{"help":"Return the metadata of a dataset (package) and its resources. :param id: the id or name of the dataset :type id: string","success":true,"result":[{"id":"01f43796-2cbf-4877-94ae-46c75f26d716","name":"upstream-regional-lidar-model-extent-trout-uprlimet-model-training-and-prediction-data","title":"UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET) model training and prediction data","author":"Burnett, Jonathan D.; Penaluna, Brooke E.; Christiansen, Kelly; Arismendi, Ivan; Johnson, Sherri L.; Griswold, Kitty; Holycross, Brett; Kolstoe, Sonja H.; Hirsch, William R.","author_email":"brooke.penaluna@usda.gov","maintainer":"RCDS Data Repository","maintainer_email":"rcds-web@uidaho.edu","notes":"\u003Cp\u003EWe present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams in Oregon using observational data collected in 2017. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. This data publication contains the geospatial data used for training, validation, and prediction by UPRLIMET (UPstream Regional LiDAR Model for Extent of Trout). Data are provided as two geodatabases with streamline (flowline) hydrography and include spatially explicit full-detail (predictions + covariates) prediction features separated by HUC12 watersheds and layers with pertinent prediction outputs merged into single spatial data layers for rapid rendering. Additionally, tabular data files are included that provide definitions of the covariates used in the model as well as the location and habitat barrier information for each stream and mainstem or tributary.\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EData Use\u003C\/strong\u003E\u003Cbr \/\u003E\n\u003Cem\u003ELicense\u003C\/em\u003E\u003Cbr \/\u003E\nCreative Commons Public Domain Dedication and Certification (\u003Ca href=\u0022https:\/\/creativecommons.org\/licenses\/publicdomain\/\u0022\u003ECC-PDDC\u003C\/a\u003E)\u003Cbr \/\u003E\nThese data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:\u003Cbr \/\u003E\nBurnett JD. Penaluna BE, Christiansen K, Arismendi I, Johnson SL, Griswold K, Holycross B, Kolstoe SH, Hirsch WR. 2023. UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET) model training and prediction data [Dataset]. Fort Collins, CO: Forest Service Research Data Archive. \u003Ca href=\u0022https:\/\/doi.org\/10.2737\/RDS-2022-0087\u0022\u003Ehttps:\/\/doi.org\/10.2737\/RDS-2022-0087\u003C\/a\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EFunding\u003C\/strong\u003E\u003Cbr \/\u003E\nUSDA Forest Service, Pacific Northwest Research Station\u003C\/p\u003E\n","url":"https:\/\/data.nkn.uidaho.edu\/dataset\/upstream-regional-lidar-model-extent-trout-uprlimet-model-training-and-prediction-data","state":"Active","log_message":"Edited by awchild.","private":true,"revision_timestamp":"Thu, 09\/07\/2023 - 12:00","metadata_created":"Wed, 06\/28\/2023 - 14:59","metadata_modified":"Thu, 09\/07\/2023 - 12:00","creator_user_id":"6cc16f2c-77c2-4d12-ac06-56bbb86b535b","type":"Dataset","resources":[{"id":"78b6c93e-881e-4ea0-be36-0f43256cab52","revision_id":"","url":"https:\/\/doi.org\/10.2737\/RDS-2022-0087","description":"","format":"html","state":"Active","revision_timestamp":"Wed, 06\/28\/2023 - 15:09","name":"Data Access via USDA | UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET) model training and prediction data","mimetype":"html","size":"","created":"Wed, 06\/28\/2023 - 15:00","resource_group_id":"e696b239-9ecb-412e-b032-03a75b2b9fd6","last_modified":"Date changed Wed, 06\/28\/2023 - 15:09"}],"tags":[{"id":"20cea8f7-bf86-480f-9ec2-edcf9e2d8e9f","vocabulary_id":"2","name":"Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii)"},{"id":"f0c7fc68-b185-4dc2-ac75-2c71a8364be2","vocabulary_id":"2","name":"UPRLIMET"},{"id":"e8a7dfd4-8cc8-4f00-88ff-646463dde9c5","vocabulary_id":"2","name":"electrofishing"},{"id":"9501d5b8-6ee1-415f-a0da-dabb2ca440c1","vocabulary_id":"2","name":"Biota"},{"id":"3570b611-323c-4b1f-8cfe-a5741ac84a8f","vocabulary_id":"2","name":"environment"},{"id":"5705a754-f4b9-4d5f-8978-54d058a11204","vocabulary_id":"2","name":"geoscientificInformation"},{"id":"670faa07-6045-4618-aca6-f82d4045cb3c","vocabulary_id":"2","name":"inlandWaters"},{"id":"425f965a-f839-486d-be20-95827298c8c0","vocabulary_id":"2","name":"ecology"},{"id":"763645d4-01b4-45cf-b61a-364ad1935976","vocabulary_id":"2","name":"ecosystems"},{"id":"67ca4e88-0aa7-414b-86ee-649699b4cf99","vocabulary_id":"2","name":"sedimentation"},{"id":"d75bd9aa-ccb4-4e5c-a5fe-367edc27c649","vocabulary_id":"2","name":"landscape ecology"},{"id":"fe489500-f4e1-4558-a370-a3b51da7520c","vocabulary_id":"2","name":"fauna"},{"id":"d26a5557-d574-4d14-8a6a-e0f5aa821f7a","vocabulary_id":"2","name":"fish"},{"id":"1a1070ed-4fb9-47f0-9800-bef28d92c0c3","vocabulary_id":"2","name":"end of fish"},{"id":"7fddc31f-14fa-45ac-898b-fa29e1b83c68","vocabulary_id":"2","name":"upper extent of fish"},{"id":"135faec4-d61a-4bf3-a2d9-cded5f66438d","vocabulary_id":"2","name":"occurrence"},{"id":"d855a797-7879-4b77-bc9f-db94393003f4","vocabulary_id":"2","name":"Oregon"},{"id":"7b1aa4f0-e197-4834-b0f5-40dde50ae103","vocabulary_id":"2","name":"biogeography"},{"id":"21f8e9cf-adf4-4741-a1a7-2eab6a3526c5","vocabulary_id":"2","name":"fisheries science"},{"id":"3674bcbf-dfe1-49a4-a7f1-02453987f97e","vocabulary_id":"2","name":"geospatial science"},{"id":"d68ec9de-1819-4160-b67e-3f00f9239991","vocabulary_id":"2","name":"hydrology"},{"id":"c57b6327-705e-4431-b293-6b751290e656","vocabulary_id":"2","name":"landscape"},{"id":"ae1ac386-aa75-4d8f-a198-43521a1c5e9c","vocabulary_id":"2","name":"watershed"},{"id":"435edef7-8f56-48e1-851a-1231e1537ef4","vocabulary_id":"2","name":"wildlife"}],"groups":[{"description":"\u003Cp\u003EThe primary objective of Idaho EPSCoR is to stimulate research in niche areas that can become fully competitive in the disciplinary and multidisciplinary research programs of the National Science Foundation and other relevant agencies. Idaho EPSCoR provides support for sustainable increases in Research and Development capacity and advances science and engineering capabilities within the state. \u003C\/p\u003E\n\u003Cp\u003EVisit them at \u003Ca href=\u0022https:\/\/www.idahoepscor.org\u0022\u003Ehttps:\/\/www.idahoepscor.org\u003C\/a\u003E\u003C\/p\u003E\n","id":"e696b239-9ecb-412e-b032-03a75b2b9fd6","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/Idaho_epscor_logo_no_white_background.png","title":"Idaho EPSCoR","name":"group\/idaho-epscor"},{"description":"\u003Cp\u003EGEM3 is an NSF EPSCoR research program seeking to understand how genetic diversity and phenotypic plasticity affect species response to environmental change, shaping both population response and adaptive capacity.\u003C\/p\u003E\n\u003Cp\u003EVisit them at: \u003Ca href=\u0022https:\/\/www.idahogem3.org\u0022\u003Ehttps:\/\/www.idahogem3.org\u003C\/a\u003E\u003C\/p\u003E\n","id":"1812a312-11a4-492d-960b-9a78b4abfe0c","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/GEM3_nov5_logo.png","title":"EPSCoR GEM3","name":"group\/epscor-gem3"}]}]} Error | RCDS Data Repository

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