Paleoenvironmental data are essential for reconstructing environmental conditions in the distant past, and these reconstructions strongly depend on proxies and age–depth models. Proxies are indirect measurements that substitute for variables that cannot be directly measured, such as past precipitation. Conversely, an age–depth model is a tool that correlates the observed proxy with a specific moment in time. Bayesian age–depth modelling has proved to be a powerful method for estimating sediment ages and their associated uncertainties. However, there remains considerable potential for further integration into proxy analysis. In this paper, we explore a mathematical justification and a computational approach that integrates uncertainty at the age–depth level and propagates it to the proxy scale in the form of a posterior predictive distribution. This method mitigates potential biases and errors by removing the need to assign a single age to a given proxy measurement. It allows for quantifying the likelihood that proxy data values correspond to modelled ages, thus enabling the quantification of uncertainty in both the temporal and proxy value domains. The use of Bayesian statistics in proxy analysis represents a relatively recent advancement. We aim to mathematically justify incorporating the Markov chain Monte Carlo output from age–depth models into proxy analysis and to present a novel methodology for constructing environmental reconstructions using this approach.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-024-00647-5","usgsCitation":"Aquino-Lopez, M.A., Anderson, L., Sanchez-Cabeza, J., Ruiz-Fernandez, A.C., and Christen, J.A., 2024, Bayesian approaches to proxy uncertainty quantification in paleoecology: A mathematical justification and practical integration: Journal of Agricultural, Biological and Environmental Statistics, v. 31, p. 162-175, https://doi.org/10.1007/s13253-024-00647-5.","productDescription":"14 p.","startPage":"162","endPage":"175","ipdsId":"IP-153062","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":463766,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":466943,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s13253-024-00647-5","text":"Publisher Index Page"}],"volume":"31","noUsgsAuthors":false,"publicationDate":"2024-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Aquino-Lopez, Marco A.","contributorId":346064,"corporation":false,"usgs":false,"family":"Aquino-Lopez","given":"Marco","email":"","middleInitial":"A.","affiliations":[{"id":82759,"text":"Cabridge University, UK","active":true,"usgs":false}],"preferred":false,"id":917924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Lysanna 0000-0001-5650-9744 landerson@usgs.gov","orcid":"https://orcid.org/0000-0001-5650-9744","contributorId":5339,"corporation":false,"usgs":true,"family":"Anderson","given":"Lysanna","email":"landerson@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":917925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanchez-Cabeza, Joan-Albert","contributorId":346065,"corporation":false,"usgs":false,"family":"Sanchez-Cabeza","given":"Joan-Albert","email":"","affiliations":[{"id":82761,"text":"CIMAT","active":true,"usgs":false}],"preferred":false,"id":917926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruiz-Fernandez, Ana Carolina","contributorId":346066,"corporation":false,"usgs":false,"family":"Ruiz-Fernandez","given":"Ana","email":"","middleInitial":"Carolina","affiliations":[{"id":82761,"text":"CIMAT","active":true,"usgs":false}],"preferred":false,"id":917927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christen, J. Andres","contributorId":346067,"corporation":false,"usgs":false,"family":"Christen","given":"J.","email":"","middleInitial":"Andres","affiliations":[{"id":82761,"text":"CIMAT","active":true,"usgs":false}],"preferred":false,"id":917928,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257885,"text":"70257885 - 2024 - Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley","interactions":[],"lastModifiedDate":"2024-08-30T12:26:31.615205","indexId":"70257885","displayToPublicDate":"2024-08-29T07:24:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley","docAbstract":"In freshwater forested wetlands, bald cypress knees (Taxodium distichum (L.) Rich.) have the potential to emit large amounts of methane (CH4), but only a few studies have examined their greenhouse gas contribution. In this study, we measured CH4 fluxes associated with cypress knees across various climate and flooding gradients of the Mississippi River Alluvial Valley in southcentral United States. Greenhouse gases were measured using a portable gas analyzer with a custom-made chamber placed over the knees. We also conducted 3D lidar scans of knees using a smartphone to estimate the surface area and volume of knees. We investigated the following: (1) What parameters influence CH4 fluxes (i.e., knee height, distance to stream, temperature, relative humidity, water level, precipitation)? and (2) Which type of knee shape measurement (i.e., cone, frustrum, or lidar scan) provides the best fit to model data while maximizing measurement efficiency? We found that knee CH4 flux rates ranged from − 0.005 to 182 mmol m− 2 d− 1. There were positive correlations between CH4 fluxes, water levels, and temperature, and a negative correlation with knee height. Sites that had been dry for longer periods of time emitted less CH4 than sites where the soil remained saturated. The frustrum shape produced a knee volume estimate that was within 12% of lidar scans, whereas cone shapes underestimate knee dimensions (-100%). Further research of emissions and fluxes in cypress knees could fill knowledge gaps within the carbon cycle and could represent a major component of wetland CH4 budgets.
Coastal imaging systems have been developed to measure wave runup and total water level (TWL) at the shoreline, which is a key metric for assessing coastal flooding and erosion. However, extracting quantitative measurements from coastal images has typically been done through the laborious task of hand-digitization of wave runup timestacks. Timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. We utilize over 7000 hand-digitized timestacks from six diverse locations to train and validate machine learning models to automate the process of TWL extraction. Using these data, we evaluate two deep learning model architectures for the task of runup detection. One is based on a fully convolutional architecture trained from scratch, and the other is a transformer-based architecture trained using transfer learning. The deep learning models provide a probability of each pixel being either wet or dry. When contoured at the 50% level (equal chance of being wet or dry), the deep learning models more accurately identified TWL maxima than minima at all sites. This resulted in accurate predictions of 2% exceedance runup, but under predictions of significant swash and over predictions of wave setup. Improved agreement with the complete TWL time series was obtained through post-processing by utilizing the wet/dry probability of each pixel to weight the contouring toward lower dryness probabilities for runup minima (maxima agreed well with observations without tuning). Overall, a transformer-based model using transfer learning provided the best agreement with wave runup statistics, including a) the 2% exceedance runup, b) significant swash, and c) wave setup at the shoreline. For a random subset of images, the model was found to be within the uncertainty range of hand-digitization. The relative success of the transfer learning model suggests that fine-tuning a large model has advantages compared to training a smaller model from scratch. Models provide per-pixel probabilistic estimates in less than 10 s per timestack on a single computational unit, versus the more than 5 min required for hand-digitization. The model is therefore well-suited for near real-time applications, allowing for the development of early warning systems for difficult to forecast events. Real-time wave runup and total water level observations can also be incorporated into coastal hazards forecasts for data assimilation and continual model validation and improvement.
The emergence of white-nose syndrome (WNS) in North America has resulted in mass mortalities of hibernating bats and total extirpation of local populations. The need to mitigate this disease has stirred a significant body of research to understand its pathogenesis. Pseudogymnoascus destructans, the causative agent of WNS, is a psychrophilic (cold-loving) fungus that resides within the class Leotiomycetes, which contains mainly plant pathogens and is unrelated to other consequential pathogens of animals. In this review, we revisit the unique biology of hibernating bats and P. destructans and provide an updated analysis of the stages and mechanisms of WNS progression. The extreme life history of hibernating bats, the psychrophilic nature of P. destructans, and its evolutionary distance from other well-characterized animal-infecting fungi translate into unique host–pathogen interactions, many of them yet to be discovered.
Climate change is expected to influence aquatic habitats and associated fish populations, yet we know little about the impact on recreational anglers. Our goal was to explore whether interannual fluctuations in waterbody surface area and other explanatory variables could be used as indicators of changes in angler fishing effort. Our approach leveraged a combination of remotely sensed waterbody surface area, environmental and fish population data, and onsite angler survey monitoring data for Devils Lake, North Dakota, USA during the open-water fishing period (May 1st to August 31st) for 9 years (1992–2021). The information was used to develop a dynamic waterbody size-angler effort model. Changes in waterbody surface area reliably predicted changes in angler effort (r2 = 0.60). Increases in waterbody surface area led to increases in angler effort, and decreases in waterbody surface area led to decreases in angler effort. Our findings show promise that remotely sensed fluctuations in waterbody surface area could be used as an indicator of interannual angler effort dynamics. Dynamic waterbody size-angler effort models could provide managers the ability to predict changes in angler effort via climate-related hydrological cycles that affect the size and distribution of waterbodies on the landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2024.107156","usgsCitation":"Maldonado, M., Mahmood, T., Coulter, D., Coulter, A., Chipps, S.R., Siller, M., Neal, M., Saha, A., and Kaemingk, M., 2024, Water-level changes impact angler effort in a large lake: Implications for climate change: Fisheries Research, v. 279, 107156, 5 p., https://doi.org/10.1016/j.fishres.2024.107156.","productDescription":"107156, 5 p.","ipdsId":"IP-160734","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466947,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.fishres.2024.107156","text":"Publisher Index Page"},{"id":466011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Devils Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.36050675879237,\n 48.39597416139583\n ],\n [\n -99.36050675879237,\n 47.77201003721444\n ],\n [\n -98.24927832713699,\n 47.77201003721444\n ],\n [\n -98.24927832713699,\n 48.39597416139583\n ],\n [\n -99.36050675879237,\n 48.39597416139583\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maldonado, Matthew L.","contributorId":347887,"corporation":false,"usgs":false,"family":"Maldonado","given":"Matthew L.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahmood, Taufique H.","contributorId":347888,"corporation":false,"usgs":false,"family":"Mahmood","given":"Taufique H.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coulter, David P.","contributorId":347889,"corporation":false,"usgs":false,"family":"Coulter","given":"David P.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":922733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coulter, Alison A.","contributorId":347890,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":922734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922735,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siller, Maddy K.","contributorId":347891,"corporation":false,"usgs":false,"family":"Siller","given":"Maddy K.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":922736,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neal, Michaela L.","contributorId":347892,"corporation":false,"usgs":false,"family":"Neal","given":"Michaela L.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922737,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saha, Ayon","contributorId":347893,"corporation":false,"usgs":false,"family":"Saha","given":"Ayon","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922738,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kaemingk, Mark A.","contributorId":347895,"corporation":false,"usgs":false,"family":"Kaemingk","given":"Mark A.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922739,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256185,"text":"70256185 - 2024 - Supporting climate adaptation for rural Mekong River Basin communities in Thailand","interactions":[],"lastModifiedDate":"2024-08-28T15:09:21.686987","indexId":"70256185","displayToPublicDate":"2024-08-28T09:57:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2764,"text":"Mitigation and Adaptation Strategies for Global Change","active":true,"publicationSubtype":{"id":10}},"title":"Supporting climate adaptation for rural Mekong River Basin communities in Thailand","docAbstract":"Climate change impacts on large river basins, such as the Mekong River Basin (MRB), are complex due to shared governance and interconnected socioeconomic areas, making them highly vulnerable to change. The MRB, spanning six countries including Thailand, is crucial for the food and economic security of > 60 million people. However, in 2021, Thailand was ranked as the 9th highest risk country affected by climate change. To integrate climate adaptation in Thailand's MRB, we examined the effects of climate change on rapidly developing farmer and fisher communities in northeastern Thailand and explored feasible adaptation options. Using an interdisciplinary approach that included literature review, participatory action methods, and the resist-accept-direct (RAD) framework, we found that climate change is projected to increase temperatures, precipitation, extreme events, erosion, and water clarity, while decreasing heavy sediment transport. These changes negatively impact agriculture, fisheries, human health, and tourism. We identified several adaptation strategies across environmental, ecological, and human health categories to accommodate local needs, such as preventing habitat degradation (e.g., from dams and deforestation), providing fish refuge and passage, and supporting technical capacity. Community-driven adaptation planning and implementation are essential for supporting global sustainable development in a changing climate.
","language":"English","publisher":"Springer","doi":"10.1007/s11027-024-10154-0","usgsCitation":"Embke, H.S., Lynch, A., and Beard, 2024, Supporting climate adaptation for rural Mekong River Basin communities in Thailand: Mitigation and Adaptation Strategies for Global Change, v. 29, 67, 29 p., https://doi.org/10.1007/s11027-024-10154-0.","productDescription":"67, 29 p.","ipdsId":"IP-153560","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true},{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":433249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Thailand","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 103.58713075502806,\n 18.415675046149772\n ],\n [\n 103.25074086691765,\n 18.334290245472285\n ],\n [\n 102.68316462566526,\n 17.813439961628113\n ],\n [\n 102.59999202106795,\n 17.84871117664857\n ],\n [\n 102.60900709222325,\n 17.95083485538415\n ],\n [\n 102.33917708734333,\n 18.048914557116312\n ],\n [\n 102.13127818612654,\n 18.217320023448924\n ],\n [\n 101.84223130367957,\n 18.12127146242109\n ],\n [\n 101.84381496679299,\n 17.360034521893695\n ],\n [\n 103.50389523271065,\n 17.326503169572362\n ],\n [\n 103.58713075502806,\n 18.415675046149772\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2024-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":907028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":169460,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":907029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907030,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260831,"text":"70260831 - 2024 - Hair mercury isotopes, a noninvasive biomarker for dietary methylmercury exposure and biological uptake","interactions":[],"lastModifiedDate":"2024-11-27T16:05:26.865603","indexId":"70260831","displayToPublicDate":"2024-08-28T09:51:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Hair mercury isotopes, a noninvasive biomarker for dietary methylmercury exposure and biological uptake","docAbstract":"Background. Fish and rice are the main dietary sources of methylmercury (MeHg); however, rice does not contain the same beneficial nutrients as fish, and these differences can impact the observed health effects of MeHg. Hence, it is important to validate a biomarker, which can distinguish among dietary MeHg sources. Methods. Mercury (Hg) stable isotopes were analyzed in hair samples from peripartum mothers in China (n = 265). Associations between mass dependent fractionation (MDF) (δ202Hg) and mass independent fractionation (MIF) (Δ199Hg) (dependent variables) and dietary MeHg intake (independent variable) were investigated using multivariable regression models. Results. In adjusted models, hair Δ199Hg was positively correlated with serum omega-3 fatty acids (a biomarker for fish consumption) and negatively correlated with maternal rice MeHg intake, indicating MIF recorded in hair can be used to distinguish MeHg intake predominantly from fish versus rice. Conversely, in adjusted models, hair δ202Hg was not correlated with measures of dietary measures of MeHg intake. Instead, hair δ202Hg was strongly, negatively correlated with hair Hg, which explained 27–29% of the variability in hair δ202Hg. Conclusions. Our results indicated that hair Δ199Hg can be used to distinguish MeHg intake from fish versus rice. Results also suggested that lighter isotopes were preferentially accumulated in hair, potentially reflecting Hg binding to thiols (i.e., cysteine); however, more research is needed to elucidate this hypothesis. Broader impacts include 1) validation of a non-invasive biomarker to distinguish MeHg intake from rice versus fish, and 2) the potential to use Hg isotopes to investigate Hg binding in tissues.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D4EM00231H","usgsCitation":"Rothenburg, S.E., Korrick, S.A., Harrington, D., Thurston, S.W., Janssen, S., Tate, M., Nong, Y., Nong, H., Liu, J., Hong, C., and Ouyang, F., 2024, Hair mercury isotopes, a noninvasive biomarker for dietary methylmercury exposure and biological uptake: Environmental Science: Processes & Impacts, v. 26, p. 1975-1985, https://doi.org/10.1039/D4EM00231H.","productDescription":"11 p.","startPage":"1975","endPage":"1985","ipdsId":"IP-167813","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497361,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11560691/","text":"External Repository"},{"id":463874,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rothenburg, Sarah E","contributorId":346139,"corporation":false,"usgs":false,"family":"Rothenburg","given":"Sarah","email":"","middleInitial":"E","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":918234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Korrick, Susan A","contributorId":346141,"corporation":false,"usgs":false,"family":"Korrick","given":"Susan","email":"","middleInitial":"A","affiliations":[{"id":82779,"text":"Harvard T.H. Chan School of Public Health","active":true,"usgs":false}],"preferred":false,"id":918235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrington, Donald","contributorId":346142,"corporation":false,"usgs":false,"family":"Harrington","given":"Donald","email":"","affiliations":[{"id":82781,"text":"University of Rochester Medical Center","active":true,"usgs":false}],"preferred":false,"id":918236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thurston, Sally W","contributorId":346143,"corporation":false,"usgs":false,"family":"Thurston","given":"Sally","email":"","middleInitial":"W","affiliations":[{"id":82781,"text":"University of Rochester Medical Center","active":true,"usgs":false}],"preferred":false,"id":918237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nong, YanFen","contributorId":346144,"corporation":false,"usgs":false,"family":"Nong","given":"YanFen","email":"","affiliations":[{"id":82782,"text":"Maternal and Child Health Hospital, Daxin County, China","active":true,"usgs":false}],"preferred":false,"id":918240,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nong, Hua","contributorId":346145,"corporation":false,"usgs":false,"family":"Nong","given":"Hua","email":"","affiliations":[{"id":82782,"text":"Maternal and Child Health Hospital, Daxin County, China","active":true,"usgs":false}],"preferred":false,"id":918241,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Liu, Jihong","contributorId":346146,"corporation":false,"usgs":false,"family":"Liu","given":"Jihong","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":918242,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hong, Chuan","contributorId":346148,"corporation":false,"usgs":false,"family":"Hong","given":"Chuan","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":918243,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ouyang, Fengxiu","contributorId":346149,"corporation":false,"usgs":false,"family":"Ouyang","given":"Fengxiu","email":"","affiliations":[{"id":82784,"text":"Ministry of Education and Shanghai Key Laboratory of Children’s Environmental Health,","active":true,"usgs":false}],"preferred":false,"id":918244,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70259255,"text":"70259255 - 2024 - Evolution and current state of continuous volcano gravimetry","interactions":[],"lastModifiedDate":"2024-10-02T14:36:37.275011","indexId":"70259255","displayToPublicDate":"2024-08-28T09:35:16","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18725,"text":"IEEE Instrumentation & Measurement Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Evolution and current state of continuous volcano gravimetry","docAbstract":"Most processes controlling volcanic activity involve underground mass redistribution (e.g., magma accumulation or withdrawal). Because of its unique ability to provide direct information on subsurface mass/density changes over time, gravimetry has important advantages over other volcano-monitoring techniques. As an example, if pre-eruptive magma accumulation occurs in voids, surface uplift or seismicity may not occur because the stress transmitted to the surrounding rock is not enough to induce fracturing or deformation; nevertheless, the addition of new mass would be associated with a gravity increase that may be measurable at the surface [1].
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","language":"English","publisher":"BioaRxiv","doi":"10.1101/2024.08.28.610102","usgsCitation":"Royle, A., Quinlan, M., and Swarth, C., 2024, Evidence for recruitment-mediated decline in an Eastern box turtle (Terrapene carolina carolina) population based on a 30-year capture-recapture data set from Maryland: BioRxiv, https://doi.org/10.1101/2024.08.28.610102.","productDescription":"22 p.","ipdsId":"IP-164958","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466948,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.08.28.610102","text":"External Repository"},{"id":465062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":920842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinlan, Mike","contributorId":347120,"corporation":false,"usgs":false,"family":"Quinlan","given":"Mike","email":"","affiliations":[{"id":83074,"text":"Jug Bay Wetlands Sanctuary","active":true,"usgs":false}],"preferred":false,"id":920843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swarth, Christopher","contributorId":347121,"corporation":false,"usgs":false,"family":"Swarth","given":"Christopher","email":"","affiliations":[{"id":83075,"text":"no affiliations","active":true,"usgs":false}],"preferred":false,"id":920844,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259258,"text":"70259258 - 2024 - From field station to forecast: Managing data at the Alaska Volcano Observatory","interactions":[],"lastModifiedDate":"2024-10-02T14:06:15.606438","indexId":"70259258","displayToPublicDate":"2024-08-28T08:56:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"From field station to forecast: Managing data at the Alaska Volcano Observatory","docAbstract":"The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions.
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elephants (Elephas maximus) reveals increasing landscape use in Nepal","interactions":[],"lastModifiedDate":"2024-09-26T13:48:12.795406","indexId":"70258798","displayToPublicDate":"2024-08-28T08:44:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal","title":"Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal","docAbstract":"Large mammals with general habitat needs can persist throughout mixed used landscapes, however, human-wildlife conflict frequently leads to their restriction to protected areas. Conservation efforts, especially for reducing conflicts with humans, can enhance tolerance of humans towards species like Asian elephants (Elephas maximus) in human-dominated landscapes. Here, we examine how elephant use in the Chure Terai Madhesh Landscape (CTML) covering the entire elephant range of Nepal changed between 2012 and 2020 in relationship to protection status and environmental conditions. We systematically surveyed ~ 42,000 km2 of potential habitat, by dividing the study area into 159 grid cells of 15 × 15 km2 and recorded elephant signs during the cool, dry season in three years (2012, 2018 and 2020). We analyzed the survey data in a single-species, multi-season (dynamic) occupancy modeling framework to test hypotheses regarding the influence of environmental conditions and protected area status on landscape use by elephants over time. The best-supported model included protected area effects on initial use, colonization, and detection probability as well as temporal variation in colonization and detection probability. Initial use and colonization rates were higher in protected areas, however elephants increasingly used cells located both inside and outside the protected areas, and the difference in use between protected areas and outside declined as elephants use became prevalent across most of the landscape. While elephant use was patchily distributed in the first year of surveys consistent with past descriptions of four sub-populations, elephant use consolidated into a western and eastern region in subsequent years with a gap in their distribution occurring between Chitwan and Bardiya National Parks. Our manuscript highlights the increasing landscape use by elephants in both protected areas and areas outside protected areas and suggests that management interventions that focus on reducing conflicts can promote greater use of both protected areas and areas outside of protected areas.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-70092-4","usgsCitation":"Ram, A.K., Lamichhane, B.R., Subedi, N., Yadav, N.K., Karki, A., Pandav, B., Brown, C., Khatri, T.B., and Yackulic, C., 2024, Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal: Scientific Reports, v. 14, 20023, 9 p., https://doi.org/10.1038/s41598-024-70092-4.","productDescription":"20023, 9 p.","ipdsId":"IP-160993","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":466949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-70092-4","text":"Publisher Index Page"},{"id":462277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[88.12044,27.87654],[88.04313,27.44582],[88.1748,26.81041],[88.06024,26.41462],[87.22747,26.3979],[86.02439,26.63098],[85.25178,26.7262],[84.67502,27.2349],[83.30425,27.36451],[81.99999,27.92548],[81.0572,28.4161],[80.08842,28.79447],[80.47672,29.72987],[81.11126,30.18348],[81.5258,30.42272],[82.32751,30.11527],[83.33712,29.46373],[83.89899,29.32023],[84.23458,28.83989],[85.01164,28.64277],[85.82332,28.20358],[86.95452,27.97426],[88.12044,27.87654]]]},\"properties\":{\"name\":\"Nepal\"}}]}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ram, Ashok Kumar","contributorId":344543,"corporation":false,"usgs":false,"family":"Ram","given":"Ashok","email":"","middleInitial":"Kumar","affiliations":[{"id":82383,"text":"Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":914072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamichhane, Babu Ram","contributorId":201694,"corporation":false,"usgs":false,"family":"Lamichhane","given":"Babu","email":"","middleInitial":"Ram","affiliations":[{"id":36232,"text":"National Trust for Nature Conservation, Khumaltar, POB 3712, Lalitpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":914073,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Subedi, Naresh","contributorId":201695,"corporation":false,"usgs":false,"family":"Subedi","given":"Naresh","email":"","affiliations":[{"id":36232,"text":"National Trust for Nature Conservation, Khumaltar, POB 3712, Lalitpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":914074,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yadav, Nabin Kumar","contributorId":344544,"corporation":false,"usgs":false,"family":"Yadav","given":"Nabin","email":"","middleInitial":"Kumar","affiliations":[{"id":82385,"text":"Ministry of Forests and Environment, Madhesh Pradesh, Janakpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":914075,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karki, Ajay","contributorId":344545,"corporation":false,"usgs":false,"family":"Karki","given":"Ajay","email":"","affiliations":[{"id":82383,"text":"Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":914076,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pandav, Bivash","contributorId":344546,"corporation":false,"usgs":false,"family":"Pandav","given":"Bivash","email":"","affiliations":[{"id":82387,"text":"Wildlife Institute of India, Dehradun","active":true,"usgs":false}],"preferred":false,"id":914077,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brown, Cory","contributorId":344547,"corporation":false,"usgs":false,"family":"Brown","given":"Cory","email":"","affiliations":[{"id":82388,"text":"US Fish and Wildlife Service, Washington D.C., USA","active":true,"usgs":false}],"preferred":false,"id":914078,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khatri, Top B.","contributorId":344548,"corporation":false,"usgs":false,"family":"Khatri","given":"Top","email":"","middleInitial":"B.","affiliations":[{"id":82389,"text":"Ecosystem Based Adaptation Program (EBA) II, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":914079,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":914080,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257859,"text":"70257859 - 2024 - Trees have similar growth responses to first-entry fires and reburns following long-term fire exclusion","interactions":[],"lastModifiedDate":"2024-08-29T12:21:04.445324","indexId":"70257859","displayToPublicDate":"2024-08-28T07:19:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Trees have similar growth responses to first-entry fires and reburns following long-term fire exclusion","docAbstract":"Managing fire ignitions for resource benefit decreases fuel loads and reduces the risk of high-severity fire in fire-suppressed dry conifer forests. However, the reintroduction of low-severity wildfire can injure trees, which may decrease their growth after fire. Post-fire growth responses could change from first-entry fires to reburns, as first-entry fires reduce fuel loads and the vulnerability among trees to fire effects, which may result in trees sustaining less damage during reburns. To determine whether trees had growth responses that varied from first-entry fires to reburns, we cored 87 ponderosa pine trees in the Gila Wilderness, New Mexico, USA that experienced 3–5 fires between 1950 and 2012 following long-term fire-exclusion and 67 unburned control trees from the Gila and Apache-Sitgreaves National Forests. We assessed tree growth response to fire by comparing tree-ring growth among burned and unburned trees from two years before to two years after fires. We compared growth between burned and unburned trees using a bootstrapping procedure to calculate annual median tree-ring width index values with 95 % confidence intervals. We compared post-fire growth after first-entry fires and reburns following long-term fire-exclusion. Burned trees had similar growth responses following first-entry fires and reburns, with lower growth during the fire year through two years post-fire compared to unburned controls. Burned tree growth returned to expected rates following these immediate post-fire growth reductions. Interestingly, trees had lower growth during the year before and the year of reburns compared to the first-entry fire, reflecting greater aridity before reburns. Greater aridity may have contributed to larger-than-expected growth reductions following reburns, which could explain similar growth responses to first-entry fires and reburns. Our results indicate that trees had consistent short-term growth responses to low-severity fires following long-term fire-exclusion. As trees retained vigor after multiple fires, managing fires for resource benefit is an effective approach to reduce the likelihood of high-severity fire without long-term negative effects on tree growth.
Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.
Our aim was to determine the movement patterns of three abundant salmonids—Brown Trout Salmo trutta, Mountain Whitefish Prosopium williamsoni, and Rainbow Trout Oncorhynchus mykiss—in the Smith River watershed of Montana.
We tagged 7172 fish with passive integrated transponder (PIT) tags, monitored their movements past 15 stationary PIT arrays over 4 years, and located tagged fish between arrays by conducting mobile surveys.
Movement patterns varied seasonally, among species, and among locations. Movement was greatest in the middle portion of the watershed, which included a pristine main‐stem canyon and lower reaches of major tributaries. Fish rarely left the canyon, but movement into the canyon from other regions was common. Mountain Whitefish were most likely to move, and Brown Trout were least likely to move. Most fish travelled less than 10 km, but some fish travelled over 100 km. Distinct movement patterns were not evident; rather, a continuous spectrum of movement behaviors was apparent. Movements by Mountain Whitefish and Rainbow Trout increased during their spawning periods. Movements peaked when mean daily water temperatures were between 11.3 and 17.1°C.
Movements were diverse and probably contributed to metapopulation dynamics, population resiliency, and species diversity. Fish movements along stream networks connect populations across diverse landscapes, and therefore, protecting and restoring stream connectivity along inland streams such as the Smith River is critical to maintaining productive fish assemblages.
No abstract available.
","language":"English","publisher":"Arizona Geological Survey","usgsCitation":"Thompson, L.A., Haxel, G.B., Peterson, D.W., May, D.J., Tosdal, R., Miller, R.J., Gray, F., LeVeque, R., and Umhoefer, P.J., 2024, Bedrock geologic map of Organ Pipe Cactus National Monument and vicinity, southwest Arizona: Arizona Geological Survey Contributed Report CR-24-B, Report: 28 p.; 1 Plate: 78.00 x 53.73 inches.","productDescription":"Report: 28 p.; 1 Plate: 78.00 x 53.73 inches","ipdsId":"IP-167019","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":464915,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://repository.arizona.edu/handle/10150/674763"},{"id":464917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Organ Pipe Cactus National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.60466467320839,\n 31.818050523947505\n ],\n [\n -112.5712178771873,\n 31.858642501376792\n ],\n [\n -112.68111449268478,\n 32.21508023390702\n ],\n [\n -113.08725415865368,\n 32.22316494823238\n ],\n [\n -113.09681038608835,\n 31.972204964089357\n ],\n [\n -112.60466467320839,\n 31.818050523947505\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Lisa A.","contributorId":347006,"corporation":false,"usgs":false,"family":"Thompson","given":"Lisa","email":"","middleInitial":"A.","affiliations":[{"id":34160,"text":"Arizona Geological Survey","active":true,"usgs":false}],"preferred":false,"id":920527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haxel, Gordon B. 0000-0002-6722-7803 gbhaxel@usgs.gov","orcid":"https://orcid.org/0000-0002-6722-7803","contributorId":261783,"corporation":false,"usgs":true,"family":"Haxel","given":"Gordon","email":"gbhaxel@usgs.gov","middleInitial":"B.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":920528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Donald W.","contributorId":347007,"corporation":false,"usgs":false,"family":"Peterson","given":"Donald","email":"","middleInitial":"W.","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":920529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"May, Daniel J.","contributorId":347008,"corporation":false,"usgs":false,"family":"May","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920530,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tosdal, Richard M.","contributorId":347010,"corporation":false,"usgs":false,"family":"Tosdal","given":"Richard M.","affiliations":[],"preferred":false,"id":920531,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Robert J.","contributorId":176277,"corporation":false,"usgs":false,"family":"Miller","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920532,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gray, Floyd","contributorId":347011,"corporation":false,"usgs":false,"family":"Gray","given":"Floyd","affiliations":[],"preferred":false,"id":920533,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeVeque, Richard A.","contributorId":347012,"corporation":false,"usgs":false,"family":"LeVeque","given":"Richard A.","affiliations":[],"preferred":false,"id":920534,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Umhoefer, Paul J.","contributorId":200335,"corporation":false,"usgs":false,"family":"Umhoefer","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920535,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257839,"text":"70257839 - 2024 - The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables","interactions":[],"lastModifiedDate":"2024-08-28T13:17:55.218007","indexId":"70257839","displayToPublicDate":"2024-08-27T08:12:09","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables","docAbstract":"A changing climate and increasing human population necessitate understanding global freshwater availability. To enable assessment of lake water variability from local-to-global and monthly-to-decadal scales, we extended the Global Lake area, Climate, and Population (GLCP) dataset, which contains monthly lake surface area for 1.42 million lakes with paired basin-level climate and population data from 1995 through 2020. In comparison to the previous version of the GLCP, the extended version is monthly and includes information on lake ice cover as well as basin-level snow area, humidity, longwave and shortwave radiation, and cloud cover. The extended GLCP emphasizes FAIR data principles by expanding its scripting repository and maintaining unique HydroLAKES identifiers, which enables the GLCP to be joined with other HydroLAKES-derived products. Compared to the original version, the extended GLCP contains a richer suite of variables that enable disparate analyses of lake water trends at broad spatial and temporal scales.
","language":"English","publisher":"EarthArxiv","doi":"10.31223/X57X31","usgsCitation":"Meyer, M.F., Virdis, S.G., Yang, X., Brousil, M., McClure, R.P., Sharma, S., Woolway, R.I., Cramer, A.N., Ren, J., Katz, S.L., Hampton, S.E., and Shi, H., 2024, The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables: EarthArXiv, https://doi.org/10.31223/X57X31.","productDescription":"35 p.","ipdsId":"IP-167090","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439189,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://dx.doi.org/10.31223/x57x31","text":"External Repository"},{"id":433245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meyer, Michael Frederick 0000-0002-8034-9434 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8034-9434","contributorId":304191,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"Frederick","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":911761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Virdis, Salvatore G.P. 0000-0003-3927-9494","orcid":"https://orcid.org/0000-0003-3927-9494","contributorId":334733,"corporation":false,"usgs":false,"family":"Virdis","given":"Salvatore","email":"","middleInitial":"G.P.","affiliations":[{"id":80222,"text":"Asian Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":911762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Xiao 0000-0002-0046-832X","orcid":"https://orcid.org/0000-0002-0046-832X","contributorId":268230,"corporation":false,"usgs":false,"family":"Yang","given":"Xiao","email":"","affiliations":[{"id":55603,"text":"University of North Carolina Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":911763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brousil, Mattew R. 0000-0001-8229-9445","orcid":"https://orcid.org/0000-0001-8229-9445","contributorId":334731,"corporation":false,"usgs":false,"family":"Brousil","given":"Mattew R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":911764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClure, Ryan P. 0000-0001-6370-3852","orcid":"https://orcid.org/0000-0001-6370-3852","contributorId":268224,"corporation":false,"usgs":false,"family":"McClure","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":911765,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sharma, Sapna","contributorId":150332,"corporation":false,"usgs":false,"family":"Sharma","given":"Sapna","email":"","affiliations":[{"id":16184,"text":"York University","active":true,"usgs":false}],"preferred":false,"id":911766,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woolway, R. Iestyn 0000-0003-0498-7968","orcid":"https://orcid.org/0000-0003-0498-7968","contributorId":297333,"corporation":false,"usgs":false,"family":"Woolway","given":"R.","email":"","middleInitial":"Iestyn","affiliations":[{"id":64373,"text":"European Space Agency Climate Office","active":true,"usgs":false}],"preferred":false,"id":911767,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cramer, Alli N. 0000-0002-0356-5782","orcid":"https://orcid.org/0000-0002-0356-5782","contributorId":268216,"corporation":false,"usgs":false,"family":"Cramer","given":"Alli","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":911768,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ren, Jianning 0000-0002-5849-2189","orcid":"https://orcid.org/0000-0002-5849-2189","contributorId":304196,"corporation":false,"usgs":false,"family":"Ren","given":"Jianning","email":"","affiliations":[{"id":16704,"text":"University of Nevada - Reno","active":true,"usgs":false}],"preferred":false,"id":911769,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Katz, Stephen L.","contributorId":245617,"corporation":false,"usgs":false,"family":"Katz","given":"Stephen","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":911770,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hampton, Stephanie E.","contributorId":178718,"corporation":false,"usgs":false,"family":"Hampton","given":"Stephanie","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":911771,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shi, Haoran 0000-0001-9543-3324","orcid":"https://orcid.org/0000-0001-9543-3324","contributorId":343708,"corporation":false,"usgs":false,"family":"Shi","given":"Haoran","email":"","affiliations":[{"id":36207,"text":"Bangor University","active":true,"usgs":false}],"preferred":false,"id":911772,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70257818,"text":"70257818 - 2024 - Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","interactions":[],"lastModifiedDate":"2024-09-11T16:26:52.60017","indexId":"70257818","displayToPublicDate":"2024-08-27T07:07:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","docAbstract":"Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real‐time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P‐ and S‐wave arrival‐time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey‐published 1D P‐wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.
Seattle Public Utilities (SPU) has a history of conducting traditional fish surveys in urban streams of Seattle, Washington. Limited staff resources have reduced SPU's capacity to monitor fish, and environmental DNA (eDNA) was recognized as an alternative survey method that could potentially improve the efficiency and capacity of SPU-sponsored fish surveys. We performed spatiotemporal surveys of eDNA to assess occupancy and distribution of Chinook Salmon (Oncorhynchus tshawytscha), Coho Salmon (O. kisutch), and Coastal Cutthroat Trout (O. clarkii clarkii) in Thornton Creek, Seattle, between October 2018 and December 2020. Peak Chinook and Coho eDNA detections occurred in October and October–November, respectively, coinciding with expected adult return time. Chinook and Coho eDNA was detected in May at the time when juveniles outmigrate through the Lake Washington basin. Coastal Cutthroat Trout eDNA was widespread and detected at high rates across seasons, reflecting their ubiquitous distribution. Results from multiscale occupancy modeling suggested that distance upstream affected site-level occupancy probabilities for adult Chinook, but not Coho. Model results also suggested that the probability of Coho and Chinook eDNA occurring in water samples was affected by survey year. Finally, model results suggested that the probability of detecting Chinook eDNA in PCR technical replicates was affected by survey year and collection day but detection of Coho eDNA was only affected by collection day. This study indicates eDNA surveys are effective for assessing distribution and occupancy of salmonids in Seattle's urban streams. Integrating eDNA surveys into urban stream monitoring programs can help alleviate the burden of limited assets facing many resource managers.
Fire facilitates erosion through changes in vegetation and soil, with major postfire erosion commonly occurring even with moderate rainfall. As climate warms, the western United States (U.S.) is experiencing an intensifying fire regime and increasing frequency of extreme rain. We evaluated whether these hydroclimatic changes are evident in patterns of postfire erosion by modeling hillslope erosion following all wildfires larger than 100 km2 in California from 1984 to 2021. Our results show that annual statewide postfire hillslope erosion has increased significantly over time. To supplement the hillslope erosion modeling, we compiled modeled and measured postfire debris-flow volumes. We find that, in northern California, more than 50% of fires triggering the top 20 values of sediment mass and sediment yield occurred in the most recent decade (between 2011 and 2021). In southern California, the postfire sediment budget was dominated by debris flows, which showed no temporal trend. Our analysis reveals that 57% of postfire sediment erosion statewide occurred upstream of reservoirs, indicating potential impacts to reservoir storage capacity and thus increased risk to water-resource security with ongoing climate change.
Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4961","usgsCitation":"Brusa, J., Farr, M., Evenson, J., Silverman, E., Murphie, B., Cyra, T., Tschaekofske, H., Spragens, K., and Converse, S.J., 2024, Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data: Ecosphere, v. 15, no. 8, e4961, 15 p., https://doi.org/10.1002/ecs2.4961.","productDescription":"e4961, 15 p.","ipdsId":"IP-157545","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4961","text":"Publisher Index Page"},{"id":482967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Salish Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.31135582023572,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 49.0056879632715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Brusa, Jamie L.","contributorId":351922,"corporation":false,"usgs":false,"family":"Brusa","given":"Jamie L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farr, Matthew T.","contributorId":351923,"corporation":false,"usgs":false,"family":"Farr","given":"Matthew T.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evenson, Joseph","contributorId":351924,"corporation":false,"usgs":false,"family":"Evenson","given":"Joseph","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silverman, Emily","contributorId":351925,"corporation":false,"usgs":false,"family":"Silverman","given":"Emily","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphie, Bryan","contributorId":351927,"corporation":false,"usgs":false,"family":"Murphie","given":"Bryan","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cyra, Thomas A.","contributorId":351929,"corporation":false,"usgs":false,"family":"Cyra","given":"Thomas A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929756,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tschaekofske, Heather","contributorId":351931,"corporation":false,"usgs":false,"family":"Tschaekofske","given":"Heather","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929757,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spragens, Kyle A.","contributorId":351933,"corporation":false,"usgs":false,"family":"Spragens","given":"Kyle A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929758,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929759,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257684,"text":"fs20243028 - 2024 - Structured science syntheses to inform decision making on Federal public lands","interactions":[],"lastModifiedDate":"2024-08-27T14:26:52.31055","indexId":"fs20243028","displayToPublicDate":"2024-08-26T15:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3028","displayTitle":"Structured Science Syntheses to Inform Decision Making on Federal Public Lands","title":"Structured science syntheses to inform decision making on Federal public lands","docAbstract":"The U.S. Geological Survey, Bureau of Land Management, and U.S. Fish and Wildlife Service partnered to develop a new type of science product: the structured science synthesis. Structured science syntheses are peer-reviewed reports that synthesize science information about a priority resource management issue on public lands. Structured science syntheses are developed explicitly to facilitate the application of science to decision making. Key characteristics of structured science syntheses include that they are coproduced with resource managers, developed using clear, repeatable methods and designed for ease of use. The syntheses include different types of science information needed for analyses completed under the National Environmental Policy Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243028","programNote":"Prepared in cooperation with the Bureau of Land Management and the U.S. Fish and Wildlife Service","usgsCitation":"Dietrich, E.I., Carter, S.K., Rutherford, T.K., Gilbert, M.A., Haby, T.S., Johnston, A.N., Jordan, S.E., Kleist, N.J., Lehrter, R.J., Masters, E.H., Mengelt, C., Stoneburner, A.L., Teige, E.C., Tull, J.C., Whipple, S.E., and Wood, D.J.A., 2024, Structured science syntheses to inform decision making on Federal public lands: U.S. Geological Survey Fact Sheet 2024–3028, 4 p., https://doi.org/10.3133/fs20243028.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-158168","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":433195,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3028"},{"id":433171,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3028/fs20243028.xml"},{"id":433170,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3028/images"},{"id":433078,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3028/coverthb.jpg"},{"id":433132,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235132","text":"Effects of Culverts on Habitat Connectivity in Streams—A Science Synthesis to Inform National Environmental Policy Act Analyses"},{"id":433131,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235114","text":"Effects of Noise from Oil and Gas Development on Ungulates and Small Mammals—A Science Synthesis to Inform National Environmental Policy Act Analyses"},{"id":433079,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3028/fs20243028.pdf","text":"Report","size":"5.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3028"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The Coastal California Gnatcatcher (Polioptila californica californica: “gnatcatcher”) is a resident species restricted to coastal sage scrub habitat in southern California. Listed as federally threatened, the gnatcatcher is subject to multiple threats, including habitat loss, fragmentation, and degradation, particularly in association with the increasing frequency of large wildfires. The California Gnatcatcher is a focal species under several habitat conservation plans and is monitored to determine population trends and evaluate the success of the plans in protecting the species.
Historically, gnatcatcher monitoring has been limited in geographic scope and has used differing methodologies, limiting the extent to which findings can be generalized across larger spatial scales and multiple populations. In 2015, we completed the first of an intended series of surveys following a standardized protocol designed to address two broad objectives. First, we sought to determine gnatcatcher occupancy at the regional scale, including habitat from throughout the species’ range in southern California, as well as in two subregions: Orange County and San Diego County, to address specific management objectives within those jurisdictions. In addition, we collected vegetation data to better understand gnatcatcher habitat associations that affect occupancy. In a parallel objective, we evaluated the effect of fire on gnatcatchers and their habitat by comparing occupancy and vegetation characteristics across sites varying in the length of time since the last fire. Data collected in 2020 allowed us to expand the study to include analyses of colonization (sites unoccupied in one year and occupied the next) and extinction (sites occupied in one year but not the next).
In 2020, we surveyed 327 regional points and 180 subregional points each in Orange and San Diego Counties. In addition, we surveyed 95–106 points within 4 postfire categories based on the year of the last fire at each point: before or during 2002 (“unburned”), 2003–06, 2007–10, and 2011–14. We surveyed for gnatcatchers during three area searches centered on each point at 2-week intervals commencing in mid-March. Vegetation data were collected during May–June using a modified point-intercept method along fixed transects.
Shrub and tree cover at our plots was dominated by California sagebrush (Artemisia californica), California buckwheat (Eriogonum fasciculatum), laurel sumac (Malosma laurina), sage (including Salvia mellifera and S. leucophylla), and sunflowers (including Encelia californica, E. farinosa, and Bahiopsis laciniata); however, most of the vegetation at plots consisted of non-native grass and herbaceous plants, indicating a high level of disturbance associated with fire. We documented vegetation differences at the subregional scale indicative of differences in fire history: in Orange County, overall shrub/tree cover was higher and herbaceous cover lower than in San Diego, where three large fires had burned within 17 years of this study. Both woody and herbaceous cover increased between 2016 and 2020 at the regional and two subregional scales, likely a response to above-average precipitation during 2 years preceding the 2020 surveys. Herbaceous vegetation also increased at postfire points; however, woody vegetation cover changed little between 2016 and 2020.
We modeled the effects of vegetation and physical (elevation, distance to Pacific coast, slope) covariates on gnatcatcher occupancy, colonization, and extinction probabilities in the regional, subregional, and postfire datasets. Cover of California sagebrush was the strongest predictor of gnatcatcher occupancy and appeared in the top models for every dataset. California buckwheat was another strong positive predictor of gnatcatcher occupancy in all datasets. Cover of sunflowers was a positive predictor of occupancy in the Orange County subregion, and both sunflowers and sage were positive predictors of occupancy at postfire points. In contrast, laurel sumac was negatively related to gnatcatcher occupancy in postfire habitats, with occupancy unlikely when sumac exceeded 50 percent cover. Herbaceous vegetation, including invasive grass, negatively affected gnatcatcher occupancy regionwide.
Covariates that were strong positive predictors of occupancy were also positive predictors of colonization and (or) negative predictors of extinction, and vice versa. Outside of the positive effects of California sagebrush and California buckwheat, and the negative effect of total herbaceous cover, we identified few covariates influencing colonization. In contrast, we identified many more predictors of extinction, including cover of laurel sumac and grass, which increased extinction risk, and cover of California sagebrush, sunflowers, and bare ground, along with time since last fire, which reduced extinction risk.
We used our modelled estimates of colonization and extinction probabilities to derive occupancy in 2020 based on starting occupancy in 2016. Gnatcatcher occupancy increased in 2020 at regional and subregional scales and in unburned habitat, growing by 19–35 percent since 2016. Among burned sites, occupancy tripled from 2016 to 2020 at points burned during 2011–14 but was unchanged at the 2007–10 postfire points and declined by 28 percent at points burned in 2003–06. The slow recovery of the gnatcatcher population in this latter category, which makes up 16 percent of the suitable habitat in San Diego County, is a matter of conservation concern warranting further attention.
Collectively, our rangewide results reveal a widespread and long-term effect of wildfire on California Gnatcatcher habitat, particularly in San Diego County. These data provide a baseline from which future monitoring can be used to evaluate changes in habitat condition over time and to improve our understanding of the factors and processes influencing gnatcatcher occupancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241015","collaboration":"Prepared in cooperation with the San Diego Association of Governments, Natural Communities Coalition, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kus, B.E., Houston, A., and Preston, K.L., 2024, Occupancy dynamics of the Coastal California Gnatcatcher in southern California: U.S. Geological Survey Open-File Report 2024–1015, 34 p., https://doi.org/10.3133/ofr20241015.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-156536","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":433034,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241015/full"},{"id":433032,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.xml"},{"id":433031,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":433029,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC30JX","text":"USGS Data Release","description":"Kus, B.E., and Houston, A., 2021, Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/F7PC30JX.","linkHelpText":"Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023)"},{"id":433033,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1015/images"},{"id":433030,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1015/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.95012019018054,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 35.34010394860475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
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Sacramento, California 95819
Geologic cross section N–N′ is the sixth in a series of geologic cross sections constructed by the U.S. Geological Survey to document and improve understanding of the geologic framework and petroleum systems of the Appalachian basin. Cross section N–N′ provides a regional view of the structural and stratigraphic framework of the Appalachian basin in the Valley and Ridge province in western and central Alabama; it spans approximately 69 miles (mi) (111 kilometers [km]).
This geologic cross section is a companion to geologic cross sections E–E′, D–D′, C–C′, I–I′, and A–A′ that are located approximately 350 to 550 mi (563 to 885 km) to the northeast. Cross section N–N' complements earlier geologic cross sections through the Alabama part of the Appalachian basin. Although some of the other cross sections show more structural and stratigraphic detail, they are of more limited extent geographically and stratigraphically.
Cross section N–N′ contains information that is useful for evaluating energy resources in the Appalachian basin. Although the Appalachian basin petroleum systems are not shown on the cross section, many of their key elements (such as source rocks, reservoir rocks, seals, and traps) can be inferred from lithologic units, unconformities, and geologic structures shown on the cross section. Other aspects of petroleum systems (such as the timing of petroleum generation and petroleum migration pathways) may be evaluated by burial history, thermal history, and fluid flow models based on what is shown on the cross section. In addition, cross section N–N′ may be used as a reconnaissance tool to identify plausible geologic structures and strata for the subsurface storage of liquid waste or for the sequestration of carbon dioxide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3524","usgsCitation":"Trippi, M.H., Coleman, J.L., and Ryder, R.T., 2024, Cross section N–N' through the Valley and Ridge province of the southern Appalachian basin, from Greene County, west-central Alabama, to Bibb County, central Alabama (ver. 1.1, November 2024): U.S. Geological Survey Scientific Investigations Map 3524, 1 sheet, 51-p. pamphlet, https://doi.org/10.3133/sim3524.","productDescription":"Report: viii, 51 p.; 1 Sheet: 71.83 x 37.31 inches","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-154937","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463685,"rank":12,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3524/versionHist.txt","size":"1.69 KB","linkFileType":{"id":2,"text":"txt"}},{"id":433016,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2985","text":"Scientific Investigations Map 2985","linkHelpText":"- Geologic Cross Section E-E' through the Appalachian Basin from the Findlay Arch, Wood County, Ohio, to the Valley and Ridge Province, Pendleton County, West Virginia"},{"id":433020,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3425","text":"Scientific Investigations Map 3425","linkHelpText":"- Geologic Cross Section A–A′ Through the Appalachian Basin from the Southern Margin of the Ontario Lowlands Province, Genesee County, Western New York, to the Valley and Ridge Province, Lycoming County, North-Central Pennsylvania"},{"id":432956,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3524/images/"},{"id":432955,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3524/sim3524.XML","linkFileType":{"id":8,"text":"xml"}},{"id":432957,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3524/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":497892,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117262.htm","linkFileType":{"id":5,"text":"html"}},{"id":432953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3524/sim3524_pamphlet.pdf","text":"Pamphlet","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3524/coverthb.jpg"},{"id":433017,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3067","text":"Scientific Investigations Map 3067","linkHelpText":"- Geologic Cross Section D-D' Through the Appalachian Basin from the Findlay Arch, Sandusky County, Ohio, to the Valley and Ridge Province, Hardy County, West Virginia"},{"id":433018,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3172","text":"Scientific Investigations Map 3172","linkHelpText":"- Geologic cross section C-C' through the Appalachian basin from Erie County, north-central Ohio, to the Valley and Ridge province, Bedford County, south-central Pennsylvania"},{"id":433019,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3343","text":"Scientific Investigations Map 3343","linkHelpText":"- Geologic Cross Section I–I′ Through the Appalachian Basin from the Eastern Margin of the Illinois Basin, Jefferson County, Kentucky, to the Valley and Ridge Province, Scott County, Virginia"},{"id":432954,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3524/sim3524_map.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.63427073659228,\n 32.37457003921131\n ],\n [\n -84.65721995534267,\n 32.37457003921131\n ],\n [\n -84.65721995534267,\n 35.006773294083345\n ],\n [\n -88.63427073659228,\n 35.006773294083345\n ],\n [\n -88.63427073659228,\n 32.37457003921131\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 26, 2024; Version 1.1: November 5, 2024","contact":"Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 954
Reston, VA 20192