CDFW BIOS GIS Dataset, Contact: Karyn L Drennen, Description: The Biological Monitoring Program is a part of the Western Riverside County Multi-Species Habitat Conservation Plan (MSHCP), which was permitted in June, 2004. The Monitoring Program monitors the status of 146 Covered Species within a designated Conservation Area to provide information to permittees, land managers, the public, and wildlife agencies (i.e., the California Department of Fish and Wildlife and the U.S. Fish and Wildlife Service).

Background:The Biological Monitoring Program is a part of the Western Riverside County Multiple Species Habitat Conservation Plan (MSHCP), which was permitted in June, 2004. The Monitoring Program monitors the status of 146 Covered Species within a designated Conservation Area to provide information to permittees, land managers, the public, and wildlife agencies (i.e., the California Department of Fish and Wildlife and the U.S. Fish and Wildlife Service). Monitoring Program activities are guided by MSHCP species objectives for each covered species, the information needs identified in Section 5.3 of the MSHCP (http://www.rctlma.org/mshcp/volume1/sec5.html#5.3) or elsewhere in the document, and the information needs of the permittees. The Biological Monitoring Program seeks to provide sufficient scientifically reliable data to assess the MSHCP's effectiveness at meeting resource objectives and achieving or maintaining a healthy MSHCP Conservation Area in perpetuity. This dataset includes all records of animal species that were acquired during formal surveys for MSHCP-Covered Species conducted by the Biological Monitoring Program within the MSHCP Conservation Area between 2005-2014.Purpose:This dataset is intended for distribution to BIOS and all users of this database as documentation of species presence at a specific place and time. These data are useful for modeling species-habitat associations, documenting species distributions, and any other presence-only data applications.Data assumptions and limitations:We assumed that all observers were equally capable of confirming all target species for each taxa-specific survey activity after completing training exercises and passing any required species-identification tests. If known, surveys were conducted within the optimal range of dates, times, and weather conditions to maximize target species detection probability. These data have all been proofed after entry and are now assumed to be free from data entry error. However, if an observer recorded data incorrectly in the field it is possible that those errors persist in this dataset. This is a presence-only dataset. Because the lack of recorded species observations in a given area may be the result of a lack of surveys in that area or the failure to detect a species during a survey when it is actually present, true absence is not implied by this dataset and should not be inferred.Known caveats of the data:The Accuracy field describes the maximum potential amount of error in the geographic coordinates associated with each observation record. The magnitude of potential error for any observation is the result of survey protocol decisions made to increase data collection efficiency and vary across taxa.Some observation records were omitted from this dataset because an accurate identification could not be made to the species, subspecies, or variety level. Others were omitted because their coordinates fell beyond 30 meters of the September 2014 Conservation Area boundary.Observations of humans recorded in camera station photographs were also omitted. If users of this dataset are interested in these observations, please contact the Biological Monitoring Program.Supplemental Information:Because many survey protocols involve gathering data for a specified period of time at a single location, and because survey locations are often repeatedly visited in a given year, many observation locations have multiple observations of the same species or of different species. Additional information and data collected during these surveys (e.g., notes made in the field, environmental conditions and habitat condition made at the time of observation) and about the survey efforts that produced these data are available by request from the Monitoring Program and are summarized where relevant in annually distributed survey summary reports.Spatial data capture methods: All spatial data were either recorded in the field using a hand-held GPS unit, digitized in ArcGIS, or generated with specialized computer software. Coordinates obtained by GPS were recorded on paper datasheets and entered by hand into the database in the office. Data generated by computer software consist of points, straight-lines, or defined-area polygons that were used to map and locate sampling stations in the field. Station names and coordinates were uploaded digitally into the Biological Monitoring Programs central database and joined through station name to results of surveys conducted at those locations.

A mesh of regular hexagons is created using a geoprocessing tool (http://www.arcgis.com/home/item.html?id=03388990d3274160afe240ac54763e57). This tool creates a mesh of hexagons overlapping a study area. The study area is the Gulf of Mexico region for GCOOS. The data is available at http://gis.gcoos.org:8080/arcgis/rest/services/Boundary/GoM_Regions/MapServer

We in our group received with great interest the publication of the HCP-MMP 1.0 parcellation by Glasser et al. (Nature) created using data from the Human Connectome Project earlier this year. Often in our connectivity pipelines we use volume files for parcellation in native space, so we decided to try and convert the Connectome Workbench files to volume masks in native structural space to try out in future studies.We were happy to find that someone had already gone through the trouble of generating FreeSurfer annotation files projected on fsaverage, so all we had to do was find a way to transform these annot files to each subject’s space and convert them to volume masks.So we wrote this Linux shell script that performs a series of conversion and transformation steps using only FreeSurfer commands. It first converts the annotation files (lh.HCPMMP1.annot and rh.HCPMMP1.annot, downloaded from https://figshare.com/articles/HCP-MMP1_0_projected_on_fsaverage/3498446) to labels using mri_annotation2label, then takes each label from fsaverage to each subject’s space with mri_label2label, converts transformed labels back to annotation with mri_label2annot, and finally converts these to volume files (nii.gz) with mris_label2annot. Seems like too many steps, but this is how we (who are not FreeSurfer experts) got satisfactory results.The default final file consists of a single .nii.gz volume containing the cortical HCP-MMP1.0 regions plus the subcortical regions from the FreeSurfer segmentation, each region assigned a unique voxel value. It should be noted that the HCP-MMP1.0 parcellation includes 180 regions per hemisphere - 179 cortical and one subcortical (hippocampus). In the final volume file, left-hemisphere cortical HCP-MMP1.0 regions will have values between 1001 and 1181, whereas right-sided regions will have values between 2001 and 2181. The correspondence between each specific region and its voxel value is given in a look-up table that is saved in each subject’s output folder. To identify the hippocampi (and other subcortical structures), one needs to check the corresponding voxel value given in the FreeSurferColorLUT.txt file provided with FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT), as they are generated based on the original aseg parcellation*.*In the previous version of the script, a few perihippocampal cortical voxels were ascribed values (1121 and 2121) that should correspond to the hippocampus in the HCPMMP1.0 parcellation. Since only the cortical regions from this parcellation are generated, these voxels are now assigned the values corresponding to the hippocampus as defined by the automatic FreeSurfer subcortical segmentation (17 and 53).Optionally, one can choose to also generate individual volume files for each cortical and/or subcortical parcellation region. This option requires FSL. If the user chooses to create individual subcortical masks, the FreeSurferColorLUT.txt must also be available in the base ($SUBJECTS_DIR/) folder.By default, the script also generates tables with anatomical information for each cortical region (number of vertices, area, volume, mean thickness, etc.).Instructions on how to use the script can be found in the script itself, or here:https://cjneurolab.org/2016/11/22/hcp-mmp1-0-volumetric-nifti-masks-in-native-structural-space/Hope this can be of use!

The ultimate goal of this project is to create an updated fine‐scale vegetation map for about 58,000 acres of Orange County, consisting of the 37,000‐acre Orange County Central and Coastal Subregions Natural Community Conservation Plan (NCCP)/Habitat Conservation Plan (HCP) Habitat Reserve System; approximately 9,500 acres of associated NCCP/HCP Special Linkages, Existing Use Areas, and Non‐Reserve Open Space; and approximately 11,000 acres of adjoining conserved open space (study area). The project consisted of three phases.Phase 1: To update vegetation mapping, the Natural Reserve of Orange County (NROC) proposes to use Manual of California Vegetation (MCV) methods (2009), which will be implemented in two stages: Stage 1 – Development of a vegetation classification system for the Central and Coastal Subregions of Orange County that is consistent with the MCV. Stage 2 – Application of the vegetation classification system to create a vegetation map through photointerpretation of available aerial imagery and ground reconnaissance. The MCV methods were developed by the California Department of Fish and Game (CDFG) Vegetation Classification and Mapping Program in collaboration with the California Native Plant Society (CNPS). This approach relies on the collection of quantifiable environmental data to identify and classify biological associations that repeat across the landscape. For areas where documentation is lacking to effectively define all of the vegetation patterns found in California, CDFG and CNPS developed the Vegetation Rapid Assessment Protocol. This protocol guides data collection and analysis to refine vegetation classifications that are consistent with CDFG and MCV standards. Based on an earlier classification by Gray and Bramlet (1992), Orange County is expected to have vegetation types not yet described in the MCV. Using the MCV approach, Rapid Assessment (RA) data was collected throughout the study area and analyzed to characterize these new vegetation types or show concurrence with existing MCV types.Phase 2: Aerial Information Systems, Inc. (AIS) was contracted by the Nature Reserve of Orange County (NROC) to create an updated fine-scale regional vegetation map consistent with the California Department of Fish & Wildlife (CDFW) classification methodology and mapping standards. The mapping area covers approximately 86,000 acres of open space and adjacent urban and agricultural lands including habitat located in both the Central and Coastal Subregions of Orange County. The map was prepared over a baseline digital image created in 2012 by the US Department of Agriculture – Farm Service Agency’s National Agricultural Imagery Program (NAIP). Vegetation units were mapped using the National Vegetation Classification System (NVCS) to the Alliance level as depicted in the second edition of the Manual of California Vegetation (MCV2). One of the most important data layers used to guide the conservation planning process for the 1996 Orange County Central & Coastal Subregion Natural Community Conservation Plan/Habitat Conservation Plan (NCCP/HCP) was the regional vegetation map created in the early 1990s by Dave Bramlett & Jones & Stokes Associates, Inc. (Jones & Stokes Associates, Inc. 1993). Up until now, this same map continues to be used to direct monitoring and management efforts in the NCCP/HCP Habitat Reserve. An updated map is necessary in order to address changes in vegetation makeup due to widespread and multiple burns in the mapping area, urban expansion, and broadly occurring vegetation succession that has occurred over the past 20 years since the original map was created. This update is further necessary in order to conform to the current NVCS, which is supported by the extensive acquisition of ground based field data and subsequent analysis that has ensued in those same 20 years over the region and adjacent similar habitats in the coastal and mountain foothills of Southern California. Vegetative and cartographic comparisons between the newly created 2012 image-based map and the original 1990s era vegetation map are documented in a separate report produced by the California Native Plant Society at the end of 2014.Phase 3: The California Native Plant Society (CNPS) Vegetation Program conducted an independent accuracy assessment of a new vegetation map completed for the natural lands of Orange County in collaboration with Aerial Information Systems (AIS), the California Department of Fish and Wildlife (CDFW), and the Nature Reserve of Orange County (NROC). This report provides a summary of the accuracy assessment allocation, field sampling methods, and analysis results; it also provides an in-depth crosswalk and comparison between the new map and the existing 1992 vegetation map. California state standards (CDFW 2007) require that a vegetation map should achieve an overall accuracy of 80%. After final scoring, the new Orange County vegetation map received an overall user’s accuracy of 87%. The new fine-scale vegetation map and supporting field survey data provide baseline information for long-term land management and conservation within the remaining natural lands of Orange County.Data made available in the OC Data Portal in partnership with UCI Libraries. Methods The project consisted of three phases, each with its own methodology.Phase 1: To update vegetation mapping, the Natural Reserve of Orange County (NROC) usedManual of California Vegetation (MCV) methods (2009), which will be implemented in two stages: Stage 1 – Development of a vegetation classification system for the Central and Coastal Subregions of Orange County that is consistent with the MCV. Stage 2 – Application of the vegetation classification system to create a vegetation map through photointerpretation of available aerial imagery and ground reconnaissance.Phase 2: Aerial Information Systems, Inc. (AIS) was contracted by the Nature Reserve of Orange County (NROC) to create an updated fine-scale regional vegetation map consistent with the California Department of Fish & Wildlife (CDFW) classification methodology and mapping standards.Phase 3: The California Native Plant Society (CNPS) Vegetation Program conducted an independent accuracy assessment of a new vegetation map completed for the natural lands of Orange County in collaboration with Aerial Information Systems (AIS), the California Department of Fish and Wildlife (CDFW), and the Nature Reserve of Orange County (NROC).For more detailed methodology information please consult the README.txt file included with dataset.

A dataset of raw synchrotron X-ray diffraction (SXRD) images, recording crystallographic texture from two different pre-processed Ti-6Al-4V (Ti-64) materials, analysing six differently orientated samples from each material. The aim of the work was to provide a large dataset for testing and improving crystallographic texture refinement software from SXRD patterns.

Prior to the experiment, the Ti-64 materials had been pre-rolled and then air-cooled to develop the microstructure, rolling to 50% and 87.5% reduction at 915ºC using a rolling mill at The University of Manchester. Rectangular samples (2 mm thick) were then machined from these rolled blocks. The samples were cut along different directions, three samples along different orthogonal rolling directions, and three at different angles to the rolling directions. The samples are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens.

Data was recorded using a high energy 99.8 keV synchrotron X-ray beam and a 5 second exposure at the detector. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phase peaks. The SXRD data was recorded across each of the specimens by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments, forming a rectangular grid of measurement points across each sample. A powder Ti-64 sample was also measured as a random texture standard.

As well as the main experiment, 3 samples (sample 1, 2 and 3) were held together in different orders (1, 2, 3 ; 2, 1, 3 ; 2, 3, 1) and analysed through-thickness, to measure how beam attenuation might affect the bulk texture measurement. In addition, different detector exposure times (1 to 0.04 seconds) were also tested to analyse the impact of exposure time on overall intensity, to see how well the α and β peaks could be resolved from background noise at very fast acquisition frequencies.

The raw data is in the form of synchrotron diffraction pattern images which has been separated according to experiment type. An accompanying YAML text file contains associated beamline metadata for each measurement. Further details of the experimental setup can be found in a pdf document.

The material data folder contains further details about the material and sample orientations, including an electron backscatter diffraction (EBSD) map that can be used to verify the crystallographic texture.