Species on Environmental Gradients

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Species on Environmental Gradients
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Environmental Gradients

• interest in community-environment relationships
• usually abiotic factors
• processes that affect composition

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Distal vs.Proximal Environmental Variables

• distal variables influence plants animals
  indirectly
• rainfall is distal and affects plants through
  other, intermediate factors (e.g., soil
  permeability and soil water-holding capacity)
• proximal variables influence plants animals
  directly
• water availability at the root hair is a proximal
  variable

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Distal vs.Proximal Environmental Variables

• distal variables influence plants animals
  indirectly
• rainfall is distal and affects plants through
  other, intermediate factors (e.g., soil
  permeability and soil water-holding capacity)
• proximal variables influence plants animals
  directly
• water availability at the root hair is a proximal
  variable

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Distal vs. Proximal Environmental Variables

• distal variables influence plants animals
  indirectly
• rainfall is distal and affects plants through
  other, intermediate factors (e.g., soil
  permeability and soil water-holding capacity)
• proximal variables influence plants animals
  directly
• water availability at the root hair is a proximal
  variable

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Direct Ordination

• Are patterns continuous or discontinuous with
  respect to underlying environmental factors?
• also known as direct gradient analysis
  (Whittaker 1956)
• presence/absence or abundance of species in
  relation to abiotic factors deemed important
• earliest methods were graphical

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Early Direct Gradient Analysis

• Insert scan from van der Maarel page 55 (and
  handout)
• main finding was that no 2 species had same
  distribution pattern

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Species on Environmental Gradients

• idealized linear responses to environment
• inappropriate except for short gradients
• many multivariate methods assume linear responses
• PCA (principal components analysis)
• discriminant analysis

Abundance
Gradient
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Species on Environmental Gradients

• Whittaker (1956, 1960) drew smooth, hump-shaped
  responses along gradients
• vary in amplitude, width
• species have nonlinear responses to each other
• Gaussian curves idealized response

Abundance
Gradient
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Problems with the Gaussian Model

• zero truncation
• curves are solid
• response curves may be more complex

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Zero Truncation

• Beals (1984)
• once absent, no information about how unfavorable
  the environment is for that species
• no negative responses outside its range

Abundance
?
?
Gradient
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Solid Curves

• species is less abundant than its potential
• other factors beside the gradient in question
  limit its abundance

Abundance
Gradient
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Complex Response Curves

• asymmetric (skewed)
• polymodal
• discontinuous

Abundance
Gradient
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Joint Distributions

• community structure is a set of associated
  species
• joint absences ambiguous and sometimes
  misleading
• shared zeroes misinterpreted in some ordinations
  (Beals 1984) PCA and CA
• some species are positively associated, others
  are negatively associated
• see pages 39 41, McCune Grace
• cant assume bivariate (or multivariate)
  normality
• more heterogeneous the data are, the greater the
  departure from normality
• often exhibit a dust bunny distribution
• clustered in corners and along axes
• requires a specialized set of techniques for
  dealing with these unruly data

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Ordination

• seek and describe pattern
• generate hypotheses
• test hypotheses
• describe strongest compositional patterns
• objectively select the most important factors
  from multiple factors
• separate strong patterns (more important) from
  weaker patterns (less important)
• reveal unexpected patterns and suggested processes

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Gradient Analysis

• underlying factors vary continuously
• Direct how are species distributed along
  specific gradients of interest?
• Indirect positions SUs according to patterns of
  association among species
• may indicate underlying gradients
• as a secondary step, environmental data can be
  fitted to the ordination