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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

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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.

Critical factors and Tolerance Limits

• Usually, many factors interact to limit species distribution.

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Critical factors and Tolerance Limits

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

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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 shiftAre niches stable?

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

Time T1 Time T2

RealizedNicheShift

NO!

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FundamentalNicheShift

•Fundamental niche shift when tolerance limits change due to EVOLUTIONARY 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: ·   

- Approximation of a Species Distribution    

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Ecological niche modelingPurpose: ·       - Potential Niche Habitat Modeling

(Invasive species, diseases)   

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Ecological niche modelingPurpose: ·   

    - Site Selection or conservation priority: Suitability Analysis

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Ecological niche modelingPurpose: ·      

- Species 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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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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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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Ecological niche modeling

Step 1: occurrence records

Step 2: environmental variables

Step 3: current ecological niche

Step 4: projected ecological niche

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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 the program 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, http://complabs.nevada.edu/~jezkovat/firefighters/niche_modelling.ppt 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 database (mammals) or Herpnet

database http://www.herpnet.org/ (reptiles)• Select “Data portals”• In Manis, click on any of the three providers (e.g. MaNIS Portal at the Museum

of Vertebrate Zoology• 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 into a folder

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Excel – prepare occurrence records csv. file• Open downloaded occurrence records in Excel (right-click and use the “open

with” function• 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• 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 generated by Maxent (your_species.asc) by selecting:

Data -> Import to Gridfile ->Single file. Choose “ESRI ascii” of file and “select integer”

• Repeat for your LGM model (your_species_ccsm.asc)• 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