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Ecological niche and ecological niche modeling. Tereza Jezkova School of Life Sciences, University of Nevada, Las Vegas March 2010. What drives species distributions?. All species have tolerance limits for environmental factors beyond which individuals cannot survive , grow , or reproduce.

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Ecological niche and ecological niche modeling

Ecological niche and ecological niche modeling

Tereza Jezkova

School of Life Sciences, University of Nevada, Las Vegas

March 2010

What drives species distributions

What drives species distributions?

  • All species have tolerance limits for environmental factors beyond which individuals cannot survive, grow, or reproduce

Environmental Gradient

Tolerance Limits and Optimum Range

Tolerance Limits

Tolerance limits exist for all important environmental factors

Critical factors and Tolerance Limits

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

  • Usually, many factors interact to limit species distribution.

Critical factors and Tolerance Limits


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

Specialist and Generalist species...

Fig. 4-11, Miller & Spoolman 2009


Biotic factors







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)

Tolerance Limits and Optimum Range

Niche shift

  • Are niches stable?

  • Realized niche shifts all the time due to

    • changing biotic interations,

    • realized environment,

    • time to disperse


Time T1

Time T2




Time T1

Time T2




Resource partitioning
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)

Niche partitioning and Law of Competitive Exclusion





Ecological niche modeling

Purpose: ·  

- Approximation of a Species Distribution

Ecological niche modeling

Purpose: ·  

- Potential Niche Habitat Modeling

(Invasive species, diseases)

Ecological niche modeling

Purpose: ·  

- Site Selection or conservation priority:

Suitability Analysis

Ecological niche modeling

Purpose: ·  

- Species Diversity Analysis

Ecological niche modeling

Two types:

1. DEDUCTIVE: A priori knowledge about the organism

Example: SWReGAP

Ecological niche modeling

Two types:

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

  • 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)


  • Variables:

  • Temperature (monthly)

  • Precipitation (monthly)

  • 19 Bioclimatic variables

  • Current, Future, Past

  • Resolution:

  • ca. 1, 5, 10 km

  • Coverage

  • World

Southwest Regional Gap Analysis Project

Northwest GAP Analysis Project

  • Variables:

  • Landcover

  • Resolution:

  • ca. 30 m

  • Coverage

  • western states

Natural Resources Conservation Service (NRCS)

SSURGO Soil Data

  • Variables:

  • Soils

  • Resolution:

  • ca. 30 m

  • Coverage

  • USA but incomplete 

Ecological niche modeling

Step 1: occurrence records

Step 2: environmental variables

Step 3: current ecological niche

Step 4: projected ecological niche

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


  • Insufficient or biased occurrence records

  • Insufficient or meaningless environmental variables


  • Inaccuracies in climate reconstructions

  • Dispersal limitations

  • Non-analogous climates

  • Niche shift (evolution)




  • 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, so are the 0K and LGM datasets

  • Detailed instructions are at the end of this PowerPoint



Chisel-toothed kangaroo rat (Dipodomys microps)

Desert kangaroo rat (Dipodomys deserti)

Pygmy rabbit (Brachylagus idahoensis)

Pika (Ochotona princeps)

Mountain beaver (Aplodontia rufa)


Desert Horned Lizard (Phrynosoma platyrhinos)

Coastal Tailed Frog (Ascaphus truei)

Long-nosed Leopard Lizard (Gambelia wislizenii)

Gila monster (Heloderma suspectum)

Download occurrence records
Download Occurrence Records

  • Choose either Manis database (mammals) or Herpnet database (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

Excel prepare occurrence records csv file
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 “


  • Download the 0K and LGM bioclimatic variables

  • 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


  • Indicate your output directory

  • Press “Run”

Diva gis
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 (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


BIO1 = Annual Mean Temperature

BIO2 = 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 Month

BIO6 = Min Temperature of Coldest Month

BIO7 = Temperature Annual Range (P5-P6)

BIO8 = Mean Temperature of Wettest Quarter

BIO9 = Mean Temperature of Driest Quarter

BIO10 = Mean Temperature of Warmest Quarter

BIO11 = Mean Temperature of Coldest Quarter

BIO12 = Annual Precipitation

BIO13 = Precipitation of Wettest Month

BIO14 = Precipitation of Driest Month

BIO15 = Precipitation Seasonality (Coefficient of Variation)

BIO16 = Precipitation of Wettest Quarter

BIO17 = Precipitation of Driest Quarter

BIO18 = Precipitation of Warmest Quarter

BIO19 = Precipitation of Coldest Quarter