Understanding and Assembling Model Input Environmental Data

Objectives By the end of this section, you should be able to: Provide a general definition of environmental data Describe the distinction among the different types of environmental data in relation to modeling species distributions Describe basic regimes of environmental variables Describe when it’s important to consider multicollinearity and problems it may cause Explain the importance of scale when selecting environmental data Articulate the best practice techniques for environmental variable selection Understand the basic components of observed and modeled climate data as well as downscaling methods September 2013 Best Practices for Systematic Conservation Planning

Environmental Data What are environmental data? Examples? Information about the geographic conditions/features of an area Examples? Species Distribution Modeling (SDM) context Causal, driving forces for a specie’s distribution and abundance Most often in raster gird format Examples from organizations field based measurements weather stations September 2013 Best Practices For Systematic Conservation Planning

Environmental data Continuous Categorical Proximal or distal Anything you can measure or count Categorical Limited number of values or groups Proximal or distal The position of the predictor in the chain of processes that link the predictor to its impact on the plant species (Austin 2002) Available soil phosphate at the root hair vs soil type or phosphate concentration September 2013 Best Practices for Systematic Conservation Planning

Types of Environmental Data Direct: Direct physiological influence but are not consumed Indirect: No physiological effect Resource: Matter and energy consumed by species Direct examples: -Temperature, precipitation, pH Indirect examples: -Elevation, distance to water, latitude, longitude Resource examples -Water availability -Prey abundance September 2013 Best Practices For Systematic Conservation Planning

Environmental Data Regimes Climate Topography Substrate (geology and soil) Land cover/ land use Remote sensing Biotic interactions Disturbances September 2013 Best Practices for Systematic Conservation Planning

Climate Data Examples Components Future conditions Temperature, precipitation, humidity Components Station data Elevation Interpolation Future conditions Global circulation model (GCM) Downscaling method Scenarios Time period Downscaling connect the circulation model with current climate data General circulation models (GCM) Future projections used in IPPC (24) September 2013 Best Practices for Systematic Conservation Planning

Climate Data Climate station data Precipitation Temperature September 2013 Best Practices for Systematic Conservation Planning

Climate Data Models Most affected by terrain and water bodies Current conditions based on averages over many years (1950-2000) Scale and location important considerations Good for applications for model transferability Errors largest at high elevations Transferability more of a direct driver Dilutes recent changes or trends Small extent  climate most likely not an important driver September 2013 Best Practices For Systematic Conservation Planning

Climate Data: Bioclim 19 biologically meaningful variables (Hutchinson) Based off of monthly and annual measures of min temp, max temp, average temp, and precipitation Heavily used in SDM Represent Annual trends Seasonality Extremes Often high collinearity Better than annual averages for species distributions (theoretically and empirically) Extremes (monthly or quarterly) E.g. Minimum temperature of coldest month September 2013 Best Practices for Systematic Conservation Planning

Topography Earth surface shape and landform features Digital elevation model (DEM) Slope and Aspect Derived from elevation in GIS Topographic position index The relative location on a slope Roughness Variability in slope and aspect in local  patches Sources: SRTM, USGS earth explorer DEM Relatively high resolution and high accuracy Can often be highly correlated with climate variables Jenness, Brost, and Beier, 2011 September 2013 Best Practices For Systematic Conservation Planning

Substrate Underlying material on which a process occurs Can be a strong driver to both plants and animals Two issues to consider Factors that proximally determine the species distribution Link between those factors and the available mapped data Often coarse units that may or may not be useful for modeling species distributions Sources: SURRGO, state and county specific datasets e.g., Animal that requires a specific soil type for burrowing September 2013 Best Practices For Systematic Conservation Planning

Land Cover/ Land Use Physical coverage or type on the earth’s surface Often categorical, but can also be continuous Important to know the intended scale and purpose of the map Temporal aspect important to consider National Land Cover Database – 1992, 2001, 2006 Sources: NLCD, Landfire Existing Vegetation, GAP analysis Land use more anthropocentrically defined , but many shades of gray September 2013 Best Practices For Systematic Conservation Planning

Remote Sensing Satellite collected information of surface reflectance Many ecologically useful indices can be derived from raw bands NDVI, Tasseled Cap, Leaf Area Index Allows for detecting spatial patterns Can be difficult to calibrate and correct Sources: USGS earth explorer (Landsat TM, MODIS) Detecting and mapping Signature of what exists (mapping of the earths surface) Indirect surrogate for functional variables September 2013 Best Practices For Systematic Conservation Planning

Potential Versus Detected Distribution What's the difference? Where is it now versus where might it be Depends on scale and species Remote sensing environmental data = more mapping September 2013 Best Practices for Systematic Conservation Planning

Biotic Interactions Interspecies interactions that impact species distributions Distribution of other species Prey sources Predators Competitors Pollinators Often assumed that these are accounted for because they co-vary with other variables Owl models: Climate only, climate and landcover, climate landcover and woodpecker interactions Global Ecology and Biogeography Volume 16, Issue 6, pages 754-763, 20 SEP 2007 DOI: 10.1111/j.1466-8238.2007.00345.x http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00345.x/full#f2 September 2013 Best Practices for Systematic Conservation Planning

Disturbances Changes to the system that may be natural or human- caused Can be a critical driver of species patterns on a landscape Temporally dependent More important at fine scales Sources: Landfire Fire history, harvest, flood, avalanche, roads Lewis, S.A.; Robichaud, P.R.; Hudak, A.T.; Austin, B.; Liebermann, R.J. Utility of Remotely Sensed Imagery for Assessing the Impact of Salvage Logging after Forest Fires. Remote Sens. 2012, 4, 2112-2132. September 2013 Best Practices for Systematic Conservation Planning

Collinearity (a.k.a. multicollinearity) Environmental variables in a model are linearly related Always some degree of collinearity Share the same information in relation to the response being modeled If not addressed can lead to poor test of variable contribution Not too important if the only objective is prediction within the sampled range Issues with extrapolation Patterns of collinearity are likely to change in new geographic regions or projected changed in climate conditions Assumptions of SDM Correlations remain constant over modeled and predicted area Always some degree of collinearity Could be intrinsic, from compositional data, Incidental Intrinsic example Number of rebounds giving the players height, or their arm length Compositional example % vegetation cover September 2013 Best Practices for Systematic Conservation Planning

Correlation Matrix September 2013 Best Practices for Systematic Conservation Planning

Scale and Environmental Data Two components of scale Extent - The geographical area considered Grain - The smallest measurement unit, the grid cell size Often default to the available data Relevant to the species and environment Large scale = small extent = small geographic area Small scale = large extent = large geographic area Dictated by goals, system, and available data Change in grain size has been shown to have only minor impacts More important to 1 : 100 vs. 1 : 1,000 A tree is small at small scales and large at large scale Small scale Large scale 1:10 > 1:1,000 September 2013 Best Practices for Systematic Conservation Planning

Best Practices for Environmental Variable Selection Represent resource gradients and other factors that determine a species distribution patterns Temporal agreement with occurrence records Direct and resource environmental data are more physiologically ‘mechanistic’ and therefore result in models that are more general If not solely interested in prediction, remove one of each pair of highly correlated environmental variables Limited to the data available rather than those most suitable Use only n/10 environmental variables Reduce the candidate predictor set using ecological understanding of the species and the system May be missing a critical environmental variable September 2013 Best Practices for Systematic Conservation Planning

September 2013 Best Practices for Systematic Conservation Planning

Activity Case study discussion within groups What are the ideal and available occurrence and environmental data for your case study? What extent and grain would you use? What are some possible errors in the occurrence data? What are some possible errors in the environmental data? Are these data appropriate for the goal and system of interest? September 2013 Best Practices for Systematic Conservation Planning