Joan C. Hagar Patricia K. Haggerty Ramiro Aragon Jeff P. Hollenbeck S. Kim Nelson 20180531 Vector Digital Data Set (Polygon) https://doi.org/10.5066/P9472SZW Joan C. Hagar Bianca N.I. Eskelson S. Kim Nelson David G. Vesely Patricia K. Haggerty 20140214 Publication (Journal) Wildlife Society Bulletin 38 USA Wildlife Society http://doi.org/10.1002/wsb.407 We developed a LiDAR-based habitat model for the threatened Marbled Murrelet (MAMU) in the Siuslaw National Forest, Oregon, using a two-step approach. First, we tested the applicability of the LiDAR-based model developed for the Coos Bay District of the Bureau of Land Management (BLM) to the Siuslaw N.F. In the second step, we tested alternative habitat models developed with forest structural data and Murrelet survey data from the Siuslaw N.F. We compared the performance of each model to provide forest managers with the best predictive tool to guide habitat management for the Marbled Murrelet. This shapefile contains the probability of Marbled Murrelet occupancy values of each model for vegetation polygons defined by the Siuslaw National Forest. The goal of this project was to develop a model that can be used to accurately map Marbled Murrelet occupancy probability to facilitate the identification and monitoring of critical nesting habitat in the Siuslaw N.F on the central Oregon coast. Models developed at one location are unlikely to be generally applicable across the entire geographic range of a given species because relationships between habitat use and environmental conditions are spatially variable (Latif et al. 2016). However, the model predicting murrelet nesting habitat developed for the Coos Bay region (Hagar et al. 2014) performed surprisingly well when applied at a new location, in the Siuslaw NF. When parameterized with coefficients derived from local data, the model discriminated habitat as well at the new location (Siuslaw National Forest) as at the development location (Coos Bay BLM District). 19880319 20160804 ground condition Complete None planned -124.425645639 -123.414185163 45.361512692 43.43593539 ISO 19115 Topic Category biota environment USGS Thesaurus remote sensing lidar vegetation endangered species birds forest ecosystems USGS Metadata Identifier USGS:5b071250e4b0ac450c98b538 Common geographic areas Oregon None Siuslaw National Forest Pacific Northwest Oregon Coast Range None. Please see 'Distribution Info' for details. None. Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations. U.S. Geological Survey FRESC Science Data Coordinator Mailing and Physical

777 NW 9th Street, Suite 400

Corvallis OR 97330 USA 541-750-1030 fresc_outreach@usgs.gov USDA Forest Service, Siuslaw National Forest USDI Geological Survey, Forest and Rangeland Ecosystem Science Center Oregon State University, Department of Fisheries and Wildlife Oregon Department of Forestry Oregon Department of Geology and Mining Industries/Oregon Lidar Consortium Environment as of Metadata Creation: Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.5 (Build 6491) Service Pack N/A (Build N/A) Quresh S. Latif Victoria A. Saab Jeff P. Hollenbeck Jonathan G. Dudley 201611 publication The Condor vol. 118, issue 4 n/a Cooper Ornithological Society ppg. 766-790 http://doi.org/10.1650/CONDOR-16-86.1 Joan C. Hagar Bianca N. I. Eskelson Patricia K. Haggerty S. Kim Nelson David G. Vesely 20140212 publication Wildlife Society Bulletin vol. 38, issue 2 n/a Wiley ppg. 237-249 http://doi.org/10.1002/wsb.407 U. S. Dept. of Agriculture, Farm Service Agency, National Agriculture Imagery Program 20160919 application/service https://gis.apfo.usda.gov/arcgis/rest/services See source descriptions for individual QA procedures. No formal logical accuracy tests were conducted. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. A horizontal accuracy assessment of the lidar data was conducted and approved by the Oregon Lidar Consortium as part of their acceptance. A formal accuracy assessment of the horizontal positional information in the vegetation dataset which is used to summarize the lidar metrics has not been conducted. A vertical accuracy assessment of the lidar data was conducted and approved by the Oregon Lidar Consortium as part of their acceptance. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Oregon Department of Geology and Mineral Industries (DOGAMI) Oregon Lidar Consortium (OLC) 20161110 Observed/Measured First-Hand Salem, Oregon DOGAMI http://www.oregongeology.org/lidar/dataviewer/index.html Digital and/or Hardcopy Resources 20080727 20141010 ground condition Lidar point data in LAS format Lidar point data as LAS files and digital terrain models were used to generate forest structure rasters as inputs to modeling. Kim Nelson, Oregon State University, Department of Fisheries and Wildlife 20170131 Vector Digital Data Set Corvallis, Oregon Oregon State University Dept. of Fisheries and Wildlife Digital and/or Hardcopy Resources 19880319 20160804 observed Siuslaw MAMU database A compilation of MAMU observations was provided from Oregon State University for the SIUS NF area. Observations were used which followed the Pacific Seabird Protocol. A determination of MAMU occupancy for observed vegetation stands was made using these source survey results. Stu Johnston 20150218 Vector Digital Data Set Corvallis, OR Siuslaw National Forest, Pacific Northwest Region, Forest Service, U.S. Department of Agriculture 12000 Digital and/or Hardcopy Resources 20030602 20110401 ground condition EV_Base/most recent update Vegetion polygons were used as analogs of forest stands, as were available for the Coos Bay BLM study. Airborne lidar data for the state of Oregon is collected through a sole source contract administered by DOGAMI. Lidar was collected by Watershed Sciences/Quantum Geospatial and quality control inspections were conducted by the DOGAMI OLC Coordinator. Data was made available to the USGS by digital transfer from external hard drives and by download from Oregon State University servers. One additional dataset for the Yaquina and Elk Creek watersheds was purchased by the US Forest Service but was conducted by the same contractor using the same standards as the OLC lidar collections. Data standards are fully described at http://www.oregongeology.org/lidar/collectinglidar.htm. Notable are that the aggregate pulse density is >= to 8 points per meter, sidelap is >= 50%, scan angle is +/- 15 degrees, and fundamental vertical accuracy is <= 9.25 cm RMSE and absolute accuracy of bare earth elevation is <= 9.25 cm RMSE. Unknown Lidar pulse data, in LAS file format, were assembled for the lidar project area. Ground terrain rasters generated by the lidar vendor were extracted to ASCII format. Processing followed the preprocessing, tiling and final processing instructions in the software 'FUSION/LDV: Software for LIDAR Data Analysis and Visualization', version 3.60+, dated October 2016. Batch files were created for each lidar project area and the final rasters generated by the GRIDMETRICS function were mosaiced to the .bil format using Esri ArcMap version 10.5. Data was processed in the Oregon Lambert HARN projection. The output raster cell size was 75 feet (22.86 m). A minimum height cutoff of 3.2808 feet (1 meter) for elevation metrics and a cutoff value of 6.5616 feet (2 meters) was used for Canopy Cover metrics. Stratum of canopy density returns were generated at 1.64, 32.4, 118.11, 170.6, 245.4 feet, based on nest height data available for the SIUS NF provided by Kim Nelson. The source for the FUSION software is http://forsys.sefs.uw.edu/fusion/fusionlatest.html 20170426 Survey sites and survey dates categorized as 'Intensive' in the OR MAMU Siuslaw database were used to assign occupancy status as 'Occupied' or 'Not observed' to vegetation polygons. Groups of surveys for particular SIUS NF harvest and project areas were evaluated in the time period between 1988 and 2016. 20170505 The EV_Base polygons were examined to assess their similarity to the forest operations inventory polygons used in the previous Coos Bay District/BLM research by overlaying them on current vegetation imagery (NAIP 2016) and the lidar canopy surface model. As there has been little clearcutting and minimal thinning conducted on the Siuslaw N.F. since 1991, these polygons do reflect effective stand boundaries. A subset of the EV_Base polygons was selected for the analysis, with hardwood dominated stands being eliminated. Some very small or sliver polygons were also eliminated. 20170508 A field for occupancy status was added to the extract EV_Base polygons. An extensive review of all the 'Intensive' Marbled Murrelet observations which met the Pacific Seabird Protocol was made by overlaying survey sites and data for individual survey dates onto the vegetation polygons. In some cases a vegetation polygon was split if it had been harvested in some portion of its area. Murrelet occupancy assignments to polygons was reviewed by Kim Nelson of OSU and David Vesely of Oregon Wildlife Institute, who had participated in the previous research in Coos Bay. A new, unique Global Id (g_id) was added to the final polygon dataset as a key field. 20170511 The USGS NAWQA Area-Characterization Tools were used to extract statistics (mean, maximum, minimum, standard deviation) from the lidar raster metric layers for each polygon and write the results into a table of each metric of interest. All the overlays were imported into an Access database to create joined tables for the modeling team. 20170518 The best modeling results from the logistic regression analysis were exported to ArcMap and incorporated into the polygon vegetation data. The probability of MAMU occupancy in the vegetation polygon, as calculated in the transferred Coos Bay model, is listed in the CO_SI_Prob field. The new Siuslaw data built with standardized lidar variables is listed in the SIUSz_Prob field. 20170613 Vector G-polygon 16088 NAD 1983 USFS R6 Albers (ESRI Full Name: NAD_1983_USFS_R6_Albers) 43.0 48.0 -120.0 34.0 600000.0 0.0 coordinate pair 0.6096 0.6096 meters D_North_American_1983 GRS_1980 6378137.0 298.257222101 SIUS_MAMU_Prob_R6ALBERS Table containing attribute information associated with the data set. Producer defined STAND_ID Identifier imported from Forest Service EV_Base.shp Producer defined 0 2639160000 unitless STAND_NUM Identifier imported from Forest Service EV_Base.shp Producer defined 0 24412820 unitless CON_HARD Conifer/Hardwood class imported from Forest Service EV_Base.shp Siuslaw National Forest {Null Value / Empty Field Entry} Unknown Producer defined C Conifer Producer defined C_AB Conifer "Abies" Producer defined CH Conifer/Hardwood: cover predominantly conifer with hardwood present Producer defined HC Hardwood/Conifer: cover a nearly equal mix of conifer and hardwood or a larger proportion of hardwood than conifer. Producer defined M Managed stands Producer defined G_ID Global identifier autogenerated from ArcMap for the MAMU project, not the original 'Global_ID' present in the EV_Base Producer defined Software generated unique identifier SIUSz_Prob Probability of Marbled Murrelet occupancy in the polygon as estimated from the SIUSz lidar model Producer defined 0.000439767267319 0.996202474254 unitless CO_SI_Prob Probability of Marbled Murrelet occupancy in the polygon as estimated from the CO_SI (Coos Bay model parameterized with Siuslaw NF coefficients) lidar model Producer defined 0.000315383610985 0.994268775195 unitless The fields STAND_ID, STAND_NUM, CON_HARD were imported from the EV_Base.shp polygon data, obtained from the US Forest Service on Febuary 3, 2017. These fields were maintained in the results of the Marbled Murrelet modeling to allow linkages to the EV_Base dataset. The EV_Base is described in the USFS metadata as "a photo-interpreted vegetation layer that is more detailed and done to a higher more consistent standard than the forestwide vegetation layer (vege). typed by photo interpretation (piveg)". FGDC Metadata record for EV_Base.shp created 2011-04-11, Rosana Costello, Willamette National Forest U.S. Geological Survey GS ScienceBase mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P9472SZW None. No fees are applicable for obtaining the data set. 20200819 U.S. Geological Survey FRESC Science Data Coordinator Mailing and Physical

777 NW 9th Street, Suite 400

Corvallis OR 97330 USA 541-750-1030 fresc_outreach@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998