Data Management Plan for the California LCC project:

Maximizing evolutionary potential under climate change in southern California protected areas.

Table of Contents
Data Input - New Collections
  1   Data on Intraspecific Variation
  2   New layers describing anthropogenic barriers

Data Input - Existing Collections
  1   Modeling software used in the project
  2   Ensemble of projected climate conditions

Data Output - Product or Deliverables
  1   Maps of the projected impacts of climate change on intraspecific variation
  2   Toolbox for assessing impacts on intraspecific variation
  3   Maps of areas of overlapping high genetic variation of multiple species

Not Data - non-data Products
  1   Determining the drivers of population structure in a highly urbanized landscape to inform conservation planning
  2   Final Report: Maximizing Evolutionary Potential Under Climate Change in Southern California Protected Areas

Data Input - Existing Collections
1Modeling software used in the project
DescriptionGeneralized Dissimilarity Modelling (GDM) models beta-diversity across a landscape. Specifically, it is a matrix regression technique and predicts biotic turnover/ dissimilarity between sites based upon environmental dissimilarity and geographic distance. The advantages of GDM are that it makes few assumptions regarding the relationship between environment and genetic diversity and can explicitly take into account the potential influence of geographic distance (i.e. isolation-by-distance) on generating population divergence. See Ferrier et al. 2007, Diversity and Distributions 13:252-264. for more details on running GDM and underlying principles. Generalized dissimilarity models were run using an Avenue script in ArcView v 3.2 in conjunction with a SPlus v 4 script obtained from the authors of GDM. All final models for this project were created using 5000 randomly distributed classification training samples and 50 final GDM classes. We ran these models in four ways; using only environmental variables, using only Euclidean distance, using only anthropogenic highways, and using all of these variables combined (full model). These different runs were used to compare the relative influence of each category of predictor variable and to detect cross-correlations among variables.
CitationFerrier et al. 2007 Diversity Distrib 13:252-264
ContactHenri Thomassen, hathomassen@ucla.edu Ryan Harrigan, iluvsa@ucla.edu

2Ensemble of projected climate conditions
DescriptionLow-resolution environmental data that was used allows for analyzing regional-scale processes, and consisted of a set of climate variables from the WorldClim database and satellite remotely sensed variables at 30 arcsec resolution. After removing cross-correlated variables (Pearson cross-correlation > 0.9), we included the following climate variables: Annual Mean Temperature (BIO01), Mean Diurnal Temperature Range (Mean of monthly maximum temp minus minimum temperature; (BIO02)), Temperature Seasonality (standard deviation * 100; (BIO04)), Maximum Temperature of warmest Month (BIO05), Minimum Temperature of Coldest Month (BIO06), Annual Precipitation (BIO12), Precipitation Seasonality (Coefficient of Variation; (BIO15), Precipitation of Warmest Quarter (BIO18), and Precipitation of Coldest Quarter (BIO19). Second a set of remotely-sensed data variables was used. These included Moderate Resolution Imaging Spectroradiometer (MODIS), we included the Normalized Difference Vegetation Index (NDVI) (calculated from the red and near infrared reflection of the earth’s surface) and its standard deviation (NDVIstd) as a measure of greenness and seasonality, as well as percent tree cover, computed from the Vegetation Continuous Field (VCF) for the year 2001. We used radar data from the Quick Scatterometer (QuickSCAT), delivering information about near-surface moisture content, and a Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) at 30 arc second resolution. In addition to this lower-resolution data, we also included high-resolution (30 m) remotely sensed data, derived from the ASTER mission. The red (RED; band 2) and far infrared (FIR; band 3n) bands served as basis to compute NDVI as follows: NDVI = (RED – NIR) / (RED + NIR) Band 11 was also used, which contains temperature data that does not need further processing. In addition to these data sets from the ASTER layers, high-resolution elevation data and a tree cover data set in 30 m resolution were used as environmental predictors.
ContactHenri Thomassen, hathomassen@ucla.edu Ryan Harrigan, iluvsa@ucla.edu
Commons Cataloged DatasetWorldClim - Global Climate Data

Data Input - New Collections
1Data on Intraspecific Variation
Deliverable TypeDatasets / Database
DescriptionGenetic distance matrix of four species, the Side-blotched lizard (UTST), the Western fence lizard (SCOC), the Western skink (EUSK), and the Wrentit (WREN), across the Southern California Study area.
Processing and WorkflowData was compiled using microsatellite data for each species and differences between these markers across a spatially-explicit landscape were modeled using measures of Euclidean distance as well as environmental data (climate, elevation, and vegetation indices) to determine how much genetic variation is explained by each.
Access and SharingTo be posted as files (one for each species) on the California Commons in ASCII and GeoTIFF format.
CitationPlease provide.
ContactHenri Thomassen, hathomassen@ucla.edu Ryan Harrigan, iluvsa@ucla.edu

2New layers describing anthropogenic barriers
Deliverable TypeDatasets / Database
DescriptionNew layers describing anthropogenic barriers. Several environmental layers included in the above toolbox of deliverables can be used by other researchers, including layers demarcating major anthropogenic barriers (road, highways) that could potentially affect gene flow in natural populations.

Data Output - Product or Deliverables
1Maps of the projected impacts of climate change on intraspecific variation
Deliverable TypeMap
DescriptionEach of full climate models (excluding vegetation and elevation variables for which there were no future projections) were subsequently projected onto predicted future climate layers from the IPCC 4th Assessment Report A1B climate change scenario for the decades 2050-2060 and 2080-2090. These predictions represent conservative efforts, however, as the predictions of atmospheric CO2 concentrations may be reached much sooner, as current emissions already exceed the trajectories of the highest scenarios. Thus, projections of genetic variation on the 2080-2090 climate scenarios are likely relevant for purposes of our study. From the predictions of genetic variation under current and future climate, we generated a change map for each of the target taxa, showing the level of predicted change in genetic variation between current conditions and those for the period 2080-2090.
FormatMaps of these predictions for decades 2050-2060, and 2080-2090, are provided in ASCII format for four species, the Side-blotched lizard (UTST), the Western fence lizard (SCOC), the Western skink (EUSK), and the Wrentit (WREN), across the Southern California Study area.
Processing and WorkflowWe used existing genetic data from 15-20 sites (depending on species) of the following bird and reptile species to identify areas important for conservation in the Santa Monica Mountains NRA: wrentit (Chamaea fasciata), western fence lizard (Sceloporus occidentalis), side-blotched lizard (Uta stansburiana), and western skink (Plestiodon skiltonianus). We used satellite remotely sensed and climate data, in conjunction with recently developed spatially explicit ecological modeling techniques to project genetic diversity across the landscape. In local to regional scale studies (with environmental layers at spatial resolutions of ~ 20m - 1km), environmental parameters can be used in a correlative approach to indirectly discern patterns of biodiversity. We then used genetic differences as a response variable and each of the environmental datasets across the study region as predictors to determine the relative role that each these predictors had in determine where genetic variation occurs across the landscape. From these models, we then identified regions that, for all species investigated, harbored high levels of beta diversity (or genetic turnover) in the Southern California area. Four major areas harboring high genetic diversity were identified; the Southern Coast (Malibu and adjacent areas south of the 101), Central/Simi Valley (Simi Valley on the eastern limit and Arroyo Vista on the western limit), Northeastern/Angeles National Forest (Angeles National Forest on the eastern limit and Santa Clarita Woodlands PArk on the western limit), and Western/Oxnard (centered in Oxnard).
Backup and StorageCommons
Access and SharingPublic
Archive OrganizationsCommons
CitationPlease provide.
ContactRyan Harrigan ; Thomas B Smith, tbsmith@ucla.edu
Commons Cataloged DatasetProjected Impacts of Climate Change on Intraspecific Variation

2Toolbox for assessing impacts on intraspecific variation
Deliverable TypeApplications and Tools
DescriptionSoftware and environmental data layers that can be used by land managers, with guidance information for carrying out conservation prioritizations.
FormatWeb page on the Commons with a "recipe" for running the model.
Access and SharingPublic.
RestrictionsNone.
Archive OrganizationsCommons
CitationPlease provide.
ContactRyan Harrigan

3Maps of areas of overlapping high genetic variation of multiple species
Deliverable TypeDatasets / Database
DescriptionAdditional maps, still to be created.

Not Data - non-data Products
1Determining the drivers of population structure in a highly urbanized landscape to inform conservation planning
Deliverable TypePublication
DescriptionUnderstanding the environmental contributors to population structure is of paramount importance for conservation in urbanized environments. We used spatially explicit models to determine genetic population structure under current and future environmental conditions across a highly fragmented, human-dominated environment in Southern California to assess the effects of natural ecological variation and urbanization. We focused on 7 common species with diverse habitat requirements, home-range sizes, and dispersal abilities. We quantified the relative roles of potential barriers, including natural environmental characteristics and an anthropogenic barrier created by a major highway, in shaping genetic variation. The ability to predict genetic variation in our models differed among species: 11–81% of intraspecific genetic variation was explained by environmental variables. Although an anthropogenically induced barrier (a major highway) severely restricted gene flow and movement at broad scales for some species, genetic variation seemed to be primarily driven by natural environmental heterogeneity at a local level. Our results show how assessing environmentally associated variation for multiple species under current and future climate conditions can help identify priority regions for maximizing population persistence under environmental change in urbanized regions.
Linkhttp://onlinelibrary.wiley.com/doi/10.1111/cobi.12969/full

2Final Report: Maximizing Evolutionary Potential Under Climate Change in Southern California Protected Areas
Deliverable TypeReport
DescriptionProject final report.
Linkhttp://climate.calcommons.org/sites/default/files/reports/Smith-LCC-Final%20Report.pdf

This Data Management Plan structure is based on recommendations from the Data Management Plan Guidance document from the National Climate Change and Wildlife Science Center