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A. cristatellus

Habitat Variability of Anolis Lizards in the Caribbean and the Spatial and Ecological Relationships of Anolis cristatellus on Puerto Rico. A. cristatellus. David Ullman May 6, 2004 ENVE 424. The Wonderful Anolis.

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A. cristatellus

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  1. Habitat Variability of Anolis Lizards in the Caribbean and the Spatial and Ecological Relationships of Anolis cristatellus on Puerto Rico A. cristatellus David Ullman May 6, 2004 ENVE 424

  2. The Wonderful Anolis • Over 200 species of Anolis lizards in United States, Mexico, Central and South America, and the Caribbean • 124 known species in the Caribbean alone • Genus well known • Ideal group of species for evolution studies - large amounts of data - island species - limited gene flow

  3. What is an ecomorph? • Definition of an ecomorph: “species with the same structural habitat/niche, similar in morphology and behavior, but not necessarily closes phyletically.” (Williams, 1972) • Microhabitat has profound impact on the morphology of Anolis • What is the effect of large scale habitat differences on species diversity and morphology? Picture taken from: Williams, E.E. 1983. Ecomorphs, faunas, island size, and diverse end points in island radiations of Anolis. In: Lizard Ecology: Studies of a Model Organism (R.B. Huey, E.R. Pianka, and T.W. Schoener, eds), pp. 326-370. Harvard University Press, Cambridge, USA.

  4. Part I: Habitat Variability and Species Diversity • Data collected to measure habitat variability in Land cover/vegetation, surface temperature, annual precipitation, and elevation • elevation, mean annual precipitation, and mean annual temperature for these analyses was obtained from the WorldClim database at the University of California (30 sec. Resolution, ESRI format) http://biogeo.berkeley.edu/worldclim/worldclim.htm • Land cover/vegetation data has been obtained from the Global Vegetation Monitoring Unit (1 km2 resolution, ESRI format) www.gvm.jrc.it/glc2000/ProductGLC2000.htm).

  5. Measuring Habitat Variability (method) • Habitat Data added to ArcMap • Masks created to outline each of the islands in the Caribbean • “Raster Calculator” used to cut out temperature, precipitation, and elevation data for each island • This data used to calculate standard deviation as a measure of variability for each habitat data on each island • For land cover/vegetation variability, number of vegetation types counted for each island • Each habitat variability measurement plotted against the log of the number of species on each island as a measure of species diversity (log transform to normalize data for parametric statistics).

  6. Habitat Variability and Species Diversity (Results) • Habitat variability does have an affect on species diversity • Land cover highly correlated with species diversity (t = 6.934, P = .00006), see right. • Elevation moderately correlated with species diversity (t = 2.772, P = .022) • Temperature moderately correlated with species diversity (t = 3.001, P = .015) • Precipitation NOT correlated with species diversity (t = 1.153, P = .279)

  7. Part II: Morphological variability in A. cristatellus • Previous research shows importance of microhabitat variability on morphology • Habitat variability is important in species diversity • Look at specific species on one island to see if broad intra-island habitat variability has an effect on morphology Anolis cristatellus on Puerto Rico

  8. Morphological variability in A. cristatellus (methods) • Morphological data from 448 museum specimens • Geographic location assigned to each of the 448 specimens based on nominal data • Temperature, Elevation, and Precipitation data recorded for each location • Female specimens filtered out due to sexual dimorphism • Filtering out specimens in same locations by averaging data • Effect of body size removed • 15 morphological measurements?  Principal Component Analysis (PCA)  condense to 3 principal components. These 3 principal components account for 82.2 % of the variance in the data • Each principal component plotted against temperature, elevation, and precipitation

  9. Spatial relationships in Morphology of A. cristatellus (Methods) • Moran’s I calculation of spatial autocorrelation • Using Rooks Case v0.9.6 (Mike Sawada, University of Ottawa, 1998) • Irregular lattice • 20 lags • 10,000 m (10 km) lag distance • Correlogram generated

  10. Morphological variability in A. cristatellus (results) • No correlation between morphology and any of the habitat conditions • Temperature (correlations): PCA1 (R2 = .0166, P > .05) PCA2 (R2 = .0332, P > .05) PCA3 (R2 = .0007, P > .05) • Precipitation: PCA1 (R2 = .0004, P > .05) PCA2 (R2 = .008, P > .05) PCA3 (R2 = .0012, P > .05) • Elevation PCA1 (R2 = .0222, P > .05) PCA2 (R2 = .0178, P > .05) PCA3 (R2 = .0037, P > .05)

  11. Spatial Autocorrelation? • Moran’s I correlograms do not show spatial autocorrelation:

  12. Kriging PCA1

  13. Kriging PCA2

  14. Kriging PCA3

  15. Conclusions • Habitat variability influences species diversity • Habitat variability has no effect on morphology of A. cristatellus • No spatial relationship in morphology • Future work: • More sampling of A. cristatellus • Apply analyses to other species on Puerto Rico and other islands • Factor in temporal scale to reflect changes in morphology or climate over time

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