1
Ecological niche and ecological niche modeling
Tereza JezkovaSchool of Life Sciences, University of Nevada, Las Vegas
March 2010
2
What drives species distributions?
•All species have tolerance limits for environmental factors beyond which individuals cannot survive, grow, or reproduce
3
Environmental Gradient
Tolerance Limits and Optimum Range
Tolerance limits exist for all important environmental factors
Tolerance Limits
4
Critical factors and Tolerance Limits
5
• 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.
6
Critical factors and Tolerance Limits
• Organism may have a wide range of tolerance to some factors and a narrow range to other factors
7
Specialist and Generalist species...
Fig. 4-11, Miller & Spoolman 2009
8
FUNDAMENTAL NICHE
Historical factors
Biotic factors
Realized environment
REALIZED NICHE
9
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)
10
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!
11
FundamentalNicheShift
•Fundamental niche shift when tolerance limits change due to EVOLUTIONARY ADAPTATION
Time T1 Time T2
12
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)
13
Niche partitioning and Law of Competitive Exclusion
Balanus
Balanus
Chthamalus
Chthamalus
14
Niche partitioning and Law of Competitive Exclusion
15
Ecological niche modelingPurpose: ·
- Approximation of a Species Distribution
16
Ecological niche modelingPurpose: · - Potential Niche Habitat Modeling
(Invasive species, diseases)
17
Ecological niche modelingPurpose: ·
- Site Selection or conservation priority: Suitability Analysis
18
Ecological niche modelingPurpose: ·
- Species Diversity Analysis
19
Ecological niche modelingTwo types:
1. DEDUCTIVE: A priori knowledge about the organismExample: SWReGAP http://fws-nmcfwru.nmsu.edu/swregap/habitatreview/
20
Ecological niche modelingTwo types:2. CORRELATIVE: Self-learning algorithms based on known occurrence records and a set of environmental variables
21
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/
22
WORLDCLIM http://worldclim.org/ Variables: • Temperature (monthly) • Precipitation (monthly)•19 Bioclimatic variables• Current, Future, Past
Resolution:• ca. 1, 5, 10 km
Coverage• World
23
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
24
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
25
Ecological niche modeling
Step 1: occurrence records
Step 2: environmental variables
Step 3: current ecological niche
Step 4: projected ecological niche
26
27
Ecological niche modeling – models from Maxent
28
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 !!!
29
sasquatch
blackbear
30
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
31
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)
32
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
33
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 “
34
• 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
35
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
36
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