In 1979, S. Uyeda and H. Kanamori proposed a tectonic model with two end members of a subduction-boundary continuum: the “Chilean” type (shallow dip of the subducting plate, great thrust events, compression, and uplift of the overriding plate) and a “Mariana” type (steep dip of the subducting plate, no great thrust events, tension, and no uplift). This concept has been used to explain variable rates of Quaternary uplift around the Pacific Rim, yet no uplift rates have been determined for the Mariana Islands themselves, one of the end members in this model. We studied the late Quaternary Tanapag Limestone, which rims much of the eastern and southern coasts of Saipan, Northern Mariana Islands, with elevations of ∼13 m to ∼30 m. Samples from 12 well-preserved corals (Acropora, Porites, and Goniastrea) yielded U-series ages ranging from ca. 134 ka to ca. 126 ka. These ages correlate the emergent reef of the Tanapag Limestone with the last interglacial period, when sea level was several meters above present. Ages and measured reef elevations from the Tanapag Limestone, along with paleo–sea-level data, yield relatively low late Quaternary uplift rates of 0.002–0.19 m/k.y., consistent with the Uyeda-Kanamori model. A review of data from other localities near subduction zones around the Pacific Basin, however, indicates that many coastlines do not fit the model. Uplift rates along the Chilean coast are predicted to be relatively high, but field studies indicate they are low. On some coastlines, relatively high uplift rates are better explained by subduction of seamounts or submarine ridges rather than subduction zone geometry. Despite the low long-term uplift rate on Saipan, the island also hosts an emergent, low-elevation (+3.9–4.0 m) reef with corals in growth position below a notch (+4.2 m). The corals are dated to 3.9–3.1 ka. The occurrence of this young, emergent reef is likely not due to tectonic uplift; instead, it is interpreted to be the result of glacial isostatic adjustment processes after the end of the last glacial period. Our findings are consistent with similar observations on tectonically stable or slowly uplifting islands elsewhere in the equatorial Pacific Ocean and agree with numerical models of a higher-than-present Holocene sea level in this region due to glacial isostatic adjustment processes.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35162.1","usgsCitation":"Muhs, D., Schweig, E.S., and Simmons, K., 2020, Late Quaternary sea-level history of Saipan, Commonwealth of the Northern Mariana Islands, USA: A test of tectonic uplift and glacial isostatic adjustment models: Geological Society of America Bulletin, v. 132, p. 863-883, https://doi.org/10.1130/B35162.1.","productDescription":"21 p.","startPage":"863","endPage":"883","ipdsId":"IP-102631","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":377440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Mariana Islands, Saipan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 145.79887390136716,\n 15.166914868426344\n ],\n [\n 145.78445434570312,\n 15.205349599759678\n ],\n [\n 145.83663940429688,\n 15.268950303672504\n ],\n [\n 145.81260681152344,\n 15.298094191660693\n ],\n [\n 145.70960998535156,\n 15.223901791042142\n ],\n [\n 145.68214416503906,\n 15.117867306000468\n ],\n [\n 145.71578979492188,\n 15.097316980284674\n ],\n [\n 145.7549285888672,\n 15.088698509791715\n ],\n [\n 145.79887390136716,\n 15.166914868426344\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","noUsgsAuthors":false,"publicationDate":"2019-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796005,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schweig, Eugene S. 0000-0003-3669-9741 schweig@usgs.gov","orcid":"https://orcid.org/0000-0003-3669-9741","contributorId":1271,"corporation":false,"usgs":true,"family":"Schweig","given":"Eugene","email":"schweig@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796006,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simmons, Kathleen R. 0000-0002-7920-094X","orcid":"https://orcid.org/0000-0002-7920-094X","contributorId":229460,"corporation":false,"usgs":false,"family":"Simmons","given":"Kathleen R.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":796007,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205560,"text":"70205560 - 2020 - Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","interactions":[],"lastModifiedDate":"2020-01-20T12:22:35","indexId":"70205560","displayToPublicDate":"2019-09-09T10:19:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","docAbstract":"Streamwater quality can be affected by climate-related variability in hydrologic state, which controls flow paths and affects biogeochemical processes. Thirty-one years of input/output solute fluxes at Panola Mountain Research Watershed, a small, forested, seasonally water-limited watershed near Atlanta, Georgia, were used to quantify the effects of climatic-related variability in storage on streamwater solute concentrations and fluxes. Streamwater fluxes were estimated for ten solutes from weekly and event sample concentrations using regression-based methods. The most pertinent storage attribute (current or antecedent watershed, shallow, and deep storage) for each solute was determined by fitting separate concentration relationships. The concentration-discharge relationships varied more for reactive solutes such as potassium, sulfate, and DOC and less for weathering products (base cations and dissolved silica) and conservative chloride. Many solutes exhibited higher concentrations when storage levels were lower or wetting up, which was likely the result of the concentrating effects of evapotranspiration and/or the buildup and flushing of weathering products associated with longer residence times. The impacts of storage modeling on annual fluxes varied by solute, ranging from about 5% (magnesium) to 52% (nitrate) as relative standard deviations, and sufficiently removed climate-related patterns observed in streamwater concentrations. Sulfate was particularly mobilized following growing season droughts but only if deep storage was sufficiently recharged, possibly indicating that sulfides in the deep storage pool were oxidized to sulfate during droughts and mobilized when re-wetted. The lack of streamwater sulfate response to 61% declines in atmospheric deposition indicates the importance of watershed biogeochemical processes on controls of streamwater export of sulfate. The approach of explicitly incorporating storage in the streamwater concentration modeling elucidated the effects of climate on streamwater water-quality and may provide insight into the effects of climatic change on future fluxes.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13589","usgsCitation":"Aulenbach, B.T., 2020, Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States: Hydrological Processes, v. 34, no. 2, p. 189-208, https://doi.org/10.1002/hyp.13589.","productDescription":"20 p.","startPage":"189","endPage":"208","ipdsId":"IP-104585","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":367690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Panola Mountain Research Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.18994903564453,\n 33.61976556057674\n ],\n [\n -84.13021087646484,\n 33.61976556057674\n ],\n [\n -84.13021087646484,\n 33.64627826509988\n ],\n [\n -84.18994903564453,\n 33.64627826509988\n ],\n [\n -84.18994903564453,\n 33.61976556057674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771652,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227104,"text":"70227104 - 2020 - Attack of the PCR clones: Rates of clonality have little effect on RAD-seq genotype calls","interactions":[],"lastModifiedDate":"2021-12-29T14:00:37.386552","indexId":"70227104","displayToPublicDate":"2019-09-03T07:57:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Attack of the PCR clones: Rates of clonality have little effect on RAD-seq genotype calls","docAbstract":"Interpretation of high-throughput sequence data requires an understanding of how decisions made during bioinformatic data processing can influence results. One source of bias that is often cited is PCR clones (or PCR duplicates). PCR clones are common in restriction site-associated sequencing (RAD-seq) data sets, which are increasingly being used for molecular ecology. To determine the influence PCR clones and the bioinformatic handling of clones have on genotyping, we evaluate four RAD-seq data sets. Data sets were compared before and after clones were removed to estimate the number of clones present in RAD-seq data, quantify how often the presence of clones in a data set causes genotype calls to change compared to when clones were removed, investigate the mechanisms that lead to genotype call changes and test whether clones bias heterozygosity estimates. Our RAD-seq data sets contained 30%–60% PCR clones, but 95% of RAD-tags had five or fewer clones. Relatively few genotypes changed once clones were removed (5%–10%), and the vast majority of these changes (98%) were associated with genotypes switching from a called to no-call state or vice versa. PCR clones had a larger influence on genotype calls in individuals with low read depth but appeared to influence genotype calls at all loci similarly. Removal of PCR clones reduced the number of called genotypes by 2% but had almost no influence on estimates of heterozygosity. As such, while steps should be taken to limit PCR clones during library preparation, PCR clones are likely not a substantial source of bias for most RAD-seq studies.
1. Reliable statistical inference is central to ecological research, much of which seeks to estimate population attributes and their interactions. The issue of sampling design and its relationship to inference has become increasingly important due to rapid proliferation of modeling methodology (line transect modeling, capture-recapture, estimation of occurrence, model selection procedures, hierarchical modeling) and new sampling approaches (adaptive sampling, other specialized designs). It is important for ecologists using these advanced methods to be aware of how the linkages between sample selection and data analysis can potentially affect inference. 2. We examine design-based and model-based inference frameworks for ecological data collected randomly, purposively, or opportunistically. We elucidate differences in the probability structures for data arising from these frameworks, clarify the assumptions that underlie them, and demonstrate their differences. 3. Design-based inference builds on a probability structure inherited from randomized data collection, whereas model-based inference relies on an assumed stochastic model of the data. By itself, a design-based approach is of limited value for inferences about causal hypotheses. In contrast, model-based inference is dependent on a conditionality principle that can seldom be shown to be met for an ecological system. We describe the conditions under which one can safely ignore sampling design in model-based analysis, along with inferential implications if these conditions are not met. The special case of opportunistic sampling is discussed. 4. We present a combined framework that takes advantage of both approaches to inference, and provides a robust methodology that can deal with the modeling of sampling problems such as nondetection and misclassification, as well as the exploration of causal hypotheses. The combined framework can be useful for identifying optimal sampling strategies. 5. Each approach to inference has its strengths and weaknesses, and practitioners should be aware of these in order to tailor designs and analyses to specific questions. We use the approaches and their underlying rationales to provide guidelines for choosing designs and estimators for reliable inference.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13279","usgsCitation":"Williams, B.K., and Brown, E., 2020, Sampling and analysis frameworks for inference in ecology: Methods in Ecology and Evolution, v. 11, no. 10, p. 1832-1842, https://doi.org/10.1111/2041-210X.13279.","productDescription":"11 p.","startPage":"1832","endPage":"1842","ipdsId":"IP-105921","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":458716,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13279","text":"Publisher Index Page"},{"id":371624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Byron K. 0000-0001-7644-1396","orcid":"https://orcid.org/0000-0001-7644-1396","contributorId":86616,"corporation":false,"usgs":true,"family":"Williams","given":"Byron","email":"","middleInitial":"K.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":false,"id":780468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Ellie 0000-0001-7798-830X ebrown@usgs.gov","orcid":"https://orcid.org/0000-0001-7798-830X","contributorId":200491,"corporation":false,"usgs":true,"family":"Brown","given":"Ellie","email":"ebrown@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":false,"id":780467,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215259,"text":"70215259 - 2020 - Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic","interactions":[],"lastModifiedDate":"2020-10-14T12:56:58.208018","indexId":"70215259","displayToPublicDate":"2019-07-22T07:50:39","publicationYear":"2020","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":"Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic","docAbstract":"Active coral restoration typically involves two interventions: crossing gametes to facilitate sexual larval propagation; and fragmenting, growing, and outplanting adult colonies to enhance asexual propagation. From an evolutionary perspective, the goal of these efforts is to establish self‐sustaining, sexually reproducing coral populations that have sufficient genetic and phenotypic variation to adapt to changing environments. Here, we provide concrete guidelines to help restoration practitioners meet this goal for most Caribbean species of interest. To enable the persistence of coral populations exposed to severe selection pressure from many stressors, a mixed provenance strategy is suggested: genetically unique colonies (genets) should be sourced both locally as well as from more distant, environmentally distinct sites. Sourcing three to four genets per reef along environmental gradients should be sufficient to capture a majority of intraspecies genetic diversity. It is best for practitioners to propagate genets with one or more phenotypic traits that are predicted to be valuable in the future, such as low partial mortality, high wound healing rate, high skeletal growth rate, bleaching resilience, infectious disease resilience, and high sexual reproductive output. Some effort should also be reserved for underperforming genets because colonies that grow poorly in nurseries sometimes thrive once returned to the reef and may harbor genetic variants with as yet unrecognized value. Outplants should be clustered in groups of four to six genets to enable successful fertilization upon maturation. Current evidence indicates that translocating genets among distant reefs is unlikely to be problematic from a population genetic perspective but will likely provide substantial adaptive benefits. Similarly, inbreeding depression is not a concern given that current practices only raise first‐generation offspring. Thus, proceeding with the proposed management strategies even in the absence of a detailed population genetic analysis of the focal species at sites targeted for restoration is the best course of action. These basic guidelines should help maximize the adaptive potential of reef‐building corals facing a rapidly changing environment.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.1978","usgsCitation":"Baums, I.B., Baker, A., Davies, S.W., Grottoli, A.G., Kenkel, C.D., Kitchen, S.A., Kuffner, I.B., LaJeunesse, T.C., Matz, M.V., Miller, M., Parkinson, J.E., and Shantz, A.A., 2020, Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic: Ecological Applications, v. 29, no. 8, e01978, 23 p., https://doi.org/10.1002/eap.1978.","productDescription":"e01978, 23 p.","ipdsId":"IP-103570","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458725,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.1978","text":"Publisher Index Page"},{"id":379349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Baums, Iliana B. 0000-0001-6463-7308","orcid":"https://orcid.org/0000-0001-6463-7308","contributorId":190566,"corporation":false,"usgs":false,"family":"Baums","given":"Iliana","email":"","middleInitial":"B.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":801288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Andrew C","contributorId":242999,"corporation":false,"usgs":false,"family":"Baker","given":"Andrew C","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":801289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davies, Sarah W","contributorId":243000,"corporation":false,"usgs":false,"family":"Davies","given":"Sarah","email":"","middleInitial":"W","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":801290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grottoli, Andrea G 0000-0001-6053-9452","orcid":"https://orcid.org/0000-0001-6053-9452","contributorId":243001,"corporation":false,"usgs":false,"family":"Grottoli","given":"Andrea","email":"","middleInitial":"G","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":801291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kenkel, Carly D","contributorId":243002,"corporation":false,"usgs":false,"family":"Kenkel","given":"Carly","email":"","middleInitial":"D","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":801292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kitchen, Sheila A","contributorId":243003,"corporation":false,"usgs":false,"family":"Kitchen","given":"Sheila","email":"","middleInitial":"A","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":801293,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":801294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LaJeunesse, Todd C","contributorId":243004,"corporation":false,"usgs":false,"family":"LaJeunesse","given":"Todd","email":"","middleInitial":"C","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":801295,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matz, Mikhail V","contributorId":243005,"corporation":false,"usgs":false,"family":"Matz","given":"Mikhail","email":"","middleInitial":"V","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":801296,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, Margaret W","contributorId":243006,"corporation":false,"usgs":false,"family":"Miller","given":"Margaret W","affiliations":[{"id":48605,"text":"SECORE International","active":true,"usgs":false}],"preferred":false,"id":801297,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Parkinson, John E 0000-0001-8386-3044","orcid":"https://orcid.org/0000-0001-8386-3044","contributorId":243007,"corporation":false,"usgs":false,"family":"Parkinson","given":"John","email":"","middleInitial":"E","affiliations":[{"id":48605,"text":"SECORE International","active":true,"usgs":false}],"preferred":false,"id":801298,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shantz, Andrew A 0000-0001-5641-8914","orcid":"https://orcid.org/0000-0001-5641-8914","contributorId":243008,"corporation":false,"usgs":false,"family":"Shantz","given":"Andrew","email":"","middleInitial":"A","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":801299,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70222620,"text":"70222620 - 2020 - Kinematic rupture modeling of ground motion from the M7 Kumamoto, Japan earthquake","interactions":[],"lastModifiedDate":"2021-08-09T13:06:50.0225","indexId":"70222620","displayToPublicDate":"2019-05-22T08:04:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Kinematic rupture modeling of ground motion from the M7 Kumamoto, Japan earthquake","docAbstract":"We analyzed a kinematic earthquake rupture generator that combines the randomized spatial field approach of Graves and Pitarka (Bull Seismol Soc Am 106:2136–2153, 2016) (GP2016) with the multiple asperity characterization approach of Irikura and Miyake (Pure Appl Geophys 168:85–104, 2011) (IM2011, also known as Irikura recipe). The rupture generator uses a multi-scale hybrid approach that incorporates distinct features of both original approaches, such as small-scale stochastic rupture variability and depth-dependent scaling of rupture speed and slip rate, inherited from GP2016, and specification of discrete high slip rupture patches, inherited from IM2011. The performance of the proposed method is examined in simulations of broadband ground motion from the 2016 Kumamoto, Japan earthquake, as well as comparisons with ground motion prediction equations (GMPEs). We generated rupture models with multi-scale heterogeneity, including a hybrid one in which the slip is a combination of high- slip patches and stochastic small scale variations. We find that the ground motions simulated with these rupture models match the general characteristics of the recorded near-fault motion equally well, over a broad frequency range (0–10 Hz). Additionally, the simulated ground motion is in good agreement with the predictions from Ground Motion Prediction Equations (GMPEs). Nonetheless, due to sensitivity of the ground motion to the local fault rupture characteristics, the performance among the models at near-fault sites is slightly different, with the hybrid model producing a somewhat better fit to the recorded ground velocity waveforms. Sensitivity tests of simulated near-fault ground motion to variations in the prescribed kinematic rupture parameters show that average rupture speeds higher than the default value in GP2016 (average rupture speed = 80% of local shear wave speed), as well as slip rate durations shorter than the default value in GP2016 (rise time coefficient = 1.6), generate ground motions that are higher than the recorded ones at periods longer than 1 s. We found that these two parameters also affect the along strike and updip rupture directivity effects, as illustrated in comparisons with the Kumamoto observations.
Currently, the deployment of tracking devices is one of the most frequently used approaches to study movement ecology of birds. Recent miniaturisation of light‐level geolocators enabled studying small bird species whose migratory patterns were widely unknown. However, geolocators may reduce vital rates in tagged birds and may bias obtained movement data.
There is a need for a thorough assessment of the potential tag effects on small birds, as previous meta‐analyses did not evaluate unpublished data and impact of multiple life‐history traits, focused mainly on large species and the number of published studies tagging small birds has increased substantially.
We quantitatively reviewed 549 records extracted from 74 published and 48 unpublished studies on over 7,800 tagged and 17,800 control individuals to examine the effects of geolocator tagging on small bird species (body mass <100 g). We calculated the effect of tagging on apparent survival, condition, phenology and breeding performance and identified the most important predictors of the magnitude of effect sizes.
Even though the effects were not statistically significant in phylogenetically controlled models, we found a weak negative impact of geolocators on apparent survival. The negative effect on apparent survival was stronger with increasing relative load of the device and with geolocators attached using elastic harnesses. Moreover, tagging effects were stronger in smaller species.
In conclusion, we found a weak effect on apparent survival of tagged birds and managed to pinpoint key aspects and drivers of tagging effects. We provide recommendations for establishing matched control group for proper effect size assessment in future studies and outline various aspects of tagging that need further investigation. Finally, our results encourage further use of geolocators on small bird species but the ethical aspects and scientific benefits should always be considered.
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With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12152468","usgsCitation":"Helder, D., Anderson, C., Beckett, K., Houborg, R., Zuleta, I., Boccia, V., Clerc, S., Kuester, M., Brian Markham, and Pagnutti, M., 2020, Observations and recommendations for coordinated calibration activities of government and commercial optical satellite systems: Remote Sensing, v. 12, no. 15, 2468, 17 p., https://doi.org/10.3390/rs12152468.","productDescription":"2468, 17 p.","ipdsId":"IP-117839","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12152468","text":"Publisher Index Page"},{"id":378354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":213606,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":798548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":813422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beckett, Keith","contributorId":240605,"corporation":false,"usgs":false,"family":"Beckett","given":"Keith","email":"","affiliations":[{"id":48112,"text":"Planet","active":true,"usgs":false}],"preferred":false,"id":813423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houborg, Rasmus","contributorId":240608,"corporation":false,"usgs":false,"family":"Houborg","given":"Rasmus","email":"","affiliations":[{"id":48112,"text":"Planet","active":true,"usgs":false}],"preferred":false,"id":813424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zuleta, Ignacio","contributorId":240611,"corporation":false,"usgs":false,"family":"Zuleta","given":"Ignacio","email":"","affiliations":[{"id":48112,"text":"Planet","active":true,"usgs":false}],"preferred":false,"id":813425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boccia, Valentina","contributorId":240606,"corporation":false,"usgs":false,"family":"Boccia","given":"Valentina","email":"","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":813426,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clerc, Sebastian","contributorId":240607,"corporation":false,"usgs":false,"family":"Clerc","given":"Sebastian","email":"","affiliations":[{"id":48113,"text":"ACRI-ST","active":true,"usgs":false}],"preferred":false,"id":798550,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kuester, Michele","contributorId":240609,"corporation":false,"usgs":false,"family":"Kuester","given":"Michele","email":"","affiliations":[{"id":48114,"text":"Maxar","active":true,"usgs":false}],"preferred":false,"id":813427,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brian Markham","contributorId":241117,"corporation":false,"usgs":false,"family":"Brian Markham","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":813428,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pagnutti, M.","contributorId":69874,"corporation":false,"usgs":true,"family":"Pagnutti","given":"M.","affiliations":[],"preferred":false,"id":813429,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70210590,"text":"70210590 - 2019 - Detection probabilities of bird carcasses along sandy beaches and marsh edges in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2020-06-11T16:04:51.976708","indexId":"70210590","displayToPublicDate":"2020-03-17T10:58:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Detection probabilities of bird carcasses along sandy beaches and marsh edges in the northern Gulf of Mexico","docAbstract":"We estimated detection probabilities of bird carcasses along sandy beaches and in marsh edge habitats in the northern Gulf of Mexico to help inform models of bird mortality associated with the Deepwater Horizon oil spill. We also explored factors that may influence detection probability, such as carcass size, amount of scavenging, location on the beach, habitat type, and distance into the marsh. Detection probability for medium-sized carcasses (200–500 g) ranged from 0.82 (SE = 0.09) to 0.93 (SE = 0.04) along sandy beaches. Within sandy beaches, we found that intact/slightly scavenged carcasses were easier to detect than heavily scavenged ones and did not find strong effects of location on the beach on detection probability. We estimated detection rate for each combination of scavenging state, carcass size, and position along sandy beaches. In marsh edge habitats, detection ranged from 0.04 (SE = 0.04) to 0.86 (SE = 0.10), with detection rates rapidly increasing from small (< 200 g) to medium carcass sizes and leveling off between medium and extra-large (> 1000 g) carcasses regardless of vegetation type (Spartina or Phragmites). Carcasses of all sizes were generally harder to locate in Spartina-dominated marshes than in Phragmites-dominated ones. A subset of the data for which we could adequately assess the effect of distance into the marsh indicated that detection rates generally declined the farther a carcass was into marsh vegetation. Based on power analyses, our ability to identify predictors that influence detection rates would be higher with larger numbers of carcasses, greater numbers of search trials per carcass, or more balanced sampling distributions across predictor values.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10661-019-7924-z","usgsCitation":"Zimmerman, G.S., Varela, V., and Yee, J.L., 2019, Detection probabilities of bird carcasses along sandy beaches and marsh edges in the northern Gulf of Mexico: Environmental Monitoring and Assessment, v. 191, no. suppl 4, 816, 15 p., https://doi.org/10.1007/s10661-019-7924-z.","productDescription":"816, 15 p.","ipdsId":"IP-094446","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-019-7924-z","text":"Publisher Index Page"},{"id":375518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2352294921875,\n 29.544787796199465\n ],\n [\n -94.0814208984375,\n 29.640320395351402\n ],\n [\n -93.61450195312499,\n 29.72145191669099\n ],\n [\n -93.218994140625,\n 29.754839972510933\n ],\n [\n -92.5213623046875,\n 29.559123451577964\n ],\n [\n -92.2247314453125,\n 29.501768632523262\n ],\n [\n -92.08740234375,\n 29.559123451577964\n ],\n [\n -91.8841552734375,\n 29.482643134466617\n ],\n [\n -91.1810302734375,\n 29.204918463909035\n ],\n [\n -90.5438232421875,\n 29.16655229520015\n ],\n [\n -90.3076171875,\n 29.28160772298835\n ],\n [\n -90.2691650390625,\n 29.017748018496047\n ],\n [\n -89.9395751953125,\n 29.176145182559758\n ],\n [\n -89.70886230468749,\n 29.3295093083647\n ],\n [\n -89.132080078125,\n 28.93124697186731\n ],\n [\n -88.978271484375,\n 29.257648503615542\n ],\n [\n -89.1485595703125,\n 30.12612436422458\n ],\n [\n -89.1925048828125,\n 30.311245603935003\n ],\n [\n -88.65966796875,\n 30.306503259848835\n ],\n [\n -88.3245849609375,\n 30.372875188118016\n ],\n [\n -88.1927490234375,\n 30.306503259848835\n ],\n [\n -87.77526855468749,\n 30.14512718337613\n ],\n [\n -87.3797607421875,\n 30.292274851024256\n ],\n [\n -86.517333984375,\n 30.358656420788783\n ],\n [\n -85.8251953125,\n 30.183121842195515\n ],\n [\n -85.45166015624999,\n 29.935895213372444\n ],\n [\n -85.3143310546875,\n 30.050076521698735\n ],\n [\n -85.7977294921875,\n 30.37761431777479\n ],\n [\n -86.3031005859375,\n 30.519681272749402\n ],\n [\n -87.0172119140625,\n 30.62845887475364\n ],\n [\n -87.4566650390625,\n 30.519681272749402\n ],\n [\n -88.39050292968749,\n 30.50548389892728\n ],\n [\n -89.1925048828125,\n 30.48181700282728\n ],\n [\n -89.62646484375,\n 30.330212685432734\n ],\n [\n -90.362548828125,\n 29.859701442126756\n ],\n [\n -90.68115234375,\n 29.506549442788593\n ],\n [\n -91.2579345703125,\n 29.654642479663647\n ],\n [\n -91.7962646484375,\n 29.92637417863576\n ],\n [\n -92.30712890625,\n 29.78821690967894\n ],\n [\n -93.3673095703125,\n 29.88351825335318\n ],\n [\n -94.47143554687499,\n 29.716681287231072\n ],\n [\n -94.2352294921875,\n 29.544787796199465\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","issue":"suppl 4","noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":790710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varela, Veronica","contributorId":225184,"corporation":false,"usgs":false,"family":"Varela","given":"Veronica","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":790711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":790712,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206935,"text":"70206935 - 2019 - Limited detection of antibodies to clade 2.3.4.4 A/Goose/Guangdong/1/1996 lineage highly pathogenic H5 avian influenza virus in North American waterfowl","interactions":[],"lastModifiedDate":"2020-01-08T16:51:16","indexId":"70206935","displayToPublicDate":"2020-01-06T16:20:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Limited detection of antibodies to clade 2.3.4.4 A/Goose/Guangdong/1/1996 lineage highly pathogenic H5 avian influenza virus in North American waterfowl","docAbstract":"During 2014, highly pathogenic (HP) influenza A viruses (IAVs) of the A/Goose/Guangdong/1/1996 lineage (GsGD-HP-H5), originating from Asia, were detected in domestic poultry and wild birds in Canada and the US. These clade 2.3.4.4 GsGD-HP-H5 viruses included reassortants possessing North American lineage gene segments; were detected in wild birds in the Pacific, Central, and Mississippi flyways; and caused the largest HP IAV outbreak in poultry in US history. To determine if an antibody response indicative of previous infection with clade 2.3.4.4 GsGD-HP-H5 IAV could be detected in North American wild waterfowl sampled before, during, and after the 2014–15 outbreak, sera from 2,793 geese and 3,715 ducks were tested by blocking enzyme-linked immunosorbent assay and hemagglutination inhibition (HI) tests using both clade 2.3.4.4 GsGD-HPH5 and North American lineage low pathogenic (LP) H5 IAV antigens. We detected an antibody response meeting a comparative titer-based criteria (HI titer observed with 2.3.4.4 GsGD-HP-H5 antigens exceeded the titer observed for LP H5 antigen by two or more dilutions) for previous infection with clade 2.3.4.4 GsGD-HP-H5 IAV in only five birds, one Blue-winged Teal (Spatula discors) sampled during the outbreak and three Mallards (Anas platyrhynchos) and one Canada Goose (Branta canadensis) sampled during the post-outbreak period. These serologic results are consistent with the spatiotemporal extent of the outbreak in wild birds in North America during 2014 and 2015 and limited exposure of waterfowl to GsGD-HP-H5 IAV, particularly in the central and eastern US.
","language":"English","publisher":"BioOne Complete","doi":"10.7589/2019-01-003","usgsCitation":"Stallknecht, D.E., Kienzle-Dean, C., Davis-Fields, N., Jennelle, C.S., Bowman, A.S., Nolting, J.M., Boyce, W., Crum, J., Santos, J., Brown, J.D., Prosser, D., De La Cruz, S.E., Ackerman, J., Casazza, M.L., Krauss, S., Perez, D., Ramey, A.M., and Poulson, R., 2019, Limited detection of antibodies to clade 2.3.4.4 A/Goose/Guangdong/1/1996 lineage highly pathogenic H5 avian influenza virus in North American waterfowl: Journal of Wildlife Diseases, v. 56, no. 1, p. 47-57, https://doi.org/10.7589/2019-01-003.","productDescription":"11 p.","startPage":"47","endPage":"57","ipdsId":"IP-102272","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research 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Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":776316,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70215873,"text":"70215873 - 2019 - Applying circuit theory and landscape linkage maps to reintroduction planning for California condors","interactions":[],"lastModifiedDate":"2020-11-02T12:50:26.995399","indexId":"70215873","displayToPublicDate":"2019-12-31T12:56:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Applying circuit theory and landscape linkage maps to reintroduction planning for California condors","docAbstract":"Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor (Gymnogyps californianus) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor’s historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0226491","usgsCitation":"D’Elia, J., Brandt, J., Burnett, L., Haig, S.M., Hollenbeck, J.P., Kirkland, S., Marcot, B.G., Punzalan, A., West, C.J., Williams-Claussen, T., Wolstenholme, R., and Young, R., 2019, Applying circuit theory and landscape linkage maps to reintroduction planning for California condors: PLoS ONE, v. 14, no. 12, e0226491, 22 p., https://doi.org/10.1371/journal.pone.0226491.","productDescription":"e0226491, 22 p.","ipdsId":"IP-115028","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":458861,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0226491","text":"Publisher Index Page"},{"id":379985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Central California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.68505859375,\n 37.96152331396614\n ],\n [\n -122.03613281249999,\n 36.96744946416934\n ],\n [\n -121.88232421875,\n 36.19109202182454\n ],\n [\n -120.4541015625,\n 34.43409789359469\n ],\n [\n -118.6083984375,\n 33.96158628979907\n ],\n [\n -118.0810546875,\n 34.03445260967645\n ],\n [\n -116.56494140625001,\n 35.24561909420681\n ],\n [\n -119.68505859375,\n 37.96152331396614\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Elia, Jesse 0000-0002-1843-8495","orcid":"https://orcid.org/0000-0002-1843-8495","contributorId":244237,"corporation":false,"usgs":false,"family":"D’Elia","given":"Jesse","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":803555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, Joe","contributorId":127746,"corporation":false,"usgs":false,"family":"Brandt","given":"Joe","email":"","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":803556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burnett, LJ","contributorId":244238,"corporation":false,"usgs":false,"family":"Burnett","given":"LJ","email":"","affiliations":[{"id":48872,"text":"Ventana Wildlife Society","active":true,"usgs":false}],"preferred":false,"id":803557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":803558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hollenbeck, Jeffrey P","contributorId":244239,"corporation":false,"usgs":false,"family":"Hollenbeck","given":"Jeffrey","email":"","middleInitial":"P","affiliations":[{"id":48873,"text":"The Northwest Habitat Institute","active":true,"usgs":false}],"preferred":false,"id":803559,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kirkland, S","contributorId":244240,"corporation":false,"usgs":false,"family":"Kirkland","given":"S","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":803560,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marcot, B G","contributorId":244241,"corporation":false,"usgs":false,"family":"Marcot","given":"B","email":"","middleInitial":"G","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":803561,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Punzalan, A","contributorId":244242,"corporation":false,"usgs":false,"family":"Punzalan","given":"A","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803562,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"West, C J","contributorId":244243,"corporation":false,"usgs":false,"family":"West","given":"C","email":"","middleInitial":"J","affiliations":[{"id":38097,"text":"Yurok Tribe","active":true,"usgs":false}],"preferred":false,"id":803563,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Williams-Claussen, T","contributorId":244244,"corporation":false,"usgs":false,"family":"Williams-Claussen","given":"T","email":"","affiliations":[{"id":48874,"text":"Yurok Tribe, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":803564,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wolstenholme, Rachel","contributorId":206084,"corporation":false,"usgs":false,"family":"Wolstenholme","given":"Rachel","email":"","affiliations":[{"id":37236,"text":"Pinnacles National Park","active":true,"usgs":false}],"preferred":false,"id":803565,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Young, Richard","contributorId":202719,"corporation":false,"usgs":false,"family":"Young","given":"Richard","affiliations":[],"preferred":false,"id":803566,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70208912,"text":"70208912 - 2019 - Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields","interactions":[],"lastModifiedDate":"2020-03-05T10:36:36","indexId":"70208912","displayToPublicDate":"2019-12-23T10:27:52","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields","docAbstract":"In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab5ccd","usgsCitation":"Davenport, F., Harrison, L., Shukla, S., Husak, G., Funk, C., and McNally, A., 2019, Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields: Environmental Research Letters, v. 14, no. 2, 124095, 13 p., https://doi.org/10.1088/1748-9326/ab5ccd.","productDescription":"124095, 13 p.","ipdsId":"IP-101895","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab5ccd","text":"Publisher Index Page"},{"id":372947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kenya, Somalia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 31.728515624999996,\n -5.615985819155327\n ],\n [\n 51.50390625,\n -5.615985819155327\n ],\n [\n 51.50390625,\n 10.833305983642491\n ],\n [\n 31.728515624999996,\n 10.833305983642491\n ],\n [\n 31.728515624999996,\n -5.615985819155327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":783964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":784025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":783965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":783966,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":783963,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNally, Amy","contributorId":53225,"corporation":false,"usgs":true,"family":"McNally","given":"Amy","affiliations":[],"preferred":false,"id":784026,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226972,"text":"70226972 - 2019 - Factors influencing neonicotinoid insecticide concentrations in floodplain wetland sediments across Missouri","interactions":[],"lastModifiedDate":"2021-12-23T13:41:18.217369","indexId":"70226972","displayToPublicDate":"2019-12-23T07:38:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing neonicotinoid insecticide concentrations in floodplain wetland sediments across Missouri","docAbstract":"Widespread use of neonicotinoid insecticides in North America has led to frequent detection of neonicotinoids in surface waters. Despite frequent surface water detection, few studies have evaluated underlying sediments for the presence of neonicotinoids. Thus, we sampled water and sediments for neonicotinoids during a one-year period at 40 floodplain wetlands throughout Missouri. Analyzed for six common neonicotinoids, sediment samples consistently (63% of samples) contained neonicotinoids (e.g., imidacloprid and clothianidin) in all sampling periods. Mean sediment and aqueous neonicotinoid concentrations were 1.19 μg kg–1 (range: 0–17.99 μg kg–1) and 0.03 μg L–1 (0–0.97 μg L–1), respectively. We used boosted regression tree analysis to explain sediment neonicotinoid concentrations and ultimately identified six variables that accounted for 31.6% of concentration variability. Efforts to limit sediment neonicotinoid contamination could include reducing agriculture within a wetland below a threshold of 25% area planted. Also, prolonging periods of overlying water >25 cm deep when water temperatures reach/exceed 18 °C could promote conditions favorable for neonicotinoid degradation. Results of this study can be useful in determining potential routes and levels of neonicotinoid exposure experienced by nontarget benthic aquatic invertebrates as well as potential means to mitigate neonicotinoid concentrations in floodplain wetlands.
PREMISE: One of the challenges in field biology is locating previously sampled plots. The Plot Locator app was developed to assist field biologists with plot identification and location, with or without GPS or online connectivity.
METHODS AND RESULTS: The Plot Locator Android app helps users locate field plots by creating a searchable database that stores study area information, such as site/plot names and numbers, distances from landmarks, optional cardinal directions and GPS coordinates, and field notes. A GPS assist and Google Maps can also be used with the app when connectivity is available. All study location data and field notes are stored in a downloadable CSV file on the user’s device.
CONCLUSIONS: The Plot Locator app provides a comprehensive searchable database of study area information, plot location information, and location aids, which are easily accessed in the field.
","language":"English","publisher":"Wiley","doi":"10.1002/aps3.11311","usgsCitation":"Boudell, J., and Middleton, B., 2019, Plot Locator: An app for locating plots in the field: Applications in Plant Science, v. 7, no. 12, e11311, 6 p., https://doi.org/10.1002/aps3.11311.","productDescription":"e11311, 6 p.","ipdsId":"IP-084359","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458913,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aps3.11311","text":"Publisher Index Page"},{"id":370878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Boudell, Jere","contributorId":221553,"corporation":false,"usgs":false,"family":"Boudell","given":"Jere","email":"","affiliations":[{"id":40405,"text":"Clayton State, Atlanta","active":true,"usgs":false}],"preferred":false,"id":778647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Beth A. 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":216869,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":778646,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208346,"text":"70208346 - 2019 - A novel picorna-like virus in a Wabash Pigtoe (Fusconaia flava) from the upper Mississippi River, USA","interactions":[],"lastModifiedDate":"2020-10-22T21:10:26.952224","indexId":"70208346","displayToPublicDate":"2019-12-19T16:34:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A novel picorna-like virus in a Wabash Pigtoe (Fusconaia flava) from the upper Mississippi River, USA","title":"A novel picorna-like virus in a Wabash Pigtoe (Fusconaia flava) from the upper Mississippi River, USA","docAbstract":"Unionid mussels are threatened by multiple environmental stressors and have experienced mass mortality events over the last several decades, but the role of infectious disease in unionid health and population declines remains poorly understood. Although several microbial agents have been found in unionids, to date only one virus has been documented—Lea plague virus (Arenaviridae) in propagated Triangle Shell mussels (Hyriopsis cumingii) in China. We used next-generation DNA sequencing to screen hemolymph of seven individuals of five unionid species from the Upper Mississippi River basin, USA for viruses. We identified the complete polyprotein gene of a novel picornalike virus in one individual of the Wabash Pigtoe (Fusconaia flava). The virus is a member of the Nora virus clade of picornalike viruses and is most closely related to viruses from arthropods in China. We did not detect viruses in another Wabash Pigtoe or in animals of the other four species. It is premature to make inferences about the role of this virus in the health of Wabash Pigtoes or other unionid species or the origin or transmission of this virus. Nevertheless, to our knowledge, our results represent the first report of a virus in wild North American unionids. Technologies based on next-generation DNA sequencing should prove useful for identifying new viruses and investigating their role in unionid health and disease.
","language":"English","publisher":"BioOne","doi":"10.31931/fmbc.v22i2.2019.81–84","usgsCitation":"Goldberg, T., Dunn, C.N., Leis, E., and Waller, D.L., 2019, A novel picorna-like virus in a Wabash Pigtoe (Fusconaia flava) from the upper Mississippi River, USA: Freshwater Mollusk Biology and Conservation, v. 22, no. 2, p. 81-84, https://doi.org/10.31931/fmbc.v22i2.2019.81–84.","productDescription":"4 p.","startPage":"81","endPage":"84","ipdsId":"IP-104237","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":372104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":372056,"type":{"id":15,"text":"Index Page"},"url":"https://bioone.org/journals/freshwater-mollusk-biology-and-conservation/volume-22/issue-2/fmbc.v22i2.2019.81%E2%80%9384/A-Novel-Picorna-Like-Virus-in-a-Wabash-Pigtoe-Fusconaia/10.31931/fmbc.v22i2.2019.81%E2%80%9384.full"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"La Crosse River, Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.07774376869202,\n 43.91427974619461\n ],\n [\n -91.07584476470946,\n 43.91427974619461\n ],\n [\n -91.07584476470946,\n 43.9152149261148\n ],\n [\n -91.07774376869202,\n 43.9152149261148\n ],\n [\n -91.07774376869202,\n 43.91427974619461\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.27235412597656,\n 43.691707903073805\n ],\n [\n -91.256046295166,\n 43.691707903073805\n ],\n [\n -91.256046295166,\n 43.71541065270114\n ],\n [\n -91.27235412597656,\n 43.71541065270114\n ],\n [\n -91.27235412597656,\n 43.691707903073805\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goldberg, Tony","contributorId":211788,"corporation":false,"usgs":false,"family":"Goldberg","given":"Tony","affiliations":[{"id":38319,"text":"UW Madison","active":true,"usgs":false}],"preferred":false,"id":781524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunn, Christopher N.","contributorId":195552,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":781525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leis, Eric","contributorId":179325,"corporation":false,"usgs":false,"family":"Leis","given":"Eric","affiliations":[],"preferred":false,"id":781526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210921,"text":"70210921 - 2019 - Inactivation of viable surrogates for the select agents virulent Newcastle disease virus and highly pathogenic avian influenza virus using either commercial lysis buffer or heat","interactions":[],"lastModifiedDate":"2020-07-03T14:07:41.942112","indexId":"70210921","displayToPublicDate":"2019-12-17T09:05:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5970,"text":"Applied Biosafety","active":true,"publicationSubtype":{"id":10}},"title":"Inactivation of viable surrogates for the select agents virulent Newcastle disease virus and highly pathogenic avian influenza virus using either commercial lysis buffer or heat","docAbstract":"Federal Select Agent Program regulations require laboratories to document a validated procedure for inactivating select agents prior to movement outside registered space. Avian influenza viruses and virulent Newcastle disease virus (vNDV) are cultured in chicken amnio-allantoic fluid (AAF), but the efficacy of commercial lysis buffers to inactivate viruses in protein-rich media has not been documented.
We assesses the efficacy of MagMAX™ lysis buffer for inactivating highly pathogenic avian influenza virus (HPAIV) and vNDV in chicken AAF and confirm the inactivation of avian influenza in serum using heat.
Low pathogenic avian influenza virus (LPAIV) and avian paramyxovirus subtype-1 (APMV-1) were incubated with lysis buffer and tested for viability. Known viable LPAIV and APMV-1 RNA was extracted from AAF using MagMAX™-96 AI/ND Viral RNA Isolation kit, and the eluate was tested for remaining infectious agent. Finally, inactivation of LPAIV in serum was examined over 3 combinations of temperature and incubation time.
MagMAX™ lysis buffer inactivated both LPAIV and APMV-1 in AAF when incubated for 30 minutes at room temperature. The full extraction process eliminated viable virus from the final RNA eluate. LPAIV in serum heated to 70°C for 30 minutes was rendered noninfectious.
The ability of a diagnostic laboratory to move samples from one space to another is critical to maintaining biosecurity as well as efficient laboratory workflow. Our study demonstrates a method to ensure the inactivation of viable avian influenza and avian paramyxoviruses in AAF, RNA eluate, and viable avian influenza virus in sera.
Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.
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","language":"English","publisher":"Springer","doi":"10.1038/s41477-019-0549-y","usgsCitation":"Fernández-Martínez, M., Pearse, I., Sardans, J., Sayol, F., Koenig, W.D., LaMontagne, J.M., Bogdziewicz, M., Collalti, A., Hacket-Pain, A., Vacchiano, G., Espelta, J., Penuelas, J., and Janssens, I.A., 2019, Nutrient scarcity as a selective pressure for mast seeding: Nature Plants, v. 5, p. 1222-1228, https://doi.org/10.1038/s41477-019-0549-y.","productDescription":"7 p.","startPage":"1222","endPage":"1228","ipdsId":"IP-109180","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":459045,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://ddd.uab.cat/record/216950","text":"Publisher Index Page"},{"id":437269,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96N15HF","text":"USGS data release","linkHelpText":"Data on interannual variability of seed production, nutrient, and weather for 219 plant species"},{"id":380416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2019-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Fernández-Martínez, M.","contributorId":244805,"corporation":false,"usgs":false,"family":"Fernández-Martínez","given":"M.","affiliations":[{"id":48983,"text":"U. 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Several of the tunicate species that are found on eelgrass in the northwest Atlantic are highly aggressive colonizers, and range expansions are predicted in association with climate-change induced increases in seawater temperature. In 2017, we surveyed tunicates within eelgrass meadows at 33 sites from New Jersey to Newfoundland. Eight tunicate species were identified colonizing eelgrass, of which four were non-native and one was cryptogenic. The most common species (Botrylloides violaceus and Botryllus schlosseri) occurred from New York to Atlantic Canada. Tunicate faunas attached to eelgrass were less diverse north of Cape Cod, Massachusetts. Artificial substrates in the vicinity of the eelgrass meadows generally supported more tunicate species than did the eelgrass, but fewer species co-occurred in northern sites than southern sites. The latitudinal gradient in tunicate diversity corresponded to gradients of summertime sea surface temperature and traditional biogeographical zones in the northwest Atlantic, where Cape Cod represents a transition between cold-water and warm-water invertebrate faunas. Tunicate density in the eelgrass meadows was low, ranging generally from 1–25% cover of eelgrass shoots, suggesting that space availability does not currently limit tunicate colonization of eelgrass. This survey, along with our 2013 survey, provide a baseline for identifying future changes in tunicate distribution and abundance in northwest Atlantic eelgrass meadows.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2019.10.4.02","usgsCitation":"Carman, M.R., Colarusso, P., Neckles, H.A., Bologna, P., Caines, S., Davidson, J.D., Evans, N.T., Fox, S., Grunden, D.W., Hoffman, S., Ma, K.C., Matheson, K., McKenzie, C., Nelson, E.P., Plaisted, H., Reddington, E., Schott, S., and Wong, M.C., 2019, Biogeographical patterns of tunicates utilizing eelgrass as substrate in the western North Atlantic between 39o and 47o north latitude (New Jersey to Newfoundland): Management of Biological Invasions, v. 10, no. 4, p. 602-616, https://doi.org/10.3391/mbi.2019.10.4.02.","productDescription":"15 p.","startPage":"602","endPage":"616","ipdsId":"IP-108765","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2019.10.4.02","text":"Publisher Index Page"},{"id":370665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Connecticut, Maine, Massachusetts, New Brunswick, Newfoundland, New Hampshire, New Jersey, New York, Nova Scotia, Prince Edward Island, Rhode Island","otherGeospatial":"North Altantic","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -53.78906249999999,\n 46.649436163350245\n ],\n [\n -58.18359375,\n 48.48748647988415\n ],\n [\n -64.951171875,\n 46.9502622421856\n ],\n [\n -67.8076171875,\n 44.74673324024678\n ],\n [\n -70.83984375,\n 43.739352079154706\n ],\n [\n -71.2353515625,\n 42.35854391749705\n ],\n [\n -71.8505859375,\n 41.44272637767212\n ],\n [\n -73.95996093749999,\n 41.0130657870063\n ],\n [\n -75.6298828125,\n 39.027718840211605\n ],\n [\n -75.41015624999999,\n 38.13455657705411\n ],\n [\n -53.78906249999999,\n 46.649436163350245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carman, Mary R.","contributorId":218699,"corporation":false,"usgs":false,"family":"Carman","given":"Mary","email":"","middleInitial":"R.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":778371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colarusso, Philip D.","contributorId":218700,"corporation":false,"usgs":false,"family":"Colarusso","given":"Philip D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":778372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Neckles, Hilary A. 0000-0002-5662-2314 hneckles@usgs.gov","orcid":"https://orcid.org/0000-0002-5662-2314","contributorId":3821,"corporation":false,"usgs":true,"family":"Neckles","given":"Hilary","email":"hneckles@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778370,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bologna, Paul","contributorId":218701,"corporation":false,"usgs":false,"family":"Bologna","given":"Paul","email":"","affiliations":[{"id":36592,"text":"Montclair State University","active":true,"usgs":false}],"preferred":false,"id":778373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caines, Scott","contributorId":218702,"corporation":false,"usgs":false,"family":"Caines","given":"Scott","email":"","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":778374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davidson, John D. 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The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.
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We assess the accuracy of pressure offset estimates that evolve over days to weeks and the confidence with which they may be attributed to tectonic deformation or to the ocean water column. One common approach to resolve this ambiguity assumes water column pressures vary insignificantly over the study region and are represented by stable reference site pressures. Assessing the validity of this assumption requires independent evidence. Correlations between pressures and colocated temperatures collected during the Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip experiment suggest temperatures might provide a useful independent proxy for water column pressures. We compared offsets estimated using several methods, with temperature and other proxies. The use of a temperature proxy was unsuccessful, because seafloor temperatures did not track the seasonal signal that contributes significantly to seafloor pressure changes over the slow slip event period. Regardless of the estimation method, offsets varied within a few cm around some uncertain reference level. Commonly used statistical measures are shown not to be reliable indicators of offset accuracy since offsets contribute minimally to the total variance. Offsets estimated using identical methods but with seafloor pressures simulated using a regional ocean model were larger than those derived from the data but had a similar pattern. Since the model simulates only water column processes, this suggests a significant fraction of the estimated pressure offsets are due to seasonal water column signal and are not of tectonic origin.