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Ecological niche and ecological niche modeling

Tereza JezkovaSchool of Life Sciences, University of Nevada, Las Vegas

March 2010

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What drives species distributions?

•All species have tolerance limits for environmental factors beyond which individuals cannot survive, grow, or reproduce, thus limiting distribution and abundance

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Environmental Gradient

Tolerance Limits and Optimum Range

Tolerance limits exist for all important environmental factors

Tolerance Limits

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Critical factors and Tolerance Limits

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• For some species, one factor may be most important in regulating a species’ distribution and abundance.

• Usually, many factors interact to limit species distribution.

• Organism may have a wide range of tolerance to some factors and a narrow range to other factors

Critical factors and Tolerance Limits

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Specialist and Generalist species...

Fig. 4-11, Miller & Spoolman 2009

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FUNDAMENTAL NICHE

Historical factors

Biotic factors

Realized environment

REALIZED NICHE

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Tolerance Limits and Optimum Range Fundamental versus realized niche

Fundamental (theoretical) niche - is the full spectrum of environmental factors that can be potentially utilized by an organism

Realized (actual) niche - represent a subset of a fundamental niche that the organism can actually utilize restricted by:

- historical factors (dispersal limitations)- biotic factors (competitors, predators)- realized environment (existent conditions)

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Tolerance Limits and Optimum Range Niche shift

Are niches stable? NO!

•Realized niche shifts all the time due to changing biotic interations, realized environment, time to disperse

Time T1 Time T2

RealizedNicheShift

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FundamentalNicheShift

•Fundamental niche shift when tolerance limits change (adaptation)

Time T1 Time T2

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Resource Partitioning

• Law of Competitive Exclusion - No two species will occupy the same niche and compete for exactly the same resources

- Extinction of one of them- Niche Partitioning (spatial, temporal)

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Niche partitioning and Law of Competitive Exclusion

Balanus

Balanus

Chthamalus

Chthamalus

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Niche partitioning and Law of Competitive Exclusion

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Ecological niche modelingPurpose: · Species Distribution Mapping

Potential Niche Habitat Modeling (Invasive species, diseases)Site Selection: Suitability AnalysisConservation Priority MappingSpecies Diversity Analysis

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Ecological niche modelingTwo types:

1. DEDUCTIVE: A priori knowledge about the organismExample: SWReGAP http://fws-nmcfwru.nmsu.edu/swregap/habitatreview/

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Ecological niche modelingTwo types:2. CORRELATIVE: Self-learning algorithms based on known occurrence records and a set of environmental variables

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WORLDCLIM http://worldclim.org/

Variables: • Temperature (monthly) • Precipitation (monthly)•19 Bioclimatic variables• Current, Future, Past

Resolution:• ca. 1, 5, 10 km

Coverage• World

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Southwest Regional Gap Analysis Project http://earth.gis.usu.edu/swgapNorthwest GAP Analysis Project http://gap.uidaho.edu/index.php/gap-home/Northwest- GAP

Variables: • Landcover

Resolution:• ca. 30 m

Coverage• western states

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Natural Resources Conservation Service (NRCS)SSURGO Soil Data http://soils.usda.gov/survey/geography/ssurgo/

Variables: • Soils

Resolution:• ca. 30 m

Coverage• USA but incomplete

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Occurrence records:

- Own surveys (small scale)- Digital Databases (e.g. museum specimens)

MANIS (mammals)ORNIS (birds)HERPNET (reptiles)

HAVE TO BE GEOREFERENCED (must have coordinates)

http://manisnet.org/http://olla.berkeley.edu/ornisnet/http://www.herpnet.org/

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Ecological niche modeling – how it worksExtract values from points

Histograms of values Calibration

Projection in time or space

Evaluation

Algorithm

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Ecological niche modeling – models from Maxent

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Problems: Models are only as good as the data that goes into it!!!

CALIBRATION MODELS• Insufficient or biased occurrence records• Insufficient or meaningless environmental variables

PROJECTION MODELS• Inaccuracies in climate reconstructions• Dispersal limitations• Non-analogous climates• Niche shift (evolution)

!!! WRONG INTERPRETATIONS !!!

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sasquatch

blackbear

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Exercise (work in pairs):

• Download museum records for one of nine species• Prepare occurrence data file• Run Maxent for current (0K) and last glacial maximum (LGM) climate• Make maps in DivaGIS (or ArcGIS if you have it)• Answer questions on the worksheet

This PowerPoint is on the website, so are the 0K and LGM datasetsDetailed instructions are at the end of this PowerPoint

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Species:MAMMALS:

Chisel-toothed kangaroo rat (Dipodomys microps)Desert kangaroo rat (Dipodomys deserti)Pygmy rabbit (Brachylagus idahoensis)Pika (Ochotona princeps)Mountain beaver (Aplodontia rufa)

REPTILES and AMPHIBIANS: Desert Horned Lizard (Phrynosoma platyrhinos)Coastal Tailed Frog (Ascaphus truei)Long-nosed Leopard Lizard (Gambelia wislizenii)Gila monster (Heloderma suspectum)

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Download Occurrence Records• Choose either Manis http://manisnet.org or Herpnet http://www.herpnet.org

database• Select “Data portals”• In Manis, click on any of the three providers (e.g. MaNIS Portal at the Museum

of Vertebrate Zoology); in Herpnet click on “Search Museum Data”• Click “build query”• Click “Arctos-MVZ catalog” and scroll down• Click on “select a concept” and choose “scientific name”• Click on “select a comparator” and choose “contains (% for wildcard)• Type in the scientific name (e.g. Dipodomys deserti)• Delete number under “Specify record limit”• Click on “submit query”• WAIT !!! • If the server crashes start over again ;)• When the server returns the result of your search, click on “Download tabular

results” and save the file

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Excel – prepare occurrence records csv. file• Open downloaded occurrence records in Excel• Delete unnecessary rows up front• Sort by “coordinate uncertainty”• Delete all records with no coordinates or those with coordinate uncertainty more

than 5000 meters• Delete all columns except the species, latitude and longitude• Select “Advanced filter” and click “Unique records only”• Copy all “unique records” and past to a new sheet• Make sure the column representing the species has the same value in all cells• Format the columns representing latitude and longitude as numbers with 4

decimal places (Font – Format cells – Number – Number – 4 decimal places)• Save as “ .csv “

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• Download the 0K and LGM bioclimatic variables http://complabs.nevada.edu/~jezkovat/firefighters/0K.zip http://complabs.nevada.edu/~jezkovat/firefighters/LGM.zip

• Unzip each dataset into a separate folder• Open Maxent (*.bat file)• Import your *.csv file of occurrence records• Import the folder with the 0K bioclimatic variables• Check all three fields • Indicate the directory with the LGM

layers• Indicate your output directory• Press “Run”

Maxent

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Diva GIS• Import your occurrence records by selecting: Data -> Import points to shapefile -

> From text file (.txt)• Add the shapefile representing “states”: Layer –> add layer –> States.shp

http://complabs.nevada.edu/~jezkovat/firefighters/states.zip (unzip first)• Import your 0K model by selecting: Data -> Import to Gridfile ->Single file.

Choose “ESRI ascii” of file and “select integer”• Double click on your model raster, Properties window opens up• Change the categories using the two thresholds you recorded from Maxent• Remove the extra two rows• Click “OK”• Repeat for your LGM model• Use the zoom tool to zoom in or out to capture the model well• Unclick the LGM model• Click on “Design” in the bottom right corner and click “OK” in the top left corner• Save as *.bmp file• Click on “data” in the bottom right corner, unclick you OK model and check your

LGM model. • Click on Design and repeat your steps as before

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BIOCLIMATIC VARIABLES

BIO1 = Annual Mean TemperatureBIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))BIO3 = Isothermality (P2/P7) (* 100)BIO4 = Temperature Seasonality (standard deviation *100)BIO5 = Max Temperature of Warmest MonthBIO6 = Min Temperature of Coldest MonthBIO7 = Temperature Annual Range (P5-P6)BIO8 = Mean Temperature of Wettest QuarterBIO9 = Mean Temperature of Driest QuarterBIO10 = Mean Temperature of Warmest QuarterBIO11 = Mean Temperature of Coldest QuarterBIO12 = Annual PrecipitationBIO13 = Precipitation of Wettest MonthBIO14 = Precipitation of Driest MonthBIO15 = Precipitation Seasonality (Coefficient of Variation)BIO16 = Precipitation of Wettest QuarterBIO17 = Precipitation of Driest QuarterBIO18 = Precipitation of Warmest QuarterBIO19 = Precipitation of Coldest Quarter