Ecological niche and ecological niche modeling Tereza Jezkova

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, thus limiting distribution and abundance 2

Tolerance Limits. Tolerance and Optimum Limits. Range 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. • Organism may have a wide range of tolerance to some factors and a narrow range to other factors 5

Specialist and Generalist species. . . 6 Fig. 4 -11, Miller & Spoolman 2009

FUNDAMENTAL NICHE Biotic factors Historical factors REALIZED NICHE Realized environment 7

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) 8

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 T 1 Time T 2 Realized Niche Shift 9

• Fundamental niche shift when tolerance limits change (adaptation) Time T 1 Time T 2 Fundamental Niche Shift 10

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) 11

Niche partitioning and Law of Competitive Exclusion Chthamalus Balanus 12

Niche partitioning and Law of Competitive Exclusion 13

Ecological niche modeling Purpose: · Species Distribution Mapping Potential Niche Habitat Modeling (Invasive species, diseases) Site Selection: Suitability Analysis Conservation Priority Mapping Species Diversity Analysis 14

Two types: Ecological niche modeling 1. DEDUCTIVE: A priori knowledge about the organism Example: SWRe. GAP http: //fws-nmcfwru. nmsu. edu/swregap/habitatreview/ 15

Ecological niche modeling Two types: 2. CORRELATIVE: Self-learning algorithms based on known occurrence records and a set of environmental variables 16

WORLDCLIM http: //worldclim. org/ Variables: • Temperature (monthly) • Precipitation (monthly) • 19 Bioclimatic variables • Current, Future, Past Resolution: • ca. 1, 5, 10 km Coverage • World 17

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 18

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 19

Occurrence records: - Own surveys (small scale) - Digital Databases (e. g. museum specimens) MANIS (mammals) http: //manisnet. org/ http: //olla. berkeley. edu/ornisnet/ ORNIS (birds) HERPNET (reptiles) http: //www. herpnet. org/ HAVE TO BE GEOREFERENCED (must have coordinates) 20

Ecological niche modeling – how it works Extract values from points Histograms of values Calibration Algorithm Projection in time or space Evaluation 21

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Ecological niche modeling – models from Maxent 23

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 !!! 24

sasquatch blackbear 25

Exercise (work in pairs): • Download museum records for one of nine species • Prepare occurrence data file • Run Maxent for current (0 K) and last glacial maximum (LGM) climate • Make maps in Diva. GIS (or Arc. GIS if you have it) • Answer questions on the worksheet This Power. Point is on the website, so are the 0 K and LGM datasets Detailed instructions are at the end of this Power. Point 26

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) 27

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. Ma. NIS 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 28

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 – 4 decimal places) Save as “. csv “ 29

Maxent • • • Download the 0 K and LGM bioclimatic variables http: //complabs. nevada. edu/~jezkovat/firefighters/0 K. 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 0 K bioclimatic variables Check all three fields Indicate the directory with the LGM layers Indicate your output directory Press “Run” 30

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 0 K 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 31

BIOCLIMATIC VARIABLES BIO 1 = Annual Mean Temperature BIO 2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO 3 = Isothermality (P 2/P 7) (* 100) BIO 4 = Temperature Seasonality (standard deviation *100) BIO 5 = Max Temperature of Warmest Month BIO 6 = Min Temperature of Coldest Month BIO 7 = Temperature Annual Range (P 5 -P 6) BIO 8 = Mean Temperature of Wettest Quarter BIO 9 = Mean Temperature of Driest Quarter BIO 10 = Mean Temperature of Warmest Quarter BIO 11 = Mean Temperature of Coldest Quarter BIO 12 = Annual Precipitation BIO 13 = Precipitation of Wettest Month BIO 14 = Precipitation of Driest Month BIO 15 = Precipitation Seasonality (Coefficient of Variation) BIO 16 = Precipitation of Wettest Quarter BIO 17 = Precipitation of Driest Quarter BIO 18 = Precipitation of Warmest Quarter BIO 19 = Precipitation of Coldest Quarter 32