Exam 3 Vocab Flashcards
(68 cards)
bisect
a scale drawing of the vegetation along a transect—time-consuming, typically not done when looking at many different plots
phanerophytes
trees (buds high above ground)
Chamaephytes
shrubs
Hemicryptophytes
grasses
Cryptophytes
perennial forbs
species richness
number of species in the community
evenness
extent to which spp in community are equally abundant
Species frequency
Percentage of quadrats (plots) in which species occurs (Inaccurate for rare or spatially clumped species; Uninformative for very common species)
Species cover
Percentage of the ground covered by a given species (can use Basal cover or Basal area; Canopy cover)
Density (e.g., stem density)
Number of individuals of a species per unit area
Species importance value
Relative cover + Relative density + Relative frequency
Species biomass
Requires destructive sampling/weighing in the lab, or calculations based on detailed field allometric measurements (never as accurate as weighing)
Univariate statistics
compare spp abundance among communities (using a single dependent variable)
-can help to see environmental factors/site history changes altering spp composition
Multivariate statistics
compare spp composition among communities–comparing different spp and communities to each other (using multiple dependent variables)
-can graph as triangles to measure dissimilarity in terms of Euclidean distance (Pythagorean theorem)
Similarity Indices
used to measure differences among communities
-presence/absence indices bt 2 sites: (# of spp—many options; Jaccard index, etc)
-abundance indices: use abundance of individual spp (% similarity, Bray-Curtis index, etc)
-reducing dimensionality of data using ordination (N spp = N dimensions for similarity calculations)
-can collapse data (from many diff spp) into 2-3 dimensions to help better visualize compositional differences among plots; and to identify the major gradients in composition (whether spp related linearly or not—line of best fit)
Ordination—aka indirect gradient analysis
-the stands are ordered by their similarity in species composition and the environmental factors responsible for the resulting patterns are inferred
– requires predetermining which environmental factors are important first
–tends to be biased toward factors that can be easily measured
–patterns imposed by biotic interactions (e.g., competition, herbivory) are ignored
EX: looking at woody composition in a tropical dry forest
-step 1: samples ordered by similarity in spp composition
-step 2: look for envr factors responsible for variation
Direct gradient analysis
-DEVELOPED BY ROBERT WHITTAKER
–the ecologist chooses environmental axes and orders vegetation samples (stands) along those axes, examining the resulting patterns *uses compositional differences to infer environmental & other gradients from species compositional changes
–can include a large number of environmental factors
–includes biotic interactions & site history
EX: looking at 63 lodgepole pine stands in the Canadian Rockies along 2 gradients (moisture & elevation)
-step 1: order samples along a few known envr gradients (ex: Jasper & Banff 63 lodgepole stands in Canada—Whittaker)
-Step 2-cluster data until certain rules are met (to make it more reasonable number of clustered communities
-step 3: relate community comp to known envr gradients & use supplemental statistics
Rarefaction
to standardize to a common number of individuals or samples
Euclidean distance
The measure of the difference between each pair of communities
dimension reduction
taking highly multivariate data and collapsing them into a small number of dimensions
multiple regression
This technique determines the relationship of the abundance of our species to each environmental variable while correcting for correlations among the environmental variables themselves
indicator species
An ideal indicator species is found in all communities of a given type and not in any other community type. The use of indicator species makes classification of communities much easier.
ground-truthing operations
in which randomly selected sites are surveyed to see whether their actual species composition matches that predicted by the remote sensing classification.
Plant Succession
directional change in community structure or composition over time