Why Quantify Landscape Pattern?

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description

Why Quantify Landscape Pattern?. Comparison (space & time) Study areas Landscapes Inference Agents of pattern formation Link to ecological processes. Programs for Quantifying Landscape Pattern. FRAGSTATS - PowerPoint PPT Presentation

Transcript of Why Quantify Landscape Pattern?

Page 1: Why Quantify Landscape Pattern?
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Why Quantify Landscape Pattern?

• Comparison (space & time)– Study areas– Landscapes

• Inference– Agents of pattern

formation– Link to ecological

processes

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Programs for Quantifying Landscape Pattern

• FRAGSTATS– http://www.umass.edu/

landeco/research/fragstats/documents/Metrics/Metrics%20TOC.htm

• Patch Analyst– http://flash.lakeheadu.ca/

~rrempel/patch/

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Quantifying Landscape Pattern

• Just because one can measure it, doesn’t mean one should– Does the metric make sense?...biologically

relevant?– Avoid correlated metrics– Cover the bases (comp., config., conn.)

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Landscape Metrics - Considerations• Selecting Metrics……

– Subset of metrics needed that:• i) explain (capture) variability in pattern• ii) minimize redundancy (i.e., correlation among

metrics = multicollinearity)

– O’Neill et al. (1988) Indices of landscape pattern. Landscape Ecology 1:153-162

• i) eastern U.S. landscapes differentiated using– dominance– contagion– fractal dimension

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Landscape Metrics - Considerations• Selecting Metrics……

– Use species-based metrics– Use Principal Components Analysis (PCA)?– Use Ecologically Scaled Landscape Indices

(ESLI; landscape indices, scale of species, and relationship to process)

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Quantifying Pattern: Corridors

• Internal:– Width

– Contrast

– Env. Gradient

• External:– Length

– Curvilinearity

– Alignment

– Env. Gradient

– Connectivity (gaps)

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Quantifying Pattern: Patches• Levels:

– Patch-level• Metrics for indiv.

patches

– Class-level• Metrics for all patches

of given type or class

– Zonal or Regional• Metrics pooled over 1

or more classes within subregion of landscape

– Landscape-level• Metrics pooled over all

patch classes over entire extent

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Quantifying Pattern: Patches

• Composition:– Variety & abundance

of elements

• Configuration:– Spatial characteristics

& dist’n of elements

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Quantifying Pattern: Patches

• Composition:– Mean (or mode,

median, min, max)

– Internal heterogeneity (var, range)

• Spatial Characters:– Area (incl. core areas)

– Perimeter

– Shape

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Quantifying Pattern: Landscapes (patch based)

• Composition:– Number of patch type

• Patch richness

– Proportion of each type

• Proportion of landscape

– Diversity

• Shannon’s Diversity Index

• Simpson’s Divesity Index

– Evenness

• Shannon’s Evenness Index

• Simpson’s Index

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Quantifying Pattern: Patches• Configuration:

– Patch Size & Density

• Mean patch size

• Patch density

• Patch size variation

• Largest patch index

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Quantifying Pattern: Patches• Configuration:

– Shape Complexity• Shape Index• Fractal Dimension

• Fractals = measure of shape complexity (also amount of edge)

• Fractal dimension (d) ranges from 1.0 (simple shapes) to 2.0 (more complex shapes)

• ln(A)/ln(P), where A = area, P = perimeter

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Quantifying Pattern: Patches• Configuration:

– Core Area (interior habitat)

• # core areas

• Core area density

• Core area variation

• Mean core area

• Core area index

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Quantifying Pattern: Patches, Zonal

• Configuration:

– Isolation / Proximity

• Proximity index

• Mean nearest neighbor distance

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• Proximity Index (PXi) = measure of relative isolation of patches; high (absolute) values indicate relative connectedness of patches