The U.S. Geological Survey (USGS) Water Resources Research Act (WRRA) Program connects our Nation’s academic capital to the USGS mission by delivering university-based research, outreach, and education services to our citizens. For more than 50 years, the WRRA Program has invested in local, State, and regionally focused water-related research; information and technology transfer; and workforce development through student training and professional internships. This report presents a long-term (10-year) vision for the WRRA Program that the USGS Water Resources Mission Area and 54 State water resources research institutes (or centers) can use to continue this Federal-State partnership to help solve the Nation’s water problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1488","usgsCitation":"Donohue, M.J., Greene, E.A., and Lerner, D.T., 2021, Water Resources Research Act Program—Current status, development opportunities, and priorities for 2020–30: U.S. Geological Survey Circular 1488, 27 p., https://doi.org/10.3133/cir1488.","productDescription":"vii, 27 p.","numberOfPages":"40","onlineOnly":"N","ipdsId":"IP-120216","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":392091,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1488/images"},{"id":392090,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1488/cir1488.XML","size":"139 kB","linkFileType":{"id":8,"text":"xml"},"description":"Circular 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U.S. Geological Survey, Water Mission Area
12201 Sunrise Valley Drive, MS 432
Reston, Virginia 20192
The noble gas temperature climate proxy is an established tool that has previously been applied to determine the source of groundwater recharge, however, unanswered questions remain. In fractured media (e.g., volcanic islands) recharge can be so rapid that groundwater is significantly depleted in heavy noble gases, indicating that the water has retained noble gas concentrations from higher elevations. Previous studies of rain samples have confirmed a match to patterns seen in fractured-rock groundwater for heavy noble gases along with a significant helium excess. Snow has been shown to be a credible source for both the helium excess and the observed heavy noble gas pattern. Here, liquid cloud water samples were collected at two mountainous sites and analyzed for noble gas concentrations. A pattern like that of rainwater was found. However, an analysis of diffusive uptake of noble gases into cloud water demonstrates that droplets of 1 mm diameter and smaller should be in constant solubility equilibrium with the atmosphere. To explain this, we present a novel hypothesis that relies on the assumption that liquid water consists of two types of water molecule clusters bounded by hydrogen bonds: a low-density ice-like structure and a high-density condensed structure. In this model, the pressure gradient near the surface of a droplet resulting from surface tension could allow for the formation of a surface layer that is rich in ice-like low density clusters. This can explain both the helium excess and the heavy noble gas depletion seen in the samples.
Indicator species are species which can be monitored as an index to measure the overall health of an ecosystem. Crocodylians have been shown to be good indicators of wetland condition as they respond to changes in hydrology, can be efficiently monitored, and are a key part of ecosystem trophic relationships. Eye shine surveys at night are a standard method used to sample alligators, but because some individuals that are present in a study area may go undetected and the proportion of individuals counted is not constant over time, appropriate modeling is required to convert counts to estimates of abundance. We analyzed 13 years of American alligator (Alligator mississippiensis) survey count data from South Florida using an N-mixture model. Alligator abundance estimates were assigned to quartiles that were then represented as color coded categories of red, yellow, or green to provide a straightforward rating of Everglades restoration based on familiar stoplight coloring. These results were then compared to a previously used method in which unadjusted counts of these same data were assigned to color coded quartile categories. Water depth played a major role in the detection probability of alligators and the stoplight colors between the two methods matched 76% of the time. This suggests that the original stoplight score method provided a good overall snapshot of the trends in alligator abundance in the Everglades; however, the hierarchical models estimate abundance and trends of alligator abundance by incorporating detection probability thus providing unbiased estimates of abundance.
Effective conservation for listed migratory species requires an understanding of how drivers of population decline vary spatially and temporally, as well as knowledge of range-wide connectivity between breeding and nonbreeding areas. Environmental conditions distant from breeding areas can have lasting effects on the demography of migratory species, yet these consequences are often the least understood. Our objectives were to 1) evaluate associations between survival and extreme environmental disturbances at nonbreeding areas, including hurricanes, harmful algal blooms, and oil spills, and 2) estimate migratory connectivity between breeding and nonbreeding areas of midcontinental piping plovers (Charadrius melodus). We used capture and resighting data from 5067 individuals collected between 2002 and 2019 from breeding areas across the midcontinent, and nonbreeding areas throughout the Gulf of Mexico and southern Atlantic coasts of North America. We developed a hidden Markov multistate model to estimate seasonal survival and account for unobservable geographic locations. Hurricanes and harmful algal blooms were negatively associated with nonbreeding season survival, but we did not detect a similarly negative relationship with oil spills. Our results indicated that individuals from separate breeding areas mixed across nonbreeding areas with low migratory connectivity. Mixing among individuals in the nonbreeding season may provide a buffering effect against impacts of extreme events on any one breeding region. Our results suggest that understanding migratory connectivity and linking seasonal threats to population dynamics can better inform conservation strategies for migratory shorebirds.
As science becomes increasingly cross-disciplinary and scientific models become increasingly cross-coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like “season” or “variability,” and that simple decompositions can be combined to represent more complex concepts, like “seasonal variability,” creating an expressive language through which to interrogate models and data.
Seismic attenuation is generally greater in the western United States (WUS) than the central and eastern United States (CEUS), but the nature of this transition or location of this boundary is poorly constrained. We conduct crustal seismic (Lg) attenuation tomography across a region that stretches from the CEUS across the Rocky Mountains to the Basin and Range using a total of 115,870 amplitude measurements from 106 earthquakes recorded on 544 stations across five frequency bands spanning 0.5–16 Hz. Similar to previous studies, we find higher attenuation in the WUS (Q0 ∼ 190) than the nominally CEUS (Q0 ∼ 250) and comparatively high attenuation on the Gulf Coast (Q0 ∼ 175). Our models defy simple east versus west regionalization, however. Heterogeneity within the Rocky Mountain region—low attenuation in the Colorado Plateau interior and Wyoming Craton (Q0 ∼ 230) compared to high attenuation in the southern Rockies (Q0 ∼ 110)—exceeds the gross differences between the CEUS and western United States. These province-scale patterns are readily interpreted in terms of intrinsic attenuation. The boundary between the Colorado Plateau and Basin and Range hosts the highest attenuation imaged in the study area (Q0 ∼ 90), consistent with localized scattering across contrasting crustal structure. Focused high attenuation in the southern Rockies may represent the effects of represent in situ partial crustal melt. Within the CEUS, second-order bands of comparatively high attenuation align with the Proterozoic Yavapai-Mazatzal suture zone and Midcontinent Rift. This complex attenuation structure defies broad regionalization and suggests a need for path-specific models near these boundaries and for critical infrastructure.
Above average precipitation from October 2018 through July 2019 in the Maumee River (R.) Basin resulted in 29% of cropland left fallow, providing a glimpse of potential effects from decreased nutrient application. Ongoing monitoring at 15 water-quality sites on the Maumee R. upstream from Defiance enabled comparison with 2017, which was hydrologically similar to 2019 in precipitation and streamflow. In 2019, nitrate (as nitrogen; NO3-N) for March-July was significantly less than previous years (2015–2018), but the response for phosphorus (P) was more complicated. Relative to 2017, total P (TP) was lower at 7 of 15 sites, but higher at 7, reflecting higher suspended sediment (SS). Dissolved P (DP) was generally lower, but less different than NO3; DP was higher at 3 sites. DP-P:NO3-N was generally higher in 2019, DP-P:TP was lower, and there was less TP relative to SS. Overall, less P was in the system in 2019. However smaller streams showed a large range of difference between 2019 and 2017 for all constituents, indicating variability in land management and physiography. In contrast, all constituents were lower in 2019 in larger (>5000 km2) streams, including the Maumee R. near Defiance, where the difference in NO3 (−37%) exceeded that for TP (−16%), DP (−10%), and SS (−20%). Differences in these relations among N, P, and SS indicate that P was available from legacy sources that are more difficult to distinguish during typical agricultural production years and that some material from 2019 was stored in the system upstream from the largest sites.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.10.004","usgsCitation":"Williamson, T.N., Shaffer, K., Runkle, D.L., Hardebeck, M.J., Dobrowolski, E.G., Frey, J.W., Baker, N.T., Collier, K.M., Huitger, C.A., Kula, S.P., Haefner, R.J., Hartley, L.M., Crates, H.F., Webber, J.J., Finnegan, D.P., Reithel, N.J., Toussant, C.A., and Weaver, T.L., 2021, Nutrient and suspended-sediment concentrations in the Maumee River and tributaries during 2019 rain-induced fallow conditions: Journal of Great Lakes Research, v. 47, no. 6, p. 1726-1736, https://doi.org/10.1016/j.jglr.2021.10.004.","productDescription":"11 p.","startPage":"1726","endPage":"1736","ipdsId":"IP-122985","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaffer, Kimberly 0000-0001-9386-7671 kshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-9386-7671","contributorId":206648,"corporation":false,"usgs":true,"family":"Shaffer","given":"Kimberly","email":"kshaffer@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runkle, Donna L. 0000-0001-8254-4316 dlrunkle@usgs.gov","orcid":"https://orcid.org/0000-0001-8254-4316","contributorId":269779,"corporation":false,"usgs":true,"family":"Runkle","given":"Donna","email":"dlrunkle@usgs.gov","middleInitial":"L.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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ntbaker@usgs.gov","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":1955,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"ntbaker@usgs.gov","middleInitial":"T.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827903,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Collier, Katie Marie 0000-0001-8130-2833","orcid":"https://orcid.org/0000-0001-8130-2833","contributorId":269780,"corporation":false,"usgs":true,"family":"Collier","given":"Katie","email":"","middleInitial":"Marie","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827904,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huitger, Carrie A. 0000-0003-4534-3245 chuitger@usgs.gov","orcid":"https://orcid.org/0000-0003-4534-3245","contributorId":207180,"corporation":false,"usgs":true,"family":"Huitger","given":"Carrie","email":"chuitger@usgs.gov","middleInitial":"A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827906,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kula, Stephanie P. 0000-0002-2249-0632 spkula@usgs.gov","orcid":"https://orcid.org/0000-0002-2249-0632","contributorId":269781,"corporation":false,"usgs":true,"family":"Kula","given":"Stephanie","email":"spkula@usgs.gov","middleInitial":"P.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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0000-0002-0656-622X","orcid":"https://orcid.org/0000-0002-0656-622X","contributorId":269783,"corporation":false,"usgs":true,"family":"Crates","given":"Hunter","email":"","middleInitial":"Frederick","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827910,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Webber, J. Jeremy 0000-0002-2512-2448","orcid":"https://orcid.org/0000-0002-2512-2448","contributorId":259209,"corporation":false,"usgs":true,"family":"Webber","given":"J.","email":"","middleInitial":"Jeremy","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827911,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Finnegan, Dennis P. 0000-0003-1934-1262 dpfinneg@usgs.gov","orcid":"https://orcid.org/0000-0003-1934-1262","contributorId":269784,"corporation":false,"usgs":true,"family":"Finnegan","given":"Dennis","email":"dpfinneg@usgs.gov","middleInitial":"P.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827912,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Reithel, Nicholas J. 0000-0002-7205-357X","orcid":"https://orcid.org/0000-0002-7205-357X","contributorId":269785,"corporation":false,"usgs":true,"family":"Reithel","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827913,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Toussant, Chad A. 0000-0002-1324-0914","orcid":"https://orcid.org/0000-0002-1324-0914","contributorId":210079,"corporation":false,"usgs":true,"family":"Toussant","given":"Chad","middleInitial":"A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827914,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Weaver, Thomas L. 0000-0002-6523-2553 tlweaver@usgs.gov","orcid":"https://orcid.org/0000-0002-6523-2553","contributorId":213949,"corporation":false,"usgs":true,"family":"Weaver","given":"Thomas","email":"tlweaver@usgs.gov","middleInitial":"L.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827915,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70226807,"text":"70226807 - 2021 - Comparative genomics analyses support the reclassification of Bisgaard taxon 40 as Mergibacter gen. nov., with Mergibacter septicus sp. nov. as type species: Novel insights into the phylogeny and virulence factors of a Pasteurellaceae family member associated with mortality events in seabirds","interactions":[],"lastModifiedDate":"2021-12-14T14:55:08.744643","indexId":"70226807","displayToPublicDate":"2021-11-22T07:28:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Comparative genomics analyses support the reclassification of Bisgaard taxon 40 as Mergibacter gen. nov., with Mergibacter septicus sp. nov. as type species: Novel insights into the phylogeny and virulence factors of a Pasteurellaceae family member associated with mortality events in seabirds","title":"Comparative genomics analyses support the reclassification of Bisgaard taxon 40 as Mergibacter gen. nov., with Mergibacter septicus sp. nov. as type species: Novel insights into the phylogeny and virulence factors of a Pasteurellaceae family member associated with mortality events in seabirds","docAbstract":"The Pasteurellaceae family has been associated with fatal diseases in numerous avian species. Several new taxa within this family, including Bisgaard taxon 40, have been recently described in wild birds, but their genomic characteristics and pathogenicity are not well understood. We isolated Bisgaard taxon 40 from four species of seabirds, including one sampled during a mass, multi-species mortality event in Florida, United States. Here, we present a comprehensive phenotypic and genetic characterization of Bisgaard taxon 40 and comparative genomic analysis with reference strains from the Pasteurellaceae family, aiming at determining its phylogenetic position, antimicrobial susceptibility profile, and identifying putative virulence factors. In silico multilocus sequence-based and whole-genome-based phylogenetic analysis clustered all Bisgaard taxon 40 strains together on a distinct branch separated from the other members of the Pasteurellaceae family, indicating that Bisgaard taxon 40 could represent a new genus. These findings were further supported by protein similarity analyses using the concatenation of 31 conserved proteins and other taxonomic approaches such as the percentage of conserved protein test. Additionally, several putative virulence factors were identified, including those associated with adhesion (capsule, ompA, ompH) and colonization (exbD, fur, galU, galE, lpxA, lpxC, and kdsA) of the host and a cytolethal distending toxin (cdt), which may have played a role in disease development leading to the mortality event. Considerably low minimum inhibitory concentrations (MICs) were found for all the drugs tested, in concordance with the absence of antimicrobial resistance genes in these genomes. The novel findings of this study highlight genomic and phenotypic characteristics of this bacterium, providing insights into genome evolution and pathogenicity. We propose a reclassification of these organisms within the Pasteurellaceae family, designated as Mergibacter gen. nov., with Mergibacter septicus sp. nov. as the type species. The type strain is Mergibacter septicus A25201T (=DSM 112696).
Research presented in the preceding chapters emphasizes recent advancements in the research, management, and aquaculture of Walleye, Sauger, and Yellow Perch in North America. These percid fishes, along with the European Perch and Pikeperch, are economically and ecologically important fishes in their native geographic range. Advances in techniques to evaluate current habitat and predict future habitat conditions provide managers with detailed baseline information and biophysical models useful for evaluating adaptive management practices. Current habitat use and movement assessments have improved substantially with technological advancements in acoustic tags and extensive receiver array networks, which, combined with genetic and genomic tools, are improving percid stock assessments and management. Advances in percid aquaculture techniques have improved growth, survival, and disease resistance, enhancing percid stocking efforts and the production of marketable fish. The exchange of information between researchers and managers will continue to advance techniques of percid management for commercial and recreational exploitation and improve aquaculture practices to provide a lucrative commercial aquaculture industry.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Yellow Perch, Walleye, and Sauger: Aspects of ecology, management, and culture","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-80678-1_12","usgsCitation":"DeBruyne, R., and Roseman, E., 2021, International importance of Percids: Summary and looking forward, chap. of Yellow Perch, Walleye, and Sauger: Aspects of ecology, management, and culture, p. 309-320, https://doi.org/10.1007/978-3-030-80678-1_12.","productDescription":"12 p.","startPage":"309","endPage":"320","ipdsId":"IP-128480","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":392676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"DeBruyne, Robin L.","contributorId":139752,"corporation":false,"usgs":false,"family":"DeBruyne","given":"Robin L.","affiliations":[{"id":12902,"text":"MI State UNiversity","active":true,"usgs":false}],"preferred":false,"id":828102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828103,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226625,"text":"70226625 - 2021 - Tissue and salinity specific Na+/Cl− cotransporter (NCC) orthologues involved in the adaptive osmoregulation of sea lamprey (Petromyzon marinus)","interactions":[],"lastModifiedDate":"2021-12-02T14:26:33.259972","indexId":"70226625","displayToPublicDate":"2021-11-22T06:44:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tissue and salinity specific Na+/Cl− cotransporter (NCC) orthologues involved in the adaptive osmoregulation of sea lamprey (Petromyzon marinus)","title":"Tissue and salinity specific Na+/Cl− cotransporter (NCC) orthologues involved in the adaptive osmoregulation of sea lamprey (Petromyzon marinus)","docAbstract":"Two orthologues of the gene encoding the Na+-Cl− cotransporter (NCC), termed ncca and nccb, were found in the sea lamprey genome. No gene encoding the Na+-K+-2Cl− cotransporter 2 (nkcc2) was identified. In a phylogenetic comparison among other vertebrate NCC and NKCC sequences, the sea lamprey NCCs occupied basal positions within the NCC clades. In freshwater, ncca mRNA was found only in the gill and nccb only in the intestine, whereas both were found in the kidney. Intestinal nccb mRNA levels increased during late metamorphosis coincident with salinity tolerance. Acclimation to seawater increased nccb mRNA levels in the intestine and kidney. Electrophysiological analysis of intestinal tissue ex vivo showed this tissue was anion absorptive. After seawater acclimation, the proximal intestine became less anion absorptive, whereas the distal intestine remained unchanged. Luminal application of indapamide (an NCC inhibitor) resulted in 73% and 30% inhibition of short-circuit current (Isc) in the proximal and distal intestine, respectively. Luminal application of bumetanide (an NKCC inhibitor) did not affect intestinal Isc. Indapamide also inhibited intestinal water absorption. Our results indicate that NCCb is likely the key ion cotransport protein for ion uptake by the lamprey intestine that facilitates water absorption in seawater. As such, the preparatory increases in intestinal nccb mRNA levels during metamorphosis of sea lamprey are likely critical to development of whole animal salinity tolerance.
Crayfish serve critical roles in aquatic ecosystems as engineers, omnivores, and prey. It is unclear how increasingly frequent extreme-flow events and warming air temperatures will affect crayfish populations, partly because there are few long-term crayfish monitoring datasets. Using a unique 10-y dataset, we asked 1) whether recruitment of crayfishes in summer responded to extreme-flow events and air temperature during spring brooding and summer growing periods and 2) whether responses were similar among 3 co-occurring crayfish species. Golden (Faxonius luteus [Creaser, 1933]), Ozark (Faxonius ozarkae [Williams, 1952]), and Spothand (Faxonius punctimanus [Creaser, 1933]) crayfishes were sampled in quadrats at 2 sites each in the Big Piney (1993–2000) and Jacks Fork (1992–2001) rivers (Missouri, USA; n = 3355 1-m2 quadrats). We used zero-inflated generalized linear models to relate variability in quadrat-level age-0 counts to mean daily maximum air temperatures and flow metrics (variability, magnitude, and frequency of extreme high- and low-flow events). Species ranged from a small-bodied, abundant habitat generalist (Golden Crayfish) to large-bodied, uncommon habitat specialists (Ozark and Spothand crayfishes). Golden Crayfish occurred in higher-velocity habitats (riffles, runs) and had variable recruitment that increased during years with few spring and summer high-flow events and summers with lower flows and warmer temperatures. In contrast, annual recruitment variability of Ozark and Spothand crayfishes was low and explained by positive effects of cooler summers and by different flow metrics. Spothand Crayfish recruitment decreased in years with frequent spring and summer high-flow events, whereas lower summer minimum flow was the only flow metric that explained slight increases in Ozark Crayfish recruitment. Relationships with the preceding year’s recruitment were quadratic for Ozark and Spothand crayfishes, suggesting potential density dependence at higher recruitment levels. Species-specific responses suggest that closely related crayfishes could respond idiosyncratically to changes in temperature and flow. Temperature- and flow-related disturbances may be key mechanisms mediating competition and, thus, may help maintain crayfish diversity. However, warming air temperatures and increasingly frequent extreme-flow events could disadvantage some species, thereby altering future crayfish assemblages.
","language":"English","publisher":"Society for Freshwater Science","doi":"10.1086/717486","usgsCitation":"Dunn, C.G., Moore, M.J., Sievert, N., Paukert, C.P., and DiStefano, R., 2021, Co-occurring lotic crayfishes exhibit variable long-term responses to extreme-flow events and temperature: Freshwater Science, v. 40, no. 4, p. 626-643, https://doi.org/10.1086/717486.","productDescription":"18 p.","startPage":"626","endPage":"643","ipdsId":"IP-127694","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450160,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/717486","text":"Publisher Index Page"},{"id":397261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big Piney River, Jacks Forks River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.84673309326172,\n 37.07791492175793\n ],\n [\n -91.80965423583984,\n 37.07791492175793\n ],\n [\n -91.80965423583984,\n 37.0921568267209\n ],\n [\n -91.84673309326172,\n 37.0921568267209\n ],\n [\n -91.84673309326172,\n 37.07791492175793\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.05821990966797,\n 37.15128685950638\n ],\n [\n -92.00122833251953,\n 37.15128685950638\n ],\n [\n -92.00122833251953,\n 37.2125580936087\n ],\n [\n -92.05821990966797,\n 37.2125580936087\n ],\n [\n -92.05821990966797,\n 37.15128685950638\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Michael J.","contributorId":274823,"corporation":false,"usgs":false,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":838224,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sievert, Nicholas A. 0000-0003-3160-7596","orcid":"https://orcid.org/0000-0003-3160-7596","contributorId":177341,"corporation":false,"usgs":false,"family":"Sievert","given":"Nicholas A.","affiliations":[],"preferred":false,"id":838448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838225,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DiStefano, Robert J.","contributorId":213268,"corporation":false,"usgs":false,"family":"DiStefano","given":"Robert J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":838226,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228224,"text":"70228224 - 2021 - Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model","interactions":[],"lastModifiedDate":"2022-02-08T15:43:48.285368","indexId":"70228224","displayToPublicDate":"2021-11-21T09:38:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model","docAbstract":"Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EF002285","usgsCitation":"Anderson, D.L., Ruggiero, P., Mendez, F.J., Barnard, P.L., Erikson, L.H., O'Neill, A., Merrifield, M., Rueda, A., Cagigal, L., and Marra, J.M., 2021, Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model: Earth's Future, v. 9, no. 12, e2021EF002285, 24 p., https://doi.org/10.1029/2021EF002285.","productDescription":"e2021EF002285, 24 p.","ipdsId":"IP-111912","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450163,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021ef002285","text":"External Repository"},{"id":395620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Diego","otherGeospatial":"Naval Base Coronado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.32986450195312,\n 32.54565554741415\n ],\n [\n -117.05795288085936,\n 32.54565554741415\n ],\n [\n -117.05795288085936,\n 32.87555050280593\n ],\n [\n -117.32986450195312,\n 32.87555050280593\n ],\n [\n -117.32986450195312,\n 32.54565554741415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, D. L.","contributorId":274874,"corporation":false,"usgs":false,"family":"Anderson","given":"D.","email":"","middleInitial":"L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":833469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruggiero, P.","contributorId":191579,"corporation":false,"usgs":false,"family":"Ruggiero","given":"P.","email":"","affiliations":[],"preferred":false,"id":833470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendez, F. J.","contributorId":274876,"corporation":false,"usgs":false,"family":"Mendez","given":"F.","email":"","middleInitial":"J.","affiliations":[{"id":27840,"text":"Universidad de Cantabria","active":true,"usgs":false}],"preferred":false,"id":833471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Merrifield, M.","contributorId":274878,"corporation":false,"usgs":false,"family":"Merrifield","given":"M.","email":"","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":833475,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rueda, A.","contributorId":274880,"corporation":false,"usgs":false,"family":"Rueda","given":"A.","email":"","affiliations":[{"id":27840,"text":"Universidad de Cantabria","active":true,"usgs":false}],"preferred":false,"id":833476,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cagigal, L.","contributorId":274882,"corporation":false,"usgs":false,"family":"Cagigal","given":"L.","affiliations":[{"id":27840,"text":"Universidad de Cantabria","active":true,"usgs":false}],"preferred":false,"id":833477,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marra, J. M.","contributorId":219619,"corporation":false,"usgs":false,"family":"Marra","given":"J.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":833478,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70226550,"text":"70226550 - 2021 - Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud","interactions":[],"lastModifiedDate":"2021-11-24T13:27:41.850893","indexId":"70226550","displayToPublicDate":"2021-11-21T07:23:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud","docAbstract":"Understanding the age and movement of groundwater is important for predicting the vulnerability of wells to contamination, constraining flow models that inform sustainable groundwater management, and interpreting geochemical signals that reflect past climate. Due to both the ubiquity of groundwater with order ten-thousand-year residence times and its importance for climate reconstruction of the last glacial period, there is a strong need for improving geochemical dating tools on this timescale. Whereas 14C of dissolved inorganic carbon and dissolved 4He are common age tracers for Late Pleistocene groundwater, each is limited by systematic uncertainties related to aquifer composition and lithology, and the extent of water-rock interaction. In principle, radiogenic 40Ar in groundwater acquired from decay of 40K in aquifer minerals should be insensitive to some processes that impact 14C and 4He and thus represent a useful, complementary age tracer. In practice, however, detection of significant radiogenic 40Ar signals in groundwater has been limited to a small number of studies of extremely old groundwater (>100 ka). Here we present the first high-precision (<1‰) measurements of triple Ar isotopes (40Ar, 38Ar, 36Ar) in groundwater. We introduce a model that distinguishes radiogenic 40Ar from atmospheric 40Ar by using the non-radiogenic Ar isotopes (36Ar, 38Ar) to correct for mass-dependent fractionation. Using this model, we investigate variability in radiogenic 40Ar excess (Δ40Ar) across 58 groundwater samples collected from 36 wells throughout California (USA). We find that Δ40Ar ranges from ~0‰ (the expected minimum value) to +4.2‰ across three study areas near Fresno, San Diego, and the western Mojave Desert. Based on measurements from a network of 23 scientific monitoring wells in San Diego, we find evidence for a strong dependence of Δ40Ar on aquifer lithology. We suggest that Δ40Ar is fundamentally controlled by the weathering of old K-bearing minerals and thus reflects both the degree of groundwater-rock interaction, which is related to groundwater age, and the integrated flow through different geological formations. Future studies of Late Pleistocene groundwater may benefit from high-precision triple Ar isotope measurements as a new tool to better interpret 14C- and 4He-based constraints on groundwater age and flow.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120458","usgsCitation":"Seltzer, A., Krantz, J.A., Ng, J., Danskin, W.R., Bekaert, D., Barry, P.H., Kimbrough, D.L., Kulongoski, J.T., and Severinghaus, J.P., 2021, The triple argon isotope composition of groundwater on ten-thousand-year timescales: Chemical Geology, v. 583, 120458, 12 p., https://doi.org/10.1016/j.chemgeo.2021.120458.","productDescription":"120458, 12 p.","ipdsId":"IP-134673","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450168,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://escholarship.org/uc/item/9kx1757b","text":"Publisher Index Page"},{"id":417210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"583","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Seltzer, Alan 0000-0003-2870-1215","orcid":"https://orcid.org/0000-0003-2870-1215","contributorId":270717,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":873138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krantz, John A.","contributorId":305541,"corporation":false,"usgs":false,"family":"Krantz","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":66250,"text":"Woods Hole Oceanographic Institution, Marine Chemistry & Geochemistry Department, Woods Hole, MA, United States of America","active":true,"usgs":false}],"preferred":false,"id":873139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ng, Jessica","contributorId":268304,"corporation":false,"usgs":false,"family":"Ng","given":"Jessica","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":873140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danskin, Wesley R. 0000-0001-8672-5501 wdanskin@usgs.gov","orcid":"https://orcid.org/0000-0001-8672-5501","contributorId":1034,"corporation":false,"usgs":true,"family":"Danskin","given":"Wesley","email":"wdanskin@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bekaert, David 0000-0002-1062-6221","orcid":"https://orcid.org/0000-0002-1062-6221","contributorId":270718,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":873142,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barry, Peter H. 0000-0002-6960-1555","orcid":"https://orcid.org/0000-0002-6960-1555","contributorId":218244,"corporation":false,"usgs":false,"family":"Barry","given":"Peter","email":"","middleInitial":"H.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":873143,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kimbrough, David L.","contributorId":211569,"corporation":false,"usgs":false,"family":"Kimbrough","given":"David","email":"","middleInitial":"L.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":873144,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873145,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Severinghaus, Jeffrey P.","contributorId":140715,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":873146,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226342,"text":"ofr20211065 - 2021 - Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19","interactions":[],"lastModifiedDate":"2021-11-22T12:06:29.314733","indexId":"ofr20211065","displayToPublicDate":"2021-11-19T13:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1065","displayTitle":"Turbidity–Suspended-Sediment Concentration Regression Equations for Monitoring Stations in the Upper Esopus Creek Watershed, Ulster County, New York, 2016–19","title":"Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19","docAbstract":"Upper Esopus Creek is the primary tributary to the Ashokan Reservoir, part of the New York City water-supply system. Elevated concentrations of suspended sediment and turbidity in the watershed of the creek are of concern for the system.
Water samples were collected through a range of streamflow and turbidity at 14 monitoring sites in the upper Esopus Creek watershed for analyses of suspended-sediment concentration (SSC) and measurements of turbidity. Analyses of the samples provided data that were used to develop cross-section coefficients and turbidity-SSC regression equations for the monitoring sites for the period October 2016 through September 2019. The equations can be used to estimate SSC at a 15-minute timestep for the monitored sites. The equations can be validated for future use by the collection and analysis of additional data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211065","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Siemion, J., Bonville, D.B., McHale, M.R., and Antidormi, M.R., 2021, Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19: U.S. Geological Survey Open-File Report 2021–1065, 27 p., https://doi.org/10.3133/ofr20211065.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120199","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":391805,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MV3NZ8","text":"USGS data release","linkHelpText":"Suspended-sediment concentration and turbidity data for sites in the upper Esopus Creek watershed New York, 2016–19"},{"id":391807,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1065/images"},{"id":391804,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.pdf","text":"Report","size":"2.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1065"},{"id":391806,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.XML"},{"id":391803,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1065/coverthb.jpg"}],"country":"United States","state":"New York","county":"Ulster County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-73.9109,42.1271],[-73.916,42.1199],[-73.9245,42.1019],[-73.9311,42.082],[-73.93,42.0765],[-73.9302,42.0679],[-73.9341,42.0575],[-73.937,42.0398],[-73.9347,42.0293],[-73.9331,42.0216],[-73.9436,41.9913],[-73.9504,41.9664],[-73.9556,41.9528],[-73.9551,41.9464],[-73.954,41.9401],[-73.9567,41.9301],[-73.9625,41.9179],[-73.9639,41.9138],[-73.9609,41.9088],[-73.9423,41.8827],[-73.9389,41.8704],[-73.939,41.8654],[-73.9423,41.8596],[-73.9448,41.8559],[-73.9461,41.851],[-73.9477,41.8346],[-73.9463,41.8142],[-73.9504,41.7979],[-73.9488,41.7847],[-73.946,41.7719],[-73.9414,41.7592],[-73.9408,41.7592],[-73.938,41.7469],[-73.9389,41.7337],[-73.9424,41.7142],[-73.9439,41.6993],[-73.9411,41.6884],[-73.9513,41.6149],[-73.9525,41.59],[-73.9999,41.5855],[-74.0521,41.5816],[-74.0575,41.5926],[-74.0677,41.604],[-74.0886,41.5988],[-74.0983,41.6089],[-74.1246,41.6133],[-74.1325,41.6152],[-74.1282,41.5833],[-74.1858,41.5944],[-74.187,41.5908],[-74.1907,41.5913],[-74.2458,41.6036],[-74.25,41.6059],[-74.2502,41.6291],[-74.2606,41.6337],[-74.2667,41.6324],[-74.2754,41.6284],[-74.281,41.6257],[-74.2989,41.6182],[-74.3156,41.6115],[-74.3187,41.6084],[-74.3404,41.5954],[-74.3521,41.5982],[-74.3583,41.5938],[-74.3675,41.5916],[-74.3681,41.5961],[-74.3705,41.597],[-74.3736,41.5975],[-74.376,41.5994],[-74.3772,41.6044],[-74.3807,41.6117],[-74.3843,41.6167],[-74.3873,41.6217],[-74.3884,41.6299],[-74.392,41.6345],[-74.3926,41.6399],[-74.3943,41.6458],[-74.4004,41.6486],[-74.4449,41.6726],[-74.4833,41.6942],[-74.5755,41.7453],[-74.4892,41.8377],[-74.4573,41.8747],[-74.5124,41.8992],[-74.6363,41.9542],[-74.7235,41.9915],[-74.78,42.0182],[-74.667,42.0697],[-74.5538,42.1212],[-74.5312,42.1464],[-74.504,42.1449],[-74.4516,42.1694],[-74.3077,42.1142],[-74.2496,42.1095],[-74.0767,42.0968],[-74.0424,42.1682],[-74.0259,42.1621],[-74.0054,42.1642],[-74.0038,42.18],[-73.9189,42.1286],[-73.9109,42.1271]]]},\"properties\":{\"name\":\"Ulster\",\"state\":\"NY\"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In 2010, tetrachloroethylene (PCE), a chlorinated volatile organic compound, was detected in groundwater from deep (more than 300 feet below land surface) fractures in monitoring wells tapping a crystalline-rock aquifer. The aquifer underlies the Milford-Souhegan glacial-drift aquifer, a high water-producing aquifer, and the Savage Municipal Water-Supply Well Superfund site in Milford, New Hampshire. Between 30 and 40 residential water-supply wells are near (0.25 mile north of) the PCE-contaminated monitoring wells. Some of the residential water-supply wells are likely installed in similar rock types and formations as those of the monitoring wells installed as part of the Superfund site. As of 2020, periodic sampling by the U.S. Environmental Protection Agency and New Hampshire Department of Environmental Services (cooperative partners for this study) since 1996 had not detected PCE in groundwater from the residential water-supply wells. Nevertheless, understanding the vulnerability of the residential water wells to capture PCE contaminated groundwater was of concern.
A numerical groundwater flow model was developed by the U.S. Geological Survey to assess groundwater flow and advective transport of PCE-contaminated groundwater in the crystalline-rock aquifer of the Milford area. The model (called the area-wide model) encompasses a 26.5-square mile area to allow for more accurate computation of water fluxes near the PCE-contaminated monitoring wells and the residential water wells. Simulations with the area-wide model show that, with the 2016 configuration of residential wells, capture of PCE by the residential water wells appears unlikely for hydrologic conditions typical of 2010 based on steady-state, advective transport modeling. However, simulations also show that adding residential water wells to the north of the PCE-contaminated monitoring wells could affect the transport of PCE. Groundwater withdrawals at other adjacent wells in the overlying Milford-Souhegan glacial-drift aquifer affect advective transport in the crystalline-rock aquifer. Therefore, the potential for future changes in withdrawals in the area, as well as changes in hydrologic conditions, including groundwater recharge and streamflow amounts, should be considered in the remedial assessment process. The development of the area-wide model and linkages established by this study with previously developed Milford-Souhegan glacial-drift aquifer transport models will help facilitate the development of remedial strategies for this Superfund site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205137","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the New Hampshire Department of Environmental Services","usgsCitation":"Harte, P.T., 2021, Numerical modeling of groundwater flow in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire: U.S. Geological Survey Scientific Investigations Report 2020–5137, 47 p., https://doi.org/10.3133/sir20205137.","productDescription":"Report: ix, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-036649","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391937,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205137/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":391330,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5137/sir20205137.XML"},{"id":391326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5137/coverthb.jpg"},{"id":391329,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5137/images/"},{"id":391328,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J102FK","text":"USGS data release","linkHelpText":"MODFLOW -2005, MODPATH, and MOC3D used for groundwater flow simulation, pathlines analysis, and solute transport in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire"},{"id":391327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5137/sir20205137.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5137"}],"country":"United States","state":"New Hampshire","city":"Milford","otherGeospatial":"Savage Municipal Water-Supply Well Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.78741455078125,\n 42.798675589844414\n ],\n [\n -71.57524108886719,\n 42.798675589844414\n ],\n [\n -71.57524108886719,\n 42.938328528472546\n ],\n [\n -71.78741455078125,\n 42.938328528472546\n ],\n [\n -71.78741455078125,\n 42.798675589844414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Eastern wild turkey (Meleagris gallopavo silvestris; hereafter turkey) is an important game species that is pursued by thousands of Alabama hunters each spring. Biologists in Alabama and other parts of the southeastern U.S. believe that turkey populations have been declining for at least two decades. Managers in many state agencies and organizations believe that liberal spring bag limits and the timing of hunting seasons are contributing to this decline. We used an expert-driven approach to develop models of turkey populations that predicted the outcomes of spring harvest management alternatives. The models were based on recent research and expert judgement regarding the effects of spring hunting regulations on turkey vital rates. We then used the relationship between the expected spring density of adult males and expected harvest elicited from experts to compare the values of the alternatives over a 30-year period. Our model suggests that if later opening dates result in increased turkey productivity and increased harvest, the result will be larger turkey populations, increased harvest, and greater value to stakeholders. In 84% of deterministic projections from 27,951 different initial populations, the highest valued alternative was to open seasons later, reduce bag limits, and shorten the season. This alternative also was best in 48% of projections that included parametric uncertainty. These results were used to produce a decision-support tool, that could be used to guide decisions about spring hunting regulations for turkeys in Alabama, and updated using the results of monitoring programs. Further research is needed to more precisely estimate the causes and effects of spring hunting seasons on turkey vital rates.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Grand, J.B., Silvano, A., Barnett, S., Moore, C., and Stewart, B., 2021, Comparing harvest management alternatives for Eastern Wild Turkeys in Alabama: Cooperator Science Series 137-2021, ii, 37 p.","productDescription":"ii, 37 p.","ipdsId":"IP-125260","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432130,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/comparing-harvest-management-alternatives-eastern-wild-turkeys-alabama","linkFileType":{"id":5,"text":"html"}},{"id":433564,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silvano, A.L.","contributorId":341811,"corporation":false,"usgs":false,"family":"Silvano","given":"A.L.","email":"","affiliations":[{"id":56927,"text":"Alabama Department of Conservation and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnett, S.","contributorId":341812,"corporation":false,"usgs":false,"family":"Barnett","given":"S.","email":"","affiliations":[{"id":56927,"text":"Alabama Department of Conservation and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, C.E.","contributorId":341815,"corporation":false,"usgs":false,"family":"Moore","given":"C.E.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908926,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, B.D.","contributorId":341818,"corporation":false,"usgs":false,"family":"Stewart","given":"B.D.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":908927,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226471,"text":"pp1868 - 2021 - Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud","interactions":[],"lastModifiedDate":"2021-11-22T12:09:52.710721","indexId":"pp1868","displayToPublicDate":"2021-11-19T10:43:51","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1868","displayTitle":"Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud","title":"Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud","docAbstract":"Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product.
Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing.
The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics.
The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing.
Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both.
More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland.
China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent.
This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues.
The GCEP30 product, which can be browsed at full resolution at www.croplands.org, has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1868","usgsCitation":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D., 2021, Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud: U.S. Geological Survey Professional Paper 1868, 63 p., https://doi.org/10.3133/pp1868.","productDescription":"Report: ix, 63 p.; Dataset","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119164","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":391888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1868/pp1868.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1868/covrthb.jpg"},{"id":391890,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/","text":"Associated data","linkHelpText":"- Release of GFSAD 30 meter Cropland Extent Products"}],"contact":"Director,
Western Geographic Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Identifying which environmental and genetic factors affect growth pattern phenotypes can help biologists predict how organisms distribute finite energy resources in response to varying environmental conditions and physiological states. This information may be useful for monitoring and managing populations of cryptic, endangered, and invasive species. Consequently, we assessed the effects of food availability, clutch, and sex on the growth of invasive Burmese pythons (Python bivittatus Kuhl) from the Greater Everglades Ecosystem in Florida, USA. Though little is known from the wild, Burmese pythons have been physiological model organisms for decades, with most experimental research sourcing individuals from the pet trade. Here, we used 60 hatchlings collected as eggs from the nests of two wild pythons, assigned them to High or Low feeding treatments, and monitored growth and meal consumption for 12 weeks, a period when pythons are thought to grow very rapidly. None of the 30 hatchlings that were offered food prior to their fourth week post-hatching consumed it, presumably because they were relying on internal yolk stores. Although only two clutches were used in the experiment, we found that nearly all phenotypic variation was explained by clutch rather than feeding treatment or sex. Hatchlings from clutch 1 (C1) grew faster and were longer, heavier, in better body condition, ate more frequently, and were bolder than hatchlings from clutch 2 (C2), regardless of food availability. On average, C1 and C2 hatchling snout-vent length (SVL) and weight grew 0.15 cm d−1 and 0.10 cm d−1, and 0.20 g d−1 and 0.03 g d−1, respectively. Additional research may be warranted to determine whether these effects remain with larger clutch sample sizes and to identify the underlying mechanisms and fitness implications of this variation to help inform risk assessments and management.
","language":"English","publisher":"The Company of Biologists","doi":"10.1242/bio.058739","usgsCitation":"Josimovich, J.M., Falk, B., Grajal-Puche, A., Hanslowe, E.B., Bartoszek, I., Reed, R., and Currylow, A.F., 2021, Clutch may predict growth of hatchling Burmese pythons better than food availability or sex: Biology Open, v. 10, no. 11, bio058739, 10 p., https://doi.org/10.1242/bio.058739.","productDescription":"bio058739, 10 p.","ipdsId":"IP-121446","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450169,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1242/bio.058739","text":"Publisher Index Page"},{"id":436113,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WHSSJ6","text":"USGS data release","linkHelpText":"Hatchling Growth Experiment Dataset from Invasive Burmese Pythons Captured in 2015 in Southern Florida"},{"id":391972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Josimovich, Jillian Maureen 0000-0002-7523-3496 jjosimovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7523-3496","contributorId":257058,"corporation":false,"usgs":true,"family":"Josimovich","given":"Jillian","email":"jjosimovich@usgs.gov","middleInitial":"Maureen","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falk, Bryan G. 0000-0002-9690-5626","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":265395,"corporation":false,"usgs":false,"family":"Falk","given":"Bryan G.","affiliations":[{"id":54672,"text":"National Park Service, Everglades National Park, 40001 SR 9336, Homestead, Florida 33034, USA","active":true,"usgs":false}],"preferred":false,"id":827123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grajal-Puche, Alejandro 0000-0003-1807-4799","orcid":"https://orcid.org/0000-0003-1807-4799","contributorId":265397,"corporation":false,"usgs":false,"family":"Grajal-Puche","given":"Alejandro","affiliations":[{"id":54677,"text":"Department of Biological Sciences, P.O. Box 5640, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":827124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanslowe, Emma B. 0000-0003-4331-6729","orcid":"https://orcid.org/0000-0003-4331-6729","contributorId":265394,"corporation":false,"usgs":false,"family":"Hanslowe","given":"Emma","email":"","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":827125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartoszek, Ian A.","contributorId":269426,"corporation":false,"usgs":false,"family":"Bartoszek","given":"Ian A.","affiliations":[{"id":55974,"text":"Conservancy of Southwest Florida, Naples, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":827126,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Currylow, Andrea Faye 0000-0003-1631-8964","orcid":"https://orcid.org/0000-0003-1631-8964","contributorId":257055,"corporation":false,"usgs":true,"family":"Currylow","given":"Andrea","email":"","middleInitial":"Faye","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827128,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227182,"text":"70227182 - 2021 - The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities","interactions":[],"lastModifiedDate":"2022-01-04T16:16:06.757333","indexId":"70227182","displayToPublicDate":"2021-11-18T10:00:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities","docAbstract":"Inland recreational fishing, defined as primarily leisure-driven fishing in freshwaters, is a popular pastime in the USA. State natural resource agencies endeavor to provide high-quality and sustainable fishing opportunities for anglers. Managers often use creel and other angler survey data to inform state- and waterbody-level management efforts. Despite the broad implementation of angler surveys and their importance to fisheries management at state scales, regional and national coordination among these activities is minimal, limiting data applicability for larger-scale management practices and research. Here, we introduce the U.S. Inland Creel and Angler Survey Catalog (CreelCat), a first-of-its-kind, publicly available national database of angler survey data that establishes a baseline of national inland recreational fishing metrics. We highlight research and management applications to help support sustainable inland recreational fishing practices, consider cautions, and make recommendations for implementation.
","language":"English","publisher":"Wiley","doi":"10.1002/fsh.10671","usgsCitation":"Lynch, A.J., Sievert, N., Embke, H.S., Robertson, A., Myers, B.J., Allen, M.S., Feiner, Z., Hoogakker, F., Knoche, S., Krogman, R., Midway, S.R., Nieman, C.L., Paukert, C., Pope, K.L., Rogers, M.W., Wszola, L.S., and Beard, 2021, The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities: Fisheries Magazine, v. 46, no. 11, p. 574-583, https://doi.org/10.1002/fsh.10671.","productDescription":"10 p.","startPage":"574","endPage":"583","ipdsId":"IP-122018","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":500804,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/oceanography_coastal_pubs/1070","text":"External Repository"},{"id":436114,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E45TNJ","text":"USGS 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In areas where it has been introduced by humans, American beaver (Castor canadensis) is an iconic example of such species due to its capacity to alter trophic dynamics of entire ecosystems and create new invasional pathways for other non-native species. The species is problematic in several watersheds within the Southern California-Northern Baja California Coast Ecoregion, a recognized hotspot of biodiversity, due to its ability to modify habitat in ways that favor invasive predators and competitors over the region's native species and habitat. Beaver was deliberately introduced across California in the mid-1900s and generally accepted as non-native to the region up to the early 2000s; however, articles promoting the idea that beaver may be a natural resident have gained traction in recent years, due in large part to the species' charismatic nature rather than by presentation of sound evidence. Here, we discuss the problems associated with beaver disturbance and its effects on conserving the region's native fauna and flora. We refute arguments underlying the claim that beaver is native to the region, and review paleontological, zooarchaeological, and historical survey data from renowned field biologists and naturalists over the past ~160 years to show that no evidence exists that beaver arrived by any means other than deliberate human introduction. Managing this ecosystem engineer has potential to reduce the richness and abundance of other non-native species because the novel, engineered habitat now supporting these species would diminish in beaver-occupied watersheds. At the same time, hydrologic functionality would shift toward more natural, ephemeral conditions that favor the regions' native species while suppressing the dominance of the most insidious invaders.
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