Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate-based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling-Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non-optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions.
The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post-wildfire hydrologic hazards including debris flows, flash floods, water-quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data-sparse, headwater catchments in complex terrain, and post-wildfire hydrologic effects are particularly sensitive to high-intensity, short-duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire-induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade-offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post-wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire-prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1728","usgsCitation":"Partridge, T.F., Johnson, Z., Sleeter, R., Qi, S.L., Walvoord, M.A., Murphy, S.F., Peterman-Phipps, C.L., and Ebel, B., 2024, Opportunities and challenges for precipitation forcing data in post-wildfire hydrologic modeling applications: WIREs Water, v. 11, no. 5, e1728, 27 p., https://doi.org/10.1002/wat2.1728.","productDescription":"e1728, 27 p.","ipdsId":"IP-155206","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":427613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439910,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1728","text":"Publisher Index Page"}],"volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":898395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":898399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterman-Phipps, Cara L. 0000-0003-1822-2552","orcid":"https://orcid.org/0000-0003-1822-2552","contributorId":259166,"corporation":false,"usgs":true,"family":"Peterman-Phipps","given":"Cara","email":"","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":898400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253160,"text":"70253160 - 2024 - Predator disturbance contributed to Common Murre Uria aalge breeding failures in Cook Inlet, Alaska following the 2014–2016 Pacific marine heatwave","interactions":[],"lastModifiedDate":"2024-04-23T12:11:34.910115","indexId":"70253160","displayToPublicDate":"2024-04-07T07:07:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7948,"text":"Marine Onithology","active":true,"publicationSubtype":{"id":10}},"title":"Predator disturbance contributed to Common Murre Uria aalge breeding failures in Cook Inlet, Alaska following the 2014–2016 Pacific marine heatwave","docAbstract":"Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.
","language":"English","publisher":"MDPI","doi":"10.3390/s24072339","usgsCitation":"Thieme, A., Prabhakara, K., Jennewein, J., Lamb, B.T., McCarty, G.T., and Hively, W.D., 2024, Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits: Sensors, v. 24, no. 7, 2339, 25 p., https://doi.org/10.3390/s24072339.","productDescription":"2339, 25 p.","ipdsId":"IP-079899","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":439911,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s24072339","text":"Publisher Index Page"},{"id":427561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","city":"Beltsville","otherGeospatial":"Beltsville Agricultural Research Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.93347699075227,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.029631686898625\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prabhakara, Kusuma","contributorId":335445,"corporation":false,"usgs":false,"family":"Prabhakara","given":"Kusuma","affiliations":[{"id":80408,"text":"University of Maryland, Department of Geographic Sciences","active":true,"usgs":false}],"preferred":false,"id":898361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennewein, Jyoti","contributorId":335446,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","email":"","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarty, Gregory T.","contributorId":335447,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"T.","affiliations":[{"id":65190,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":898364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898365,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252825,"text":"70252825 - 2024 - Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US","interactions":[],"lastModifiedDate":"2024-04-08T23:51:25.344452","indexId":"70252825","displayToPublicDate":"2024-04-06T09:01:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US","docAbstract":"We developed an open source, extensible Python-based framework, that we call the Versatile Modeling of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi-source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non-linear least squares and Markov Chain Monte-Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel-1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm3/yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm3/yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi-source models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC011341","usgsCitation":"Angarita, M., Grapenthin, R., Henderson, S., Christoffersen, M.S., and Anderson, K.R., 2024, Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US: Geochemistry, Geophysics, Geosystems, v. 25, e2023GC011341, 19 p., https://doi.org/10.1029/2023GC011341.","productDescription":"e2023GC011341, 19 p.","ipdsId":"IP-159327","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439914,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc011341","text":"Publisher Index Page"},{"id":427557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Unimak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.24763757080336,\n 55.437699893466515\n ],\n [\n -166.24763757080336,\n 53.85001704010827\n ],\n [\n -162.27182660949765,\n 53.85001704010827\n ],\n [\n -162.27182660949765,\n 55.437699893466515\n ],\n [\n -166.24763757080336,\n 55.437699893466515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Angarita, Mario","contributorId":215655,"corporation":false,"usgs":false,"family":"Angarita","given":"Mario","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":898366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grapenthin, Ronni","contributorId":257035,"corporation":false,"usgs":false,"family":"Grapenthin","given":"Ronni","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":898367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Scott","contributorId":206392,"corporation":false,"usgs":false,"family":"Henderson","given":"Scott","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":898368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christoffersen, Michael S","contributorId":237038,"corporation":false,"usgs":false,"family":"Christoffersen","given":"Michael","email":"","middleInitial":"S","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":898379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":898370,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70253068,"text":"70253068 - 2024 - Eutrophication saturates surface elevation change potential in tidal mangrove forests","interactions":[],"lastModifiedDate":"2024-08-26T14:38:23.436483","indexId":"70253068","displayToPublicDate":"2024-04-06T06:52:04","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}},"title":"Eutrophication saturates surface elevation change potential in tidal mangrove forests","docAbstract":"Coastal mangrove forests are at risk of being submerged due to sea-level rise (SLR). However, mangroves have persisted with changing sea levels due to a variety of biotic and physical feedback mechanisms that allow them to gain and maintain relative soil surface elevation. Therefore, mangrove’s resilience to SLR is dependent upon their ability to build soil elevation at a rate that tracks with SLR, or well-enough to migrate inland. Anthropogenic disturbances, such as altered hydrology and eutrophication, can degrade mangrove forest health and compromise this land building process, placing mangroves at greater risk. Much of Florida’s mangroves are adjacent to highly urbanized areas that produce nutrient-loaded runoff. This study assesses how experimental nutrient inputs in the eutrophic Caloosahatchee Estuary influence the soil surface elevation change (SEC) in two distinct mangrove zones. Annual rates of SEC were reduced by phosphorus additions and differed by mangrove zone, ranging from 0.67 ± 0.59 to 2.13 ± 0.61 and 4.21 ± 0.58 to 6.39 ± 0.59 mm year−1 in the fringe and basin zone, respectively. This suggests that eutrophication can reduce the maximum potential SEC response to SLR and that a mangrove forest’s vulnerability to SLR is not uniform throughout forest but can differ by mangrove zone.
Potential changes in wildland fire regimes due to anthropogenic climate change can be projected using data from climate models, but directly applying these meteorological variables to long-term planning and adaptive management activities may be difficult for decision makers. Analog mapping, in contrast, creates more intuitive assessments of changing fire regimes that also recognize the complex, multivariate, and multi-scalar nature of ecosystems. Here, we use data from 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5), to identify and map future climate-fire analogs for 655 protected areas in the conterminous U.S. based on annual temperature, cumulative precipitation amount and seasonality, and fire regime potentials derived from a simple process-based fire frequency model. Patterns of analogs were heavily influenced by gradients in latitude and topography, with longer time frames (end-of-century conditions) and the more extreme climate forcing scenario resulting in greater analog distances and more ensemble entropy (i.e., less consensus among climate models regarding the closest analog for a given management unit). Finer scale analyses for three protected areas (Yellowstone and Great Smoky Mountains National Parks, White Mountain National Forest) illustrate how climate-fire analog mapping can improve insight into the types of ecosystem responses that might occur under similar management conditions. Federally protected areas such as national parks, forests, and wildlife refuges have long served as reference sites for the study of fire regimes, a role that is likely to continue because many of these units are managed to allow at least some ecosystem processes to operate independently. The results suggest that analog mapping approaches are well-suited as part of qualitative assessments within climate- and fire-aware adaptive management processes. The use of analogs to depict relatable, real-world depictions of possible ecosystem changes in a given place, can help managers make more strategic choices about when and where to resist, accept, or direct climate change-driven ecological change.
This study presents a comparison of measured versus modeled total organic carbon (TOC) in the Upper Cretaceous Tuscaloosa marine shale (TMS) of southwestern Mississippi as a case study to evaluate the effects of mineralogy on the TOC estimated from the ΔlogR method. The ΔlogR method is utilized to calculate TOC, which involves baselining sonic transit time and resistivity log curves in a non-source rock section of the formation. In our application, the well log curves were baselined in the upper TMS, which is described as a non-source rock section, and in the lower TMS above the high resistivity zone (HRZ), which is described as having a higher carbonate content. The ΔlogR calculated TOC values from these two baselining approaches show that the lower baseline results in improved agreement between measured TOC and calculated TOC. This improvement is likely to be due to the lower baseline accounting for the increase in resistivity caused by higher carbonate content, in addition to any presence of TOC. The upper baseline, which has a lower carbonate content, does not account for this resistivity increase. Additionally, sample type appears to affect the comparison of measured and ΔlogR calculated TOC. Most of the samples used in this study are legacy cuttings that were not preserved during storage, exposing the high surface area cuttings to increased rates of oxidation, whereas geophysical logs record the rock properties in situ. To account for this oxidation effect, the difference between the medians of the TMS HRZ TOC core and cuttings values was added to each TOC measurement in this study, resulting in a median measured TOC value that is similar to the median of the lower TMS-baselined ΔlogR calculated TOC value. Overall, this study demonstrates that carbonate content and sample type can affect how well measured and ΔlogR-modeled TOC values compare.
Unknown causes behind the loss of freshwater mussel populations have prompted population restoration as a tool to recover these imperiled species. However, water quality conditions that support mussel species within natural environments and potential causes of water quality impairment in systems with declining populations are typically unknown and may be critical knowledge needed before reintroducing mussels. Our objective was to relate the growth and survival of declining freshwater mussel populations of the Brook Floater Alasmidonta varicosa (Lamarck, 1819) to water quality parameters within 4 Massachusetts, USA, rivers containing extant populations. We deployed propagated age-1 and age-2 Brook Floater in contained systems (silos) for 1 growing season (June–October). Through biweekly sampling, we tracked the growth and survival of mussels then modeled their relationships with water quality variables. Mussels had a higher growth rate at a higher chlorophyll a (Chl a) value (2.82 µg/L) over the temperature range measured (biweekly mean = 16–26°C) when compared with lower Chl a values (0.61 and 1.17 µg/L). Age-1 mussel growth rate was negatively affected by low Chl a concentration (0.61 µg/L) across the temperature range, but age-2 mussel growth rate was not negatively affected until temperatures were above ~22°C. Na+ limited the growth rate of mussels, with the rate of change in growth rate for age-1 mussels greater than for age-2 mussels. Other cations (Mg2+, K+, and Ca2+)—potentially linked to road deicers—also negatively affected growth rate in all 4 rivers but may have had a greater impact on mussels in rivers with reduced growth rates from lower temperatures and Chl a. However, survival was uniformly high across all rivers, indicating water quality parameters may have sublethal but not lethal effects. Additional assessments for chronic water quality stressors along with changing land cover, land management, and climates are important considerations for restoration potential and the long-term persistence of populations.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/730247","usgsCitation":"Skorupa, A., Roy, A.H., Hazelton, P., Perkins, D., Timothy Warren, T., and Cheng, B.S., 2024, Food, water quality, and the growth of a freshwater mussel: Implications for population restoration: Freshwater Science, v. 43, no. 2, p. 107-123, https://doi.org/10.1086/730247.","productDescription":"7 p.","startPage":"107","endPage":"123","ipdsId":"IP-154695","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.92801492613863,\n 42.333950606928596\n ],\n [\n -71.9376914557071,\n 42.33232039224919\n ],\n [\n -73.077586638902,\n 42.32546816564172\n ],\n [\n -73.17435193458931,\n 42.06443189078277\n ],\n [\n -71.94349737344804,\n 42.081670744671186\n ],\n [\n -71.92801492613863,\n 42.333950606928596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.62300789778979,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.70452095814687\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":340492,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelton, Peter D.","contributorId":340493,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, David","contributorId":340494,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Timothy Warren, Timothy","contributorId":340495,"corporation":false,"usgs":false,"family":"Timothy Warren","given":"Timothy","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907297,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Brian S.","contributorId":340496,"corporation":false,"usgs":false,"family":"Cheng","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907298,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264807,"text":"70264807 - 2024 - Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA","interactions":[],"lastModifiedDate":"2025-03-25T14:09:06.120979","indexId":"70264807","displayToPublicDate":"2024-04-05T08:59:18","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"displayTitle":"Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA","title":"Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA","docAbstract":"This field trip examines the results of integrated geologic studies of the 9 August 2020, Mw 5.1 earthquake near Sparta, North Carolina, USA. The earthquake generated ~4 km of coseismic surface rupture of the Little River fault and uplifted a surface area of ~11 km2. The Little River fault is a thrust fault oriented 110–130°/45–70°SW, and mapped fault segments are en echelon with scarp heights from <5–30 cm. The epicenter is in polydeformed rocks of the Ashe and Alligator Back Metamorphic Suites in the eastern Blue Ridge. Bedrock structure formed during multiple Paleozoic orogenies; the regional foliation strikes NE-SW and dips SE (mean orientation 063°/52°SE). Mapping identified late Paleozoic veins and shear zones, a regional joint set striking 330–340° and 250–240°, and brittle faults that cut the Paleozoic foliation. Brittle faults oriented similar to the Little River fault are mapped up to 4 km along strike from the coseismic rupture along Bledsoe Creek valley, and the combined length of the Little River fault system is ~8 km. Paleoseismic trenches across the Little River fault corroborate the reactivation of an older fault by the 2020 earthquake and reveal two events during late Pleistocene (<50 ka). Surficial mapping identified several terrace deposits, including a deposit along Bledsoe Creek that yielded a 26Al/10Be isochron burial age of 0.46 ± 0.13 Ma and overlies a brittle fault, thus constraining the timing of movement of the fault at that location. Paleoliquefaction studies document soft-sediment deformation features in alluvium that may represent paleoseismic events. Collectively, these results highlight long-lived paleoseismicity of the Blue Ridge and that the 9 August 2020 earthquake reactivated an older, suitably oriented brittle fault in the bedrock. The Little River fault is an example of a previously unknown but active fault lying outside of known seismic zones with demonstrated recurrence of paleo-ruptures, raising questions about the assumption that damaging earthquakes are limited to areas of ongoing background seismicity, which is counter to seismic hazard assessments in the eastern United States.
Bedrock mapping separates eastern Blue Ridge lithostratigraphy of the Lynchburg Group and Ashe and Alligator Back Metamorphic Suites into separate fault-bound packages juxtaposed over various 1.3–1.0 Ga basement rocks of the northern French Broad massif by the Gossan Lead fault.
","largerWorkTitle":"Geology and geologic hazards of the Blue Ridge: Field excursions for the 2024 GSA southeastern section meeting, Asheville, North Carolina, USA","language":"English","publisher":"Geological Society of America","doi":"10.1130/2024.0067(03)","usgsCitation":"Merschat, A.J., Carter, M.W., Lynn, A., Stewart, K., Figueiredo, P., Odom, W.E., McAleer, R.J., Vazquez, J.A., Powell, N.E., and Holm-Denoma, C.S., 2024, Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA, chap. of Geology and geologic hazards of the Blue Ridge: Field excursions for the 2024 GSA southeastern section meeting, Asheville, North Carolina, USA, v. 67, p. 69-106, https://doi.org/10.1130/2024.0067(03).","productDescription":"38 p.","startPage":"69","endPage":"106","ipdsId":"IP-177390","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":483771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Sparta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.20449414311922,\n 36.5814103989881\n ],\n [\n -81.2095938748342,\n 36.44322251293683\n ],\n [\n -81.04130272824062,\n 36.44322251293683\n ],\n [\n -81.04275979444436,\n 36.57614511920512\n ],\n [\n -81.20449414311922,\n 36.5814103989881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Merschat, Arthur J. 0000-0002-9314-4067 amerschat@usgs.gov","orcid":"https://orcid.org/0000-0002-9314-4067","contributorId":4556,"corporation":false,"usgs":true,"family":"Merschat","given":"Arthur","email":"amerschat@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":931956,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern 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mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynn, Ashley S.","contributorId":352582,"corporation":false,"usgs":false,"family":"Lynn","given":"Ashley S.","affiliations":[{"id":24614,"text":"North Carolina Geological Survey","active":true,"usgs":false}],"preferred":false,"id":931777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Kevin G.","contributorId":352583,"corporation":false,"usgs":false,"family":"Stewart","given":"Kevin G.","affiliations":[{"id":84275,"text":"University of North Carolina-Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":931778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Figueiredo, Paula","contributorId":248217,"corporation":false,"usgs":false,"family":"Figueiredo","given":"Paula","affiliations":[{"id":49830,"text":"North Carolina University","active":true,"usgs":false}],"preferred":false,"id":931779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":931782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Powell, Nicholas Edwin 0000-0003-3654-8759","orcid":"https://orcid.org/0000-0003-3654-8759","contributorId":304622,"corporation":false,"usgs":true,"family":"Powell","given":"Nicholas","email":"","middleInitial":"Edwin","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931783,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440 cholm-denoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":2442,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher","email":"cholm-denoma@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":931784,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70263160,"text":"70263160 - 2024 - Morbidity in California giant salamander (Dicamptodon ensatus Eschscholtz, 1833) caused by Euryhelmis sp. Poche, 1926 (Trematoda: Heterophyiidae)","interactions":[],"lastModifiedDate":"2025-01-30T15:13:06.533454","indexId":"70263160","displayToPublicDate":"2024-04-05T08:04:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Morbidity in California giant salamander (Dicamptodon ensatus Eschscholtz, 1833) caused by Euryhelmis sp. Poche, 1926 (Trematoda: Heterophyiidae)","docAbstract":"In the fall of 2021, California Department of Fish and Wildlife reported larval and adult California giant salamanders (Dicamptodon ensatus Eschscholtz, 1833) with skin lesions at multiple creeks in Santa Clara and Santa Cruz Counties, California, USA. Field signs in both stages included rough, lumpy textured skin, and larvae with tails that were disproportionately long, flat, wavy, and flaccid. Presence of large-bodied larvae suggested delayed metamorphosis, with some larvae having cloudy eyes and suspected blindness. To determine the cause of the disease, three first-of-the-year salamanders from one location were collected, euthanized with 20% benzocaine, and submitted for necropsy to the U.S. Geological Survey, National Wildlife Health Center. Upon gross examination, all salamanders were emaciated with no internal fat stores, and had multiple pinpoint to 1.5-mm diameter raised nodules in the skin over the body, including the head, gills, dorsum, ventrum, all four limbs, and the tail; one also had nodules in the oral cavity and tongue. Histologically all salamanders had multiple encysted metacercariae in the dermis, subcutis, and skeletal muscles of the head, body, and tail that were often associated with granulomatous and granulocytic inflammation and edema. A small number of encysted meta cercariae or empty cysts were present in the gills with minimal inflammation, and rarely in the kidney with no associated inflammation. Morphology of live metacercariae (Trematoda: Heterophyiidae), and sequencing of the 28S rRNA gene identified a species of Euryhelmis (Poche, 1926). Artificial digestion of a 1.65 g, decapitated, eviscerated carcass yielded 773 metacercariae, all of similar size and morphology as the live specimens. Based on these findings, the poor body condition of these salamanders was concluded to be due to heavy parasite burden. Environmental factors such as drought, increased temperature, and overcrowded conditions may be exacerbating parasite infections in these populations of salamander.
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Jaimie L. 0000-0002-2305-6862 jaimiemiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2305-6862","contributorId":272836,"corporation":false,"usgs":false,"family":"Miller","given":"Jaimie","email":"jaimiemiller@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":925700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erickson, Lawrence","contributorId":350328,"corporation":false,"usgs":false,"family":"Erickson","given":"Lawrence","affiliations":[{"id":83717,"text":"Independent Contractor, Boulder Creek, CA","active":true,"usgs":false}],"preferred":false,"id":925701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fork, Susanne","contributorId":350329,"corporation":false,"usgs":false,"family":"Fork","given":"Susanne","affiliations":[{"id":40430,"text":"Elkhorn Slough National Estuarine Research Reserve","active":true,"usgs":false}],"preferred":false,"id":925702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roderick, Constance 0000-0001-8330-8024","orcid":"https://orcid.org/0000-0001-8330-8024","contributorId":215346,"corporation":false,"usgs":true,"family":"Roderick","given":"Constance","email":"","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":925703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":925704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cole, Rebecca A. 0000-0003-2923-1622 rcole@usgs.gov","orcid":"https://orcid.org/0000-0003-2923-1622","contributorId":2873,"corporation":false,"usgs":true,"family":"Cole","given":"Rebecca","email":"rcole@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":925705,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252769,"text":"ofr20241020 - 2024 - Using structured decision making to assess management alternatives to inform the 2024 update of the Minnesota Invasive Carp Action Plan","interactions":[],"lastModifiedDate":"2024-04-05T13:51:41.672255","indexId":"ofr20241020","displayToPublicDate":"2024-04-05T07:43:17","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1020","displayTitle":"Using Structured Decision Making to Assess Management Alternatives to Inform the 2024 Update of the Minnesota Invasive Carp Action Plan","title":"Using structured decision making to assess management alternatives to inform the 2024 update of the Minnesota Invasive Carp Action Plan","docAbstract":"This report summarizes the results of a structured decision making process started by the Minnesota Department of Natural Resources to develop and evaluate various invasive carp management strategies to inform a 2024 update of the Minnesota Invasive Carp Action Plan. The Minnesota Department of Natural Resources invited State, Federal, Tribal, and nongovernmental organization partners to participate in online and in-person workshops to elicit concerns and perform an expert-based assessment of potential invasive carp management strategies to address those concerns. The participatory group divided into two subgroups: the values team and the technical team. The values team specified 12 management objectives that captured the major interest group concerns. The technical and values teams developed 18 different invasive carp management strategies to compare. The technical team then scored each strategy in terms of how well it met each management objective. The values team weighted the management objectives to assess where tradeoffs might need to be made. The results of this process captured the wide range of partner concerns and technical opinions using a transparent, repeatable, and rigorous method. The analyses suggest that tradeoffs between management efficacy and cost are likely. Furthermore, considerable uncertainty exists among the technical experts regarding which strategy is likely to be most effective or cost-efficient. Followup analyses were done to assess how resolving uncertainty among experts could affect decision making and guide future monitoring efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241020","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources","usgsCitation":"Post van der Burg, M., and Colvin, M.E., 2024, Using structured decision making to assess management alternatives to inform the 2024 update of the Minnesota Invasive Carp Action Plan: U.S. Geological Survey Open-File Report 2024–1020, 19 p., https://doi.org/10.3133/ofr20241020.","productDescription":"Report: vi, 19 p; 1 Table","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-161101","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":427385,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241020/full"},{"id":427383,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1020/ofr20241020.XML"},{"id":427384,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1020/images/"},{"id":427382,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2024/1020/downloads/","text":"Table 1.1"},{"id":427381,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1020/ofr20241020.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 20241020"},{"id":427380,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1020/coverthb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, South Dakota, Wisconsin","otherGeospatial":"Upper Mississippi River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.28863011626855,\n 36.967503821513446\n ],\n [\n -87.94391643617755,\n 40.58553678179527\n ],\n [\n -85.83165826135074,\n 41.39856936972541\n ],\n [\n -86.26582742157316,\n 41.79170899467056\n ],\n [\n -87.56529202081786,\n 41.69802278026626\n ],\n [\n -88.28550154004203,\n 43.465559915400405\n ],\n [\n -89.81947003232136,\n 43.27701839489646\n ],\n [\n -88.441517663658,\n 45.88964868854825\n ],\n [\n -92.89455996568974,\n 46.6688703992705\n ],\n [\n -93.90294960750411,\n 46.954074901936195\n ],\n [\n -93.89057371275759,\n 47.45968753805238\n ],\n [\n -95.66287916799946,\n 47.30832878269692\n ],\n [\n -96.0833744059119,\n 45.445266641443965\n ],\n [\n -97.25196294733773,\n 45.73968681198525\n ],\n [\n -97.57385808711409,\n 45.5014351606913\n ],\n [\n -95.14621163333918,\n 43.21030588453809\n ],\n [\n -95.56457215116306,\n 42.47496837126678\n ],\n [\n -91.70810834882887,\n 38.50319929325272\n ],\n [\n -89.94905595458137,\n 36.68584673279122\n ],\n [\n -89.28863011626855,\n 36.967503821513446\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
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8711 37th Street Southeast
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Many freshwater lakes are groundwater flow-through systems. Although lakes commonly are considered to be sinks for nitrogen inputs, relatively little is known about carbon and nitrogen export from lakes to groundwater. The current study focused on lake-bottom biogeochemical processes accompanying the transport of nitrogen, dissolved oxygen (O2), and dissolved organic carbon (DOC) during lake-water recharge from a groundwater flow-through lake. Lake-water and porewater (15–100 cm below lakebed) samples were collected along transects within the lake downwelling zone. Infiltrating porewater O2 and DOC concentrations decreased with depth while nitrate (NO3−) concentrations increased, indicating nitrification of organic matter within the profiles. The depth of NO3− production and transport was seasonally dependent. In winter, NO3− and O2 were exported beyond 100-cm depth; whereas in summer, shallow nitrification zones were underlain by deeper NO3− reduction zones, and diel patterns of O2 and NO3− penetration depths were observed. Microbial community compositions and stable isotope profiles (δ15N[NO3−], δ18O[NO3−], δ18O[O2]) were consistent with apparent C–N–O reaction stoichiometries indicating O2 reduction and nitrification in shallower porewater, followed by varying NO3− reduction at depth. Maximum porewater NO3− concentrations (∼10–20 μM) were limited by infiltrating O2 concentrations and C/N ratios of reacting organic matter. Lake-water level variations caused changes in shoreline position and porewater velocities, while variations in lake-water temperature, DOC, and O2 contributed to changes in reaction rates and depth of O2 and NO3− penetration into the lakebed. The quality of groundwater recharged by lake water reflected temporally and spatially varying physical and biogeochemical processes in the sediment porewater.
Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause (n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation.
Carbon dioxide (CO2) is gaining interest as a tool to combat aquatic invasive species, including zebra mussels (Dreissena polymorpha). However, the effects of water chemistry on CO2 efficacy are not well described. We conducted five trials in which we exposed adult zebra mussels to a range of CO2 in water with adjusted total hardness and specific conductance. We compared dose–responses and found differences in lethal concentration to 50% of organisms (LC50) estimates ranging from 108.3 to 179.3 mg/L CO2 and lethal concentration to 90% of organisms (LC90) estimates ranging from 163.7 to 216.6 mg/L CO2. We modeled LC50 and LC90 estimates with measured water chemistry variables from the trials. We found sodium (Na+) concentration to have the strongest correlation to changes in the LC50 and specific conductance to have the strongest correlation to changes in the LC90. Our results identify water chemistry as an important factor in considering efficacious CO2 concentrations for zebra mussel control. Additional research into the physiological responses of zebra mussels exposed to CO2 may be warranted to further explain mode of action and reported selectivity. Further study could likely develop a robust and relevant model to refine CO2 applications for a wider range of water chemistries. Environ Toxicol Chem 2024;00:1–8. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
An understanding of oceanographic conditions and processes important to marine animal ecology is fundamental to the development of effective management and conservation actions. Longfin Smelt (Spirinchus thaleichthys) is a pelagic forage fish found in coastal and estuarine waters along the Pacific coast of North America from Alaska to central California. Substantial population declines in California’s San Francisco Estuary, where Longfin Smelt are protected under California’s Endangered Species Act, have prompted extensive study of estuarine factors associated with the decline. However, coastal factors that affect up to two-thirds of the Longfin Smelt life cycle are poorly understood and may be important drivers of population dynamics. We compiled coastal observations from numerous sources to estimate the range-wide coastal marine distribution of Longfin Smelt and assess habitat factors affecting distribution in the northeast Pacific Ocean. Based on maximum entropy species distribution models, Longfin Smelt distribution was correlated with depth, distance from the nearest estuary, sea surface temperature, and sea surface chlorophyll. Longfin Smelt were found in shallow, higher productivity coastal waters closer to estuaries, with depth and temperature the most consistent factors influencing distribution. Habitat suitability was highly variable at the southern extent of the range, particularly off the California coast, and was largely driven by habitat contractions associated with warm-water conditions. Study results provide insights into the habitat and range-wide distribution of an at-risk estuarine-reliant forage fish and are the first step toward identifying processes that affect the marine portion of the Longfin Smelt life cycle.
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.
He‘eia and ‘Ioleka‘a Streams in the He‘eia watershed on O‘ahu, Hawai‘i, receive substantial discharge from dike-impounded groundwater. Previous studies indicated that groundwater withdrawals from the watershed affect streamflow. Resource managers and users seek information that can be used to balance the needs of competing uses of groundwater and streamflow in the watershed.
In this study, analyses of historical streamflow and withdrawal data indicate that when groundwater withdrawals from Haiku Tunnel (a groundwater development tunnel built in the 1940s in the watershed) of 1.73–1.87 million gallons per day (Mgal/d) were introduced in the first few decades of the tunnel’s operation, base flow at a gage on He‘eia Stream decreased by 1.37–1.40 Mgal/d. Changes in rainfall during this period were not sufficient to account for the changes in base flow. The tunnel withdrawal also affected ‘Ioleka‘a Stream, but the effect was less. In the 1980s, average withdrawal from the tunnel decreased by 0.73–1.00 Mgal/d and base flow at the He‘eia streamgage increased by 0.15–0.21 Mgal/d; a concurrent rainfall increase may partly account for the base-flow increase. Withdrawal from another well (Haiku well) starting in the late 1980s had a much smaller effect than the tunnel did on flow at the He‘eia streamgage.
Numerical groundwater-model simulations indicate that shutting down withdrawals from Haiku Tunnel and Haiku well would increase base flows in streams inside and outside of the He‘eia watershed. Simulated shutdown of 0.35 Mgal/d withdrawal from Haiku well caused base flow of streams in the He‘eia watershed to increase by 0.09 Mgal/d or 26 percent of the withdrawal reduction, and shutdown of 0.60 Mgal/d withdrawal from Haiku Tunnel caused base flow of streams within the watershed to increase by 0.12 Mgal/d or 20 percent of withdrawal reduction. Shutdown of a combined 0.95 Mgal/d withdrawal from the tunnel and well caused base flow of streams within the watershed to increase by 0.22 Mgal/d or 23 percent of the withdrawal reduction.
The model simulations and analyses of streamflow data demonstrate that, climate changes notwithstanding, reducing or shutting down withdrawal from Haiku Tunnel has not in the past, and will not in the future, restore base flow to predevelopment rates. The nearly pristine condition that existed prior to the construction of the Haiku Tunnel no longer exists because other large-producing tunnels and wells near the He‘eia watershed have since begun withdrawing water from the same dike-impounded aquifer. Reduction or shutdown of withdrawals from the wells and tunnel in the He‘eia watershed cannot restore streamflow to predevelopment rates if withdrawals from all other wells and tunnels continue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245020","collaboration":"Prepared in cooperation with the Honolulu Board of Water Supply","usgsCitation":"Izuka, S.K., Kāne, H.L., and Rotzoll, K., 2024, Groundwater and surface-water interactions in the He‘eia watershed, O‘ahu, Hawai‘i—Insights from analysis of historical data and numerical groundwater-model simulations: U.S. Geological Survey Scientific Investigations Report 2024–5020, 22 p., https://doi.org/10.3133/sir20245020.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-149791","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499449,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116223.htm","linkFileType":{"id":5,"text":"html"}},{"id":427359,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5020/sir20245020.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":427358,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5020/covrthb.jpg"},{"id":427357,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91JM5FZ","text":"USGS Data Release","description":"Rotzoll, K., 2024, MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P91JM5FZ.","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii"}],"country":"United States","state":"Hawaii","otherGeospatial":"He‘eia Watershed, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.841667,\n 21.441667\n ],\n [\n -157.841667,\n 21.391667\n ],\n [\n -157.791667,\n 21.391667\n ],\n [\n -157.791667,\n 21.441667\n ],\n [\n -157.841667,\n 21.441667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors. Climate conditions can directly impact ungulates via changes in the costs of thermoregulation and locomotion, or indirectly, via changes in habitat and forage availability, predation, and species interactions. Many studies have documented the effects of climate variability and climate change on North America’s ungulates, recording impacts to population demographics, physiology, foraging behavior, migratory patterns, and more. However, ungulate responses are not uniform and vary by species and geography. Here, we present a systematic map describing the abundance and distribution of evidence on the effects of climate variability and climate change on native ungulates in North America.
We searched for all evidence documenting or projecting how climate variability and climate change affect the 15 ungulate species native to the U.S., Canada, Mexico, and Greenland. We searched Web of Science, Scopus, and the websites of 62 wildlife management agencies to identify relevant academic and grey literature. We screened English-language documents for inclusion at both the title and abstract and full-text levels. Data from all articles that passed full-text review were extracted and coded in a database. We identified knowledge clusters and gaps related to the species, locations, climate variables, and outcome variables measured in the literature.
We identified a total of 674 relevant articles published from 1947 until September 2020. Caribou (Rangifer tarandus), elk (Cervus canadensis), and white-tailed deer (Odocoileus virginianus) were the most frequently studied species. Geographically, more research has been conducted in the western U.S. and western Canada, though a notable concentration of research is also located in the Great Lakes region. Nearly 75% more articles examined the effects of precipitation on ungulates compared to temperature, with variables related to snow being the most commonly measured climate variables. Most studies examined the effects of climate on ungulate population demographics, habitat and forage, and physiology and condition, with far fewer examining the effects on disturbances, migratory behavior, and seasonal range and corridor habitat.
The effects of climate change, and its interactions with stressors such as land-use change, predation, and disease, is of increasing concern to wildlife managers. With its broad scope, this systematic map can help ungulate managers identify relevant climate impacts and prepare for future changes to the populations they manage. Decisions regarding population control measures, supplemental feeding, translocation, and the application of habitat treatments are just some of the management decisions that can be informed by an improved understanding of climate impacts. This systematic map also identified several gaps in the literature that would benefit from additional research, including climate effects on ungulate migratory patterns, on species that are relatively understudied yet known to be sensitive to changes in climate, such as pronghorn (Antilocapra americana) and mountain goats (Oreamnos americanus), and on ungulates in the eastern U.S. and Mexico.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-024-00331-8","usgsCitation":"Malpeli, K., Endyke, S.C., Weiskopf, S.R., Thompson, L., Johnson, C.G., Kurth, K.A., and Carlin, M.A., 2024, Existing evidence on the effects of climate variability and climate change on ungulates in North America: A systematic map: Environmental Evidence, v. 13, 8, 21 p., https://doi.org/10.1186/s13750-024-00331-8.","productDescription":"8, 21 p.","ipdsId":"IP-159301","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439938,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-024-00331-8","text":"Publisher Index Page"},{"id":434994,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EMV7HO","text":"USGS data release","linkHelpText":"Catalogue of the literature assessing climate effects on ungulates in North America 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Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":221497,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":898182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Ciara G.","contributorId":271273,"corporation":false,"usgs":false,"family":"Johnson","given":"Ciara","email":"","middleInitial":"G.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":898183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kurth, Katherine Anne 0000-0002-6883-8307","orcid":"https://orcid.org/0000-0002-6883-8307","contributorId":334177,"corporation":false,"usgs":true,"family":"Kurth","given":"Katherine","email":"","middleInitial":"Anne","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":898184,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carlin, Maxfield A.","contributorId":335367,"corporation":false,"usgs":false,"family":"Carlin","given":"Maxfield","email":"","middleInitial":"A.","affiliations":[{"id":37275,"text":"none","active":true,"usgs":false}],"preferred":false,"id":898185,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254886,"text":"70254886 - 2024 - Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","interactions":[],"lastModifiedDate":"2024-06-10T15:45:06.315386","indexId":"70254886","displayToPublicDate":"2024-04-04T10:36:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","title":"Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk","docAbstract":"Ungulates commonly select habitat with higher forage biomass and or nutritional quality to improve body condition and fitness. However, predation risk can alter ungulate habitat selection and foraging behavior and may affect their nutritional condition. Ungulates often choose areas with lower predation risk, sometimes sacrificing higher quality forage. This forage–predation risk trade-off can be important for life history strategies and influences individual nutritional condition and population vital rates. We used GPS collar data from adult female mule deer (Odocoileus hemionus) and mountain lions (Puma concolor) to model mule deer habitat selection in relation to forage conditions, stalking cover and predation risk from mountain lions to determine if a forage-predation risk trade-off existed for mule deer in central New Mexico. We also examined mountain lion kill sites and mule deer foraging locations to assess trade-offs at a finer scale. Forage biomass and protein content were inversely correlated with horizontal visibility, hence associated with higher stalking cover for mountain lions, suggesting a forage-predation risk trade-off for mule deer. Mule deer habitat selection was influenced by forage biomass and protein content at the landscape and within home range spatial scales, with forage protein being related to habitat selection during spring and summer and forage biomass during winter. However, mule deer selection for areas with better foraging conditions was constrained by landscape-scale encounter risk for mountain lions, such that increasing encounter risk was associated with diminished selection for areas with better foraging conditions. Mule deer also selected for areas with higher visibility when mountain lion predation risk was higher. Mountain lion kill sites were best explained by decreasing horizontal visibility and available forage protein, suggesting that deer may be selecting for forage quality at the cost of predation risk. A site was 1.5 times more likely to be a kill site with each 1-meter decrease in visibility (i.e., increased stalking cover). Mule deer selection of foraging sites was related to increased forage biomass, further supporting the potential for a trade-off scenario. Mule deer utilized spatio-temporal strategies and risk-conditional behavior to reduce predation risk, and at times selected suboptimal foraging areas with lower predation risk.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2024.1121439","usgsCitation":"Cain, J.W., Kay, J.H., Liley, S.G., and Gedir, J.V., 2024, Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk: Frontiers in Ecology and Evolution, v. 12, 1121439, 17 p., https://doi.org/10.3389/fevo.2024.1121439.","productDescription":"1121439, 17 p.","ipdsId":"IP-148026","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2024.1121439","text":"Publisher Index Page"},{"id":429764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Cibola National Forest, Gallinas Mountains area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.59348062065897,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.340783560738174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kay, Jacob H.","contributorId":337909,"corporation":false,"usgs":false,"family":"Kay","given":"Jacob","email":"","middleInitial":"H.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liley, Stewart G.","contributorId":337910,"corporation":false,"usgs":false,"family":"Liley","given":"Stewart","email":"","middleInitial":"G.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gedir, Jay V.","contributorId":337911,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":902779,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252794,"text":"70252794 - 2024 - Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","interactions":[],"lastModifiedDate":"2024-04-05T15:19:37.198461","indexId":"70252794","displayToPublicDate":"2024-04-04T10:14:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17157,"text":"Frontiers in Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","docAbstract":"Introduction: Information on spatial patterns of water depth in river channels is valuable for numerous applications, but such data can be difficult to obtain via traditional field methods. Ongoing developments in remote sensing technology have enabled various image-based approaches for mapping river bathymetry; this study evaluated the potential to retrieve depth from multispectral images acquired by an uncrewed aircraft system (UAS).
Methods: More specifically, we produced depth maps for a 4 km reach of a clear-flowing, relatively shallow river using an established spectrally based algorithm, Optimal Band Ratio Analysis. To assess accuracy, we compared image-derived estimates to direct measurements of water depth. The field data were collected by wading and from a boat equipped with an echo sounder and used to survey cross sections and a longitudinal profile. We partitioned our study area along the Sacramento River, California, USA, into three distinct sub-reaches and acquired a separate image for each one. In addition to the typical, self-contained, per-image depth retrieval workflow, we also explored the possibility of exporting a relationship between depth and reflectance calibrated using data from one site to the other two sub-reaches. Moreover, we evaluated whether sampling configurations progressively more sparse than our full field survey could still provide sufficient calibration data for developing robust depth retrieval models.
Results: Our results indicate that under favorable environmental conditions like those observed on the Sacramento River during low flow, accurate, precise depth maps can be derived from images acquired by UAS, not only within a sub-reach but also across multiple, adjacent sub-reaches of the same river.
Discussion: Moreover, our findings imply that the level of effort invested in obtaining field data for calibration could be significantly reduced. In aggregate, this investigation suggests that UAS-based remote sensing could facilitate highly efficient, cost-effective, operational mapping of river bathymetry at the reach scale in clear-flowing streams.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2024.1305991","usgsCitation":"Legleiter, C.J., and Harrison, L.R., 2024, Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images: Frontiers in Remote Sensing, v. 5, 1305991, 16 p., https://doi.org/10.3389/frsen.2024.1305991.","productDescription":"1305991, 16 p.","ipdsId":"IP-156864","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439943,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2024.1305991","text":"Publisher Index Page"},{"id":434995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEXVAR","text":"USGS data release","linkHelpText":"Multispectral images and field measurements of water depth from the Sacramento River near Glenn, California, acquired September 14-16, 2021"},{"id":427518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 40\n ],\n [\n -122.5,\n 39\n ],\n [\n -121.75,\n 39\n ],\n [\n -121.75,\n 40\n ],\n [\n -122.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":898242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":898243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252803,"text":"70252803 - 2024 - Apparent non-double-couple components as artifacts of moment tensor inversion","interactions":[],"lastModifiedDate":"2024-04-05T15:09:16.834651","indexId":"70252803","displayToPublicDate":"2024-04-04T10:05:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"Apparent non-double-couple components as artifacts of moment tensor inversion","docAbstract":"Compilations of earthquake moment tensors from global and regional catalogs find pervasive non-double-couple (NDC) components with a mean deviation from a double-couple (DC) source of around 20%. Their distributions vary only slightly with magnitude, faulting mechanism, or geologic environments. This consistency suggests that for most earthquakes, especially smaller ones whose rupture processes are expected to be simpler, the NDC components are largely artifacts of the moment tensor inversion procedure. This possibility is also supported by the fact that NDC components for individual earthquakes with Mw<6.5 are only weakly correlated between catalogs. We explore this possibility by generating synthetic seismograms for the double-couple components of earthquakes around the world using one Earth model and inverting them with a different Earth model. To match the waveforms with a different Earth model, the inversion changes the mechanisms to include a substantial NDC component while largely preserving the fault geometry (DC component). The resulting NDC components have a size and distribution similar to those reported for the earthquakes in the Global Centroid Moment Tensor (GCMT) catalog. The fact that numerical experiments replicate general features of the pervasive NDC components reported in moment tensor catalogs implies that these components are largely artifacts of the inversions not adequately accounting for the effects of laterally varying Earth structure.
","language":"English","publisher":"Seismica","doi":"10.26443/seismica.v3i1.1157","usgsCitation":"Rosler, B., Stein, S., Ringler, A.T., and Vackar, J., 2024, Apparent non-double-couple components as artifacts of moment tensor inversion: Seismica, v. 3, no. 1, 1157, 11 p., https://doi.org/10.26443/seismica.v3i1.1157.","productDescription":"1157, 11 p.","ipdsId":"IP-140079","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v3i1.1157","text":"Publisher Index Page"},{"id":427517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosler, Boris","contributorId":335403,"corporation":false,"usgs":false,"family":"Rosler","given":"Boris","email":"","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":898273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":898274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":898275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vackar, Jiri","contributorId":335404,"corporation":false,"usgs":false,"family":"Vackar","given":"Jiri","email":"","affiliations":[{"id":80396,"text":"The Czech Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":898276,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252629,"text":"sir20235119 - 2024 - Groundwater hydrology, groundwater and surface-water interactions, aquifer testing, and groundwater-flow simulations for the Fountain Creek alluvial aquifer, near Colorado Springs, Colorado, 2018–20","interactions":[],"lastModifiedDate":"2026-01-30T19:12:11.399508","indexId":"sir20235119","displayToPublicDate":"2024-04-03T12:40:00","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":"2023-5119","displayTitle":"Groundwater Hydrology, Groundwater and Surface-Water Interactions, Aquifer Testing, and Groundwater-Flow Simulations for the Fountain Creek Alluvial Aquifer, near Colorado Springs, Colorado, 2018–20","title":"Groundwater hydrology, groundwater and surface-water interactions, aquifer testing, and groundwater-flow simulations for the Fountain Creek alluvial aquifer, near Colorado Springs, Colorado, 2018–20","docAbstract":"From 2018 through 2020, the U.S. Geological Survey, in cooperation with the Air Force Civil Engineering Center, conducted an integrated study of the Fountain Creek alluvial aquifer located near Colorado Springs, Colorado. The objective of the study was to characterize hydrologic conditions for the alluvial aquifer pertinent to the potential for transport of solutes. Specific goals of this report were to characterize the groundwater hydrology of the area, to quantify groundwater and surface-water interactions, to estimate hydraulic properties of the aquifer using aquifer testing, and to complete numerical simulations of groundwater flow.
Synoptic groundwater-level elevation measurements completed throughout this study, and as part of other U.S. Geological Survey programs between 1994 and 2020, indicate groundwater-level elevations fluctuate on annual and interannual timeframes. Groundwater-level fluctuations likely were caused by temporally variable groundwater recharge and discharge components in the area, with many wells showing maximum groundwater-level elevations during the winter months (November through March). From an interannual perspective, groundwater-level fluctuations appear to have reached maximum values during 2000 to 2003, decreased during 2003 to 2006, and remained relatively constant since that time, with the exception of several wells which have displayed rising groundwater-level elevations since 2018. Spatial evaluation of groundwater-level elevations indicates groundwater flow is generally from northeast to southwest within the vicinity of several alluvial paleochannels occurring along the northeastern margin of the aquifer. Within the center of the aquifer along Fountain Creek, groundwater flow is generally from north to south, approximately paralleling surface-water flow. To quantitatively understand the potential effect of groundwater recharge and groundwater pumping on fluctuations in groundwater-level elevation, a statistical transfer-function-noise model was applied. Results of the statistical model indicate throughout most of the aquifer, fluctuations were primarily the result of recharge seasonality. In the main stem of the aquifer where groundwater pumping wells were more concentrated, however, groundwater-level elevation fluctuations were also attributable to groundwater pumping through time.
Three-dimensional evaluation of the aquifer geometry near Fountain Creek was combined with synoptic streamflow measurement and accounting of stream gains and losses to evaluate groundwater and surface-water interactions in the study area. Streamflow gain or loss calculations indicate Fountain Creek both gains from and loses flow to the alluvial aquifer, and gaining or losing reaches of the stream may be partially controlled by the depth to bedrock near the stream. Reaches with streamflow gains tend to coincide with areas where the estimated depth to bedrock is decreasing, meaning the alluvial aquifer is likely thinning in these areas and groundwater-flow paths may be converging and discharging groundwater to the stream. Losing reaches tended to coincide with locally greater depth to bedrock where the alluvial aquifer is likely thicker and has greater storage potential for surface water lost from Fountain Creek.
Results of aquifer testing indicate hydraulic conductivity, estimated from slug tests and single-well pumping tests, ranged from 0.32 to 1,410 feet per day (ft/d) and 4.13 to 664 ft/d, respectively. These results are similar to the range of values from previous aquifer tests in the study area. Hydraulic conductivities from aquifer testing for this study were generally greater than the estimates of previous slug tests and had a mean value less than the estimates from previous pumping tests. Spatial evaluation of aquifer testing results indicates hydraulic conductivity tends to be greater in the main stem of the alluvial aquifer and lower in paleochannels upgradient from the main stem of the aquifer. The spatial variation in hydraulic conductivity may be attributed to the geomorphologic processes that formed the alluvial aquifer. Compacted sediment in the paleochannels has not been potentially transported sufficient distance to cause grain-size sorting, resulting in a poorly sorted deposit and lower hydraulic conductivities. In the central portion of the alluvial aquifer, near Fountain Creek, the sediments have been transported farther from their source areas and are likely better sorted, removing finer grained sediments that would cause lower hydraulic conductivity.
A numerical groundwater-flow model was calibrated for the Fountain Creek alluvial aquifer for 2000–19 to simulate water-budget components, groundwater-flow directions, and groundwater-flow paths. The model simulated precipitation recharge, groundwater and surface-water interactions, evapotranspiration, high-volume groundwater pumping by pumping wells, and external inflows and outflows occurring along the boundaries of the alluvial aquifer. Model calibration was completed using manual and automated approaches, the latter of which assisted in quantifying model results sensitivity to input parameters. The calibrated model corresponds well with groundwater-level elevation observations, with a mean residual (observed minus simulated groundwater-level elevation) equal to −0.60 feet. Simulated groundwater base flow to streams was typically within 10 percent of base flow estimated by independent methods. Groundwater and surface-water interactions represented the largest water-budget components of the aquifer, with the second largest groundwater discharge component coming from pumping wells. Groundwater and surface-water interactions represent both the largest gain and loss terms in the water budget, because these interactions differ spatially, meaning in some areas of the model domain groundwater is being recharged by streams, whereas in other areas, groundwater is discharged to streams. Estimates of advective groundwater-flow paths indicate pumping wells may capture groundwater recharged from losing streams and groundwater that flows into the main stem of the alluvial aquifer from paleochannels.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Flood-flow estimates were computed at over 5,000 Federal Emergency Management Agency (FEMA) flood insurance study (FIS) flow locations across Pennsylvania for the 1-percent annual exceedance probability flood event (1-percent AEP). Depending on a point of interest’s proximity to a streamgage, weighting techniques may be applied to obtain flood-flow estimates for ungaged flow locations using observed peak-flow data from a nearby streamgage. Following the U.S. Geological Survey’s (USGS) published guidance, stream segments were identified where the drainage-area ratio method could be leveraged. Using updated regional regression equations and recently published flood-flow estimates at USGS streamgage locations following USGS Bulletin 17C guidelines, weighted and transferred flood flows were computed, where appropriate. For locations not applicable for the drainage-area ratio method, regression equations were used to compute flood-flow estimates. These flood-flow estimates were then compared to FEMA FIS 1-percent AEP flood-flow estimates. Percentage-difference values were computed for 3,599 FIS flow locations determined to be suitable for analysis, finding that USGS-derived flood-flow estimates were consistently lower than FEMA FIS flood-flow estimates with a statewide median percentage difference of −10.1 percent. The dataset was normally distributed with a standard deviation of 45.7 percent. Allegheny County was found to have 74 FIS flow locations with percentage-difference values greater than or equal to 67 percent or less than or equal to −67 percent. The flood-flow region in which Allegheny County is contained, Region 2, had a median percentage-difference value of −39 percent. Although removed from the final analysis, flow locations with drainage-area values above the recommended threshold for regression-based estimation (about 1,000 square miles [mi2]) were observed to have consistently higher percentage-difference values; a reminder of the limitations of use for regression-based flood-flow estimates. This report, the comparisons within, and a companion data release are intended to serve as tools to FEMA in assisting with the ongoing assessment of FIS flow locations across Pennsylvania.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235133","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Weaver, M.R., Stuckey, M.H., Colgin, J.E., and Roland, M.A., 2024, Estimation and comparison of 1-percent annual exceedance probability flood flows at Federal Emergency Management Agency flood insurance study flow locations across Pennsylvania: U.S. Geological Survey Scientific Investigations Report 2023–5133, 33 p., https://doi.org/10.3133/sir20235133.","productDescription":"Report: viii, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151288","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":427275,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FG2MF2","text":"USGS data release","linkHelpText":"USGS-derived 1-percent 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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road,
New Cumberland, PA 17070