zhenduo","contributorId":340263,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","affiliations":[{"id":81528,"text":"Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":906767,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":906768,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260439,"text":"70260439 - 2024 - Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","interactions":[],"lastModifiedDate":"2024-11-01T13:35:38.261218","indexId":"70260439","displayToPublicDate":"2024-06-15T08:26:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","docAbstract":"According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m*. We further identify site-specific m* and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007875","usgsCitation":"Yi, K., Novick, K.A., Zhang, Q., Wang, L., Hwang, T., Yang, X., Mallick, K., Beland, M., Senay, G.B., and Baldocchi, D., 2024, Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations: Journal of Geophysical Research Biogeosciences, v. 129, no. 6, e2023JG007875, 19 p., https://doi.org/10.1029/2023JG007875.","productDescription":"e2023JG007875, 19 p.","ipdsId":"IP-163083","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007875","text":"Publisher Index Page"},{"id":463530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Yi, Koong","contributorId":345841,"corporation":false,"usgs":false,"family":"Yi","given":"Koong","email":"","affiliations":[{"id":82725,"text":"Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, CA, U.S.A","active":true,"usgs":false}],"preferred":false,"id":917685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Novick, Kimberly A.","contributorId":196379,"corporation":false,"usgs":false,"family":"Novick","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":917686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Quan","contributorId":345842,"corporation":false,"usgs":false,"family":"Zhang","given":"Quan","email":"","affiliations":[{"id":82726,"text":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China.","active":true,"usgs":false}],"preferred":false,"id":917687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Lixin","contributorId":300466,"corporation":false,"usgs":false,"family":"Wang","given":"Lixin","affiliations":[{"id":65165,"text":"Department of Earth Sciences, Indiana University–Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.","active":true,"usgs":false}],"preferred":false,"id":917688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Taehee","contributorId":345843,"corporation":false,"usgs":false,"family":"Hwang","given":"Taehee","email":"","affiliations":[{"id":82727,"text":"Department of Geography, Indiana University Bloomington, Bloomington, IN, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":917690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallick, Kanishka","contributorId":345844,"corporation":false,"usgs":false,"family":"Mallick","given":"Kanishka","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beland, Martin","contributorId":345845,"corporation":false,"usgs":false,"family":"Beland","given":"Martin","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":917693,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":917694,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70256593,"text":"70256593 - 2024 - Designing count-based studies in a world of hierarchical models","interactions":[],"lastModifiedDate":"2024-08-23T15:45:06.312749","indexId":"70256593","displayToPublicDate":"2024-06-14T10:36:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Designing count-based studies in a world of hierarchical models","docAbstract":"Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count-based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count-based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count-based studies, while pointing to relevant literature elaborating on particular tools and concepts.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22622","usgsCitation":"Latif, Q., Valente, J., Johnston, A., Davis, K., Fogarty, F., Green, A.W., Jones, G.M., Leu, M., Michel, N.L., Pavlacky, D.C., Rigby, E., Rushing, C.S., Sanderlin, J., Tingley, M.W., and Zhao, Q., 2024, Designing count-based studies in a world of hierarchical models: Journal of Wildlife Management, v. 88, no. 7, e22622, 31 p., https://doi.org/10.1002/jwmg.22622.","productDescription":"e22622, 31 p.","ipdsId":"IP-153768","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/content/qt7h5896rk/qt7h5896rk.pdf","text":"External Repository"},{"id":433103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Latif, Quresh S.","contributorId":341286,"corporation":false,"usgs":false,"family":"Latif","given":"Quresh S.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnston, Alison","contributorId":341287,"corporation":false,"usgs":false,"family":"Johnston","given":"Alison","email":"","affiliations":[{"id":65006,"text":"University of St Andrews","active":true,"usgs":false}],"preferred":false,"id":908191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Kayla L.","contributorId":341288,"corporation":false,"usgs":false,"family":"Davis","given":"Kayla L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":908192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fogarty, Frank A.","contributorId":341289,"corporation":false,"usgs":false,"family":"Fogarty","given":"Frank A.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":908193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Green, Adam W.","contributorId":341290,"corporation":false,"usgs":false,"family":"Green","given":"Adam","email":"","middleInitial":"W.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":908194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Gavin M.","contributorId":341291,"corporation":false,"usgs":false,"family":"Jones","given":"Gavin","email":"","middleInitial":"M.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":908195,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leu, Matthias","contributorId":341292,"corporation":false,"usgs":false,"family":"Leu","given":"Matthias","affiliations":[{"id":57314,"text":"William & Mary","active":true,"usgs":false}],"preferred":false,"id":908196,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Michel, Nicole L.","contributorId":341293,"corporation":false,"usgs":false,"family":"Michel","given":"Nicole","email":"","middleInitial":"L.","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":908197,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pavlacky, David C. Jr.","contributorId":341294,"corporation":false,"usgs":false,"family":"Pavlacky","given":"David","suffix":"Jr.","email":"","middleInitial":"C.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908198,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rigby, Elizabeth A.","contributorId":341295,"corporation":false,"usgs":false,"family":"Rigby","given":"Elizabeth A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908199,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rushing, Clark S.","contributorId":341296,"corporation":false,"usgs":false,"family":"Rushing","given":"Clark","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":908200,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sanderlin, Jamie S.","contributorId":341297,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie S.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":908201,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tingley, Morgan W.","contributorId":341298,"corporation":false,"usgs":false,"family":"Tingley","given":"Morgan","email":"","middleInitial":"W.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908202,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zhao, Qing","contributorId":341299,"corporation":false,"usgs":false,"family":"Zhao","given":"Qing","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908203,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70254920,"text":"sim3522 - 2024 - Bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire","interactions":[],"lastModifiedDate":"2026-01-29T21:54:14.125073","indexId":"sim3522","displayToPublicDate":"2024-06-13T12:35:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3522","displayTitle":"Bedrock Geologic Map of the Woodstock Quadrangle, Grafton County, New Hampshire","title":"Bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire","docAbstract":"The bedrock geology of the Woodstock 7.5-minute quadrangle consists of highly deformed metasedimentary rocks of the Central Maine trough, including the Silurian Rangeley and Perry Mountain Formations and the Devonian Littleton Formation. The central, northern, and eastern parts of the quadrangle are underlain by the oldest rocks in the area, the Rangeley Formation. In the southwest and south-central part of the quadrangle, metaturbidites of the Perry Mountain Formation and subsequent Littleton Formation overly the Rangeley Formation in a deformed F1 synform, herein informally called the Bagley Brook basin. The metasedimentary rocks were intruded by widespread syn- to post-tectonic granitoids of the Devonian New Hampshire Plutonic Suite and minor post-metamorphic Jurassic-Cretaceous mafic dikes of the White Mountain Plutonic-Volcanic Suite. The metasedimentary rocks were affected by at least two episodes of deformation in the Devonian Acadian orogeny. The dominant regional foliation is second-generation (S2/D2) and formed during the development of sillimanite-muscovite mineral assemblages. Large bodies of the Early Devonian Kinsman Granodiorite intruded the metasedimentary rocks semi-concordantly during D2 deformation. Dikes of the Late Devonian Concord Granite cut the Kinsman Granodiorite and the metasedimentary rocks and were emplaced either syn- or post-D2. The map pattern in the Rangeley Formation is dominated by northeast to northwest trending, moderately to steeply north-dipping F2 and F3 folds. Map-scale F1 folds are defined by the Bagley Brook basin. Previous division of Rangeley Formation stratigraphy in this region into “upper” and “lower” parts was not corroborated by 1:24,000-scale mapping of lithodemic units, and rocks previously mapped as part of the Smalls Falls and Madrid Formations are here reassigned to the Rangeley Formation. Some rocks previously mapped as the lower part of the Littleton Formation are now assigned to the Perry Mountain Formation. The Littleton Formation on this map is approximately equivalent to rocks previously mapped as the upper part of the same formation.
Steeply dipping fractures in the quadrangle show a preferred northeast orientation, consistent with subsurface fracture orientations in the well fields near Mirror Lake. Jurassic-Cretaceous mafic dikes and normal faults show preferred northeast orientations, similar to the fractures, suggesting that the extensional stress field that controlled dike orientation during the Mesozoic also produced the dominant brittle fabrics in the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3522","collaboration":"Prepared in cooperation with the State of New Hampshire, Department of Environmental Services, New Hampshire Geological Survey; and the U.S. Department of Agriculture Forest Service","usgsCitation":"Walsh, G.J., Burton, W.C., Armstrong, T.R., and Crider, E.A., Jr., 2024, Bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire: U.S. Geological Survey Scientific Investigations Map 3522, 1 sheet, scale 1:24,000, includes 20-p. pamphlet, https://doi.org/10.3133/sim3522.","productDescription":"Report: viii, 20 p.; 1 Sheet: 38.22 x 36.27 inches; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133738","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":429746,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3522/sim3522_sheet.pdf","text":"Sheet","size":"131 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3522 Sheet"},{"id":429740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3522/coverthb.jpg"},{"id":429895,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3522/images/"},{"id":429741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3522/sim3522_pamphlet.pdf","text":"Pamphlet","size":"24.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3522 PDF"},{"id":429743,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3522/sim3522.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3522 XML"},{"id":429745,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96TJ9OI","text":"USGS data release","linkHelpText":"Database for the bedrock geologic map of the Woodstock quadrangle, Grafton County, New Hampshire"},{"id":499290,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117075.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Hampshire","county":"Grafton County","otherGeospatial":"Woodstock quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.75,\n 44\n ],\n [\n -71.75,\n 43.875\n ],\n [\n -71.625,\n 43.875\n ],\n [\n -71.625,\n 44\n ],\n [\n -71.75,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 926A
Reston, VA 20192
Northern myotis Myotis septentrionalis are one of the bat species most affected by white-nose syndrome (WNS), and disease-induced declines may cause compounding effects when combined with other threats such as habitat loss and fragmentation. Recent evidence suggests that peripheral populations are persisting in post-WNS years; however, the environmental factors that influence the occurrence of this species along the Atlantic Coastal Plain are virtually unknown. We conducted a large-scale acoustic survey on 3 islands: Long Island, New York, and Martha’s Vineyard and Nantucket, Massachusetts, USA, and used a multi-scale occupancy modeling approach to determine the landscape and abiotic factors affecting the distribution of northern myotis. Our estimates of occupancy and detection probability suggest widespread presence across the islands. At the local (200 m) scale, we identified strong negative effects of development on Long Island and Nantucket and a strong positive effect of forest habitat on Martha’s Vineyard. None of the variables we measured sufficiently explained the landscape (1 km2 ) occupancy of this species, which was very high (ψ = 0.81–0.97), representing an outlier for this species in the post-WNS landscape. The lack of association at the landscape scale suggests that general differences in land cover are not a driving factor of higher occupancy of peripheral northern myotis populations, while local site- specific conditions may be supporting critical foraging or roosting habitat. Because islands are particularly vulnerable to human-driven habitat alteration due to the constraint of limited space, and development pressure is expected to increase, this study provides a baseline to enable managers to assess the effects of future environmental disturbances and monitor population trends to support long-term survival of northern myotis.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01335","usgsCitation":"Hoff, S., Mosher, B., Watson, M., Johnson, L., Olson, E., O’Dell, D., Pendergast, C.J., Bogan, D.A., Herzog, C.J., and Turner, W.C., 2024, Widespread occupancy of the endangered northern myotis on northeastern Atlantic Coastal Plain islands: Endangered Species Research, v. 54, p. 141-153, https://doi.org/10.3354/esr01335.","productDescription":"13 p.","startPage":"141","endPage":"153","ipdsId":"IP-158096","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439405,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01335","text":"Publisher Index Page"},{"id":433568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, New York","otherGeospatial":"Long Island, Martha's Vineyard, Nantucket","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.72456524610662,\n 41.44291621427166\n ],\n [\n -73.72456524610662,\n 40.57243247892035\n ],\n [\n -69.85737774610656,\n 40.57243247892035\n ],\n [\n -69.85737774610656,\n 41.44291621427166\n ],\n [\n -73.72456524610662,\n 41.44291621427166\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","noUsgsAuthors":false,"publicationDate":"2024-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoff, Samantha","contributorId":342962,"corporation":false,"usgs":false,"family":"Hoff","given":"Samantha","email":"","affiliations":[{"id":81956,"text":"University at Albany","active":true,"usgs":false}],"preferred":false,"id":910538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mosher, Brittany A.","contributorId":342963,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany A.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":910539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watson, Mandy","contributorId":342964,"corporation":false,"usgs":false,"family":"Watson","given":"Mandy","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Luanne","contributorId":342965,"corporation":false,"usgs":false,"family":"Johnson","given":"Luanne","affiliations":[{"id":81959,"text":"BiodiversityWorks","active":true,"usgs":false}],"preferred":false,"id":910541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, Elizabeth","contributorId":342966,"corporation":false,"usgs":false,"family":"Olson","given":"Elizabeth","email":"","affiliations":[{"id":81959,"text":"BiodiversityWorks","active":true,"usgs":false}],"preferred":false,"id":910542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Dell, Danielle","contributorId":342967,"corporation":false,"usgs":false,"family":"O’Dell","given":"Danielle","email":"","affiliations":[{"id":81960,"text":"Nantucket Conservation Foundation","active":true,"usgs":false}],"preferred":false,"id":910543,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pendergast, Casey J.","contributorId":342968,"corporation":false,"usgs":false,"family":"Pendergast","given":"Casey","email":"","middleInitial":"J.","affiliations":[{"id":81956,"text":"University at Albany","active":true,"usgs":false}],"preferred":false,"id":910544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bogan, Daniel A.","contributorId":342969,"corporation":false,"usgs":false,"family":"Bogan","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":81961,"text":"Siena College","active":true,"usgs":false}],"preferred":false,"id":910545,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herzog, Carl J.","contributorId":342970,"corporation":false,"usgs":false,"family":"Herzog","given":"Carl","email":"","middleInitial":"J.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":910546,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910547,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259099,"text":"70259099 - 2024 - Low rate of population establishment of a freshwater invertebrate (Gammarus lacustris) in experimental conservation translocations","interactions":[],"lastModifiedDate":"2024-09-27T11:52:54.565701","indexId":"70259099","displayToPublicDate":"2024-06-13T06:48:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Low rate of population establishment of a freshwater invertebrate (Gammarus lacustris) in experimental conservation translocations","docAbstract":"Conservation translocations may be a useful tool for the restoration of declining freshwater invertebrates, but they are poorly represented in the literature. We conducted a before-after/control-impact (BACI) experiment to test the efficacy of conservation translocation for re-establishing abundant populations of the amphipod Gammarus lacustris, a declining species and wildlife food resource in depressional wetlands in the upper Midwest of the United States of America. Each study site (n = 19) contained at least one treatment wetland receiving translocated G. lacustris from a local donor and one control wetland. We selected study wetlands based on a suite of wetland characteristics and randomly assigned recipient versus control treatment. Gammarus lacustris was detected post-translocation at only 2 of 22 recipient wetlands (1 of 19 sites). Overall, there was a statistical increase in G. lacustris density in recipient wetlands compared to controls; however, the results were of minimal biological significance due to being driven by a single site with low G. lacustris densities. Accordingly, our results suggest that future conservation translocations of amphipods might be successful if limited to recently restored wetlands or informed by a more complex habitat suitability model to differentiate dispersal limitations from habitat limitations. To develop such a model would involve identifying the fewest, most influential physical and biological factors (e.g. wetland size/structure, fish, aquatic vegetation, and water chemistry) from the numerous inter-related factors that correlate with the abundance of naturally occurring G. lacustris; candidate wetlands to receive amphipods would be those for which the model predicts abundant G. lacustris but in which they do not presently occur.
Two Monitoring Avian Productivity and Survivorship (MAPS) stations were operated at Marine Corps Base Camp Pendleton (MCBCP), California, in 2021: one at De Luz Creek and one at the Santa Margarita River. The stations were established to provide data on Neotropical migratory birds at MCBCP to support the dual missions of environmental stewardship and military readiness.
A total of 1,227 individual birds were captured in 2021 between the two stations: 395 at De Luz and 832 at Santa Margarita (both 15 banding days). Of these 1,227 individuals captured, 955 were newly banded (273 at De Luz and 682 at Santa Margarita), 150 were recaptures banded before 2021 (28 at De Luz and 122 at Santa Margarita, excluding recaptures released before reading band number [1 at De Luz and 3 at Santa Margarita]), and 118 were unbanded (93 at De Luz and 25 at Santa Margarita). Return rate in 2021 was much lower than the annual mean at De Luz (1995–2019) and similar to the annual mean at the Santa Margarita station (1998–2020). The sex ratio of known-sex adult birds was skewed toward males at both stations in 2021.
Species richness was similar at De Luz from 2019 to 2021, increased at Santa Margarita from 2020 to 2021 and was above annual means at both sites (1995–2019 and 1998–2020, respectively). The most abundant species at De Luz were Wrentit (Chamaea fasciata) and Allen’s Hummingbird (Selasphorus sasin). Song Sparrow (Melospiza melodia) and Common Yellowthroat (Geothlypis trichas) were most abundant at Santa Margarita.
Since 2002, we have examined the population trends of 12 species at De Luz and 13 species at Santa Margarita for which numbers of known-age individuals were adequate for statistical analysis. We estimated population size and calculated indices of productivity and survival for a subset of these species with sufficient captures and recaptures for valid parameter estimation—four at De Luz and six at Santa Margarita. We determined that in 2021, abundance of 42 percent (5 of 12) of focal species at De Luz and 38 percent (5 of 13) of focal species at Santa Margarita was below the annual mean abundance. Of the focal species below mean abundance, 40 percent (2 of 5) at De Luz and 60 percent (3 of 5) at Santa Margarita were migrant populations. Of the focal species, 25 (3 of 12) percent at De Luz and 31 percent (4 of 13) at Santa Margarita had declining population trends during the span of station operation. With few exceptions, these declines appeared to be associated with conditions on the breeding grounds.
Annual productivity (calculated as the ratio of juveniles to adults among individual captures) was zero for all focal species at De Luz in 2021. At Santa Margarita, productivity increased from year 2020 to 2021 for Common Yellowthroat, Song Sparrow, and Yellow Warbler (Setophaga petechia) and declined from year 2020 to 2021 for Least Bell’s Vireo (Vireo bellii pusillus), but productivity was above the 1998–2020 mean for all four species, whereas productivity was maintained for Orange-crowned Warbler (Leiothlypis celata) and Yellow-breasted Chat (Icteria virens). Winter precipitation affected productivity of Black-headed Grosbeak (Pheucticus melanocephalus), Common Yellowthroat, and Song Sparrow at De Luz and affected productivity of Common Yellowthroat, Orange-crowned Warbler, Song Sparrow, Yellow-breasted Chat, and Yellow Warbler at Santa Margarita.
We calculated the mean annual adult survival for 1998–2020 at Santa Margarita, excluding years when the station was not operated. Survival could not be calculated for De Luz in 2021 because the station was not operated in 2020. Model-averaged annual adult survival ranged from 42 to 66 percent for residents and from 30 to 66 percent for migrants at Santa Margarita. Survival of Common Yellowthroat, Song Sparrow, and possibly Yellow Warbler was found to be affected by winter precipitation. Sex was a significant predictor of survival for Common Yellowthroat, Least Bell’s Vireo, Orange-crowned Warbler, and Yellow-breasted Chat at Santa Margarita, where females were found to have lower survival than males.
At Santa Margarita, multiple regression analyses examining adult survival and productivity as predictors of future population size indicated that resident Song Sparrow and migrant Yellow Warbler populations were affected by population size from the previous year, migrant Yellow-breasted Chat populations were affected by productivity from the previous year, and migrant Orange-Crowned Warbler populations were affected by survival from the previous year. Updated previous-year population size predictions could not be calculated for De Luz because the station was not operated in 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241024","collaboration":"Prepared in cooperation with Assistant Chief of Staff, Environmental Security, U.S. Marine Corps Base Camp Pendleton","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Mendia, S.M., and Kus, B.E., 2024, Neotropical migratory bird monitoring study at Marine Corps Base Camp Pendleton, California—2021 annual data summary: U.S. Geological Survey Open-File Report 2024–1024, 69 p., https://doi.org/10.3133/ofr20241024.","productDescription":"viii, 69 p.","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-155196","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":429918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1024/covrthb.jpg"},{"id":429919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1024/ofr20241024.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429920,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1024/ofr20241024.xml"},{"id":429921,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1024/images"},{"id":429922,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241024/full"}],"country":"United States","state":"California","otherGeospatial":"Marine Corps Base Camp Pendleton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.38,\n 33.275\n ],\n [\n -117.38,\n 33.26\n ],\n [\n -117.35,\n 33.26\n ],\n [\n -117.35,\n 33.275\n ],\n [\n -117.38,\n 33.275\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.33,\n 33.38\n ],\n [\n -117.33,\n 33.37\n ],\n [\n -117.32,\n 33.37\n ],\n [\n -117.32,\n 33.38\n ],\n [\n -117.33,\n 33.38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Sources and fluxes of methane to the atmosphere from permafrost are significant but poorly constrained in global climate models. We present data collected from the variable permafrost setting of the outer Mackenzie River Delta, including observations of aquatic methane seepage, core determinations of in situ methane occurrence and seep gas isotope geochemistry. The sources and locations of in situ geologic methane occurrence and aquatic and atmospheric gas release appear to be controlled by the regional geology and permafrost conditions. Where permafrost is >250 m thick, thermogenic gas deposits at depth are isolated by laterally continuous, low permeability ice-bearing sediments with few through-going thawed taliks. Thus, the observed in situ methane and aquatic gas seepage appears to be dominated by microbial methane. In contrast, where permafrost is <80 m thick, taliks are more likely to be through-going, providing permeable conduits from depth and migration pathways for both thermogenic and biogenic gas. Continuous annual fluid sampling of two lakes and a river channel documents aquatic methane flux from microbial sources, more deeply buried thermogenic sources, and mixtures of both. Using estimates of in situ methane concentration from deep core samples and observations of in situ free gas occurrences, we conclude that the reservoir of in situ geologic methane within ice bonded permafrost is substantial and that this methane is presently migrating with ongoing atmospheric release. It is our assessment that the permafrost setting, and processes described are sensitive to future climate change as the permafrost warms.
The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.
Large wood (LW) plays important geomorphic and ecological roles in rivers and is widely used as a restoration tool. Changes to floodplain land use and historical removal have altered wood dynamics in fluvial systems globally. We know little about the distribution and dynamics of LW in great rivers (approximately >105 km2) like the Upper Mississippi and Illinois Rivers despite its ecosystem importance and use in restoration projects. We assessed LW occurrence data collected by the fisheries component of the Upper Mississippi River Restoration Program's Long Term Resource Monitoring element. We analysed 25 years of data collected across six reaches of the Upper Mississippi and Illinois Rivers that represented contrasting physiographic settings, and across four aquatic area types comprising gradients of hydrology, connectivity and geomorphology. We tested hypotheses on drivers of LW occurrence using generalised linear mixed effects models, where occurrence was predicted by reach- and local-scale environmental variables. Occurrence varied significantly across reaches and aquatic area types. In general, wood occurred more frequently upriver and in side channels compared to other aquatic areas. Large wood was most strongly predicted systemically by reach identity but not local-scale variables, underscoring the importance of broad-scale physiographic gradients in defining hydrogeomorphic processes. Floodplain forests and shoreline revetment were consistently important predictors across reaches. Our findings show that the spatial variability of LW occurrence reflects the physical variability of the Upper Mississippi and Illinois Rivers. They also reveal the value in using geomorphic classifications as frameworks for understanding physical processes like LW dynamics because of their ability to contextualise site-scale conditions. The baseline understanding of LW abundance across different hydrogeomorphic gradients and scales presented here can give insight into how to more effectively target restoration efforts in great rivers and contribute to a broader understanding of LW dynamics where such studies have been lacking.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5911","usgsCitation":"Van Appledorn, M., Jankowski, K.J., Gahm, K., Budd, S., Baumann, D., Bennie, B., Erickson, R.A., Haro, R.J., and Rohweder, J.J., 2024, The where and why of large wood occurrence in the Upper Mississippi and Illinois Rivers: Earth Surface Processes and Landforms, v. 49, no. 11, p. 3383-3398, https://doi.org/10.1002/esp.5911.","productDescription":"16 p.","startPage":"3383","endPage":"3398","ipdsId":"IP-156995","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":430017,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois River, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.73578092131349,\n 45.12080357067936\n ],\n [\n -91.01148319916909,\n 42.06683000374717\n ],\n [\n -92.03223565670272,\n 40.356697147602766\n ],\n [\n -91.38931153946832,\n 38.69309735425202\n ],\n [\n -89.14510076231794,\n 36.650083721260515\n ],\n [\n -88.69332581972627,\n 40.70515855929407\n ],\n [\n -89.58402008394364,\n 41.68908264434286\n ],\n [\n -90.9223085282444,\n 44.553043048533425\n ],\n [\n -93.73578092131349,\n 45.12080357067936\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gahm, Kaija","contributorId":338731,"corporation":false,"usgs":false,"family":"Gahm","given":"Kaija","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":903502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budd, Serenity","contributorId":338732,"corporation":false,"usgs":false,"family":"Budd","given":"Serenity","email":"","affiliations":[{"id":38728,"text":"Virginia Commonwealth University","active":true,"usgs":false}],"preferred":false,"id":903503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumann, Douglas","contributorId":328549,"corporation":false,"usgs":false,"family":"Baumann","given":"Douglas","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":903504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennie, Barbara","contributorId":328550,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":903505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903506,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haro, Roger J.","contributorId":139538,"corporation":false,"usgs":false,"family":"Haro","given":"Roger","email":"","middleInitial":"J.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":903507,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903508,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255313,"text":"70255313 - 2024 - Temporal habitat use of mule deer in the Pueblo of Santa Ana, New Mexico","interactions":[],"lastModifiedDate":"2024-07-15T15:38:36.010069","indexId":"70255313","displayToPublicDate":"2024-06-11T06:39:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Temporal habitat use of mule deer in the Pueblo of Santa Ana, New Mexico","docAbstract":"Mule deer (Odocoileus hemionus) are important economically, culturally, and recreationally to the Pueblo of Santa Ana in central New Mexico, USA. Studies of habitat selection improve our understanding of mule deer ecology in central New Mexico and provide the Tribe with valuable information for management of mule deer. We used global positioning system telemetry-collar data collected on mule deer around the Pueblo of Santa Ana to create resource selection functions from proximity-based habitat predictors using a generalized linear mixed model. We created separate resource selection functions for females and males during summer and winter at different times of the day. Season generally had a greater effect on mule deer habitat use than the time of day. Female and male mule deer selected for similar habitats but were sexually segregated in their summer distributions. These findings are consistent with results from other locations where mule deer partitioned habitat similarly between seasons and sexes. Supported models reaffirm accepted patterns of habitat selection for mule deer to the Pueblo of Santa Ana where local results were lacking. Our results can help managers identify locations in and around the Pueblo of Santa Ana where future development such as highway expansion are likely to conflict with mule deer activity and locations where habitat enhancement projects such as adding water sources can have the greatest effect for the deer population.