We develop and apply a dynamic economic simulation model to analyze the multi-regional impacts of, and mechanisms of recovery from, a major disaster, the HayWired scenario — a hypothetical Magnitude 7.0 earthquake affecting California’s San Francisco Bay Area. The model integrates loss pathways: capital stock damage, labor supply shocks due to short-term population displacement and longer-run out-migration from damaged areas, and the exacerbating effects of damage to transportation infrastructure capital, as well as various aspects of static and dynamic economic resilience. With input substitution-based static inherent resilience and dynamic resilience in the form of optimal intertemporal and spatial investment allocation, gross output losses range from 0.5 percent to 6 percent across regions, and welfare losses are 0.4 percent statewide but can be ten times as large in hardest-hit areas. Large-scale reconstruction investment is supported by substantial interregional transfers of resources through intra-state trade. Increased output via firms engaging in the key adaptive resilience tactic of production recapture can alleviate a substantial fraction of losses—but only if upstream and downstream barriers to recovery can be lowered quickly.
","language":"English","publisher":"Sage Journals","doi":"10.1177/01600176231202451","usgsCitation":"Sue Wing, I., Rose, A.Z., Wei, D., and Wein, A., 2024, The long shadow of a major disaster: Modeled dynamic impacts of the hypothetical HayWired earthquake on California’s economy: International Regional Science Review, v. 47, no. 5-6, p. 655-696, https://doi.org/10.1177/01600176231202451.","productDescription":"42 p.","startPage":"655","endPage":"696","ipdsId":"IP-138450","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":421891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124,\n 39\n ],\n [\n -124,\n 36\n ],\n [\n -120,\n 36\n ],\n [\n -120,\n 39\n ],\n [\n -124,\n 39\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"5-6","noUsgsAuthors":false,"publicationDate":"2023-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Sue Wing, Ian","contributorId":304246,"corporation":false,"usgs":false,"family":"Sue Wing","given":"Ian","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":886077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Adam Z","contributorId":330862,"corporation":false,"usgs":false,"family":"Rose","given":"Adam","email":"","middleInitial":"Z","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":886078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wei, Dan","contributorId":248873,"corporation":false,"usgs":false,"family":"Wei","given":"Dan","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":886079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":886080,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250169,"text":"70250169 - 2024 - Machine learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India","interactions":[],"lastModifiedDate":"2024-01-04T14:57:18.934706","indexId":"70250169","displayToPublicDate":"2023-10-04T08:34:25","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17094,"text":"Journal Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India","docAbstract":"Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. The results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.
","language":"English","publisher":"Society of Exploration Geophysicists and American Association of Petroleum Geologists","doi":"10.1190/int-2023-0056.1","usgsCitation":"Chong, L., Collett, T., Creason, C.G., Seol, Y., and Myshakin, E., 2024, Machine learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India: Journal Interpretation, v. 12, p. T63-T75, https://doi.org/10.1190/int-2023-0056.1.","productDescription":"13 p.","startPage":"T63","endPage":"T75","ipdsId":"IP-151255","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467055,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2059620","text":"External Repository"},{"id":422953,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 81,\n 16.75\n ],\n [\n 81,\n 15.4\n ],\n [\n 83.25,\n 15.4\n ],\n [\n 83.25,\n 16.75\n ],\n [\n 81,\n 16.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2023-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Chong, Leebyn","contributorId":331733,"corporation":false,"usgs":false,"family":"Chong","given":"Leebyn","email":"","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":888643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":888644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Creason, C. Gabriel","contributorId":331734,"corporation":false,"usgs":false,"family":"Creason","given":"C.","email":"","middleInitial":"Gabriel","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":888645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seol, Yongkoo","contributorId":195139,"corporation":false,"usgs":false,"family":"Seol","given":"Yongkoo","email":"","affiliations":[],"preferred":false,"id":888646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myshakin, E.M.","contributorId":229601,"corporation":false,"usgs":false,"family":"Myshakin","given":"E.M.","email":"","affiliations":[{"id":41691,"text":"Office of Research and Development, National Energy Technology Laboratory, Morgantown, WV, USA","active":true,"usgs":false}],"preferred":false,"id":888647,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249268,"text":"70249268 - 2024 - Spatial variation in density of American black bears in northern Yellowstone National Park","interactions":[],"lastModifiedDate":"2023-12-04T17:20:03.177492","indexId":"70249268","displayToPublicDate":"2023-10-03T07:29:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variation in density of American black bears in northern Yellowstone National Park","docAbstract":"The quality and availability of resources are known to influence spatial patterns of animal density. In Yellowstone National Park, relationships between the availability of resources and the distribution of grizzly bears (Ursus arctos) have been explored but have yet to be examined in American black bears (Ursus americanus). We conducted non-invasive genetic sampling during 2017–2018 (mid-May to mid-July) and applied spatially explicit capture-recapture models to estimate density of black bears and examine associations with landscape features. In both years, density estimates were higher in forested vegetation communities, which provide food resources and thermal and security cover preferred by black bears, compared with non-forested areas. In 2017, density also varied by sex, with female densities being higher than males. Based on our estimates, the northern range of Yellowstone National Park supports one of the highest densities of black bears (20 black bears/100 km2) in the northern Rocky Mountains (6–12 black bears/100 km2 in other regions). Given these high densities, black bears could influence other wildlife populations more than previously thought, such as through displacement of sympatric predators from kills. Our study provides the first spatially explicit estimates of density for black bears within an ecosystem that contains the majority of North America's large mammal species. Our density estimates provide a baseline that can be used for future research and management decisions of black bears, including efforts to reduce human–bear conflicts.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.22497","usgsCitation":"Bowersock, N.R., Litt, A.R., Sawaya, M.A., Gunther, K.A., and van Manen, F.T., 2024, Spatial variation in density of American black bears in northern Yellowstone National Park: The Journal of Wildlife Management, v. 88, no. 1, e22497, 16 p., https://doi.org/10.1002/jwmg.22497.","productDescription":"e22497, 16 p.","ipdsId":"IP-152759","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":441146,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22497","text":"Publisher Index Page"},{"id":421535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.03494952723108,\n 43.72561860928255\n ],\n [\n -106.75486658878216,\n 43.72561860928255\n ],\n [\n -106.75486658878216,\n 45.330355263304455\n ],\n [\n -110.03494952723108,\n 45.330355263304455\n ],\n [\n -110.03494952723108,\n 43.72561860928255\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Bowersock, Nathaniel R.","contributorId":268804,"corporation":false,"usgs":false,"family":"Bowersock","given":"Nathaniel","email":"","middleInitial":"R.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":884948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Litt, Andrea R.","contributorId":208358,"corporation":false,"usgs":false,"family":"Litt","given":"Andrea","email":"","middleInitial":"R.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":884949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawaya, Michael A.","contributorId":330440,"corporation":false,"usgs":false,"family":"Sawaya","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":78897,"text":"Sinopah Wildlife Research Associates","active":true,"usgs":false}],"preferred":false,"id":884950,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gunther, Kerry A.","contributorId":84621,"corporation":false,"usgs":false,"family":"Gunther","given":"Kerry","email":"","middleInitial":"A.","affiliations":[{"id":5118,"text":"Yellowstone National Park, Yellowstone Center for Resources, Bear Management Office, P.O. Box 168, Yellowstone National Park, WY 82190","active":true,"usgs":false}],"preferred":false,"id":884951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":884952,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251209,"text":"70251209 - 2024 - The inevitability of large shallow craters on Callisto and Ganymede: Implications for crater depth-diameter trends","interactions":[],"lastModifiedDate":"2024-01-29T12:40:51.426987","indexId":"70251209","displayToPublicDate":"2023-10-02T06:35:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"The inevitability of large shallow craters on Callisto and Ganymede: Implications for crater depth-diameter trends","docAbstract":"Complex craters with diameters (D) ≥ 40 km on Callisto and Ganymede are shallower than would be expected from simply extrapolating the depth-diameter trend from smaller (D ≤ 40 km) craters. This unusual depth-diameter (d-D) trend, and associated changes in crater morphology, have been hypothesized to result from rheological transitions, including the existence of an ocean, within the moons' ice shell. Simulations of impact crater formation can reproduce the observed shallow depths but require heat fluxes roughly twice the maximum radiogenic flux to do so. Here we demonstrate that the d-D trends on Callisto and Ganymede can instead be explained as a direct consequence of viscous relaxation under radiogenic heating. We use numerical simulations of viscous relaxation to show that if craters form at the depth expected from an extrapolation of the complex crater d-D trend, they will evolve to the observed depths over timescales of 200 Myrs to 1 Gyrs. Large craters (e.g., D ≥ 80 km) younger than 200 Myrs, which would retain greater depths, should be relatively rare. If we instead assume that the craters formed at their observed depths, as proposed by previous impact modeling, they quickly become much shallower than observed. We find excellent agreement between observed crater depths on Ganymede and our simulated crater depths by assuming a pure-water ice composition and a diurnally averaged surface temperature of 120 K, but require either larger-grained or “dirty” ice with a modestly higher viscosity to match observations at Callisto, where the surface temperature is warmer (130 K). We favor the latter explanation because it is consistent with the existence of a dusty lag on Callisto's surface and the absence of a similar lag on Ganymede. Our results predict that, for a given crater diameter, post-relaxation crater depth should increase with increasing latitude, a hypothesis best tested on Callisto, whose relatively quiescent geologic history best preserves the signature of viscous relaxation under radiogenic heating.
Convergent margins play a fundamental role in the construction and modification of Earth's lithosphere and are characterized by poorly understood episodic processes that occur during the progression from subduction to terminal collision. On the northern margin of the active Arabia-Eurasia collision zone, the Greater Caucasus Mountains provide an opportunity to study a protracted convergent margin that spanned most of the Phanerozoic and culminated in Cenozoic continental collision. However, the main episodes of lithosphere formation and deformation along this margin remain enigmatic. Here, we use detrital zircon U–Pb geochronology from Paleozoic and Mesozoic (meta)sedimentary rocks in the Greater Caucasus, along with select zircon U–Pb and Hf isotopic data from coeval igneous rocks, to link key magmatic and depositional episodes along the Caucasus convergent margin. Devonian to Early Carboniferous rocks were deposited prior to Late Carboniferous accretion of the Greater Caucasus crystalline core onto the Laurussian margin. Permian to Triassic rocks document a period of northward subduction and forearc deposition south of a continental margin volcanic arc in the Northern Caucasus and Scythian Platform. Jurassic rocks record the opening of the Caucasus Basin as a back-arc rift during southward migration of the arc front into the Lesser Caucasus. Cretaceous rocks have few Jurassic-Cretaceous zircons, indicating a period of relative magmatic quiescence and minimal exhumation within this basin. Late Cenozoic closure of the Caucasus Basin juxtaposed the Lesser Caucasus arc to the south against the crystalline core of the Greater Caucasus to the north and led to the formation of a hypothesized terminal suture. We expect this suture to be within ~20 km of the southern range front of the Greater Caucasus because all analysed rocks to the north exhibit a provenance affinity with the crystalline core of the Greater Caucasus.
Insights from conservation genomics have dramatically improved recovery plans for numerous endangered species. However, most taxa have yet to benefit from the full application of genomic technologies. The mountain yellow-legged frog species complex, Rana muscosa and Rana sierrae, inhabits the Sierra Nevada mountains and Transverse/Peninsular Ranges of California and Nevada. Both species have declined precipitously throughout their historical distributions. Conservation management plans outline extensive ongoing recovery efforts but are still based on the genetic structure determined primarily using a single mitochondrial sequence. Our study used two different sequencing strategies – amplicon sequencing and exome capture – to refine our understanding of the population genetics of these imperiled amphibians. We used buccal swabs, museum tissue samples, and archived skin swabs to genotype frog populations across their range. Using the amplicon sequencing and exome capture datasets separately and combined, we document five major genetic clusters. Notably, we found evidence supporting previous species boundaries within Kings Canyon National Park with some exceptions at individual sites. Though we see evidence of genetic clustering, especially in the R. muscosa clade, we also found evidence of some admixture across cluster boundaries in the R. sierrae clade, suggesting a stepping-stone model of population structure. We also find that the southern R. muscosa cluster had large runs of homozygosity and the lowest overall heterozygosity of any of the clusters, consistent with previous reports of marked declines in this area. Overall, our results clarify management unit designations across the range of an endangered species and highlight the importance of sampling the entire range of a species, even when collecting genome-scale data.
Five metal mixture dose–response models were used to predict the toxicity of porewater to young sturgeon at areas of interest in the Upper Columbia River (WA, USA/BC, Canada) and to evaluate these models as tools for risk assessments. Dose components of metal mixture models included exposure to free metal ion activities or metal accumulation by biotic ligands or humic acid, and links of dose to response used logistic equations, independent joint action equations, or additive toxicity functions. Laboratory bioassay studies of single metal exposures to juvenile sturgeon, porewater collected in situ in the fast-flowing Upper Columbia River, and metal mixture models were used to evaluate toxicity. The five metal mixture models were very similar in their predictions of adverse response of juvenile sturgeon and in identifying copper (Cu) as the metal responsible for the most toxic conditions. Although the modes of toxic action and the 20% effective concentration values were different among the dose models, predictions of adverse response were consistent among models because all doses were tied to the same biological responses. All models indicated that 56% ± 5% of 122 porewater samples were predicted to have <20% adverse response, 25% ± 5% of samples were predicted to have 20% to 80% adverse response, and 20% ± 4% were predicted to have >80% adverse response in juvenile sturgeon. The approach of combining bioassay toxicity data, compositions of field porewater, and metal mixture models to predict lack of growth and survival of aquatic organisms due to metal toxicity is an important tool that can be integrated with other information (e.g., survey studies of organism populations, life cycle and behavior characteristics, sediment geochemistry, and food sources) to assess risks to aquatic organisms in metal-enriched ecosystems. Environ Toxicol Chem 2023;00:1–12. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.
Local biodiversity monitoring is important to assess the effects of global change, but also to evaluate the performance of landscape and wildlife protection, since large-scale assessments may buffer local fluctuations, rare species tend to be underrepresented, and management actions are usually implemented on local scales. We estimated population trends of 58 bird species using open-population N-mixture models based on count data in two localities in southeastern Spain, which have been collected according to a citizen science monitoring program (SACRE, Monitoring Common Breeding Birds in Spain) over 21 and 15 years, respectively. We performed different abundance models for each species and study area, accounting for imperfect detection of individuals in replicated counts. After selecting the best models for each species and study area, empirical Bayes methods were used for estimating abundances, which allowed us to calculate population growth rates (λ) and finally population trends. We also compared the two local population trends and related them with national and European trends, and species functional traits (phenological status, dietary, and habitat specialization characteristics). Our results showed increasing trends for most species, but a weak correlation between populations of the same species from both study areas. In general, local population trends were consistent with the trends observed at national and continental scales, although contrasting patterns exist for several species, mainly with increasing local trends and decreasing Spanish and European trends. Moreover, we found no evidence of a relationship between population trends and species traits. We conclude that using open-population N-mixture models is an appropriate method to estimate population trends, and that citizen science-based monitoring schemes can be a source of data for such analyses. This modeling approach can help managers to assess the effectiveness of their actions at the local level in the context of global change.
Survival estimates are critical components of avian ecology. In well-intentioned efforts to maximize the utility of one's research, survival estimates often derive from data that were not originally collected for survival assessments, and such post hoc analyses may include unintentional biases. We estimated the survival of Whimbrels captured and marked at two breeding sites in Alaska using divergent data streams that in isolation were subject to methodological biases. Although both capture sites were chosen to study the migration ecology of Alaska-breeding Whimbrels, maximizing the conservation value of the data we collected was obviously desirable. We used multi-year telemetry information to infer survival from one site (Colville River) and mark-resight techniques to estimate survival from a second site (Kanuti River). At the Colville River, we could not feasibly include a control group of birds to assess potential survival effects of externally mounted transmitters, while at Kanuti River we were unable to accurately account for potential emigration events because we used resightings alone. We integrated these datasets in a Bayesian hierarchical framework, an approach that permitted insights across sites that moderated methodological biases within sites. Using telemetry enabled us to detect permanent emigration events from breeding sites in two of ten birds; results that informed estimates for birds without tracking devices. These datasets yielded point estimates of true survival of Whimbrels from Colville River equipped with solar-powered satellite transmitters that were higher (0.83) than true survival estimates of Whimbrels from Kanuti River marked with leg flags alone (0.74) or equipped with surgically implanted satellite transmitters (0.50), but the 95% credible intervals on these estimates overlapped across groups. For species like Whimbrels that are difficult and costly to study, combining information from disparate data streams allowed us to derive novel demographic estimates, an approach with clear application to other similar studies.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.13273","usgsCitation":"Ruthrauff, D.R., Harwood, C.M., Tibbitts, T.L., and Patil, V.P., 2024, Disparate data streams together yield novel survival estimates of Alaska-breeding Whimbrels: Ibis, v. 166, no. 2, p. 622-632, https://doi.org/10.1111/ibi.13273.","productDescription":"11 p.","startPage":"622","endPage":"632","ipdsId":"IP-141546","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":498851,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1111/ibi.13273","text":"Publisher Index Page"},{"id":420836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Colville River, Kanuti River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.03574093661132,\n 68.75774490548673\n ],\n [\n -160.03574093661132,\n 65.24300636559764\n ],\n [\n -145.19869372665494,\n 65.24300636559764\n ],\n [\n -145.19869372665494,\n 68.75774490548673\n ],\n [\n -160.03574093661132,\n 68.75774490548673\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"166","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruthrauff, Daniel R. 0000-0003-1355-9156 druthrauff@usgs.gov","orcid":"https://orcid.org/0000-0003-1355-9156","contributorId":4181,"corporation":false,"usgs":true,"family":"Ruthrauff","given":"Daniel","email":"druthrauff@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":883058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harwood, Christopher M.","contributorId":260398,"corporation":false,"usgs":false,"family":"Harwood","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":52582,"text":"US Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":883059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592 ltibbitts@usgs.gov","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":102185,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T.","email":"ltibbitts@usgs.gov","middleInitial":"Lee","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":883060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":883061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248745,"text":"70248745 - 2024 - Hepatotoxic response of perfluorooctane sulfonamide (PFOSA) in early life stage zebrafish (Danio rerio) is greater than perfluorooctane sulfonate (PFOS)","interactions":[],"lastModifiedDate":"2023-09-19T11:52:38.073821","indexId":"70248745","displayToPublicDate":"2023-09-14T06:49:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Hepatotoxic response of perfluorooctane sulfonamide (PFOSA) in early life stage zebrafish (Danio rerio) is greater than perfluorooctane sulfonate (PFOS)","docAbstract":"Perfluorooctane sulfonamide (PFOSA), a typical perfluorooctane sulfonate precursor (PreFOS), has been detected in the aquatic environment globally. However, the effects of PFOSA at levels measured in the environment have not been well characterized in aquatic organisms. In this study, we evaluated the transcriptional, biochemical, histopathological, and morphological effects of PFOSA to characterize the underlying mechanisms of toxicity by using a universal model in aquatic ecotoxicology, zebrafish (Danio rerio). Transcriptional changes in PFOSA-exposed zebrafish predicted hepatic fibrosis and associated immune function. Subsequent, sublethal impacts were observed, which included significant alterations in liver-specific protein levels, increased immune cell numbers, and liver pathological structural damage. In addition, we compared the effects caused by PFOSA and perfluorooctane sulfonate (PFOS) at the same exposure concentration and found a greater hepatotoxic effect of PFOSA relative to PFOS, indicating that the adverse impacts of PFOSA may be more severe. This was the first study to comparatively explore the hepatotoxic response of PFOSA and PFOS in aquatic organisms, which can be used for ecological risk assessments of PreFOS compounds.
Coastal managers are facing imminent decisions regarding the fate of coastal wetlands, given ongoing threats to their persistence. There is a need for objective methods to identify which wetland parcels are candidates for restoration, monitoring, protection, or acquisition due to limited resources and restoration techniques. Here, we describe a new spatially comprehensive data set for Chesapeake Bay salt marshes, which includes the unvegetated-vegetated marsh ratio, elevation metrics, and sediment-based lifespan. Spatial aggregation across regions of the Bay shows a trend of increasing deterioration with proximity to the seaward boundary, coherent with conceptual models of coastal landscape response to sea-level rise. On a smaller scale, the signature of deterioration is highly variable within subsections of the Bay: fringing, peninsular, and tidal river marsh complexes each exhibit different spatial patterns with regards to proximity to the seaward edge. We then demonstrate objective methods to use these data for mapping potential management options on to the landscape, and then provide methods to estimate lifespan and potential changes in lifespan in response to restoration actions as well as future sea level rise. We account for actions that aim to increase sediment inventories, revegetate barren areas, restore hydrology, and facilitate salt marsh migration into upland areas. The distillation of robust geospatial data into simple decision-making metrics, as well as the use of those metrics to map decisions on the landscape, represents an important step towards science-based coastal management.
Umbrella species and other surrogate species approaches to conservation provide an appealing framework to extend the reach of conservation efforts beyond single species. For the umbrella species concept to be effective, populations of multiple species of concern must persist in areas protected on behalf of the umbrella species. Most assessments of the concept, however, focus exclusively on geographic overlap among umbrella and background species, and not measures that affect population persistence (e.g. habitat quality or fitness). We quantified the congruence between the habitat preferences and nesting success of a high-profile umbrella species (greater sage-grouse, Centrocercus urophasianus, hereafter ‘sage-grouse’), and three sympatric species of declining songbirds (Brewer's sparrow Spizella breweri, sage thrasher Oreoscoptes montanus and vesper sparrow Pooecetes gramineus) in central Wyoming, USA during 2012–2013. We used machine-learning methods to create data-driven predictions of sage-grouse nest-site selection and nest survival probabilities by modeling field-collected sage-grouse data relative to habitat attributes. We then used field-collected songbird data to assess whether high-quality sites for songbirds aligned with those of sage-grouse. Nest sites selected by songbirds did not coincide with sage-grouse nesting preferences, with the exception that Brewer's sparrows preferred similar nest sites to sage-grouse in 2012. Moreover, the areas that produced higher rates of songbird nest survival were unrelated to those for sage-grouse. Our findings suggest that management actions at local scales that prioritize sage-grouse nesting habitat will not necessarily enhance the reproductive success of sagebrush-associated songbirds. Measures implemented to conserve sage-grouse and other purported umbrella species at broad spatial scales likely overlap the distribution of many species, however, broad-scale overlap may not translate to fine-scale conservation benefit beyond the umbrella species itself. The maintenance of microhabitat heterogeneity important for a diversity of species of concern will be critical for a more holistic application of the umbrella species concept.
Ecological restoration is considered an essential activity as we attempt to repair anthropogenic degradation. Yet, resources are limited and it is important that efforts focus on activities that are effective and yield successful restoration. Structured decision making (SDM) is an organized framework that is designed to incorporate differing values across stakeholders and evaluate alternatives. The SDM framework typically consists of six steps: define the decision problem, define objectives and evaluation criteria, develop alternatives, estimate consequences, evaluate trade-offs, and decide, implement, and monitor. Here, we posit that SDM is well suited for ecological restoration, yet remains underused. Specifically, tools such as stakeholder surveys, conceptual modeling, and multi-criteria decision analysis are notably useful in ecological restoration and can be applied under the SDM framework to ensure robust and transparent decision making. We illustrate the application of SDM to ecological restoration with case studies that used SDM alongside ecosystem service assessments, for species-as-risk management, and to assess action desirability across large and diverse stakeholder groups. Finally, we demonstrate how SDM is equipped to handle many of the challenges associated with ecological restoration by identifying commonalities. We contend that increased use of SDM for ecological restoration by environmental managers has the potential to yield wise use of limited resources and more effective restoration outcomes.
The Prairie Pothole Region (PPR) is the primary breeding ground for many species of North American waterfowl. The PPR was historically dominated by mixed and tallgrass prairies interspersed with wetlands, but >70% of the native grassland area has been lost due to widespread conversion to croplands. Cover cropping is a reemerging farming technique that may provide suitable nesting cover for grassland nesting waterfowl in active croplands, but waterfowl nest survival in fall cover-cropped fields has not been evaluated. We studied use (nest abundance and density) and nest survival of breeding waterfowl in fall-seeded cover crops and perennial cover during 2018 and 2019. We searched 2,094 ha of cover crops and 1,604 ha of perennial cover and found 123 and 304 duck nests, respectively, in each cover type. Estimated nest success (34-day interval) was 3.7% and 16.6% in cover crops during 2018 and 2019, respectively, versus 22.1% in 2018 and 24.9% in 2019 in perennial cover, with increased success of cover-crop fields in 2019 resulting from precipitation that prevented most fields from being planted to row crops. In a model that included effects of planting, daily nest survival in perennial cover was 0.944 (SD = 0.026) in 2018 and 0.960 (SD = 0.019) in 2019. Estimated daily nest survival was 0.912 (SD = 0.040) in 2018 and 0.960 (SD = 0.019) in 2019 during intervals when planting did not occur, but was only 0.417 (SD = 0.124) in 2018 and 0.612 (SD = 0.117) in 2019 on the day that planting occurred. Estimated nest densities in 2018 and 2019, adjusted for nests that failed prior to discovery, were 5.1 (SE = 1.1) and 11.0 (SE = 3.1) nests 100-ha−1 in perennial cover, but only 2.1 (SE = 0.8) and 2.6 (SE = 0.7) in cover crops, respectively. Based on observed nest initiation and planting dates, about 70% of duck nests in cover crops would experience planting events in a typical growing season. Our results suggest that under current management techniques, fall-seeded cover crops offer poor nesting habitat for waterfowl; however, the important benefits cover crops provide to soil health, water quality, and other ecosystem services remain.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1484","usgsCitation":"Gallman, C.W., Arnold, T., Michel, E.S., and Stafford, J.D., 2024, Evaluation of fall-seeded cover crops for grassland nesting waterfowl in eastern South Dakota: Wildlife Society Bulletin, https://doi.org/10.1002/wsb.1484.","ipdsId":"IP-138726","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1484","text":"Publisher Index Page"},{"id":432037,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.62300789778979,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.70452095814687\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.06563969108848,\n 45.92537600884873\n ],\n [\n -101.06563969108848,\n 42.51351369305877\n ],\n [\n -96.13503097856136,\n 42.51351369305877\n ],\n [\n -96.13503097856136,\n 45.92537600884873\n ],\n [\n -101.06563969108848,\n 45.92537600884873\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2023-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallman, Charles W.","contributorId":340511,"corporation":false,"usgs":false,"family":"Gallman","given":"Charles","email":"","middleInitial":"W.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arnold, Todd W.","contributorId":340512,"corporation":false,"usgs":false,"family":"Arnold","given":"Todd W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":907321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michel, Eric S.","contributorId":204829,"corporation":false,"usgs":false,"family":"Michel","given":"Eric","email":"","middleInitial":"S.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stafford, Joshua D. 0000-0001-7590-8708 jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907323,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248391,"text":"70248391 - 2024 - Nonlinear patterns of surface elevation change in coastal wetlands: The value of generalized additive models for quantifying rates of change","interactions":[],"lastModifiedDate":"2024-08-26T14:08:55.764687","indexId":"70248391","displayToPublicDate":"2023-09-04T07:07:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Nonlinear patterns of surface elevation change in coastal wetlands: The value of generalized additive models for quantifying rates of change","docAbstract":"In the face of accelerating climate change and rising sea levels, quantifying surface elevation change dynamics in coastal wetlands can help to develop a more complete understanding of the implications of sea-level rise on coastal wetland stability. The surface elevation table-marker horizon (SET-MH) approach has been widely used to quantify and characterize surface elevation change dynamics in coastal marshes and mangrove forests. Whereas past studies that utilized the SET-MH approach have most often quantified rates of surface elevation change using simple linear regression analyses, several recent studies have shown that elevation patterns can include a diverse combination of linear and non-linear patterns. Generalized additive models (GAMs) are an extension of generalized linear models (GLMs) that have previously been used to analyze a variety of complex ecological processes such as cyclical changes in water quality, species distributions, long-term patterns in wetland area change, and palaeoecological time series. Here, we use long-term SET data to demonstrate the value of generalized additive models for analyzing non-linear patterns of surface elevation change in coastal wetlands. Additionally, we illustrate how the GAM approach can be used to effectively quantify rates of elevation change at both landscape- and local site-level scales.
Broad-scale mapping of stream channel and floodplain geomorphic metrics is critical to improve the understanding of geomorphic change, biogeochemical processes, riverine habitat quality, and opportunities for management intervention. The Floodplain and Channel Evaluation Tool (FACET) was developed to provide an open-source tool for automated processing of digital elevation models (DEMs) to generate regional-scale estimates of bank height, channel width, floodplain width, and a suite of other fluvial geomorphic dimensions that can be summarized at the stream reach- or catchment-scale. FACET was tested on 3-m DEMs covering the Delaware River watershed and 85% of the Chesapeake Bay watershed in the United States (U.S.) and on 1-m DEMs for a subset of the study area. Accuracy was assessed from data collected at 67 field sites in the study area. FACET successfully measured geomorphometry for over 270,000 stream reaches (88% of streams attempted) in the study area. Factors that reduced the ability of FACET to accurately estimate geomorphic metrics included errors in DEM hydro-conditioning, gradually sloping banks, incised stream channels, and the use of fixed input parameters to define buffer lengths. Even with these limitations, FACET was able to map regional patterns in stream and floodplain geomorphometry providing a robust dataset that can enhance modeling and management efforts throughout the mid-Atlantic region, U.S.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13163","usgsCitation":"Hopkins, K.G., Ahmed, L., Claggett, P.R., Lamont, S., Metes, M.J., and Noe, G.E., 2024, Mapping stream and floodplain geomorphometry with the Floodplain and Channel Evaluation Tool: Journal of the American Water Resources Assocation, v. 60, no. 2, p. 480-498, https://doi.org/10.1111/1752-1688.13163.","productDescription":"19 p.","startPage":"480","endPage":"498","ipdsId":"IP-122007","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":498279,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13163","text":"Publisher Index Page"},{"id":435100,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RQJPT1","text":"USGS data release","linkHelpText":"Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds"},{"id":420407,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Maryland, Pennsylvania, Virginia, West Virginia","otherGeospatial":"Chesapeake Bay Watershed, Delaware Bay Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.0949618969048,\n 36.62653336736905\n ],\n [\n -73.85224340648863,\n 36.62653336736905\n ],\n [\n -73.85224340648863,\n 43.03867782373945\n ],\n [\n -82.0949618969048,\n 43.03867782373945\n ],\n [\n -82.0949618969048,\n 36.62653336736905\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":881561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmed, Labeeb 0000-0003-4524-9611","orcid":"https://orcid.org/0000-0003-4524-9611","contributorId":303117,"corporation":false,"usgs":true,"family":"Ahmed","given":"Labeeb","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":881562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Claggett, Peter R. 0000-0002-5335-2857 pclaggett@usgs.gov","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":176287,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","email":"pclaggett@usgs.gov","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":881563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamont, Samuel","contributorId":328860,"corporation":false,"usgs":false,"family":"Lamont","given":"Samuel","affiliations":[{"id":78512,"text":"Athenium Analytics","active":true,"usgs":false}],"preferred":false,"id":881564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Metes, Marina J. 0000-0002-6797-9837","orcid":"https://orcid.org/0000-0002-6797-9837","contributorId":204835,"corporation":false,"usgs":true,"family":"Metes","given":"Marina","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":881565,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":881566,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252092,"text":"70252092 - 2024 - MODFLOW as a configurable multi-model hydrologic simulator","interactions":[],"lastModifiedDate":"2024-03-14T11:52:46.995688","indexId":"70252092","displayToPublicDate":"2023-09-01T06:47:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"MODFLOW as a configurable multi-model hydrologic simulator","docAbstract":"MODFLOW 6 is the latest in a line of six “core” versions of MODFLOW released by the U.S. Geological Survey. The MODFLOW 6 architecture supports incorporation of additional hydrologic processes, in addition to groundwater flow, and allows interaction between processes. The architecture supports multiple model instances and multiple types of models within a single simulation, a flexible approach to formulating and solving the equations that represent hydrologic processes, and recent advances in interoperability, which allow MODFLOW to be accessed and controlled by external programs. The present version of MODFLOW 6 consolidates popular capabilities available in MODFLOW variants, such as the unstructured grid support in MODFLOW-USG, the Newton-Raphson formulation in MODFLOW-NWT, and the support for partitioned stress boundaries in MODFLOW-CDSS. The flexible multi-model capability allows users to configure MODFLOW 6 simulations to represent the local-grid refinement (LGR) capabilities available in MODFLOW-LGR, the multi-species transport capabilities in MT3DMS, and the coupled variable-density capabilities available in SEAWAT. This paper provides a new, holistic and integrated overview of simulation capabilities made possible by the MODFLOW 6 architecture, and describes how ongoing and future development can take advantage of the program architecture to integrate new capabilities in a way that is minimally invasive and automatically compatible with the existing MODFLOW 6 code.
Arctic marine mammals live in a rapidly changing environment due to the amplified effects of global warming. Pacific walruses (Odobenus rosmarus divergens) have responded to declines in Arctic sea-ice extent by increasingly hauling out on land farther from their benthic foraging habitat. Energy models can be useful for better understanding the potential implications of changes in behavior on body condition and reproduction but require behavior-specific metabolic rates. Here we measured the resting metabolic rates (RMR) of three captive, adult female Pacific walruses through breath-by-breath respirometry when fed and fasted resting out of water (sitting and lying down) and while fed resting in water. RMR in and out of water were positively related with pretrial energy intake when not fasted and 25% higher than RMR when walruses were fasted and out of water. Overall, RMR was higher than previously estimated for this species. Fasting RMR out of water was only 25% lower than subsurface swimming metabolic rates suggestive of relatively efficient swimming in adult females. Our results identify the importance of considering feeding status and species-specific differences in affecting metabolic costs. Further research is needed to better understand potential energetic costs of thermoregulation at temperatures experienced by wild walruses.
","language":"English","publisher":"Wiley","doi":"10.1111/mms.13065","usgsCitation":"Rode, K.D., Rocabert, J., Borque-Espinosa, A., Ferrero-Fernandez, D., and Fahlman, A., 2024, Effects of feeding and habitat on resting metabolic rates of the Pacific walrus: Marine Mammal Science, v. 40, no. 1, p. 184-195, https://doi.org/10.1111/mms.13065.","productDescription":"12 p.","startPage":"184","endPage":"195","ipdsId":"IP-145412","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":498278,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/mms.13065","text":"Publisher Index Page"},{"id":420359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Spain","city":"Valencia","otherGeospatial":"Oceanogràfic Aquarium","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -0.34989761056129964,\n 39.45284564008105\n ],\n [\n -0.34791806836184946,\n 39.452167673433905\n ],\n [\n -0.34636955551226833,\n 39.451822524241294\n ],\n [\n -0.3414366640649007,\n 39.450614488592265\n ],\n [\n -0.34180383721491125,\n 39.45273470053647\n ],\n [\n -0.3416601607646328,\n 39.45399200502928\n ],\n [\n -0.34461351001357343,\n 39.45374547652608\n ],\n [\n -0.3473752906621712,\n 39.45420155357053\n ],\n [\n -0.3483012055616257,\n 39.45469460647391\n ],\n [\n -0.34900362376129124,\n 39.45427551172804\n ],\n [\n -0.34989761056129964,\n 39.45284564008105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":881513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rocabert, Joan","contributorId":328857,"corporation":false,"usgs":false,"family":"Rocabert","given":"Joan","email":"","affiliations":[{"id":78510,"text":"Adm+ engineering","active":true,"usgs":false}],"preferred":false,"id":881514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borque-Espinosa, Alicia","contributorId":269982,"corporation":false,"usgs":false,"family":"Borque-Espinosa","given":"Alicia","email":"","affiliations":[{"id":56054,"text":"Universitat de Valencia","active":true,"usgs":false}],"preferred":false,"id":881515,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferrero-Fernandez, Diana","contributorId":328858,"corporation":false,"usgs":false,"family":"Ferrero-Fernandez","given":"Diana","email":"","affiliations":[{"id":78511,"text":"Avanqua Oceanografic SL","active":true,"usgs":false}],"preferred":false,"id":881516,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fahlman, Andreas","contributorId":269986,"corporation":false,"usgs":false,"family":"Fahlman","given":"Andreas","email":"","affiliations":[{"id":56058,"text":"Fundacion Oceanografic de la Comunitat Valenciana","active":true,"usgs":false}],"preferred":false,"id":881517,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252624,"text":"70252624 - 2024 - Wind River Subbasin Restoration Annual Report of USGS Activities January 2021 through December 2022","interactions":[],"lastModifiedDate":"2024-04-01T11:59:57.750462","indexId":"70252624","displayToPublicDate":"2023-08-31T06:58:06","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River Subbasin Restoration Annual Report of USGS Activities January 2021 through December 2022","docAbstract":"We sampled juvenile wild Steelhead Trout Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize population attributes and investigate life-history metrics, particularly migratory patterns, and early life-stage survival. We used passive integrated transponder (PIT) tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles and adults. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead Trout have been released in the Wind River subbasin since 1997, and hatchery adults are estimated at less than one percent of spawners in most years. Over twenty years of Steelhead Trout status and trend monitoring and research in the subbasin is contributing to understanding of population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, which had an outdated adult ladder and contributed to increased water temperatures reducing performance of juvenile Steelhead Trout. \n\nData from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administrations (BPA) Research, Monitoring, and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (https://www.cbfish.org/ProgramStrategy.mvc/Index). Specifically, this work addresses the sub-strategies of 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and Uncertainties Research regarding differing life histories of a wild Steelhead Trout population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives. \n\nDuring summer and fall 2021 and 2022, we PIT-tagged 1,889 and 1,391 Steelhead parr (age-0 and age-1), respectively, in the Trout Creek and upper Wind River watersheds. Age-0 parr were at lower densities in 2022 than many years due to a poor return of adult Steelhead spawners in 2022. Steelhead Trout parr were recaptured and detected through repeat headwater sampling, smolt trap operations, and instream PTISs and Columbia River PIT-tag detection infrastructure. We maintained, and upgraded in 2022, a series of six instream PTISs to monitor movement of tagged Steelhead Trout parr, smolts, and adults, providing data to population assessments, and life-cycle research and modeling. \n\nWe continue to improve our PTISs in the Wind River subbasin. The improvements in siting and addition of grid power to the upper Wind River PTIS (site code WRU, rkm 27.6) during 2016 and 2017, and the addition of the Mine Reach site (site code MIN, rkm 36.0) have much improved PIT-tagged fish monitoring in the upper Wind River watershed. The paired PTIS design in the upper Wind River watershed (sites WRU and MIN) matches that in the Trout Creek watershed (sites TRC, rkm 2.0; and TC4, rkm 11.5) and will allow comparisons of Steelhead Trout population metrics between the two watersheds as response to Hemlock Dam removal continues and future restoration efforts occur in Trout Creek. \n\nDuring summer 2022, we upgraded three PTISs with new transceivers and new or reconfigured antennas. We replaced the Biomark 1001 Multiplexing Transceivers with Biomark MTS IS1001 Master Controller and individual IS1001 Transceivers at WRU, TRC (Trout Creek, rkm 2.0), and TC4 (Trout Creek at 43 Road Bridge, rkm 11.5). These new transceivers and antennas will improve detection performance due to increased read range and decreased susceptibility to noise. We also installed an additional IS1001 Transceiver and 11-foot antenna at WRA in summer 2021 to increase cross-channel and water column coverage.\n\nDetection data from PIT-tagged adult Steelhead Trout at PTISs allow assessment of adult escapement to tributary watersheds within the Wind River subbasin. Adult Steelhead Trout detection efficiency estimates at our primary PTIS in Trout Creek have been greater than 99 percent during six of the past eight years and have exceeded 97% at our primary PTIS in the Wind River during seven of the past eight years. Adult escapement estimates to tributary watersheds are helping us evaluate the efficacy of the 2009 removal of Hemlock Dam from rkm 2.0 of Trout Creek. The dam had potential negative effects on Steelhead Trout populations in Trout Creek due to hydrologic impairment, increased temperatures, and adult passage issues. Hemlock Dam was laddered for adult passage, but not to modern standards, which likely resulted in avoidance by some adult Steelhead Trout. \n\nDetections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. We have estimated that from 15 to 51% of parr tagged as age-0 fish in headwater areas make downstream migrations at age 1 for additional rearing. Downstream movement occurs primarily during spring but also in fall. We have estimated that up to 27% of Steelhead Trout parr, tagged as age-1 fish, make downstream migrations during fall. Fall migration of age-1 parr has been more common in the upper Wind River watershed than the Trout Creek watershed. These findings raise questions about where parr most successfully rear and whether migrations are density- or habitat-quality driven. Broader monitoring programs would give a more comprehensive understanding of juvenile Steelhead Trout production and rearing and contributions to adult recruitment from varied rearing strategies. \n\nRepeat sampling at consistent locations in the subbasin has enabled assessment of juvenile Steelhead Trout growth patterns. Growth rates (relative change in weight) of age-0 PIT-tagged parr during summer were similar across the subbasin, though slightly lower in the Trout Creek watershed. The greatest summer growth rate was in the mainstem of the Wind River (rkm 37 and 41). Summer growth rates were lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly relative growth was similar across the subbasin for both age-0 and age-1 tagged parr. Lower Layout Creek had the highest yearly growth rate of parr from age-0 to age-1. Mainstem Wind River (rkm 37) had the highest yearly growth rate of parr from age-1 to age-2. \n\nNon-native Brook Trout Salvelinus fontinalis are present in the subbasin, chiefly the Trout Creek watershed, and repeat sampling provides an index of their prevalence. Mean percent-of-catch that is Brook Trout, at four sample sites in Trout Creek, has declined from the period 1998 2003 to the period 2011 2022. Percent-of-catch and number of Brook Trout at the Trout Creek sites from 2011 through 2022 has generally declined, though both metrics have been somewhat variable. \n\nEvaluation and planning of habitat restoration efforts are critical to ensure efficient use of money and resources. Assessing Steelhead Trout life history variation in the Wind River subbasin will inform research and tracking of many populations and help inform habitat restoration and water allocation planning. Movement of Steelhead Trout parr from natal areas to other rearing areas raises questions regarding juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT-tagging of age-0 and age-1 Steelhead Trout parr allow investigation of such questions. Increasingly detailed viable salmonid population information, such as that provided by PIT-tagging and instream PTIS networks like those in the Wind River, can provide data to inform fisheries policy and management and understand life-history strategies and limiting factors. Such efforts also enable assessment of long-term effects of habitat restoration actions such as the removal of Hemlock Dam on Trout Creek, and the proposed Stage-0 restoration effort for upper Trout Creek, which would be a large-scale effort to reset sections of stream within their floodplain, restoring connectivity and interaction with surrounding landscape.","language":"English","publisher":"Bonneville Power Administration","collaboration":"Bonneville Power Administration","usgsCitation":"Jezorek, I., 2024, Wind River Subbasin Restoration Annual Report of USGS Activities January 2021 through December 2022, 68 p.","productDescription":"68 p.","ipdsId":"IP-156916","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":427265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427258,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P204538"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217811,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":897744,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70256540,"text":"70256540 - 2024 - Scenario planning and multispecies occupancy models reveal positive avian responses to restoration of afforested woodlands","interactions":[],"lastModifiedDate":"2024-08-15T23:13:54.162082","indexId":"70256540","displayToPublicDate":"2023-08-29T18:09:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Scenario planning and multispecies occupancy models reveal positive avian responses to restoration of afforested woodlands","docAbstract":"Scenario planning is a powerful approach for assessing restoration outcomes under alternative futures. However, developing plausible scenarios remains daunting in complex systems like ecological communities. Here, we used Bayesian multispecies occupancy modeling to develop scenarios to assess woodland restoration outcomes in afforested communities in seven wildlife management areas in Arkansas, U.S.A. Our objectives were (1) to define plausible woodland restoration and afforestation scenarios by quantifying historic ranges of variation in mean tree cover and tree cover heterogeneity from 1986 to 2021 and (2) to predict changes in bird species richness and occupancy patterns for six species of greatest conservation need under two future scenarios: complete afforestation (100% tree cover) and woodland restoration (based on remotely sensed historic tree cover). Using 35 years of remotely sensed tree cover data and 6 years of bird monitoring data, we developed multispecies occupancy models to predict future bird species richness and occupancy under the complete afforestation and woodland restoration scenarios. Between 1986 and 2021, tree cover increased in all study areas—with one increasing 70%. Under the woodland restoration scenario, avian species richness increased up to 20%, and four of six species of greatest conservation need exhibited gains in occupancy probability. The complete afforestation scenario had negligible effects on richness and occupancy. Overall, we found decreasing tree cover to historic levels prior to widespread afforestation would provide community-level benefits and would do little harm even to forest-dependent species of conservation concern. Applying multispecies occupancy modeling within a scenario planning framework allows for comparing multiscale trade-offs between plausible futures.
Assimilating recent observations improves model outcomes for real-time assessments of groundwater processes. This is demonstrated in estimating time-varying recharge to a shallow fractured-rock aquifer in response to precipitation. Results from estimating the time-varying water-table altitude (h) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed-lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data-driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of h, filtering and FLS produce estimates of recharge that better represent time-varying observations of h and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real-time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. Results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.13349","usgsCitation":"Shapiro, A.M., and Day-Lewis, F., 2024, Benefits and cautions in data assimilation strategies: An example of modeling groundwater recharge: Groundwater, v. 62, no. 3, p. 405-416, https://doi.org/10.1111/gwat.13349.","productDescription":"12 p.","startPage":"405","endPage":"416","ipdsId":"IP-145008","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":498221,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13349","text":"Publisher Index Page"},{"id":420617,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":882392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick","contributorId":214659,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":882393,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247941,"text":"70247941 - 2024 - Genetic analysis of federally endangered Cape Sable seaside sparrow subpopulations in the Greater Everglades, USA","interactions":[],"lastModifiedDate":"2024-02-07T16:38:44.10256","indexId":"70247941","displayToPublicDate":"2023-08-25T08:36:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Genetic analysis of federally endangered Cape Sable seaside sparrow subpopulations in the Greater Everglades, USA","docAbstract":"The federally endangered Cape Sable seaside sparrow (Ammospiza maritima mirabilis) is endemic to the Greater Everglades ecosystem in southern Florida, inhabiting fragmented marl prairies in six individual subpopulations. The subspecies is threatened by loss of breeding habitat from fire and water management. Genetic information is severely limited for the subspecies but could help inform decisions regarding subpopulation protections and potential translocations for genetic rescue. To provide genetic data and inform management efforts, feather samples were collected across five subpopulations (designated A–E) and protocols were tested to optimize DNA extraction yields. We assessed four mitochondrial DNA markers (N = 36–69) and 12 nuclear microsatellite loci (N = 55) in 108 sparrows. Mitochondrial DNA sequences revealed low haplotype diversity, with NADH dehydrogenase-2 haplotypes matching to most other extant subspecies and to the Atlantic coast subspecies. Nuclear diversity was low compared to other subspecies, but similar across subpopulations. Samples grouped as one population when analyzed by Principal Component Analysis, Bayesian modelling and genetic distance metrics. Limited genetic emigration was detected from one putative migrant. Relatedness was significantly different for sparrows in the most geographically distant subpopulation (A), likely reflecting high self-recruitment and natal site fidelity (P = 0.003). The low to moderate effective population size (NE = 202.4; NE:NC = 0.06) and generation time estimates indicated that unique genetic variation could be lost quickly during stochastic events. The sample sizes were limited, which reduced the power to comprehensively address recent population size reductions and any subsequent loss of genetic diversity.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-023-01551-0","usgsCitation":"Beaver, C., Virzi, T., and Hunter, M., 2024, Genetic analysis of federally endangered Cape Sable seaside sparrow subpopulations in the Greater Everglades, USA: Conservation Genetics, v. 25, p. 101-116, https://doi.org/10.1007/s10592-023-01551-0.","productDescription":"16 p.; Data Release","startPage":"101","endPage":"116","ipdsId":"IP-129514","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441213,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-023-01551-0","text":"Publisher Index Page"},{"id":420243,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NYGMI1","linkFileType":{"id":5,"text":"html"}},{"id":420152,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.92502664678194,\n 26.153665277276858\n ],\n [\n -81.92502664678194,\n 24.937300882586968\n ],\n [\n -80.0519651651226,\n 24.937300882586968\n ],\n [\n -80.0519651651226,\n 26.153665277276858\n ],\n [\n -81.92502664678194,\n 26.153665277276858\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2023-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, Caitlin 0000-0002-9269-7604","orcid":"https://orcid.org/0000-0002-9269-7604","contributorId":219703,"corporation":false,"usgs":true,"family":"Beaver","given":"Caitlin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":881149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Virzi, Thomas","contributorId":328736,"corporation":false,"usgs":false,"family":"Virzi","given":"Thomas","email":"","affiliations":[{"id":78474,"text":"Conservation InSight","active":true,"usgs":false}],"preferred":false,"id":881150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214958,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":881151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259482,"text":"70259482 - 2024 - Banking on strong rural livelihoods and the sustainable use of natural capital in post-conflict Colombia","interactions":[],"lastModifiedDate":"2024-10-09T15:27:44.6585","indexId":"70259482","displayToPublicDate":"2023-08-20T10:23:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18742,"text":"Environment, Development, and Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Banking on strong rural livelihoods and the sustainable use of natural capital in post-conflict Colombia","docAbstract":"In post-conflict Colombia, the government has prioritized resettlement of displaced people through development of strong rural livelihoods and the sustainable use of natural capital. In this paper, we considered government proposals for expanding payment for ecosystem services (PES) and sustainable silvopastoral systems, and private-sector investment in habitat banking. We coupled the Integrated Economic-Environmental Model (IEEM) with spatially explicit land use and land cover change and ecosystem services models to assess the potential impacts of these programs through the lens of wealth and sustainable economic development. This innovative workflow integrates dynamic endogenous feedbacks between natural capital, ecosystem services and the economic system, and can be applied to other country contexts. Results show that PES and habitat banking programs are strong investment propositions (Net Present Value of US$4.4 and $4.9 billion, respectively), but only when moving beyond conventional economic analysis to include non-market ecosystem services. Where a portfolio investment approach is taken and PES is implemented with sustainable silvopastoral systems, investment returns would reach US$7.1 billion. This paper provides a detailed evaluation of the benefits of investing in rural livelihoods and enhancing Colombia’s natural capital base, with empirical evidence to inform the spatial targeting of policies to maximize economic, environmental and social outcomes.
","language":"English","publisher":"Springer","doi":"10.1007/s10668-023-03740-w","usgsCitation":"Banerjee, O., Cicoweiz, M., Malek, Z., Verburg, P.H., Vargas, R., Goodwin, S., Bagstad, K.J., and Murillo, J.A., 2024, Banking on strong rural livelihoods and the sustainable use of natural capital in post-conflict Colombia: Environment, Development, and Sustainability, v. 26, p. 26517-26538, https://doi.org/10.1007/s10668-023-03740-w.","productDescription":"22 p.","startPage":"26517","endPage":"26538","ipdsId":"IP-136226","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467057,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10810/64357","text":"External Repository"},{"id":462748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Colombia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-75.37322,-0.15203],[-75.80147,0.0848],[-76.29231,0.41605],[-76.57638,0.25694],[-77.42498,0.39569],[-77.66861,0.82589],[-77.85506,0.80993],[-78.85526,1.38092],[-78.99094,1.69137],[-78.61783,1.7664],[-78.66212,2.26736],[-78.42761,2.62956],[-77.93154,2.69661],[-77.51043,3.32502],[-77.12769,3.84964],[-77.49627,4.08761],[-77.3076,4.66798],[-77.53322,5.58281],[-77.31882,5.84535],[-77.47666,6.69112],[-77.88157,7.22377],[-77.75341,7.70984],[-77.43111,7.63806],[-77.24257,7.93528],[-77.47472,8.52429],[-77.35336,8.6705],[-76.83667,8.63875],[-76.08638,9.33682],[-75.6746,9.44325],[-75.6647,9.774],[-75.48043,10.61899],[-74.9069,11.08304],[-74.27675,11.10204],[-74.19722,11.31047],[-73.41476,11.22702],[-72.62784,11.73197],[-72.23819,11.95555],[-71.75409,12.4373],[-71.39982,12.37604],[-71.13746,12.11298],[-71.33158,11.77628],[-71.97392,11.60867],[-72.22758,11.1087],[-72.61466,10.82198],[-72.90529,10.45034],[-73.0276,9.73677],[-73.30495,9.152],[-72.78873,9.08503],[-72.66049,8.62529],[-72.43986,8.40528],[-72.3609,8.00264],[-72.47968,7.63251],[-72.44449,7.42378],[-72.19835,7.34043],[-71.96018,6.99161],[-70.67423,7.08778],[-70.09331,6.96038],[-69.38948,6.09986],[-68.98532,6.2068],[-68.26505,6.15327],[-67.69509,6.26732],[-67.34144,6.09547],[-67.52153,5.55687],[-67.7447,5.22113],[-67.82301,4.50394],[-67.62184,3.83948],[-67.33756,3.54234],[-67.30317,3.31845],[-67.80994,2.82066],[-67.44709,2.60028],[-67.18129,2.25064],[-66.87633,1.25336],[-67.06505,1.13011],[-67.26,1.72],[-67.53781,2.03716],[-67.86857,1.69246],[-69.81697,1.71481],[-69.8046,1.08908],[-69.21864,0.98568],[-69.25243,0.60265],[-69.4524,0.70616],[-70.01557,0.54141],[-70.02066,-0.18516],[-69.57707,-0.54999],[-69.42049,-1.12262],[-69.4441,-1.55629],[-69.89364,-4.29819],[-70.39404,-3.76659],[-70.69268,-3.74287],[-70.04771,-2.72516],[-70.81348,-2.25686],[-71.41365,-2.3428],[-71.77476,-2.16979],[-72.32579,-2.43422],[-73.07039,-2.30895],[-73.6595,-1.26049],[-74.1224,-1.00283],[-74.4416,-0.53082],[-75.10662,-0.05721],[-75.37322,-0.15203]]]},\"properties\":{\"name\":\"Colombia\"}}]}","volume":"26","noUsgsAuthors":false,"publicationDate":"2023-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Banerjee, Onil","contributorId":224437,"corporation":false,"usgs":false,"family":"Banerjee","given":"Onil","email":"","affiliations":[{"id":40887,"text":"Inter-American Development Bank","active":true,"usgs":false}],"preferred":false,"id":915446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cicoweiz, Martin 0000-0001-6616-7370","orcid":"https://orcid.org/0000-0001-6616-7370","contributorId":345057,"corporation":false,"usgs":false,"family":"Cicoweiz","given":"Martin","email":"","affiliations":[{"id":40888,"text":"Universidad Nacional de la Plata","active":true,"usgs":false}],"preferred":false,"id":915447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malek, Ziga 0000-0002-6981-6708","orcid":"https://orcid.org/0000-0002-6981-6708","contributorId":299652,"corporation":false,"usgs":false,"family":"Malek","given":"Ziga","email":"","affiliations":[{"id":64916,"text":"Vrije Univeriteit Amsterdam","active":true,"usgs":false}],"preferred":false,"id":915448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verburg, Peter H.","contributorId":222519,"corporation":false,"usgs":false,"family":"Verburg","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":915449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vargas, Renato 0000-0002-2302-1141","orcid":"https://orcid.org/0000-0002-2302-1141","contributorId":299655,"corporation":false,"usgs":false,"family":"Vargas","given":"Renato","email":"","affiliations":[{"id":64919,"text":"CHW Research","active":true,"usgs":false}],"preferred":false,"id":915450,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goodwin, Sean 0000-0001-8968-8160","orcid":"https://orcid.org/0000-0001-8968-8160","contributorId":299654,"corporation":false,"usgs":false,"family":"Goodwin","given":"Sean","email":"","affiliations":[{"id":64916,"text":"Vrije Univeriteit Amsterdam","active":true,"usgs":false}],"preferred":false,"id":915451,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":915452,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murillo, Josue Avila","contributorId":345058,"corporation":false,"usgs":false,"family":"Murillo","given":"Josue","email":"","middleInitial":"Avila","affiliations":[{"id":64921,"text":"Interamerican Development Bank","active":true,"usgs":false}],"preferred":false,"id":915453,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248802,"text":"70248802 - 2024 - Paleomagnetism and geochronology of the Gwalior Sills, Bundelkhand craton, Northern India Block: New constraints on Greater India assembly","interactions":[],"lastModifiedDate":"2023-09-21T13:24:18.997547","indexId":"70248802","displayToPublicDate":"2023-08-18T08:17:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1848,"text":"Gondwana Research","active":true,"publicationSubtype":{"id":10}},"title":"Paleomagnetism and geochronology of the Gwalior Sills, Bundelkhand craton, Northern India Block: New constraints on Greater India assembly","docAbstract":"We present an updated paleomagnetic pole from the Gwalior Sills in the Bundelkhand craton within the Northern India Block (NIB). Geochronological results from baddeleyite grains from one of the sills yielded an age of 1719 ± 7 Ma which together with a previously published age indicates the emplacement of sills between 1712 and 1756 Ma (∼1730 Ma). The paleomagnetic pole calculated from additional sites in this study, combined with previous studies, falls at 13.5°N, 173.7°E (A95 = 3.6°, K = 98) indicating near equatorial latitudes for northern India. Limestone sampled a few meters above the contact with the sill exhibits similar directions consistent with having been baked by the sill. The pole does not resemble any younger poles from Peninsular India and receives a reliability score of R = 5. Dykes in the Singhbhum craton are slightly older (1765 Ma) and indicate low paleolatitudes for the Southern Indian Block (SIB). Although the Gwalior and Singhbhum poles data indicate low latitudes for both the NIB and SIB, they are statistically different and indicate that a rotation of at least 65° is required to bring the poles into accord. We propose that the NIB and SIB were in proximity but were separated by an ocean basin. We propose the name Gotosindhu (‘Ancient Sea’) for the body of water separating the NIB and SIB. We also review previous models for the assembly of the Columbia supercontinent during this time and critically examine the position of the NIB/SIB in those reconstructions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gr.2023.08.004","usgsCitation":"Meert, J., Miller, S.W., Pivarunas, A.F., Pandit, M.K., Mueller, P.A., Sinha, A.K., Kamenov, G., Kwafo, S., and Singha, A., 2024, Paleomagnetism and geochronology of the Gwalior Sills, Bundelkhand craton, Northern India Block: New constraints on Greater India assembly: Gondwana Research, v. 125, p. 29-48, https://doi.org/10.1016/j.gr.2023.08.004.","productDescription":"20 p.","startPage":"29","endPage":"48","ipdsId":"IP-143323","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":441221,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gr.2023.08.004","text":"Publisher Index Page"},{"id":421022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Bundelkhand craton, Gwalior Sills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 78,\n 27\n ],\n [\n 78,\n 24\n ],\n [\n 81,\n 24\n ],\n [\n 81,\n 27\n ],\n [\n 78,\n 27\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meert, Joseph 0000-0003-0297-3239","orcid":"https://orcid.org/0000-0003-0297-3239","contributorId":329970,"corporation":false,"usgs":false,"family":"Meert","given":"Joseph","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":883712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Scott W.","contributorId":237002,"corporation":false,"usgs":false,"family":"Miller","given":"Scott","email":"","middleInitial":"W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":883713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pivarunas, Anthony Francis 0000-0002-0003-2059","orcid":"https://orcid.org/0000-0002-0003-2059","contributorId":301014,"corporation":false,"usgs":true,"family":"Pivarunas","given":"Anthony","email":"","middleInitial":"Francis","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":883714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pandit, Manoj K. 0000-0002-0404-3337","orcid":"https://orcid.org/0000-0002-0404-3337","contributorId":329971,"corporation":false,"usgs":false,"family":"Pandit","given":"Manoj","email":"","middleInitial":"K.","affiliations":[{"id":78752,"text":"University of Rajasthan","active":true,"usgs":false}],"preferred":false,"id":883715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Paul A.","contributorId":191457,"corporation":false,"usgs":false,"family":"Mueller","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sinha, Anup K.","contributorId":329972,"corporation":false,"usgs":false,"family":"Sinha","given":"Anup","email":"","middleInitial":"K.","affiliations":[{"id":78754,"text":"Indian Institute Of Geomagnetism","active":true,"usgs":false}],"preferred":false,"id":883717,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kamenov, George 0000-0002-6041-6687","orcid":"https://orcid.org/0000-0002-6041-6687","contributorId":329973,"corporation":false,"usgs":false,"family":"Kamenov","given":"George","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":883718,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kwafo, Samuel","contributorId":329974,"corporation":false,"usgs":false,"family":"Kwafo","given":"Samuel","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":883719,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Singha, Ananya","contributorId":329975,"corporation":false,"usgs":false,"family":"Singha","given":"Ananya","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":883720,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255248,"text":"70255248 - 2024 - Native fish abundance and habitat selection changes in the presence of nonnative piscivores","interactions":[],"lastModifiedDate":"2024-06-14T15:49:15.303193","indexId":"70255248","displayToPublicDate":"2023-08-13T10:39:55","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Native fish abundance and habitat selection changes in the presence of nonnative piscivores","docAbstract":"We compared abundance patterns and developed resource selection models for imperilled native southwestern (USA) fishes in the presence and absence of Black Bass (Micropterus spp.) to evaluate how fishes alter their selection for habitats when sympatric with a nonnative piscivore. We collected data using snorkel surveys and in-stream habitat sampling in Fossil Creek (AZ), upstream (native fish only) and downstream (native and nonnative fish) of a fish barrier. The abundance of all Roundtail Chub (Gila robusta), small (≤127 mm total length [TL]; vulnerable to predation) Sonora Sucker (Catostomus insignis) and Speckled Dace (Rhinichthys osculus) was significantly reduced, but the abundance of both small and large (>127 mm TL; invulnerable to predation) Desert Sucker (Catostomus clarkii) was similar in sampling reaches with and without Black Bass. When sympatric with Black Bass, small Roundtail Chub increased their selection for riffles by 2.57 times and small Desert Sucker reduce their selection for pools by 6.90 times while also selecting for faster flow velocity and finer substrates in lotic mesohabitats. Large native fishes altered selection least, notwithstanding an increased selection for canopy cover in sampling reaches with Black Bass. Observed shifts in resource selection are consistent with predator avoidance strategies. Our study highlights the behavioural consequences of nonnative piscivores on native fish communities and stresses the importance of maintaining lotic mesohabitats as potential refugia for vulnerable native fishes when nonnative piscivores are present.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12742","usgsCitation":"Jenney, C.J., Bauder, J.M., and Bonar, S.A., 2024, Native fish abundance and habitat selection changes in the presence of nonnative piscivores: Ecology of Freshwater Fish, v. 33, no. 1, e12742, 14 p., https://doi.org/10.1111/eff.12742.","productDescription":"e12742, 14 p.","ipdsId":"IP-152854","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":441226,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12742","text":"Publisher Index Page"},{"id":430210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Fossil Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.65799555233461,\n 34.30417660701174\n ],\n [\n -111.66485798143397,\n 34.30417660701174\n ],\n [\n -111.66485798143397,\n 34.29393959091287\n ],\n [\n -111.65799555233461,\n 34.29393959091287\n ],\n [\n -111.65799555233461,\n 34.30417660701174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenney, Christopher J.","contributorId":288206,"corporation":false,"usgs":false,"family":"Jenney","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":903856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":903857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903858,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}