Habitat Variability of Anolis Lizards in the Caribbean and the Spatial and Ecological Relationships of Anolis cristatellus on Puerto Rico

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/P
  roductGLC2000.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)

• Morans 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 gt .05)
• PCA2 (R2 .0332, P gt .05)
• PCA3 (R2 .0007, P gt .05)
• Precipitation
• PCA1 (R2 .0004, P gt .05)
• PCA2 (R2 .008, P gt .05)
• PCA3 (R2 .0012, P gt .05)
• Elevation
• PCA1 (R2 .0222, P gt .05)
• PCA2 (R2 .0178, P gt .05)
• PCA3 (R2 .0037, P gt .05)

11
Spatial Autocorrelation?

• Morans 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