Significant Findings for Year 2020: Note that due to covid-19 restrictions, offshore sampling was limited in 2020.
1) May – Oct total phosphorus (TP) in 2020 was 10.6 µg/L (offshore) and 7.7 µg/L (nearshore), higher than the long-term (1995-2019) average in the offshore (6.2 µg/L) and close to average in the nearshore (7.8 µg/L); mean TP values for the past decade (2010-2019) were 6.0 µg/L and 7.9 µg/L in the offshore and nearshore, respectively. In 2020, TP concentrations were significantly higher (p=0.03) in the offshore compared to the nearshore. Note that offshore duplicate samples had high relative percent difference (average 54%. 6-117%) making inferences for the offshore in 2020 uncertain.
2) May – Oct epilimnetic chlorophyll-a was similar at nearshore (1.9 µg/L) and offshore (2.1 µg/L) sites. These values were slightly higher than the average for 1995 – 2019 (1.7 µg/L, offshore; 1.5 µg/L, nearshore) and higher than for the last decade (1.4 µg/L, both offshore and nearshore).
3) May – Oct Secchi depth ranged from 3.5 m to 10.4 m (11 ft to 34 ft) at individual sites and was not significantly different between nearshore (6.3 m; 20.7 ft) and offshore (6.5 m; 21.3 ft) locations. Long-term (1995-2019) average was 7.2 m in the offshore and 6.4 m in the nearshore; means for the last decade were 7.8 m in the offshore and 6.2 m in the nearshore.
4) Despite higher TP values in 2020 than in recent years, TP, chlorophyll-a and Secchi depth are indicative of oligotrophic conditions in the offshore of Lake Ontario.
5) Nearshore summer zooplankton biomass was 10.5 µg/L, near the all-time low (9.4 µg/L, 2017) since monitoring began in 1995. Offshore epilimnetic summer zooplankton biomass was 11.7 µg/L. These values are similar to biomass in the last decade (2010-2019).
6) Peak (July) epilimnetic biomass of Cercopagis was 3.0 µg/L in the nearshore and represented 25% of the zooplankton community at that time; Cercopagis was absent from the July offshore epilimnetic samples in 2020 but was present in whole water column samples taken in August by other agencies. Epilimnetic biomass of Bythotrephes peaked in late-September in both the nearshore (1.0 µg/L) and offshore (1.8 µg/L) and represented 10% and 18% of the zooplankton community at those times, respectively.
7) Summer nearshore and offshore epilimnetic zooplankton density and biomass declined significantly 1995 – 2020. The declines were due mainly to reductions in cyclopoid copepods in both habitats.
No abstract available.
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Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass (Bromus tectorum) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Invasive species in forests and rangelands of the United States: A comprehensive science synthesis for the United States Forest Sector","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"U.S. Forest Service","doi":"10.1007/978-3-030-45367-1_11","collaboration":"U.S. Forest Service","usgsCitation":"Reeves, M., Ibanez, I., Blumenthal, D., Chen, G., Guo, Q., Jarnevich, C.S., Koch, J., Sapio, F., Schwartz, M.D., Meentemeyer, R.K., Wylie, B., and Boyte, S.P., 2021, Tools and technologies for quantifying spread and impacts of invasive species, chap. 11 of Invasive species in forests and rangelands of the United States: A comprehensive science synthesis for the United States Forest Sector, p. 243-265, https://doi.org/10.1007/978-3-030-45367-1_11.","productDescription":"23 p.","startPage":"243","endPage":"265","ipdsId":"IP-082001","costCenters":[{"id":222,"text":"Earth Resources Observation and Science 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In this study, we develop and report aquaculture practices for Red Shiner that ensure consistent year-round production in laboratory settings and evaluate the timing of sexual differentiation via histological gonad examinations. Our methods resulted in a mean of 56.00% (SD = 8.98%) survival through the larval stages of development, and we obtained spawns from captive-reared Red Shiners 138 d posthatch. Red Shiners are gonochoristic, and both ovaries and testes differentiate directly from undifferentiated gonads. Ovaries begin to differentiate in females 45 d posthatch, while testes begin differentiating in males 105 d posthatch. This study provides in-depth protocols for the closed-cycle aquaculture of Red Shiners and describes the gonadal differentiation and development of both sexes.
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Ontario","interactions":[],"lastModifiedDate":"2023-05-09T14:14:35.142575","indexId":"70225737","displayToPublicDate":"2021-07-01T08:52:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5114,"text":"NYSDEC Lake Ontario Annual Report ","active":true,"publicationSubtype":{"id":2}},"chapter":"12","title":"Bottom trawl assessment of benthic preyfish community in Lake Ontario","docAbstract":"Since 1978, the Lake Ontario benthic preyfish survey has provided information on the status and trends of the benthic preyfish community related to Lake Ontario Fish Community Objectives that include understanding preyfish population dynamics and community diversity. Beginning in 2015, the benthic preyfish survey expanded from US-only to incorporate lake-wide sampling sites which drastically increased the survey’s spatial coverage. In 2020, the collaborative benthic preyfish survey completed 82 bottom trawl tows across main lake and embayment sites at depths from 6 to 226 m. Compared to previous years, the survey was largely confined to ports east of Rochester on the US side, and east of Wesleyville on the Canadian side due to COVID-19 travel constraints. In total, the survey sampled 109,315 fish from 32 species in 2020. Round goby was the most numerically abundant species comprising 59% of total catch, followed by alewife (27%), and deepwater sculpin (7%). Despite being a common species represented in historical catches, slimy sculpin comprised only 0.05% of the total catch, with only 53 fish sampled in 2020. Annual biomass indices were among the lowest reported for slimy sculpin across the entire time series, whereas deepwater sculpin biomass remained high but showed a decrease from values observed in 2019. Deepwater sculpin condition in 2020 was lower than in 2019 but similar to values observed in 2017-2018. Community diversity remained high relative to historically lower values when round goby and deepwater sculpin were not captured in trawl catches. Round goby biomass in 2020 was the highest on the US time series, but not on the lake-wide series, highlighting the need to resume lake-wide sampling to further understand how regional vs lake-wide sampling affects benthic preyfish population estimates.
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Conservation","active":true,"usgs":false}],"preferred":false,"id":826459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holden, Jeremy P.","contributorId":190415,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","email":"","middleInitial":"P.","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":826460,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226458,"text":"70226458 - 2021 - Gopherus agassizii","interactions":[],"lastModifiedDate":"2021-11-18T14:41:18.5924","indexId":"70226458","displayToPublicDate":"2021-07-01T08:40:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9925,"text":"The IUCN Red List of Threatened Species 2021","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gopherus agassizii","title":"Gopherus agassizii","docAbstract":"A provisional Red List Assessment of the widespread Desert Tortoise, Gopherus agassizii (sensu lato), was performed at a Desert Tortoise Council workshop in 2010 and updated by the IUCN Tortoise and Freshwater Turtle Specialist Group (TFTSG) in 2011, at which time the Mojave Desert subpopulation, now considered G. agassizii (sensu stricto) following taxonomic analysis and splitting into three separate species (G. agassizii, G. morafkai, and G. evgoodei), was assessed as Critically Endangered A2bce+A4bce based on population reduction (decreasing density), habit loss of over 80% over three generations (90 years), including past reductions and predicted future declines, as well as the effects of disease (upper respiratory tract disease / mycoplasmosis). Gopherus agassizii (sensu stricto) comprises tortoises in the most well-studied 30% of the larger range; this portion of the original range has seen the most human impacts and is where the largest past population losses had been documented. A recent rigorous range-wide population reassessment of G. agassizii (sensu stricto) has demonstrated continued adult population and density declines of about 90% over three generations (two in the past and one ongoing) in four of the five G. agassizii recovery units and inadequate recruitment with decreasing percentages of juveniles in all five recovery units. As such, we reaffirm the prior assessment of the taxonomically restricted Mojave Desert Tortoise, G. agassizii, as Critically Endangered, and add criterion “a” for direct population observations: CR A2abce+A4abce. The previously defined widespread species G. agassizii (sensu lato) was last assessed as Vulnerable on the IUCN Red List in 1996; a separate assessment currently in progress by the TFTSG for the Sonoran Desert Tortoise, G. morafkai (previously considered part of G. agassizii) has provisionally assessed that species as Vulnerable.
","language":"English","publisher":"IUCN","doi":"10.2305/IUCN.UK.2021-2.RLTS.T97246272A3150871.en","usgsCitation":"Berry, K.H., Allison, L.J., McLuckie, A., Vaughn, M., and Murphy, R.W., 2021, Gopherus agassizii: The IUCN Red List of Threatened Species 2021, HTML Document, https://doi.org/10.2305/IUCN.UK.2021-2.RLTS.T97246272A3150871.en.","productDescription":"HTML Document","ipdsId":"IP-125270","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2305/iucn.uk.2021-2.rlts.t97246272a3150871.en","text":"Publisher Index Page"},{"id":391863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, Utah","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.444580078125,\n 32.48196313217176\n ],\n [\n -113.64257812499999,\n 34.30714385628804\n ],\n [\n -113.499755859375,\n 34.768691457552706\n ],\n [\n -114.466552734375,\n 35.951329861522666\n ],\n [\n -113.291015625,\n 36.500805317604794\n ],\n [\n -112.950439453125,\n 37.51844023887861\n ],\n [\n -113.5986328125,\n 37.64903402157866\n ],\n [\n -114.14794921875,\n 37.64903402157866\n ],\n [\n -114.76318359375,\n 36.58024660149866\n ],\n [\n -115.11474609375001,\n 36.155617833818525\n ],\n [\n -116.56494140625001,\n 36.82687474287728\n ],\n [\n -118.09204101562501,\n 36.35052700542763\n ],\n [\n -117.48779296875,\n 34.88593094075317\n ],\n [\n -116.883544921875,\n 34.252676117101515\n ],\n [\n -115.49926757812499,\n 33.247875947924385\n ],\n [\n -115.3564453125,\n 32.759562025650126\n ],\n [\n -114.444580078125,\n 32.48196313217176\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":826969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allison, L. J.","contributorId":269368,"corporation":false,"usgs":false,"family":"Allison","given":"L.","email":"","middleInitial":"J.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":826970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLuckie, A. M.","contributorId":269369,"corporation":false,"usgs":false,"family":"McLuckie","given":"A. M.","affiliations":[{"id":49122,"text":"Utah Division of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":826971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vaughn, M.","contributorId":269372,"corporation":false,"usgs":false,"family":"Vaughn","given":"M.","email":"","affiliations":[{"id":55949,"text":"Turtle Conservancy","active":true,"usgs":false}],"preferred":false,"id":826972,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphy, R. W.","contributorId":269374,"corporation":false,"usgs":false,"family":"Murphy","given":"R.","email":"","middleInitial":"W.","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":826973,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238599,"text":"70238599 - 2021 - Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets","interactions":[],"lastModifiedDate":"2022-12-01T14:47:41.476167","indexId":"70238599","displayToPublicDate":"2021-07-01T08:39:03","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets","docAbstract":"Coastal wetland restoration can be used to offset past wetland losses and/or reduce future losses due to land-use changes, rising sea levels, and accelerating climate change. However, there is a need for information regarding the restoration-relevant performance of foundation species like mangrove and marsh plants, including their responses to acute and chronic stressors that can affect restoration outcomes. Mangrove encroachment and poleward range expansion into marsh, facilitated by warming winters, has provided restoration practitioners in the northern Gulf of Mexico with a new foundation plant species to consider using during restoration. To evaluate the performance of transplanted mangroves and characterize restoration-relevant marsh–mangrove interactions, we planted nursery-raised black mangrove (Avicennia germinans) seedlings within different marsh cover treatments along an eroding marsh-dominated shoreline in Louisiana. Mangrove seedling survival increased with greater densities of marsh cover, indicating that marsh grass (Spartina alterniflora) may facilitate mangrove establishment. However, only 35% of transplanted mangrove seedlings established after 10 weeks, suggesting a low return on resources expended in raising seedlings for 1–3 years in greenhouse conditions. Moreover, a 2018 freeze event killed 100% of transplanted mangrove seedlings, while nearby naturally established mangroves suffered minor damage. Our results, along with those in the mangrove restoration literature, indicate that planting mangroves in the northern Gulf of Mexico may not be the most efficient use of limited resources. Rather, restoration efforts may benefit from focusing initially on the restoration of abiotic conditions (e.g. elevation and hydrologic regimes), followed by using marsh plants (rather than transplanted mangroves) to jump-start ecosystem development.
","language":"English","publisher":"John Wiley & Sons, Inc.","doi":"10.1111/rec.13373","usgsCitation":"Macy, A., Osland, M., Cherry, J.A., and Cebrian, J., 2021, Effects of chronic and acute stressors on transplanted black mangrove (Avicennia germinans) seedlings along an eroding Louisiana shoreline: Restoration Ecology, v. 29, no. 5, e13373, 8 p., https://doi.org/10.1111/rec.13373.","productDescription":"e13373, 8 p.","ipdsId":"IP-117905","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":397219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","city":"Port Fourchon","otherGeospatial":"Bayou Lafourche","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.49163818359375,\n 29.045965338037213\n ],\n [\n -89.86061096191406,\n 29.045965338037213\n ],\n [\n -89.86061096191406,\n 29.398926652739164\n ],\n [\n -90.49163818359375,\n 29.398926652739164\n ],\n [\n -90.49163818359375,\n 29.045965338037213\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Macy, Aaron","contributorId":218917,"corporation":false,"usgs":false,"family":"Macy","given":"Aaron","email":"","affiliations":[{"id":39936,"text":"Dauphin Island Sea Lab, Dauphin Island, AL USA","active":true,"usgs":false}],"preferred":false,"id":838419,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":219805,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cherry, Julia A.","contributorId":195565,"corporation":false,"usgs":false,"family":"Cherry","given":"Julia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":838421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cebrian, Just","contributorId":218914,"corporation":false,"usgs":false,"family":"Cebrian","given":"Just","email":"","affiliations":[{"id":39936,"text":"Dauphin Island Sea Lab, Dauphin Island, AL USA","active":true,"usgs":false}],"preferred":false,"id":838422,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225152,"text":"70225152 - 2021 - Using landscape metrics to characterize towns along an urban-rural gradient","interactions":[],"lastModifiedDate":"2021-10-14T12:33:11.708912","indexId":"70225152","displayToPublicDate":"2021-07-01T07:29:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using landscape metrics to characterize towns along an urban-rural gradient","docAbstract":"Urban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation.
We present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability.
We develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation.
The resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient.
Understanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01287-7","usgsCitation":"Kaminski, A.R., Bauer, D.M., Bell, K., Loftin, C., and Nelson, E., 2021, Using landscape metrics to characterize towns along an urban-rural gradient: Landscape Ecology, v. 36, p. 2937-2956, https://doi.org/10.1007/s10980-021-01287-7.","productDescription":"20 p.","startPage":"2937","endPage":"2956","ipdsId":"IP-105928","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451686,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01287-7","text":"Publisher Index Page"},{"id":390516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut. 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\"}}]}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaminski, Abigail R.","contributorId":267710,"corporation":false,"usgs":false,"family":"Kaminski","given":"Abigail","email":"","middleInitial":"R.","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":825174,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauer, Dana Marie","contributorId":267711,"corporation":false,"usgs":false,"family":"Bauer","given":"Dana","email":"","middleInitial":"Marie","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":825175,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Kathleen P.","contributorId":267712,"corporation":false,"usgs":false,"family":"Bell","given":"Kathleen P.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":825176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":825173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Erik","contributorId":267713,"corporation":false,"usgs":false,"family":"Nelson","given":"Erik","affiliations":[{"id":33315,"text":"Bowdoin College","active":true,"usgs":false}],"preferred":false,"id":825177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232405,"text":"70232405 - 2021 - The distribution of anadromy in steelhead / rainbow trout in the Eel River, northwestern California","interactions":[],"lastModifiedDate":"2022-07-04T17:12:05.965896","indexId":"70232405","displayToPublicDate":"2021-07-01T06:58:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10946,"text":"California Fish and Wildlife Journal","active":true,"publicationSubtype":{"id":10}},"title":"The distribution of anadromy in steelhead / rainbow trout in the Eel River, northwestern California","docAbstract":"To inform management and conservation of the species, we investigated the distribution of anadromy and residency of steelhead/rainbow trout (Oncorhynchus mykiss) in the Eel River of northwestern California. We determined maternal anadromy versus residency for 106 juvenile O. mykiss using otolith microchemistry. To attempt to relate patterns of anadromy with environmental factors known to influence its distribution in O. mykiss in other places, fish were collected from 52 sites throughout the drainage covering a range of stream size (0.1–7.7 m3/s estimated mean annual run-off) and distance from the ocean (23–219 km). Sixty-one of 91 fish sampled below prospective barriers had anadromous mothers, while 1 of 15 fish sampled above barriers had an anadromous mother. We did not detect any influence of stream size or distance from the ocean on the occurrence of anadromy. Fish with resident mothers were found at 21 of 46 sites below barriers. The current broad distribution of fish with resident mothers indicates the importance of maintaining freshwater conditions suitable for resident adults and juveniles age-1 and older, such as preserving dry-season streamflows.
","language":"English","publisher":"California Department of Fish and Wildlife","doi":"10.51492/cfwj.107.7","usgsCitation":"Harvey, B.C., Nakamoto, R.J., Kent, A.J., and Zimmerman, C.E., 2021, The distribution of anadromy in steelhead / rainbow trout in the Eel River, northwestern California: California Fish and Wildlife Journal, v. 107, no. 2, p. 77-88, https://doi.org/10.51492/cfwj.107.7.","productDescription":"12 p.","startPage":"77","endPage":"88","ipdsId":"IP-129368","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":451690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.51492/cfwj.107.7","text":"Publisher Index Page"},{"id":402802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.76074218749999,\n 37.89219554724437\n ],\n [\n -121.06933593749999,\n 37.89219554724437\n ],\n [\n -121.06933593749999,\n 41.11246878918088\n ],\n [\n -124.76074218749999,\n 41.11246878918088\n ],\n [\n -124.76074218749999,\n 37.89219554724437\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Bret C.","contributorId":292678,"corporation":false,"usgs":false,"family":"Harvey","given":"Bret","email":"","middleInitial":"C.","affiliations":[{"id":62967,"text":"U.S. Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":845439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nakamoto, Rodney J.","contributorId":292679,"corporation":false,"usgs":false,"family":"Nakamoto","given":"Rodney","email":"","middleInitial":"J.","affiliations":[{"id":62967,"text":"U.S. Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":845440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Adam J.R.","contributorId":292680,"corporation":false,"usgs":false,"family":"Kent","given":"Adam","email":"","middleInitial":"J.R.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":845441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":845442,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223163,"text":"70223163 - 2021 - Conspecific and congeneric interactions shape increasing rates of breeding dispersal of northern spotted owls","interactions":[],"lastModifiedDate":"2021-10-06T15:55:10.235775","indexId":"70223163","displayToPublicDate":"2021-07-01T06:45:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Conspecific and congeneric interactions shape increasing rates of breeding dispersal of northern spotted owls","docAbstract":"Breeding dispersal, the movement from one breeding territory to another, is rare for philopatric species that evolved within relatively stable environments, such as the old-growth coniferous forests of the Pacific Northwest. Although dispersal is not inherently maladaptive, the consequences of increased dispersal on population dynamics in populations whose historical dispersal rates are low could be significant, particularly for a declining species. We examined rates and possible causes of breeding dispersal based on a sample of 4,118 northern spotted owls (Strix occidentalis caurina) monitored in seven study areas over 28 yr, 1990–2017, in Oregon and Washington, USA. Using a multistate mark–resight analysis, we investigated the potential impacts of an emergent congeneric competitor (barred owl Strix varia) and forest alteration (extrinsic factors), and social and individual conditions (intrinsic factors) on 408 successive and 1,372 nonsuccessive dispersal events between years. The annual probability of breeding dispersal increased for individual owls that had also dispersed in the previous year and decreased for owls on territories with historically high levels of reproduction. Intrinsic factors including pair status, prior reproductive success, and experience at a site, were also associated with breeding dispersal movements. The percent of monitored owls dispersing each year increased from ˜7% early in the study to ˜25% at the end of the study, which coincided with a rapid increase in numbers of invasive and competitively dominant barred owls. We suggest that the results presented here can inform spotted owl conservation efforts as we identify factors contributing to changing rates of demographic parameters including site fidelity and breeding dispersal. Our study further shows that increasing rates of breeding dispersal associated with population declines contribute to population instability and vulnerability of northern spotted owls to extinction, and the prognosis is unlikely to change unless active management interventions are undertaken.
From 2014 to 2017, the U.S. Geological Survey’s Florence Bascom Geoscience Center (FBGC) entered into an inter-agency agreement with the Federal Highway Administration’s Turner-Fairbank Highway Research Center (TFHRC) to assist in field site selection and auger drilling fieldwork. The TFHRC was developing a device to measure the erosional properties of clay-rich sediments to be used for in situ testing at locations of bridge pier construction. FBGC scientists selected 15 drilling locations at 14 different field sites across Northern Virginia and Southern Maryland for the investigation of near-surface sediment properties and the development and testing of the TFHRC’s in situ scour testing device (ISTD). This report provides information about the project and summarizes the data collected during fieldwork including sediment descriptions of the borehole cores and the methods used during fieldwork.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211038","collaboration":"Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Borehole sampling of surficial sediments in Northern Virginia and Southern Maryland: U.S. Geological Survey Open-File Report 2021–1038, 27 p., https://doi.org/10.3133/ofr20211038.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-120037","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":386418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1038/ofr20211038.pdf","text":"Report","size":"6.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1038"},{"id":386421,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A8G5LQ","text":"USGS Data Release","linkHelpText":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Datasheets associated with borehole sampling of surficial sediments in Northern Virginia and Southern Maryland conducted by the U.S. Geological Survey for the Federal Highways Administration Turner-Fairbanks Research Center In Situ Scour Testing Device: U.S. Geological Survey data release"},{"id":386417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1038/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.06884765624999,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 37.77071473849609\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Reintroducing predators is a promising conservation tool to help remedy human-caused ecosystem changes. However, the growth and spread of a reintroduced population is a spatiotemporal process that is driven by a suite of factors, such as habitat change, human activity, and prey availability. Sea otters (Enhydra lutris) are apex predators of nearshore marine ecosystems that had declined nearly to extinction across much of their range by the early 20th century. In Southeast Alaska, which is comprised of a diverse matrix of nearshore habitat and managed areas, reintroduction of 413 individuals in the late 1960s initiated the growth and spread of a population that now exceeds 25,000.
Periodic aerial surveys in the region provide a time series of spatially-explicit data to investigate factors influencing this successful and ongoing recovery. We integrated an ecological diffusion model that accounted for spatially-variable motility and density-dependent population growth, as well as multiple population epicenters, into a Bayesian hierarchical framework to help understand the factors influencing the success of this recovery.
Our results indicated that sea otters exhibited higher residence time as well as greater equilibrium abundance in Glacier Bay, a protected area, and in areas where there is limited or no commercial fishing. Asymptotic spread rates suggested sea otters colonized Southeast Alaska at rates of 1–8 km/yr with lower rates occurring in areas correlated with higher residence time, which primarily included areas near shore and closed to commercial fishing. Further, we found that the intrinsic growth rate of sea otters may be higher than previous estimates suggested.
This study shows how predator recolonization can occur from multiple population epicenters. Additionally, our results suggest spatial heterogeneity in the physical environment as well as human activity and management can influence recolonization processes, both in terms of movement (or motility) and density dependence.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-021-00270-w","usgsCitation":"Eisaguirre, J., Willliams, P.J., Lu, X., Kissling, M.L., Beatty, W.S., Esslinger, G.G., Womble, J.N., and Hooten, M., 2021, Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator: Movement Ecology, v. 9, 34, 14 p., https://doi.org/10.1186/s40462-021-00270-w.","productDescription":"34, 14 p.","ipdsId":"IP-126602","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00270-w","text":"Publisher Index Page"},{"id":387131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.15234374999997,\n 60.28340847828243\n ],\n [\n -142.470703125,\n 59.60109549032134\n ],\n [\n -135.6591796875,\n 55.50374985927514\n ],\n [\n -131.7919921875,\n 51.781435604431195\n ],\n [\n -130.693359375,\n 52.1874047455997\n ],\n [\n -129.9462890625,\n 55.27911529201561\n ],\n [\n -130.0341796875,\n 56.29215668507645\n ],\n [\n -135.7470703125,\n 59.88893689676585\n ],\n [\n -139.2626953125,\n 60.34869562531862\n ],\n [\n -141.15234374999997,\n 60.28340847828243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph M. 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":260861,"corporation":false,"usgs":false,"family":"Eisaguirre","given":"Joseph M.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":818988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willliams, Perry J.","contributorId":260862,"corporation":false,"usgs":false,"family":"Willliams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":818989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Xinyi","contributorId":260863,"corporation":false,"usgs":false,"family":"Lu","given":"Xinyi","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kissling, Michelle L.","contributorId":172675,"corporation":false,"usgs":false,"family":"Kissling","given":"Michelle","email":"","middleInitial":"L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":818991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":146301,"corporation":false,"usgs":false,"family":"Beatty","given":"William","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":818992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Esslinger, George G. 0000-0002-3459-0083 gesslinger@usgs.gov","orcid":"https://orcid.org/0000-0002-3459-0083","contributorId":131009,"corporation":false,"usgs":true,"family":"Esslinger","given":"George","email":"gesslinger@usgs.gov","middleInitial":"G.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":818993,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Womble, Jamie N.","contributorId":198631,"corporation":false,"usgs":false,"family":"Womble","given":"Jamie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":818994,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":818995,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221716,"text":"ofr20211048 - 2021 - Literature review for candidate chemical control agents for nonnative crayfish","interactions":[],"lastModifiedDate":"2021-07-01T11:45:35.778315","indexId":"ofr20211048","displayToPublicDate":"2021-06-30T12:02:12","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":"2021-1048","displayTitle":"Literature Review for Candidate Chemical Control Agents for Nonnative Crayfish","title":"Literature review for candidate chemical control agents for nonnative crayfish","docAbstract":"Nonnative crayfish are an immediate and pervasive threat to aquatic environments and their biodiversity. Crayfish control can be achieved by physical methods, water chemistry modification, biological methods, biocidal application, and application of crayfish physiology modifiers. The purpose of this report is to identify suitable candidates for potential control of nonnative crayfish through a comprehensive literature review. This review focuses on control methods, specifically on the available data to support registration of a crayfish pesticide. The literature search resulted in 28,058 documents, which were searched to determine if they contained information on physical, chemical, biological, and (or) biocidal approaches to control crayfish. Pesticides directly toxic to crayfish in this literature review include: pyrethroids (natural pyrethrins and synthetic), fipronil, mirex, antimycin-A, and rotenone. Some chemicals, such as diflubenzuron and emamectin benzoate, alter crayfish physiology resulting in a lower pesticide dose needed to control crayfish. Environmental damage, application rate, exposure duration, nontarget effects, environmental persistence, and registration data gaps were used as criteria to define which pesticides are potentially selective to crayfish, along with which have the greatest amount of data to support registration by the U.S. Environmental Protection Agency.
Synthetic pyrethroids were identified as the most likely candidate to be developed into a crayfish pesticide. A type-2 synthetic pyrethroid, cyfluthrin, has the greatest potential for eradicating nonnative crayfish. Although other invertebrate species will be negatively affected at the concentrations required for crayfish control, compared with other pyrethroids and other potential control chemicals, cyfluthrin offers rapid ecosystem recovery due to being more selective, having fewer effects on native fish, and having a short aquatic persistence. Cyfluthrin also has few data gaps for U.S. Environmental Protection Agency registration purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211048","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Schueller, J.R., Smerud, J.R., Fredricks, K.T., and Putnam, J.G., 2021, Literature review for candidate chemical control agents for nonnative crayfish: U.S. Geological Survey Open-File Report 2021–1048, 32 p., https://doi.org/10.3133/ofr20211048.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-115061","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1048/ofr20211048.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1048"},{"id":386878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1048/coverthb.jpg"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Wetlands of the Prairie Pothole Region (PPR) in the Great Plains of central North America are numerous, densely distributed, and have highly productive plant and animal communities (Photo 49). When in a natural, unaltered condition, these wetlands store relatively large amounts of organic carbon in their soils (Photo 50). Human alterations, such as extensive drainage and land-use conversion for agriculture (Figure 7), have been linked with the loss of soil organic carbon (SOC) and associated emission of carbon dioxide (CO2), as well as impacts to other ecosystem services provided by these wetlands, such as wildlife and waterfowl habitat, plant biodiversity, flood mitigation, groundwater recharge, nutrient removal and retention, and recreation (Gleason et al., 2011). It has been estimated that more than half of the wetlands of the PPR have been lost due to drainage and other disturbances, with losses approaching 90 percent in some areas (Dahl, 2014; Serran et al., 2018). The goal of this case study was to identify land-management strategies that are consistent with maintaining and increasing SOC stocks of PPR wetlands.
Two overarching strategies generally are promoted to preserve and enhance SOC stocks of PPR wetlands: avoided drainage and rewetting or restoration. Avoided drainage involves protecting natural, unaltered wetlands from impacts of human actives with the purpose of retaining wetland functions and services such as carbon storage. Rewetting or restoration involves reestablishing natural hydrology and land use with the purpose of enhancing wetland functions and services that were previously lost due to human activities. Avoided drainage provides immediate and long-lasting benefits, while replenishing SOC through rewetting and restoration requires many decades. Both strategies are associated with higher methane (CH4) emissions but lower CO2 and nitrous oxide (N2O) emissions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Recarbonizing global soils– A technical manual of recommended management practices","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"United Nations","collaboration":"United Nations","usgsCitation":"Bansal, S., and Tangen, B., 2021, Preserving soil organic carbon in prairie wetlands of central North America, chap. 19 of Recarbonizing global soils– A technical manual of recommended management practices, v. 6, p. 203-212.","productDescription":"10 p.","startPage":"203","endPage":"212","ipdsId":"IP-120160","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":389552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389551,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fao.org/documents/card/en/c/cb6605en"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"central North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.8564453125,\n 41.27780646738183\n ],\n [\n -92.3291015625,\n 43.26120612479979\n ],\n [\n -93.8232421875,\n 45.1510532655634\n ],\n [\n -95.44921875,\n 46.40756396630067\n ],\n [\n -96.6357421875,\n 47.69497434186282\n ],\n [\n -97.42675781249999,\n 49.866316729538674\n ],\n [\n -98.9208984375,\n 50.708634400828224\n ],\n [\n -100.1953125,\n 50.28933925329178\n ],\n [\n -102.3046875,\n 51.45400691005982\n ],\n [\n -107.490234375,\n 52.64306343665892\n ],\n [\n -112.763671875,\n 53.9560855309879\n ],\n [\n -114.5654296875,\n 53.61857936489517\n ],\n [\n -113.7744140625,\n 49.468124067331644\n ],\n [\n -109.1162109375,\n 49.06666839558117\n ],\n [\n -105.2490234375,\n 48.951366470947725\n ],\n [\n -104.23828125,\n 48.40003249610685\n ],\n [\n -101.4697265625,\n 47.931066347509784\n ],\n [\n -99.6240234375,\n 46.437856895024204\n ],\n [\n -97.734375,\n 43.35713822211053\n ],\n [\n -95.3173828125,\n 42.90816007196054\n ],\n [\n -94.0869140625,\n 41.21172151054787\n ],\n [\n -92.8564453125,\n 41.27780646738183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222518,"text":"70222518 - 2021 - Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah","interactions":[],"lastModifiedDate":"2021-08-02T16:10:26.071312","indexId":"70222518","displayToPublicDate":"2021-06-30T11:01:45","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5437,"text":"Utah Geological Survey Miscellaneous Publication","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"MP-173DM","title":"Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah","docAbstract":"The Newfoundland Mountains map area (Newfoundland Mountains and adjacent part of Wells 30' x 60' quadrangles) is located in Box Elder County, northwestern Utah. The map encompasses broad expanses of the Great Salt Lake Desert as well as several picturesque mountain ranges (figures 1, 2, and 3). The geology of the area was last mapped and summarized by Doelling (1980). Since that landmark study, much of the area has been mapped in greater detail and new paleontologic, geochronologic, and structural data provide for an updated view of the geology. In addition, new geophysical studies (Langenheim and others, 2013; Langenheim, 2016) provide key data for improved interpretation of subsurface geology.
The geologic map (plate 1) was compiled from fifteen 7.5' quadrangles mapped at a scale of 1:24,000 (mostly in the western part of the area), one map covering the Newfoundland Mountains at a scale of 1:31,680 (Allmendinger and Jordan, 1989), unpublished geologic mapping at scales from 1:24,000 to 1:50,000 (most by Miller; Bovine Mountain by T.E. Jordan), and reconnaissance mapping and aerial photo interpretation in intervening areas by Miller. Some published maps were remapped or reinterpreted by the authors in light of more recent studies north of the map area. Geologic mapping conducted as part of several theses/dissertations and a few published papers also were used (plate 2, index to geologic mapping). Concealed faults under valley bottoms were interpreted from gravity and aeromagnetic data.
Our approach for this map was to integrate across the main themes of the mapped geology by generalizing units, structures, and polygons. This has the aim of illustrating the principal tectonic and stratigraphic packages, as well as illustrating the patterns of surficial units and geomorphology. Cross sections (plate 2) were constructed to coincide with representative cross sections for several detailed geologic maps. This approach required large bends across valleys. Basin geometry shown in the cross sections was constrained by gravity data and a seismic line since few deep drill holes are available.
","language":"English","publisher":"Utah Geological Survey","usgsCitation":"Miller, D., Felger, T.J., and Langenheim, V., 2021, Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah: Utah Geological Survey Miscellaneous Publication MP-173DM, 34 p.","productDescription":"34 p.","ipdsId":"IP-021940","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":387633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387618,"type":{"id":11,"text":"Document"},"url":"https://ugspub.nr.utah.gov/publications/misc_pubs/mp-173/mp-173.pdf"}],"country":"United States","state":"Utah","county":"Box Elder County","otherGeospatial":"Newfoundland Mountains, Wells 30' x 60' 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Center","active":true,"usgs":true}],"preferred":true,"id":820418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":206978,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":820419,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223832,"text":"70223832 - 2021 - Toward improved decision-support tools for Delta Smelt management actions","interactions":[],"lastModifiedDate":"2021-09-09T16:00:10.030009","indexId":"70223832","displayToPublicDate":"2021-06-30T10:48:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":419,"text":"White Paper","active":false,"publicationSubtype":{"id":9}},"title":"Toward improved decision-support tools for Delta Smelt management actions","docAbstract":"The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level.
Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions.
Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance.
Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement.
• DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions.
• DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth.
• DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt.
There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address.
For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models.
Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time.
Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires:
- Engagement of a committed group of technical experts with appropriate expertise.
- The development of a coordinated workplan including appropriate project management and tracking.
- Dialog between potential users (i.e., managers and policy makers) and technical experts.
- Resources to pursue DST development including personnel and computational resources.
This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.
","language":"English","publisher":"Collaborative Adaptive Management Team","usgsCitation":"Reed, D., Acuna, S., Ateljevich, E., Brown, L.R., Geske, B., Gross, E., Hobbs, J., Kimmerer, W.J., Lucas, L., Nobriga, M., and Rose, K.A., 2021, Toward improved decision-support tools for Delta Smelt management actions: White Paper, v, 34 p.","productDescription":"v, 34 p.","ipdsId":"IP-127826","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.baydeltalive.com/CSAMP/docs/24756"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":822849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acuna, Shawn","contributorId":257756,"corporation":false,"usgs":false,"family":"Acuna","given":"Shawn","email":"","affiliations":[{"id":52106,"text":"Metropolitan Water District of Southern California","active":true,"usgs":false}],"preferred":false,"id":822850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ateljevich, Eli","contributorId":187437,"corporation":false,"usgs":false,"family":"Ateljevich","given":"Eli","email":"","affiliations":[],"preferred":false,"id":822851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geske, Ben","contributorId":265520,"corporation":false,"usgs":false,"family":"Geske","given":"Ben","email":"","affiliations":[{"id":54715,"text":"Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":822853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gross, Edward","contributorId":264402,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":822854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hobbs, Jim","contributorId":200389,"corporation":false,"usgs":false,"family":"Hobbs","given":"Jim","email":"","affiliations":[],"preferred":false,"id":822855,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":822856,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822857,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nobriga, Matthew","contributorId":139247,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":822858,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kenneth A","contributorId":147274,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","email":"","middleInitial":"A","affiliations":[{"id":16815,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge","active":true,"usgs":false}],"preferred":false,"id":822859,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70221876,"text":"70221876 - 2021 - Global tropical reef fish richness could decline by around half if corals are lost","interactions":[],"lastModifiedDate":"2021-07-13T10:01:20.128986","indexId":"70221876","displayToPublicDate":"2021-06-30T09:50:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3173,"text":"Proceedings of the Royal Society B","active":true,"publicationSubtype":{"id":10}},"title":"Global tropical reef fish richness could decline by around half if corals are lost","docAbstract":"Reef fishes are a treasured part of marine biodiversity, and also provide needed protein for many millions of people. Although most reef fishes might survive projected increases in ocean temperatures, corals are less tolerant. A few fish species strictly depend on corals for food and shelter, suggesting that coral extinctions could lead to some secondary fish extinctions. However, secondary extinctions could extend far beyond those few coral-dependent species. Furthermore, it is yet unknown how such fish declines might vary around the world. Current coral mass mortalities led us to ask how fish communities would respond to coral loss within and across oceans. We mapped 6964 coral-reef-fish species and 119 coral genera, and then regressed reef-fish species richness against coral generic richness at the 1° scale (after controlling for biogeographic factors that drive species diversification). Consistent with small-scale studies, statistical extrapolations suggested that local fish richness across the globe would be around half its current value in a hypothetical world without coral, leading to more areas with low or intermediate fish species richness and fewer fish diversity hotspots.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2021.0274","usgsCitation":"Strona, G., Lafferty, K.D., Fattorini, S., Beck, P., Guilhaumon, F., Arrigoni, R., Montano, S., Seveso, D., Galli, P., Planes, S., and Parravicini, V., 2021, Global tropical reef fish richness could decline by around half if corals are lost: Proceedings of the Royal Society B, v. 288, no. 1953, 8 p., https://doi.org/10.1098/rspb.2021.0274.","productDescription":"8 p.","ipdsId":"IP-130442","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2021.0274","text":"Publisher Index Page"},{"id":387113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Southeast Asia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 178.59375,\n -32.842673631954305\n ],\n [\n 198.28125,\n -1.0546279422758742\n ],\n [\n 160.3125,\n 20.632784250388028\n ],\n [\n 125.15625000000001,\n 28.613459424004414\n ],\n [\n 108.984375,\n 24.206889622398023\n ],\n [\n 97.3828125,\n 29.84064389983441\n ],\n [\n 88.59374999999999,\n 16.97274101999902\n ],\n [\n 94.21875,\n -12.211180191503997\n ],\n [\n 129.0234375,\n -9.44906182688142\n ],\n [\n 152.9296875,\n -12.897489183755892\n ],\n [\n 178.59375,\n -32.842673631954305\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"288","issue":"1953","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Strona, Giovanni","contributorId":237089,"corporation":false,"usgs":false,"family":"Strona","given":"Giovanni","affiliations":[{"id":47601,"text":"University of Helsinki, Research Centre for Ecological Change, Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":819166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":819167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fattorini, Simone","contributorId":260938,"corporation":false,"usgs":false,"family":"Fattorini","given":"Simone","email":"","affiliations":[{"id":52729,"text":"Department of Life, Health and Environmental Sciences, University of L'Aquila, Italy","active":true,"usgs":false}],"preferred":false,"id":819168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Pieter","contributorId":260939,"corporation":false,"usgs":false,"family":"Beck","given":"Pieter","email":"","affiliations":[{"id":52730,"text":"European Commission, Joint Research Centre (JRC), Ispra, Italy","active":true,"usgs":false}],"preferred":false,"id":819169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guilhaumon, Francois","contributorId":260940,"corporation":false,"usgs":false,"family":"Guilhaumon","given":"Francois","email":"","affiliations":[{"id":52731,"text":"MARBEC, IRD, CNRS, University of Montpellier, Ifremer, France","active":true,"usgs":false}],"preferred":false,"id":819170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arrigoni, Roberto","contributorId":260941,"corporation":false,"usgs":false,"family":"Arrigoni","given":"Roberto","email":"","affiliations":[{"id":52730,"text":"European Commission, Joint Research Centre (JRC), Ispra, Italy","active":true,"usgs":false}],"preferred":false,"id":819171,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Montano, Simone","contributorId":260942,"corporation":false,"usgs":false,"family":"Montano","given":"Simone","email":"","affiliations":[{"id":52732,"text":"Department of Earth and Environmental Sciences (DISAT), University of Milan - Bicocca, Italy","active":true,"usgs":false}],"preferred":false,"id":819172,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Seveso, Davide","contributorId":260943,"corporation":false,"usgs":false,"family":"Seveso","given":"Davide","email":"","affiliations":[{"id":52732,"text":"Department of Earth and Environmental Sciences (DISAT), University of Milan - Bicocca, Italy","active":true,"usgs":false}],"preferred":false,"id":819173,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Galli, Paolo","contributorId":260944,"corporation":false,"usgs":false,"family":"Galli","given":"Paolo","affiliations":[{"id":52732,"text":"Department of Earth and Environmental Sciences (DISAT), University of Milan - Bicocca, Italy","active":true,"usgs":false}],"preferred":false,"id":819174,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Planes, Serge","contributorId":260945,"corporation":false,"usgs":false,"family":"Planes","given":"Serge","email":"","affiliations":[{"id":52733,"text":"PSL Research University: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Université de Perpignan, France","active":true,"usgs":false}],"preferred":false,"id":819175,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Parravicini, Valeriano","contributorId":260946,"corporation":false,"usgs":false,"family":"Parravicini","given":"Valeriano","email":"","affiliations":[{"id":52733,"text":"PSL Research University: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Université de Perpignan, France","active":true,"usgs":false}],"preferred":false,"id":819176,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70220178,"text":"70220178 - 2021 - Strength recovery and sealing under hydrothermal conditions","interactions":[],"lastModifiedDate":"2021-09-30T15:11:55.401134","indexId":"70220178","displayToPublicDate":"2021-06-30T09:29:44","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Strength recovery and sealing under hydrothermal conditions","docAbstract":"While there is significant evidence for healing in natural faults, geothermal reservoirs, and lab experiments, the thermal, hydraulic, mechanical, and chemical interactions that influence healing are poorly understood. We present preliminary results of triaxial slide-hold-slide experiments to constrain rates and mechanisms of healing. Experiments were conducted on gouge composed of Westerly granite and on bare surfaces of Westerly granite and Eureka quartzite. Tests were run at 22, 100, and 200˚C. In some experiments, we also determined the in-plane fluid transmissivity. In bare surface experiments we observe that restrengthening depends on both time and temperature. At 200˚C the simulated fractures restrengthen at a rate of ∆µ/∆log(thold) = 0.009/decade while at 22˚C the healing rate is ~ 0.002/decade. In the gouge experiments restrengthening appears to be independent of temperature. This may be related to the heterogenous mineral composition and thickness of the gouge layer which could allow shearing to be accommodated in unhealed zones. In the experiments, an overall reduction in fluid transmissivity is observed but sliding periods are often associated with increases in the fluid transmissivity. The transmissivity reduction tends to be greater at 200˚C relative to room temperature. Our preliminary results suggest that multiple healing mechanisms are operating under hydrothermal conditions.
","conferenceTitle":"55th US Rock Mechanics/Geomechanics Symposium","conferenceDate":"Jun 20-23, 2021","conferenceLocation":"Houston, TX","language":"English","publisher":"American Rock Mechanics Association","usgsCitation":"Jeppson, T.N., Lockner, D., Kilgore, B.D., Beeler, N.M., and Taron, J., 2021, Strength recovery and sealing under hydrothermal conditions, 55th US Rock Mechanics/Geomechanics Symposium, Houston, TX, Jun 20-23, 2021, 11 p.","productDescription":"11 p.","ipdsId":"IP-127005","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":390036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jeppson, Tamara Nicole 0000-0001-5526-5530","orcid":"https://orcid.org/0000-0001-5526-5530","contributorId":248768,"corporation":false,"usgs":true,"family":"Jeppson","given":"Tamara","email":"","middleInitial":"Nicole","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":814644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockner, David A. 0000-0001-8630-6833","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":257574,"corporation":false,"usgs":true,"family":"Lockner","given":"David A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":814645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kilgore, Brian D. 0000-0003-0530-7979 bkilgore@usgs.gov","orcid":"https://orcid.org/0000-0003-0530-7979","contributorId":3887,"corporation":false,"usgs":true,"family":"Kilgore","given":"Brian","email":"bkilgore@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":814646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":814647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taron, Joshua M. 0000-0003-2719-3917","orcid":"https://orcid.org/0000-0003-2719-3917","contributorId":257575,"corporation":false,"usgs":true,"family":"Taron","given":"Joshua M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":814648,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223729,"text":"70223729 - 2021 - Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","interactions":[],"lastModifiedDate":"2021-09-16T15:12:14.688708","indexId":"70223729","displayToPublicDate":"2021-06-30T09:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","docAbstract":"Green Lake is the deepest natural inland lake in Wisconsin, USA, with a maximum depth of about 72 meters (m). In the early 1900’s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity), with low dissolved oxygen (DO) concentrations only in the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake increased, which led to increased algal production and low DO concentrations not only occurring in its deepest areas but also in the middle of the water column (metalimnion). Routine monitoring of the lake and its tributaries has been conducted by the U.S. Geological Survey since 2004 and 1988, respectively. Results from this monitoring led to the Wisconsin Department of Natural Resources (WDNR) listing the lake as impaired because of low DO concentrations in the metalimnion, with elevated TP concentrations identified as the cause of impairment.
As part of this study, comprehensive sampling of the lake and its tributaries was conducted in 2017–2018 to augment ongoing monitoring and further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water quality models were then used to determine the causes of the low DO concentrations and the magnitude of P load reductions needed to improve the water quality of the lake to meet multiple water-quality goals, including the WDNR criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900’s, but since the late 1970s have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–2018 (baseline period for this study), the near-surface geometric-mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were below the 0.015 mg/L WDNR criterion for the lake), and the minimum metalimnetic DO concentrations measured in August ranged from 1.0 to 4.7 mg/L. It was believed that the degradation in water quality was caused by excessive P inputs to the lake; therefore, the total P inputs to the lake were estimated. The mean annual external P load during 2014–2018 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. At fall turnover, internal sediment recycling contributed an additional 7,040 kg that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there is a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, indicated by Secchi disk depths, had a larger response to decreases in P loading than to increases in loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration on the east side of the lake, with higher TP concentrations than the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr equates to a 46-percent reduction in the potentially controllable external P sources (all external sources except precipitation, atmospheric deposition, and waterfowl) from that measured during water years (WYs) 2014–2018. The total external P loading would need to be decreased to 7,680 kg/yr (17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to be decreased to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June-September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM-AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that metalimnetic DO minima are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM-AED results indicated that settling of organic material during summer may be slowed by the colder, denser, and more viscous water in the metalimnion and increase DO consumption. Based on empirical evidence comparing minimum metalimnetic DO concentrations with various meteorological, hydrologic, water quality, and in-lake physical factors, lower metalimnetic DO concentrations occurred when there was warmer metalimnetic water temperatures, higher near-surface Chl-a and TP concentrations, and lower Secchi depths during summer. GLM-AED results indicated that the external P load would need to be reduced to about 4,010 kg/yr, a 57-percent reduction from that measured in 2014–2018, to eliminate the occurrence of metalimnetic DO minima of less than 5 mg/L in over 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake. However, it may take several years for the full effects of the external load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Diagnostic and feasibility study findings: Water quality improvements for Green Lake, Wisconsin","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Green Lake Association","usgsCitation":"Robertson, D., Siebers, B.J., Ladwig, R., Hamilton, D., Reneau, P., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2021, Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading, vii, 115 p.","productDescription":"vii, 115 p.","ipdsId":"IP-129488","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":389346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388824,"type":{"id":15,"text":"Index Page"},"url":"https://www.greenlakeassociation.org/research/"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07920837402344,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.75894467245554\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":822505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":822506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Cory P. 0000-0002-1208-8471","orcid":"https://orcid.org/0000-0002-1208-8471","contributorId":261754,"corporation":false,"usgs":false,"family":"McDonald","given":"Cory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":822508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prellwitz, Stephanie","contributorId":265281,"corporation":false,"usgs":false,"family":"Prellwitz","given":"Stephanie","email":"","affiliations":[{"id":54642,"text":"Green Lake Association","active":true,"usgs":false}],"preferred":false,"id":822509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lathrop, Richard C","contributorId":172075,"corporation":false,"usgs":false,"family":"Lathrop","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822510,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221867,"text":"70221867 - 2021 - Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","interactions":[],"lastModifiedDate":"2022-04-13T20:17:41.090462","indexId":"70221867","displayToPublicDate":"2021-06-30T09:14:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","docAbstract":"Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.
","language":"English","publisher":"MDPI","doi":"10.3390/f12070864","usgsCitation":"McKerrow, A., Davidson, A., Rubino, M., Faber-Langendoen, D., and Dockter, D., 2021, Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups: Forests, v. 12, no. 7, 864, 15 p., https://doi.org/10.3390/f12070864.","productDescription":"864, 15 p.","ipdsId":"IP-129762","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":451704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f12070864","text":"Publisher Index Page"},{"id":387107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidson, Anne","contributorId":197967,"corporation":false,"usgs":false,"family":"Davidson","given":"Anne","email":"","affiliations":[],"preferred":false,"id":819085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubino, Matthew J. 0000-0003-0651-3053","orcid":"https://orcid.org/0000-0003-0651-3053","contributorId":215500,"corporation":false,"usgs":false,"family":"Rubino","given":"Matthew J.","affiliations":[{"id":39268,"text":"North Carolina State University, NC Cooperative Fish & Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":819086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faber-Langendoen, Don","contributorId":260895,"corporation":false,"usgs":false,"family":"Faber-Langendoen","given":"Don","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":819087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":819088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236252,"text":"70236252 - 2021 - The drying regimes of non-perennial rivers and streams","interactions":[],"lastModifiedDate":"2022-08-31T13:36:51.788646","indexId":"70236252","displayToPublicDate":"2021-06-30T08:34:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The drying regimes of non-perennial rivers and streams","docAbstract":"The flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non-perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no-flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL093298","usgsCitation":"Price, A.N., Jones, C.N., Hammond, J., Zimmer, M., and Zipper, S., 2021, The drying regimes of non-perennial rivers and streams: Geophysical Research Letters, v. 48, no. 14, e2021GL093298, 12 p., https://doi.org/10.1029/2021GL093298.","productDescription":"e2021GL093298, 12 p.","ipdsId":"IP-127641","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":405993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"14","noUsgsAuthors":false,"publicationDate":"2021-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":850332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, C. Nathan 0000-0002-5804-0510","orcid":"https://orcid.org/0000-0002-5804-0510","contributorId":295972,"corporation":false,"usgs":false,"family":"Jones","given":"C.","email":"","middleInitial":"Nathan","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":850333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmer, Margaret 0000-0001-8287-1923","orcid":"https://orcid.org/0000-0001-8287-1923","contributorId":225158,"corporation":false,"usgs":false,"family":"Zimmer","given":"Margaret","affiliations":[{"id":41054,"text":"Earth and Planetary Sciences, University of California, Santa Cruz, CA, 95064, USA","active":true,"usgs":false}],"preferred":false,"id":850335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zipper, Samuel 0000-0002-8735-5757","orcid":"https://orcid.org/0000-0002-8735-5757","contributorId":225160,"corporation":false,"usgs":false,"family":"Zipper","given":"Samuel","email":"","affiliations":[{"id":41056,"text":"Kansas Geological Survey, University of Kansas, Lawrence KS 66047, USA","active":true,"usgs":false}],"preferred":false,"id":850336,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221670,"text":"ofr20211060 - 2021 - Estimated water withdrawals and use in Puerto Rico, 2015","interactions":[],"lastModifiedDate":"2021-07-01T11:41:28.037511","indexId":"ofr20211060","displayToPublicDate":"2021-06-30T08:33:16","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":"2021-1060","displayTitle":"Estimated Water Withdrawals and Use in Puerto Rico, 2015","title":"Estimated water withdrawals and use in Puerto Rico, 2015","docAbstract":"Water withdrawals and use in Puerto Rico for 2015 were estimated at 2,372 million gallons per day (Mgal/d), which was 21 percent less than withdrawals and use for 2010. The 2015 total water withdrawal and use estimates were the lowest since 1990 and coincided with a substantial decline of 25 percent in saline-water withdrawals for thermoelectric-power cooling processes from 2010 to 2015. Freshwater withdrawals were 671 Mgal/d, or 28 percent of total water withdrawals, and saline-water withdrawals were 1,701 Mgal/d, or 72 percent of total withdrawals. Fresh surface-water withdrawals were estimated at 548 Mgal/d, 10 percent less than in 2010, whereas fresh groundwater withdrawals were estimated at 122 Mgal/d, 2 percent less than in 2010. Saline surface-water withdrawals were 25 percent less than in 2010.
Freshwater withdrawals were greatest for public-supply water and irrigation in 2015 and, combined, accounted for 98 percent of Puerto Rico’s total freshwater withdrawals. Withdrawals in 2015 for public-supply water (576 Mgal/d) were 14 percent lower and withdrawals for irrigation (78 Mgal/d) were 104 percent greater than in 2010, possibly because of drought conditions in agricultural counties along the south and southeast coasts in 2015. The sources for public-supply water withdrawals in 2015 included surface water (88 percent) and groundwater (12 percent). Withdrawals for other uses, which account for the remaining 2 percent of Puerto Rico’s total freshwater withdrawals, were lower in 2015 than in 2010; specifically, withdrawals for domestic self-supplied use decreased by 78 percent, industrial withdrawals decreased by 15 percent, and withdrawals for livestock decreased by 25 percent. Freshwater withdrawals for thermoelectric power and mining were greater in 2015 than in 2010, increasing by 23 percent and 5 percent, respectively.
The total population of Puerto Rico decreased by 7 percent from 2010 to 2015, from 3.73 million people in 2010 to 3.47 million people in 2015. The number of people who obtained potable water from public-supply water facilities in 2015 was about 3.47 million, or about 100 percent of the population of Puerto Rico.
Public-supply water deliveries for domestic use accounted for 338 Mgal/d in 2015, which is 47 percent greater than in 2010, indicating an increase in domestic per capita use from 62 to 98 gallons per person per day from 2010 to 2015. Domestic self-supplied withdrawals were estimated at 0.52 Mgal/d in 2015, for an estimated 4,708 people (less than 1 percent of Puerto Rico’s population). All domestic self-supplied withdrawals were assumed to be from groundwater sources.
Irrigation freshwater withdrawals were 78 Mgal/d in 2015 and accounted for 12 percent of the total freshwater withdrawals for all uses. Surface-water deliveries from irrigation districts accounted for 44 percent of total irrigation withdrawals, whereas groundwater withdrawals accounted for 56 percent. About 37,000 acres were irrigated in 2015, a decrease of 11 percent or about 4,000 acres compared to 2010. About 99 percent of the acreage was irrigated by micro-irrigation and sprinkler systems in 2015. About 65 percent of the irrigation withdrawals were accounted for by four municipalities: Santa Isabel, Salinas, Lajas, and Juana Díaz.
Altogether, freshwater withdrawals for livestock, industrial, mining, and thermoelectric power accounted for 2 percent (16.2 Mgal/d) of freshwater withdrawals for all uses, 9 percent less than in 2010. About 71 percent of the freshwater withdrawn for these categories was from groundwater sources.
In 2015, 50 percent of the total freshwater withdrawn in Puerto Rico was apportioned to six municipalities: Arecibo, Trujillo Alto, Toa Alta, Villalba, Aguada, and Mayagüez. Arecibo accounted for about 18 percent of the total freshwater withdrawals, predominantly for public-supply water use. Trujillo Alto, Toa Alta, Villalba, Aguada, and Mayagüez accounted for about 32 percent (213 Mgal/d) of the total freshwater withdrawals, which were predominantly for public-supply water uses. Withdrawals in some of these municipalities are subsequently distributed to other municipalities such as those in the San Juan metro area. The Puerto Rico Aqueduct and Sewer Authority water service area for the San Juan metro area (referred to as W–102) accounted for about 28 percent of the total water delivered from public-supply water facilities to domestic users, which includes about 34 percent of the total population of Puerto Rico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211060","collaboration":"Prepared in cooperation with the Puerto Rico Aqueduct and Sewer Authority and the Puerto Rico Environmental Quality Board","usgsCitation":"Molina-Rivera, W.L., and Irizarry-Ortiz, M.M., 2021, Estimated water withdrawals and use in Puerto Rico, 2015: U.S. Geological Survey Open-File Report 2021–1060, 38 p., https://doi.org/10.3133/ofr20211060.","productDescription":"Report: vii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-096352","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":386796,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9POVNC6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Spatial and tabular datasets of water withdrawals and use in Puerto Rico, 2015"},{"id":386795,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1060/ofr20211060.pdf","text":"Report","size":"6.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1060"},{"id":386794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1060/coverthb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.445068359375,\n 17.764381077782076\n ],\n [\n -65.1873779296875,\n 17.764381077782076\n ],\n [\n -65.1873779296875,\n 18.651449894396634\n ],\n [\n -67.445068359375,\n 18.651449894396634\n ],\n [\n -67.445068359375,\n 17.764381077782076\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559