Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.
In 2020, natural resource managers at Camp Edwards, Barnstable County, MA, observed Terrapene carolina carolina (Eastern Box Turtle) individuals infected by myiasis, where parasitic flesh flies larviposit into the living tissue of a host. The hypothesized parasite was Dexosarcophaga cistudinis, but its impacts on the host's body condition, movement, and habitat use were unknown. Our objectives were to identify the parasite at Camp Edwards and to compare the body condition, movement, and habitat characteristics at capture locations of Eastern Box Turtles for infected and noninfected individuals. We radio-tracked turtles weekly and encountered 48 individuals from May to August 2022 at Camp Edwards, MA. Upon capture, we recorded turtle infection status, mass, carapace length, shell surface temperature, GPS location, and habitat characteristics of the capture location. We confirmed D. cistudinis as the parasite and found that myiasis-infected turtles had a significantly higher shell temperature (27.92 ± 5.28 °C) than noninfected turtles (26.77 ± 5.64 °C). However, we did not find an effect of myiasis on body condition, habitat use, or average daily distance moved. Collectively, our results suggest that infected turtles may exhibit behavioral fever, a mechanism by which ectotherms move to warmer microclimates to raise their body temperature in response to infections. Eastern Box Turtles at Camp Edwards may be able to use behavioral fever in response to myiasis infection because of the habitat mosaic made available through detailed habitat-management regimes.
In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.
The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high‐risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low‐lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high‐risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near‐real‐time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240112","usgsCitation":"Kramer, R., Thelen, W., Iezzi, A.M., Moran, S.C., and Pauk, B., 2024, Recent expansion of the Cascades Volcano Observatory geophysical network at Mount Rainier for improved volcano and lahar monitoring: Seismological Research Letters, v. 95, no. 5, p. 2707-2721, https://doi.org/10.1785/0220240112.","productDescription":"15 p.","startPage":"2707","endPage":"2721","ipdsId":"IP-163746","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":484133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Ranier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.99784760683576,\n 47.05336015194894\n ],\n [\n -121.99784760683576,\n 46.64802462725589\n ],\n [\n -121.42808425851092,\n 46.64802462725589\n ],\n [\n -121.42808425851092,\n 47.05336015194894\n ],\n [\n -121.99784760683576,\n 47.05336015194894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"95","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Rebecca 0000-0002-4873-1983 rkramer@usgs.gov","orcid":"https://orcid.org/0000-0002-4873-1983","contributorId":195070,"corporation":false,"usgs":true,"family":"Kramer","given":"Rebecca","email":"rkramer@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":304206,"corporation":false,"usgs":true,"family":"Iezzi","given":"Alexandra","email":"","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932571,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pauk, Benjamin 0000-0003-3036-5927 bpauk@usgs.gov","orcid":"https://orcid.org/0000-0003-3036-5927","contributorId":195069,"corporation":false,"usgs":true,"family":"Pauk","given":"Benjamin","email":"bpauk@usgs.gov","affiliations":[],"preferred":true,"id":932572,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257027,"text":"70257027 - 2024 - Feedbacks: A new synthesis of causal loops across ecology","interactions":[],"lastModifiedDate":"2024-11-22T15:56:13.864419","indexId":"70257027","displayToPublicDate":"2024-07-22T08:28:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Feedbacks: A new synthesis of causal loops across ecology","docAbstract":"Feedbacks are the basic linkages of living systems. In organisms, they regulate the processes of growth and homeostasis, as well as their interactions with their world. Feedback, which Judson (1980) called ‘one of the chief themes of scientific understanding,' is equally important in ecological systems. The ecological literature is rich in papers dealing with the role of feedback in various phenomena. However, we know of no comprehensive synthesis of feedbacks in ecology. Pichon et al. (2024) accomplish this, and for the first time show that ecological feedbacks can be categorized in terms of a small number of fundamental attributes. The paper brings the array of different types of feedbacks into a manageable order, providing not only the relevant theoretical framework but also guidance on methods for applying understanding to practical issues.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1111/ecog.07460","usgsCitation":"DeAngelis, D.L., and Xu, L., 2024, Feedbacks: A new synthesis of causal loops across ecology: Ecography, v. 2024, no. 11, e07460, 3 p., https://doi.org/10.1111/ecog.07460.","productDescription":"e07460, 3 p.","ipdsId":"IP-164923","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439260,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07460","text":"Publisher Index Page"},{"id":432334,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2024","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":909198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Linhao","contributorId":221358,"corporation":false,"usgs":false,"family":"Xu","given":"Linhao","email":"","affiliations":[{"id":40353,"text":"Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key","active":true,"usgs":false}],"preferred":false,"id":909199,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257094,"text":"70257094 - 2024 - Predictor importance in habitat suitability models for invasive terrestrial plants","interactions":[],"lastModifiedDate":"2024-09-16T16:12:33.446909","indexId":"70257094","displayToPublicDate":"2024-07-22T07:13:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predictor importance in habitat suitability models for invasive terrestrial plants","docAbstract":"Due to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.
The contiguous United States.
We explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.
Human modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.
This study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.
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Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2024.100850","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2024, Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease: Spatial Statistics, v. 62, 100850, 15 p., https://doi.org/10.1016/j.spasta.2024.100850.","productDescription":"100850, 15 p.","ipdsId":"IP-155343","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1016/j.spasta.2024.100850","text":"Publisher Index Page"},{"id":485559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":354688,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":354689,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":936164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":354690,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":354692,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":936167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":354695,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270064,"text":"70270064 - 2024 - Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","interactions":[],"lastModifiedDate":"2025-08-18T15:27:11.945136","indexId":"70270064","displayToPublicDate":"2024-07-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22165,"text":"Reliability Engineering and System Safety (RESS)","active":true,"publicationSubtype":{"id":10}},"title":"Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","docAbstract":"Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions.
Dams have negatively affected freshwater biodiversity throughout the world. These negative effects tend to be exacerbated for aquatic taxa with migratory life histories, and for taxa whose habitat is fundamentally altered by the formation of large reservoirs. Sauger (Sander candadensis; Percidae), large-bodied migratory fishes native to North America, have seen population declines over much of the species' range, and dams are often implicated for their role in blocking access to spawning habitat and otherwise negatively affecting river habitat. Furthermore, hybridization appears to be more frequent between sauger and walleye in the reservoirs formed by large dams. In this study, we examine the role of dams in altering sauger population connectivity and facilitating hybridization with introduced walleye in Wyoming's Wind River and Bighorn River systems. We collected genomic data from individuals sampled over a large spatial scale and replicated sampling throughout the spawning season, with the intent to capture potential variation in hybridization prevalence or genomic divergence between sauger with different life histories. The timing of sampling was not related to hybridization prevalence or population divergence, suggesting limited genetic differences between sauger spawning in different time and places. Overall, there was limited hybridization detected, however, hybridization was most prevalent in Boysen Reservoir (a large impounded section of the Wind River). Dams in the lower Wind River and upper Bighorn River were associated with population divergence between sauger upstream and downstream of the dams, and demographic models suggest that this divergence has occurred in concordance with the construction of the dam. Sauger upstream of the dams exhibited substantially lower estimates of genetic diversity, which implies that disrupted connectivity between Wind River and Bighorn River sauger populations may already be causing negative demographic effects. This research points towards the importance of considering the evolutionary consequences of dams on fish populations in addition to the threats they pose to population persistence.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11706","usgsCitation":"Rosenthal, W., Mandeville, E., Pilkerton, A., Gerrity, P.C., Skorupski, J., Walters, A.W., and Wagner, C., 2024, Influence of dams on sauger population structure and hybridization with introduced walleye, v. 14, no. 7, e11706, 16 p., https://doi.org/10.1002/ece3.11706.","productDescription":"e11706, 16 p.","ipdsId":"IP-145629","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11706","text":"Publisher Index Page"},{"id":485447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bighorn River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.2,\n 45\n ],\n [\n -109.2,\n 42.8\n ],\n [\n -107.6,\n 42.8\n ],\n [\n -107.6,\n 45\n ],\n [\n -109.2,\n 45\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenthal, William C.","contributorId":244630,"corporation":false,"usgs":false,"family":"Rosenthal","given":"William C.","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":935772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":935773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilkerton, Ashleigh","contributorId":346434,"corporation":false,"usgs":false,"family":"Pilkerton","given":"Ashleigh","affiliations":[{"id":63974,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":935774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerrity, Paul C.","contributorId":104198,"corporation":false,"usgs":true,"family":"Gerrity","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":935775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skorupski, Joseph A.","contributorId":354495,"corporation":false,"usgs":false,"family":"Skorupski","given":"Joseph A.","affiliations":[],"preferred":false,"id":935776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Catherine E.","contributorId":337377,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine E.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":935778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259268,"text":"70259268 - 2024 - Experimental assessment of egg mat gear retention and collection efficacy","interactions":[],"lastModifiedDate":"2024-12-10T15:25:10.409709","indexId":"70259268","displayToPublicDate":"2024-07-21T06:46:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Experimental assessment of egg mat gear retention and collection efficacy","docAbstract":"Assessment of egg deposition is widely used to provide an index of spawning efforts for lithophilic spawning fishes. However, little is known about the collection efficacy and bias of fish egg collection methods. We conducted a two-phased study consisting of a simulated-river flume study (two-part design), and a field study (an egg drift comparison with capture on mats) to assess egg collection methods and evaluate egg retention and capture on egg mats. Lake whitefish Coregonus clupeaformis egg retention on seeded mats decreased with increasing velocity and walleye Sander vitreus egg retention was variable as velocity increased. Fewer lake whitefish eggs were collected on egg mats when limestone reef rock was present in the flume study during the simulated spawned trials, but the inverse was true for walleye. Similarly, during field collections more lake whitefish eggs were collected in benthic D-shaped frame (D-frame) drift nets set near a known spawning reef compared to egg mats set on the reef, indicating lake whitefish eggs were drifting downstream along the river bottom. In contrast, fewer walleye eggs were observed in D-frame drift nets compared to number of eggs captured on the egg mats. Therefore, egg mats are an informative tool for evaluating walleye egg deposition in an immediate area but may underestimate egg deposition of lake whitefish, especially in lotic systems. Compared to other egg collection methods in the current literature, our study indicates that egg mats are useful for assessing egg deposition by lithophilic spawning fishes, but that the collection and retention efficacy and bias of this gear may vary between species and habitat types.
Wildfires and climate change are having transformative effects on vegetation composition and structure, and post-fire management may have long-lasting impacts on ecosystem reorganization. Post-fire aerial seeding treatments are commonly used to reduce runoff and soil erosion, but little is known about how seeding treatments affect native vegetation recovery over long periods of time, particularly in type-converted forests which have been dramatically transformed by the effects of repeated, high-severity fire. In this study, we analyze and report on a rare long-term (23-year) dataset that documents vegetation dynamics following a 1996 post-fire aerial seed treatment and subsequent 2011 high-severity reburn in a dry conifer forest of northern New Mexico in the southwestern United States. Repeated surveys between 1997 – 2019 of 49 permanent transects were used to test for differences in vegetation cover, richness, and diversity between seeded and unseeded areas, and to characterize the development of seeded and unseeded vegetation communities through time and across gradients of burn severity, elevation, and soil-available water capacity. Post-fire seeding led to a clear and sustained divergence in herbaceous community composition. Seeded plots had much higher cover of non-native graminoids, primarily Bromus inermis, a likely contaminant in the seed mix. High-severity reburning in all plots in 2011 reduced native graminoid cover by half at seeded plots compared to both pre-fire levels and to plots that were unseeded following the initial 1996 fire. In addition, increased fire severity was associated with increased non-native graminoid cover and reduced native graminoid cover, native species richness, and species diversity. This study documents a fire-driven ecosystem transformation from a former conifer forest into a shrub-grass system, reinforced by aerial seeding of grasses and high-severity reburning. This unique long-term dataset illustrates that post-fire seeding carries significant risk of unwanted non-native species invasions that persist through subsequent fires – indicating that alternative post-fire management actions merit consideration to better support native ecosystem resilience in the face of emergent climate change and increasing disturbance. Lastly, this study highlights the importance of long-term monitoring of post-fire vegetation dynamics, as short-term assessments will miss key elements of the full complexity of ecosystem responses to fire and post-fire management actions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.3008","usgsCitation":"Wion, A.P., Stevens, J., Beeley, K., Oertel, R., Margolis, E.Q., and Allen, C., 2024, Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding: Ecological Applications, v. 34, no. 6, e3008, 21 p., https://doi.org/10.1002/eap.3008.","productDescription":"e3008, 21 p.","ipdsId":"IP-158911","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":498298,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.3008","text":"Publisher Index Page"},{"id":431346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains, San Miguel Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.61072300933778,\n 36.66992633929999\n ],\n [\n -107.61072300933778,\n 35.363708672581055\n ],\n [\n -105.69910191558768,\n 35.363708672581055\n ],\n [\n -105.69910191558768,\n 36.66992633929999\n ],\n [\n -107.61072300933778,\n 36.66992633929999\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wion, Andreas Paul 0000-0002-0701-2843","orcid":"https://orcid.org/0000-0002-0701-2843","contributorId":335166,"corporation":false,"usgs":true,"family":"Wion","given":"Andreas","email":"","middleInitial":"Paul","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Jens T. 0000-0002-2234-1960","orcid":"https://orcid.org/0000-0002-2234-1960","contributorId":289230,"corporation":false,"usgs":false,"family":"Stevens","given":"Jens T.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":906779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beeley, Kay","contributorId":340264,"corporation":false,"usgs":false,"family":"Beeley","given":"Kay","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":906780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oertel, Rebecca","contributorId":340265,"corporation":false,"usgs":false,"family":"Oertel","given":"Rebecca","email":"","affiliations":[{"id":81531,"text":"Fort Collins Science Center *retired","active":true,"usgs":false}],"preferred":false,"id":906781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allen, Craig D.","contributorId":289211,"corporation":false,"usgs":false,"family":"Allen","given":"Craig D.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":906783,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266352,"text":"70266352 - 2024 - Stability concepts in ecology","interactions":[],"lastModifiedDate":"2025-05-06T13:29:07.014175","indexId":"70266352","displayToPublicDate":"2024-07-20T08:26:10","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Stability concepts in ecology","docAbstract":"The term stability, as applied to ecological systems, whether populations, communities, or ecosystems, means the tendency either to stay either close to some initial state, or to stay within certain bounds, or to persist in the face of environmental disturbances or changes. Here, a historical overview of stability concepts in ecology is outlined and measures of stability are discussed and described mathematically, including local stability, engineering resilience, resistance, persistence, and structural stability. Examples of instabilities caused by both pulse and press disturbances are given.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference module in earth systems and environmental sciences-Encyclopedia of ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-443-21964-1.00008-2","usgsCitation":"DeAngelis, D.L., and Xu, L., 2024, Stability concepts in ecology, chap. of Reference module in earth systems and environmental sciences-Encyclopedia of ecology, HTML Document, https://doi.org/10.1016/B978-0-443-21964-1.00008-2.","productDescription":"HTML Document","ipdsId":"IP-162704","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":485438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":935750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Linhao","contributorId":221358,"corporation":false,"usgs":false,"family":"Xu","given":"Linhao","email":"","affiliations":[{"id":40353,"text":"Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key","active":true,"usgs":false}],"preferred":false,"id":935751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256109,"text":"70256109 - 2024 - Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","interactions":[],"lastModifiedDate":"2024-07-22T11:47:51.294971","indexId":"70256109","displayToPublicDate":"2024-07-20T06:43:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","docAbstract":"In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km2 scales under conditions without debris flows.
Vesicularity of individual pyroclasts from airfall tephra deposits is an important parameter that is commonly measured at basaltic volcanoes. Conventional methods used to determine pyroclast vesicularity on a large number of clasts has the potential to be time consuming, particularly when rapid analysis is required. Here we propose dynamic image analysis on two-dimensional (2D) projection shapes of crushed pyroclasts from tephra deposits as a new method to estimate vesicularity. This method relies on the influence of vesicles and uses grain morphology as a proxy for vesicle size and abundance. Pyroclasts from a variety of basaltic tephra deposits from the volcanoes of Mauna Loa and Kīlauea were analyzed. Vesicularities between 52–98% were measured via nitrogen-gas pycnometry. The same pyroclasts were then crushed and sieved, and their grain shapes measured using dynamic image analysis on a CAMSIZER®. This yields values for the mean sphericity, elongation, compactness, and Krumbein roundness of the grains. Our data show that grains become increasingly irregular with increasing vesicularity, with the degree of correlation between shape parameters and vesicularity depending on the size of measured grains. Shape irregularities in small grains (60–250 µm) are mostly area-based, with elongation being the best vesicularity indicator, whereas shape irregularities in large grains (250–700 µm) are mostly perimeter-based, with Krumbein roundness as the best vesicularity indicator. Using mean shape parameter values with all grain sizes included, grain elongation is the most well-correlated shape parameter with vesicularity, with the best fitted model explaining 76% of variation in the observations. Microscope images of thin sections of intact pyroclasts, as well as from crushed pyroclasts, were analyzed using CSDCorrections 1.6 software in ImageJ to find local vesicularity, vesicle size, grain size, grain elongation, and vesicle spatial distribution by stereological conversion. Observed correlation between grain shape and vesicularity can be explained by the local effect of vesicles on the shape of the solid structure in between those vesicles. Grain shape depends not only on vesicularity, but also on vesicle to grain size ratio and the spatial distribution of vesicles. The influence of vesicles on grain shape is best captured by grains with the size of the solid structure in between vesicles, which generally increases with decreasing vesicularity. Dynamic image analysis is a useful tool to quickly gauge vesicularity, which could be used in near-real-time during an eruption response. However, this method is best suited for highly vesicular (> 80%) basaltic pyroclasts from tephra deposits with few microlites and phenocrysts. Further research on crushing techniques, optimum grain size for shape measurements, and Krumbein roundness measurements for the grain size range of 250–700 µm might enable application of this method to lower vesicularity pyroclasts.
Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.
To explain the drivers of historical water use in the public water systems (PWSs) that serve populations in Providence, Rhode Island, and surrounding areas, and to forecast future water use, a machine-learning model (cubist regression) was developed by the U.S. Geological Survey in cooperation with Providence Water to model daily per capita rates of domestic, commercial, and industrial water use. The PWSs in this area form a connected network that sources water from the Scituate Reservoir in Rhode Island. The cubist regression model was trained and tested on daily per capita rates for three categories of water use (domestic, commercial, and industrial) that were developed from quarterly water sales data and U.S. Census Bureau population estimates within each PWS service area from January 2005 through December 2021. The model was then used to make forecasts of future water use under varying scenarios of climate change, population growth, and economic growth for the years 2030 and 2040.
The resulting daily per capita rates, which were modeled from the historical data, had an r2 value of 0.94 and root mean square error of 6.7 gallons per capita daily. Results of the model were used to estimate total water use (the product of daily per capita rates and population) for all public water systems over the historical study period. Daily per capita rates in the study area decreased from 2005 to 2021, while population increased during that same period. “Category of water use” was the variable with the greatest explanatory power for modeling daily per capita rates. Overall, both daily per capita rates and total water use were projected to decrease in 2030 and 2040, in comparison to historical values from 2005 to 2021. Daily per capita rates and total water use were forecasted to decrease as economic growth rates increase. Daily per capita rates were expected to decrease as population growth rates increase; however, total water use was less sensitive to population growth rates than daily per capita rates. Effects of climate change were minimal over the 2030 and 2040 forecasting horizon for the scenarios tested.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245052","collaboration":"Prepared in cooperation with Providence Water","usgsCitation":"Chamberlin, C.A., 2024, A predictive analysis of water use for Providence, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2024–5052, 36 p., https://doi.org/10.3133/sir20245052.","productDescription":"Report: viii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152679","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499474,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117188.htm","linkFileType":{"id":5,"text":"html"}},{"id":431062,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94XIQ7W","text":"USGS data release","linkHelpText":"Model archive, input data, modeled estimates of water use 2005-2021, and forecasts of water use in 2030 and 2040 in Providence, Rhode Island"},{"id":431061,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5052 XML"},{"id":431060,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5052/images/"},{"id":431059,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245052/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5052 HTML"},{"id":431058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.pdf","text":"Report","size":"4.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5052 PDF"},{"id":431057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5052/coverthb.jpg"}],"country":"United States","state":"Rhode Island","city":"Providence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.6320030327788,\n 41.56235835697041\n ],\n [\n -71.17676197086665,\n 41.56235835697041\n ],\n [\n -71.17676197086665,\n 42.025783641742635\n ],\n [\n -71.6320030327788,\n 42.025783641742635\n ],\n [\n -71.6320030327788,\n 41.56235835697041\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
1. This report summaries field surveys conducted during July 2023 through April 2024 to control introduced American bullfrogs (Lithobates [Rana] catesbeianus; hereafter bullfrog) and support Chiricahua leopard frog (Lithobates [Rana] chiricahuensis) conservation in southeast Arizona. We conducted 394 field surveys across 141 sites in Chiricahua leopard frog Recovery Units 1 and 2 during this survey period.
2. During these surveys, we detected bullfrogs at 36 sites and removed a total of 452 bullfrogs. Bullfrog reproduction was only observed during this survey period at sites on private property where we did not have landowner permission to remove bullfrogs. We found bullfrogs and Chiricahua leopard frogs co-occurring at only a single site (Chulo Tank). Only a single bullfrog was present at this site, and it was removed.
3. Within the Cobre Ridge/Recovery Unit 1 region, we detected Chiricahua leopard frogs at 15 sites, with reproductive activity confirmed at two sites. We performed or assisted with Chiricahua leopard frog translocations at five sites. We detected Chiricahua leopard frog overwinter survival at two sites in this region although moribund and dead frogs were observed at one of these two sites.
4. We did not detect Chiricahua leopard frog within our Canelo Hills Buffer Zone or stock tanks along the foothills of the San Rafael Valley. We detected Chiricahua leopard frogs at four sites on the Appleton-Whittell Research Ranch with reproduction documented at two sites. We also removed two adult bullfrogs from a single site.
5. We detected Chiricahua leopard frogs at 17 sites on the Las Cienegas National Conservation Area (LCNCA). While Chiricahua leopard frog reproduction was not confirmed during July–November of 2023, as of 24 April 2025 we detected Chiricahua leopard frog reproduction at three sites. We did detect a moribund Chiricahua leopard frog at Lower Empire Gulch in March 2024. No bullfrogs were detected on the LCNCA and a single adult bullfrog was removed from one site in the Elgin Buffer Zone.
6. We began bullfrog removal efforts at a new eradication site along the Babocomari River on the Babacomari Ranch. This planned four-year eradication project will remove a significant non-urban bullfrog source population that threatens existing Chiricahua leopard frog metapopulations.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css82950145","usgsCitation":"Bauder, J.M., and Chris L. Prewitt, 2024, Control of introduced American bullfrogs and support of Chiricahua leopard frog conservation in southeast Arizona: Cooperator Science Series CSS-156-2024, ii, 23 p., https://doi.org/10.3996/css82950145.","productDescription":"ii, 23 p.","ipdsId":"IP-166478","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":494896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.50920319467511,\n 32.17286359287779\n ],\n [\n -111.50920319467511,\n 31.343181994125658\n ],\n [\n -110.12808318858993,\n 31.343181994125658\n ],\n [\n -110.12808318858993,\n 32.17286359287779\n ],\n [\n -111.50920319467511,\n 32.17286359287779\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":947208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chris L. Prewitt","contributorId":360556,"corporation":false,"usgs":false,"family":"Chris L. Prewitt","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":947209,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70258153,"text":"70258153 - 2024 - Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends","interactions":[],"lastModifiedDate":"2024-11-05T15:40:27.353457","indexId":"70258153","displayToPublicDate":"2024-07-19T09:30:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends","docAbstract":"Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false-positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white-nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population-wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance-based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false-negative and false-positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1617","usgsCitation":"Udell, B.J., Straw, B., Loeb, S.C., Irvine, K., Thogmartin, W.E., Lausen, C., Reichard, J.D., Coleman, J.T., Cryan, P.M., Frick, W.F., and Reichert, B., 2024, Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends: Ecological Monographs, v. 94, no. 4, e1617, 25 p.; Data Release, https://doi.org/10.1002/ecm.1617.","productDescription":"e1617, 25 p.; Data Release","ipdsId":"IP-153066","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":434925,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R3W0EZ","text":"USGS data release","linkHelpText":"Ecosystems-nabat-FPabund: software for fitting false-positive N-mixture models using NABat mobile acoustic data (version 1.0.0)"},{"id":439265,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecm.1617","text":"Publisher Index Page"},{"id":433498,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Udell, Bradley James 0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":271174,"corporation":false,"usgs":true,"family":"Udell","given":"Bradley","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Straw, Bethany R. 0000-0001-9086-4600","orcid":"https://orcid.org/0000-0001-9086-4600","contributorId":271020,"corporation":false,"usgs":true,"family":"Straw","given":"Bethany","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loeb, Susan C. 0000-0002-9264-3614","orcid":"https://orcid.org/0000-0002-9264-3614","contributorId":337070,"corporation":false,"usgs":false,"family":"Loeb","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":912387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":221555,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":912389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lausen, Cori","contributorId":343919,"corporation":false,"usgs":false,"family":"Lausen","given":"Cori","affiliations":[{"id":36893,"text":"Wildlife Conservation Society Canada","active":true,"usgs":false}],"preferred":false,"id":912390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reichard, Jonathan D. 0000-0002-4792-2868","orcid":"https://orcid.org/0000-0002-4792-2868","contributorId":337073,"corporation":false,"usgs":false,"family":"Reichard","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":912391,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coleman, Jeremy T.H. 0000-0002-2762-947X","orcid":"https://orcid.org/0000-0002-2762-947X","contributorId":239956,"corporation":false,"usgs":false,"family":"Coleman","given":"Jeremy","email":"","middleInitial":"T.H.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":912392,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912393,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Frick, Winifred F. 0000-0002-9469-1839","orcid":"https://orcid.org/0000-0002-9469-1839","contributorId":337076,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred","email":"","middleInitial":"F.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":912394,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912395,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70257688,"text":"70257688 - 2024 - The effects of flow extremes on native and non-native stream fishes in Puerto Rico","interactions":[],"lastModifiedDate":"2024-08-23T14:23:44.291409","indexId":"70257688","displayToPublicDate":"2024-07-19T09:20:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"The effects of flow extremes on native and non-native stream fishes in Puerto Rico","docAbstract":"The Chesapeake Bay Partnership is implementing conservation practices (CPs) throughout the Chesapeake Bay watershed to reduce nutrient and sediment delivery to the Bay. This study intends to provide an integrated and detailed understanding of how local streams respond to these CP-driven management efforts.
Key issue: To what extent do CPs positively affect the health of local streams in the nontidal watershed (cobenefits)?
Critical unknown: How do CPs change water quality and the stressors that affect stream aquatic life? Which CPs improve stream health more effectively?
Critical knowledge to be delivered to stakeholders includes—
Chesapeake Bay Activities
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
This is the 24th supplement since publication of the 7th edition of the Check-list of North American Birds (American Ornithologists’ Union [AOU] 1998). It summarizes decisions made between April 25, 2023 and April 30, 2024 by the American Ornithological Society’s (formerly American Ornithologists’ Union) Committee on Classification and Nomenclature—North and Middle America. The Committee has continued to operate in the manner outlined in the 42nd Supplement (Banks et al. 2000). During the past year Oscar Johnson joined the committee, and J. V. Remsen, Jr. and Kevin Winker left the committee.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithology/ukae019","usgsCitation":"Chesser, R., Billerman, S., Burns, K., Cicero, C., Dunn, J.L., Hernandez-Banos, B., Jimenez, R.A., Johnson, O.W., Kratter, A.W., Mason, N., Ramussen, P., and Remsen, J., 2024, Sixty-fifth supplement to the American Ornithological Society’s check-list of North American birds: Ornithology, v. 141, no. 3, ukae019, 21 p., https://doi.org/10.1093/ornithology/ukae019.","productDescription":"ukae019, 21 p.","ipdsId":"IP-166997","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":433691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Chesser, R. Terry 0000-0003-4389-7092","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":87669,"corporation":false,"usgs":true,"family":"Chesser","given":"R. Terry","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":912863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Billerman, Shawn","contributorId":344111,"corporation":false,"usgs":false,"family":"Billerman","given":"Shawn","email":"","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":912864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Kevin J","contributorId":145564,"corporation":false,"usgs":false,"family":"Burns","given":"Kevin J","affiliations":[{"id":5088,"text":"SDSU","active":true,"usgs":false}],"preferred":false,"id":912865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cicero, Carla","contributorId":145565,"corporation":false,"usgs":false,"family":"Cicero","given":"Carla","email":"","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":912866,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunn, Jon L.","contributorId":145566,"corporation":false,"usgs":false,"family":"Dunn","given":"Jon","email":"","middleInitial":"L.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":912867,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hernandez-Banos, Blanca","contributorId":344114,"corporation":false,"usgs":false,"family":"Hernandez-Banos","given":"Blanca","email":"","affiliations":[{"id":82289,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":912868,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jimenez, Rosa Alicia","contributorId":344115,"corporation":false,"usgs":false,"family":"Jimenez","given":"Rosa","email":"","middleInitial":"Alicia","affiliations":[{"id":82292,"text":"Universidad de San Carlos de Guatemala","active":true,"usgs":false}],"preferred":false,"id":912869,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Oscar W.","contributorId":224103,"corporation":false,"usgs":false,"family":"Johnson","given":"Oscar","email":"","middleInitial":"W.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":912870,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kratter, Andrew W.","contributorId":145567,"corporation":false,"usgs":false,"family":"Kratter","given":"Andrew","email":"","middleInitial":"W.","affiliations":[{"id":16151,"text":"Univ Fla","active":true,"usgs":false}],"preferred":false,"id":912871,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mason, Nicholas","contributorId":344116,"corporation":false,"usgs":false,"family":"Mason","given":"Nicholas","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":912872,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ramussen, Pamela","contributorId":344117,"corporation":false,"usgs":false,"family":"Ramussen","given":"Pamela","email":"","affiliations":[{"id":82293,"text":"Cornell Lab of Ornithology; Michigan State University","active":true,"usgs":false}],"preferred":false,"id":912873,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Remsen, J.V. Jr.","contributorId":344118,"corporation":false,"usgs":false,"family":"Remsen","given":"J.V.","suffix":"Jr.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":912874,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70257566,"text":"70257566 - 2024 - Tire-derived contaminants 6PPD and 6PPD-Q: Analysis, sample handling, and reconnaissance of United States stream exposures","interactions":[],"lastModifiedDate":"2024-09-06T13:17:17.948487","indexId":"70257566","displayToPublicDate":"2024-07-19T08:11:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1226,"text":"Chemosphere","active":true,"publicationSubtype":{"id":10}},"title":"Tire-derived contaminants 6PPD and 6PPD-Q: Analysis, sample handling, and reconnaissance of United States stream exposures","docAbstract":"The environmental ubiquity of tire and road wear particles (TRWP) underscores the need to understand the occurrence, persistence, and environmental effects of tire-related chemicals in aquatic ecosystems. One such chemical is 6PPD-quinone (6PPD-Q), a transformation product of the tire antioxidant 6PPD. In urban stormwater runoff 6PPD-Q can exceed acute toxicity thresholds for several salmonid species and is being implicated in significant coho salmon losses in the Pacific Northwest. There is a critical need to understand the prevalence of 6PPD-Q across watersheds to identify habitats heavily affected by TRWPs. We conducted a reconnaissance of 6PPD and 6PPD-Q in surface waters across the United States from sites (N = 94) with varying land use (urban, agricultural, and forested) and streamflow to better understand stream exposures. A rapid, low-volume direct-inject, liquid chromatography mass spectrometry method was developed for the quantitation of 6PPD-Q and screening for 6PPD. Laboratory holding times, bottle material, headspace, and filter materials were investigated to inform best practices for 6PPD-Q sampling and analysis. Glass bottles with PTFE-lined caps minimized sorption and borosilicate glass fiber filters provided the highest recovery. 6PPD-Q was stable for at least 5 months in pure laboratory solutions and for 75 days at 5 °C with minimal headspace in the investigated surface water and stormwaters. Results also indicated samples can be frozen to extend holding times. 6PPD was not detected in any of the 526 analyzed samples and there were no detections of 6PPD-Q at agricultural or forested sites. 6PPD-Q was frequently detected in stormwater (57%, N = 90) and from urban impacted sites (45%, N = 276) with concentrations ranging from 0.002 to 0.29 μg/L. The highest concentrations, above the lethal level for coho salmon, occurred during stormwater runoff events. This highlights the importance of capturing episodic runoff events in urban areas near ecologically relevant habitat or nursery grounds for sensitive species.
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As a result, saltwater intrusion affects the productivity of Louisiana’s coastal bald cypress forests. To study the long-term effects of hydrology and salinity on the health of these systems, we fitted dendrometer bands on selected trees to record basal area increment as a measure of growth in permanent forest productivity plots established within six bald cypress stands. Three stands were in freshwater sites with low salinity rooting zone groundwater (0.1–1.3 ppt), while the other three had higher salinity rooting zone groundwater (0.2–4.9 ppt). Water level was logged continuously, and salinity was measured monthly to quarterly on the surface and in groundwater wells. Higher groundwater salinity levels were related to decreased bald cypress radial growth, while higher freshwater flooding increased radial growth. With these data, coastal managers can model rates of bald cypress forest change as a function of salinity and flooding.
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