1. Understanding and Assembling Model Input
Environmental Data

2. 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

3. 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

4. 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

5. 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

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

7. 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

8. Climate Data Climate station data Precipitation Temperature
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Best Practices for Systematic Conservation Planning

9. Climate Data Models Most affected by terrain and water bodies
Current conditions based on averages over many years ( )
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

10. 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

11. 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
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Best Practices For Systematic Conservation Planning

12. 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

13. 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

14. 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
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Best Practices For Systematic Conservation Planning

15. 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
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Best Practices for Systematic Conservation Planning

16. 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 , 20 SEP 2007 DOI: /j x
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Best Practices for Systematic Conservation Planning

17. 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,
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18. 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

19. Correlation Matrix September 2013
Best Practices for Systematic Conservation Planning

20. 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

21. 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

22. September 2013
Best Practices for Systematic Conservation Planning

23. 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