Transcript Understanding Our Environment

Ecological niche and ecological niche
modeling
Tereza Jezkova
School of Life Sciences, University of Nevada, Las Vegas
March 2010
1
What drives species distributions?
•All species have tolerance limits for environmental factors beyond
which individuals cannot survive, grow, or reproduce
2
Tolerance LimitsTolerance
and Optimum
Range
Limits
Environmental Gradient
Tolerance limits exist for all important environmental factors
3
Critical factors and Tolerance Limits
4
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.
5
Critical factors and Tolerance Limits
• Organism may have a wide range of
tolerance to some factors and a narrow range
to other factors
6
Specialist and Generalist species...
7
Fig. 4-11, Miller & Spoolman 2009
FUNDAMENTAL NICHE
Biotic factors
Historical
factors
REALIZED
NICHE
Realized
environment
8
Tolerance
Limits andversus
Optimum
Range
Fundamental
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)
9
Tolerance Limits and
Optimum
Niche
shift Range
Are niches stable?
NO!
Realized niche shifts all the time due to
•changing biotic interations,
•realized environment,
•time to disperse
Time T1
Time T2
Realized
Niche
Shift
10
•Fundamental niche shift when tolerance limits change  due
to EVOLUTIONARY ADAPTATION
Time T1
Time T2
Fundamental
Niche
Shift
11
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)
12
Niche partitioning and Law of Competitive Exclusion
Chthamalus
Chthamalus
Balanus
Balanus
13
Niche partitioning and Law of Competitive Exclusion
14
Ecological niche modeling
Purpose: ·
- Approximation of a Species Distribution
15
Ecological niche modeling
Purpose: ·
- Potential Niche Habitat Modeling
(Invasive species, diseases)
16
Ecological niche modeling
Purpose: ·
- Site Selection or conservation priority:
Suitability Analysis
17
Ecological niche modeling
Purpose: ·
- Species Diversity Analysis
18
Ecological niche modeling
Two types:
1. DEDUCTIVE: A priori knowledge about the organism
Example: SWReGAP http://fws-nmcfwru.nmsu.edu/swregap/habitatreview/
19
Ecological niche modeling
Two types:
2. CORRELATIVE: Self-learning algorithms based on known
occurrence records and a set of environmental variables
20
Occurrence records:
- Own surveys (small scale)
- Digital Databases (e.g. museum specimens)
MANIS (mammals) http://manisnet.org/
ORNIS (birds)
http://olla.berkeley.edu/ornisnet/
HERPNET (reptiles) http://www.herpnet.org/
HAVE TO BE GEOREFERENCED (must have coordinates)
21
WORLDCLIM http://worldclim.org/
Variables:
• Temperature (monthly)
• Precipitation (monthly)
•19 Bioclimatic variables
• Current, Future, Past
Resolution:
• ca. 1, 5, 10 km
Coverage
• World
22
Southwest Regional Gap Analysis Project http://earth.gis.usu.edu/swgap
Northwest GAP Analysis Project http://gap.uidaho.edu/index.php/gap-home/Northwest-GAP
Variables:
• Landcover
Resolution:
• ca. 30 m
Coverage
• western states
23
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 
24
Ecological niche modeling
Step 1: occurrence records
Step 2: environmental variables
Step 3: current ecological niche
Step 4: projected ecological niche
25
26
Ecological niche modeling – models from Maxent
27
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 !!!
28
sasquatch
blackbear
29
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_mode
lling.ppt so are the 0K and LGM datasets
Detailed instructions are at the end of this PowerPoint
30
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)
31
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
32
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 “
33
Maxent
•
•
•
•
•
•
•
•
•
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”
34
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
35
BIOCLIMATIC VARIABLES
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
36