environmental data

Ryan DiGaudio

Environmental Data

Understanding and Assembling Model Input

Best Practices for Systematic Conservation Planning2

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

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Environmental Data• What are environmental data?

• 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

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Environmental data• Continuous

• 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)

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LegendAnas_acuta_points

NVC class

Agricultural Vegetation

Aquatic Vegetation

Developed & Other Human Use

Forest & Woodland

Introduced & Semi Natural Vegetation

Nonvascular & Sparse Vascular Rock Vegetation

Open Water

Polar & High Montane Vegetation

Recently Disturbed or Modified

Semi-Desert

Shrubland & Grassland

dem_30gr_proValue

High : 4396 Low : 984

PrecipitationHigh : 302

Low : 2.96969

LegendAnas_acuta_points

NVC class

Agricultural Vegetation

Aquatic Vegetation

Developed & Other Human Use

Forest & Woodland

Introduced & Semi Natural Vegetation

Nonvascular & Sparse Vascular Rock Vegetation

Open Water

Polar & High Montane Vegetation

Recently Disturbed or Modified

Semi-Desert

Shrubland & Grassland

dem_30gr_proValue

High : 4396 Low : 984

PrecipitationHigh : 302

Low : 2.96969

Types of Environmental Data• Direct: Direct physiological influence but are not consumed

• Indirect: No physiological effect

• Resource: Matter and energy consumed by species


Winter temperature

Surv

ival


Environmental Data Regimes• Climate• Topography• Substrate (geology and soil)• Land cover/ land use• Remote sensing• Biotic interactions• Disturbances

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Climate Data• Examples

• Temperature, precipitation, humidity• Components

• Station data• Elevation• Interpolation

• Future conditions• Global circulation model (GCM)• Downscaling method• Scenarios• Time period

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Climate Data• Climate station data

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Precipitation

Temperature

Climate Data• Most affected by terrain and water bodies

• Current conditions based on averages over many years (1950-2000)

• Many local, regional and global sources

• Good for applications for model transferability



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 collinearitySeptember

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Topography• Earth surface shape and landform features

• Digital elevation model (DEM)

• Slope and Aspect

• Topographic position index

• RoughnessSeptember

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Jenness, Brost, and Beier, 2011

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


U.S. Department of the Interior, U.S. Geological SurveyURL: http://pubs.usgs.gov/pp/2004/1686a/1686a.html

Land Cover/ Land Use• Physical coverage or type on the earth’s surface

• Important to know the intended scale and purpose of the map

• Temporal aspect important to consider• National Land Cover Database – 1992, 2001, 2006


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



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

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Global Ecology and BiogeographyVolume 16, Issue 6, pages 754-763, 20 SEP 2007 DOI: 10.1111/j.1466-8238.2007.00345.xhttp://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00345.x/full#f2

http://onlinelibrary.wiley.com/doi/10.1111/geb.2007.16.issue-6/issuetoc

http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00345.x/full#f2

http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00345.x/full#f2


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

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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.


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

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Correlation Matrix

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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 detection

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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

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1:10 > 1:1,000

Large scale

Small scale


Sample Unit Size

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¼ of area

3/4 of area


Resampling

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Nearest Neighbor Bilinear Cubic convolution

Bilinear interpolation ?


Best Practices for Environmental Variable Selection• Use only n/10 environmental variables

• Limited to the data available rather than those most suitable

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Still biologically relevant? Will they still inform conservation goals?

Problem


Best Practices for Environmental Variable Selection• If not solely interested in prediction, remove one of each pair of highly correlated environmental variables

• Reduce the candidate predictor set using ecological understanding of the species and the system

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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

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Best Practices for Systematic Conservation Planning26 September 2013


Part 4• How do the file formats between the two layers compare?

• What is the extent of the us_tmax_2010.05.tiff and how does this compare to the other rasters?

• Check the grain (cell size) of the two layers – any differences?

• What about the coordinate reference system between the two layers?

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• Part 4 continue• Once the tool has finished running, check the properties of the output:• Do the number of rows and columns match the other layers?• Is grain size the size the same as the other layers?• Coordinate Reference System?

• What might some issues with using this method for resampling? Are we forcing a downscale or upscale of our new raster?

• Is there only one way to resample? • What if this was a categorical variable? How would we want to modify our methods to make sure we are using the appropriate resampling methods?

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Part 5

• Spend some time deciding what variables you would remove and why. Write these down, you will use them later.

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Part 1• What is the coordinate reference system of the data? How would you find this out?

• How many records don’t have any coordinates?• Are there any duplicate records?• How many records are unique?• What are some field that might help provide a measure of data quality/accuracy

• What is the accuracy of a location with a latitude of 31.9 vs. 29.73457? How might this impact any analysis performed with these data

• Below is the map of the occurrences. Are there any points the look suspect to further evaluation?

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Part 2• These data came from eBird which comprises of data contributed by volunteer and professional• What might be some concerns with this data set?

• If you were handed this data set, what would be some questions you would ask to better understand the assumptions and limitations of the data?

• Are there any points that look suspicious?• How many occurrences are there?• Are there any duplicate locations? How might this impact a species distribution model? How might two occurrences that have the same location have different environmental data?

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Part 3• What do you notice about the distribution of the background points?• Are they a random sample of the environment?

• Where are they concentrated?• How is the distribution of the background points similar to the occurrence points?

• These points represent all eBrid observations for all other species during the spring months. Why would we want to use these points as our background sample?

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Part 3 cont.• Take a look at the column names in the background sample spreadsheet. Do the match the original names of the environmental rasters?

• Maxent requires the names of the columns in the background file to match the file names of the environmental rasters. What would we need to do to make sure we don’t hit any errors when running Maxent

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Best Practices for Systematic Conservation Planning34 September 2013

environmental data

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modeled climate data

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area examples

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