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356 results for “Pacific Northwest”
Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
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Data from: Climatic damage cause variations of agricultural insurance loss for the Pacific Northwest region of the United States
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The role of multiple Pleistocene refugia in promoting diversification in the Pacific Northwest
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United States Pacific Northwest surveys of coastal foredune topography and vegetation abundance, 2012-2014
These datasets document our measurements of dune plant species abundance and topography from paired vegetation and topographic cross-shore foredune surveys in the United States Pacific Northwest (Oregon and southern Washington coastlines) in Summer 2012 and Summer 2014. In 2012, we conducted cross-shore paired topographic and vegetation surveys at 126 transect locations, and performed three replicate cross-shore transects per location (for a total of 378 transects). Of these surveys, 58 transect locations were positioned within Habitat Restoration Areas (as described in Biel et al. 2017). In 2014, we repeated these topographic and vegetation surveys at 83 of the 2012 transect locations (performing a single transect survey per location). Within each transect, we measure elevation (using RTK GPS) and plant species abundance (using 0.25 m^2 quadrats) at 5 m intervals between the vegetation line and the foredune heel. Within each quadrat, we measure the percent cover of all plant species present, and the tiller abundance of the three dune building grasses, Ammophila arenaria (invasive), Ammophila breviligulata (invasive), and Elymus mollis (native). Together, these datasets encompass measurements of elevation and plant abundance from 7953 quadrats in 2012, and 1616 quadrats in 2014.
Data from: Fire catalyzed rapid ecological change in lowland coniferous forests of the Pacific Northwest over the past 14,000 years
Disturbance can catalyze rapid ecological change by causing widespread mortality and initiating successional pathways, and during times of climate change, disturbance may contribute to ecosystem state changes by initiating a new successional pathway. In the Pacific Northwest of North America (PNW), disturbance by wildfires strongly shapes the composition and structure of lowland forests, but understanding the role of fire over periods of climate change is challenging, because fire-return intervals are long (e.g., millennia) and the coniferous trees dominating these forests can live for many centuries. We developed stand-scale paleorecords of vegetation and fire that span nearly the past 14,000 yr to study how fire was associated with state changes and rapid dynamics in forest vegetation at the stand scale (1–3 ha). We studied forest history with sediment cores from small hollow sites in the Marckworth State Forest, located ~1 km apart in the Tsuga heterophylla Zone in the Puget Lowland ecoregion of western Washington, USA. The median rate of change in pollen/spore assemblages was similar between sites (0.12 and 0.14% per year), but at both sites, rates of change increased significantly following fire events (ranging up to 1% per year, with a median of 0.28 and 0.38%, P < 0.003). During times of low climate velocity, forest composition was resilient to fires, which initiated successional pathways leading back to the dominant vegetation type. In contrast, during times of high climate variability and velocity (e.g., the early Holocene) forests were not resilient to fires, which triggered large-scale state changes. These records provide clear evidence that disturbance, in the form of an individual fire event, can be an important catalyst for rapid state changes, accelerating vegetation shifts in response to large-scale climate change.
A novel hybrid beachgrass is invading U.S. Pacific Northwest dunes with potential ecosystem consequences
<p>Invasive plants formed via hybridization, especially those that modify the structure and function of their ecosystems, are of particular concern given the potential for hybrid vigor. In the U.S. Pacific Northwest, two invasive, dune-building beachgrasses, <em>Ammophila arenaria</em> (European beachgrass) and <em>A. breviligulata</em> (American beachgrass) have hybridized and formed a new beachgrass taxa (<em>Ammophila arenaria</em> × <em>A. breviligulata</em>) but little is known about its distribution, spread, and ecological consequences. Here we report on surveys of the hybrid beachgrass conducted across a 250-km range from Moclips, Washington to Pacific City, Oregon in 2021 and 2022. We detected nearly 300 hybrid individuals, or an average of 8–14 hybrid individuals per km of surveyed foredune. The hybrid was more common at sites within southern Washington and northern Oregon where <em>A. breviligulata</em> is abundant (75–90% cover) and <em>A. arenaria</em> is sparse and patchy. The hybrid displayed morphological traits such as shoot density and height that typically exceeded its parent species suggesting hybrid vigor. We measured an average growth rate of 30% over one year, with individuals growing faster at the leading edge of the foredune, nearest the beach. We also found a positive relationship between hybrid abundance and that of <em>A. arenaria</em>, suggesting that <em>A. arenaria</em> density may be a controlling factor for hybridization rate. The hybrid showed similar sand deposition and associated plant species richness patterns compared to its parent species, although longer-term studies are needed. Finally, we found hybrid individuals within and near conservation habitat of two Endangered Species Act-listed, threatened bird species, the western snowy plover (<em>Charadrius alexandrinus nivosus</em>) and the streaked horned lark (<em>Eremophila alpestris strigata</em>), a concern for conservation management. Documenting this emerging hybrid beachgrass provides insights into how hybridization affects the spread of novel species and the consequences for communities in which they invade.</p>
Dataset of forb composition in a Pacific Northwest Bunchgrass Prairie
<p>This dataset supports the research article "Forb composition gradients and intra-annual variation in a threatened Pacific Northwest Bunchgrass Prairie; Averett and Endress. In Print. Ecology and Evolution". This data includes: (1) perennial forb species composition data from Pacific Northwest Bunchgrass Pairie habitat in the Starkey Experimental Forest and Range, northeastern Oregon; (2) environmental, abiotic, and species trait variables measured from each sampling site; (3) plant species list; (4) densities of culturally important forb species; and (5) long-term sample dates for vegetation plots from the Starkey Experimental Forest and surrounding National Forest lands. Forb composition data was collected from 29 plots in the Starkey Experimental Forest and Range, northeastern Oregon, at three different times during 2016 (April; May; July).</p>
Leaf traits predict water‐use eficiency in U.S. Pacific Northwest grasslands under rain exclusion treatment
<p>Does drought stress in temperate grasslands alter the relationship between plant structure and function? Here we report data from an experiment focusing on growth form and species traits that affect the critical functions of water‐ and nutrient‐use efficiency in prairie and pasture plant communities. A total of 139 individuals of 12 species (11 genera and four families) were sampled in replicated plots maintained for three years across a 520-km latitudinal gradient in the Pacific Northwest, USA. Rain exclusion did not alter the interspecific relationship between foliar traits and stoichiometry or intrinsic water‐use efficiency (iWUE). Rain exclusion reduced iWUE in grasses, and effect was primarily species‐specific, although leaf morphology, life history strategy, and phylogenetic distance predicted iWUE for all 12 species when analyzed together. Variation in specific leaf area explained most of the variation in iWUE between different functional groups, with annual forbs and annual grasses at opposite ends of the resource‐use spectrum. Our findings are consistent with expected trait‐driven tradeoffs between productivity and resource‐use efficiency and provide insight into strategies for the sustainable use and conservation of temperate grasslands.</p>
FIGURE 7 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 7. Dorsal views of heads. (A) Medusapyga alsea. (B) M. chehalis. Scale bars 100 µm.
FIGURE 8 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 8. Dorsal views of pronota. (A) Medusapyga alsea. (B) M. chehalis. Scale bars 100 µm.
FIGURE 2 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 2. Dorsal habitus. (A) Medusapyga alsea, female. (B) M. chehalis, female. Scale bar 1 mm.
Physical Features for my study on Automatic Seismic Event Classification System in Pacific Northwest (Origin time - 50, +100)
<p>These features used the revised version of the feature extraction code. The revision involves changing the envelope filtering options and some minor modifications. </p>
FIGURE 9 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 9. Left elytron of female Medusapyga alsea, showing fixed setae. Scale bar 100 µm.
Downscaled North American Multi-Model Ensemble Forecast for the Pacific Northwest USA
<h1>Downscaled North American Multi-Model Ensemble Forecast of Meteorological Variables for the Pacific Northwest</h1> <p>Monthly retrospective hindcasts (1982-2010) and forecasts (2011-2020) of temperature and precipitation are acquired for the Pacific Northwest region of the United States from five models (CFSv2, NASA GEOS5v2, CanCM4i, GEM-NEMO, and NCAR-CCSM) participating in the North American Multi-Model Ensemble project <a href="https://www.zotero.org/google-docs/?WlFE7n">(Kirtman et al., 2014)</a>. These models, detailed in Table 1 with more recent information available in <a href="https://www.zotero.org/google-docs/?PjZZHw">(Becker et al., 2022)</a>, are initialized monthly to provide a forecast of 0-9 months at a 1.0̊ × 1.0̊ spatial resolution. The multi-model ensemble mean (ENSMEAN) is then generated for each initialization by simply averaging all considered models and their ensemble members. Monthly ENSMEAN forecast is bias-corrected and spatially downscaled to 1/24th degree using the methodology described in <a href="https://www.zotero.org/google-docs/?jwPLzP">Wood et al. (2002)</a> and <a href="https://www.zotero.org/google-docs/?2b3lkj">Barbero et al. (2017)</a> using historical meteorological data <a href="https://www.zotero.org/google-docs/?zN8gKh">(gridMET; Abatzoglou, 2013)</a> as the baseline. Then, the downscaled ENSMEAN data are temporally disaggregated to daily timescales using an analog approach. The closest analog month for the ENSMEAN forecast is found from the gridMET dataset by minimizing the root mean square error (RMSE) of monthly gridMET and forecast precipitation (excluding gridMET data for the target month). Other daily meteorological variables (such as maximum and minimum temperature, maximum and minimum relative humidity, wind speed, and specific humidity) are extracted from the same analog month to use as input for the coupled crop-hydrology model. As a last step to the analog approach, the process corrects the bias between the forecast and analog month to ensure that monthly mean temperature and accumulated precipitation match those of the original forecast. </p> <p>Table 1. List of NMME models used to create Ensemble Mean.</p> <div> <table> <tbody> <tr> <td>Model </td> <td>Model Expansion </td> <td>Ensemble Size </td> <td>References</td> </tr> <tr> <td>NCEP- CFSv2 </td> <td>Climate Forecast System, version 2 </td> <td>24 </td> <td><a href="https://www.zotero.org/google-docs/?XTr15U">(Saha et al., 2014)</a></td> </tr> <tr> <td>NASA GEOS5v2</td> <td>Goddard Earth Observing System, version 5 </td> <td>4</td> <td><a href="https://www.zotero.org/google-docs/?fQzQZL">(Molod et al., 2020)</a></td> </tr> <tr> <td>CanCM4i </td> <td>Fourth Generation Canadian Coupled Global Climate Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?AoXCO8">(Merryfield et al., 2013)</a></td> </tr> <tr> <td>GEM - NEMO </td> <td>Global Environmental Multiscale Model – Nucleus for European Modelling of the Ocean </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?N4JwM9">(Lin et al., 2020)</a></td> </tr> <tr> <td>NCAR - CCSM </td> <td>Community Climate System Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?NMkImR">(Kirtman & Min, 2009)</a></td> </tr> </tbody> </table> </div> <p>The dataset has *.mat files which are MATLAB data files. </p> <h3>References</h3> <ol> <li> <p>Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. <a href="https://doi.org/10.1002/joc.3413">https://doi.org/10.1002/joc.3413</a></p> </li> <li> <p>Barbero, R., Abatzoglou, J. T., & Hegewisch, K. C. (2017). Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States. Weather and Forecasting, 32(1), 327–341. https://doi.org/10.1175/WAF-D-16-0117.1</p> </li> <li> <p>Becker, E. J., Kirtman, B. P., L’Heureux, M., Muñoz, Á. G., & Pegion, K. (2022). A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions. Bulletin of the American Meteorological Society, 103(3), E973–E995. <a href="https://doi.org/10.1175/BAMS-D-20-0327.1">https://doi.org/10.1175/BAMS-D-20-0327.1</a></p> </li> <li> <p>Kirtman, B. P., & Min, D. (2009). Multimodel Ensemble ENSO Prediction with CCSM and CFS. Monthly Weather Review, 137(9), 2908–2930. https://doi.org/10.1175/2009MWR2672.1</p> </li> <li> <p>Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., Dool, H. van den, Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., … Wood, E. F. (2014). The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95(4), 585–601. <a href="https://doi.org/10.1175/BAMS-D-12-00050.1">https://doi.org/10.1175/BAMS-D-12-00050.1</a></p> </li> <li> <p>Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., & Erfani, A. (2020). The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). Weather and Forecasting, 35(4), 1317–1343. <a href="https://doi.org/10.1175/WAF-D-19-0259.1">https://doi.org/10.1175/WAF-D-19-0259.1</a></p> </li> <li> <p>Merryfield, W. J., Lee, W.-S., Boer, G. J., Kharin, V. V., Scinocca, J. F., Flato, G. M., Ajayamohan, R. S., Fyfe, J. C., Tang, Y., & Polavarapu, S. (2013). The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization. Monthly Weather Review, 141(8), 2910–2945. <a href="https://doi.org/10.1175/MWR-D-12-00216.1">https://doi.org/10.1175/MWR-D-12-00216.1</a></p> </li> <li> <p>Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres, G., Borovikov, A., Kovach, R. M., Marshak, J., Schubert, S., Li, Z., Lim, Y.-K., Andrews, L. C., Cullather, R., Koster, R., Achuthavarier, D., Carton, J., Coy, L., Friere, J. L. M., … Pawson, S. (2020). GEOS-S2S Version 2: The GMAO High-Resolution Coupled Model and Assimilation System for Seasonal Prediction. Journal of Geophysical Research: Atmospheres, 125(5), e2019JD031767. https://doi.org/10.1029/2019JD031767</p> </li> <li> <p>Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., Dool, H. van den, Zhang, Q., Wang, W., Chen, M., & Becker, E. (2014). The NCEP Climate Forecast System Version 2. Journal of Climate, 27(6), 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1</p> </li> <li> <p>Wood, A. W., Maurer, E. P., Kumar, A., & Lettenmaier, D. P. (2002). Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research: Atmospheres, 107(D20), ACL 6-1-ACL 6-15. https://doi.org/10.1029/2001JD000659</p> </li> </ol>
Phylogenetic alignments and trees (16S, COI, 16S+COI) of Haploniscidae (Crustacea: Isopoda) from the Northwest Pacific Ocean
<p>The role of geomorphological features as drivers for benthic deep-sea biodiversity remains poorly understood. By disentangling the putative <em>Haploniscus belyaevi </em>Birstein, 1963 species complex from the abysso-hadal Kuril-Kamchatka Trench (KKT) region in the Northwest Pacific Ocean, we aim to shed light on deep-sea differentiation and how it is related to potential bathymetric barriers such as the KKT and the Kuril-Island Ridge (KIR). Our integrative taxonomic approach featured morphological and molecular delimitation methods, also considering the post-marsupial development due to pronounced sexual dimorphism. Mitochondrial 16S and COI markers were sequenced and several molecular species delimitation methods were applied. By combining the different results we were able to delineate six distinct species within the <em>belyaevi </em>complex, including several morphologically cryptic species, and found hints of three additional species groups in the complex. Even though several of these species were distributed across the KKT and/or KIR, limited gene flow and depth-differentiation were indicated supporting previous notions that these geomorphological features play a role in deep-sea benthos speciation.</p> <p>These files comprise the 16S, COI and 16S+COI alignments of these analyses and their phylogenetic trees as calculated by IQTree.</p>
Pacific Northwest Extreme Event - 06/29/2021
<p>This archive includes the data for the extreme event (06/29/2021) used for the manuscript "The 3-week-long transport history and deep tropical origin of the 2021 extreme heat wave in the Pacific Northwest" submitted to Geophysical Research Letter.</p>
The impact of varying spatiotemporal scales on different joint species distribution models: A case study of pelagic fish species in the northwest Pacific Ocean
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Data from: Restoration of riparian forest cover increases carbon stocks in the Pacific Northwest
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Data from: Fire catalyzed rapid ecological change in lowland coniferous forests of the Pacific Northwest over the past 14,000 years
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Data from: Prairie plant phenology driven more by temperature than moisture in climate manipulations across a latitudinal gradient in the Pacific Northwest, USA
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.