Lecture 1 - Forest Landscape Ecology Lab, University of Wisconsin ...

Lecture 2, Landscape Elements
January 20, 2005
Principles of Landscape Ecology
FOR 565
1. What is a landscape?
There are many definitions of landscapes. Overall, all definitions include an area of land
containing patches or elements, and all generally emphasize interactions between
systems.
Forman and Godron (1986):
“A heterogeneous land area composed of a cluster of interacting ecosystems that is
repeated in similar form throughout.” This definition emphasizes the repeated patterns of
ecosystems that warrant classification as a landscape.
Turner et al. (2001):
“An area that is spatially heterogeneous in at least one factor of interest.” This definition
emphasizes spatial heterogeneity at any scale or in any form – not necessarily repeated.
There is no absolute size for a landscape: it depends on your perspective and the
questions that you are asking. The landscape is defined by an interacting mosaic of
patches relevant to the phenomenon under consideration, and the scientist or manager
must define the landscape appropriately.
From a wildlife management perspective, a landscape may contain a mosaic of habitat
patches. Landscape size would differ among organisms, depending on what constitutes a
mosaic of habitat or resource patches meaningful to that particular organism. This differs
from anthropocentric definitions that give landscapes a certain minimum size – which has
limited utility in wildlife management if you accept that organisms experience the
environment at different scales. The best course of action may be to manage habitats
over the broadest possible range of spatial scales, because each scale (stand, watershed,
etc.) will be important for some suite of species, and each species will respond to more
than one scale.
Two Models of Landscape Cover
There are two general conceptual models of landscapes: the corridor-patch-matrix model
and the landscape continuum model. We will focus on the first while recognizing the
value of the second.
2. Patch-Corridor-Matrix Model
2.1 Patches
Landscapes are composed of a mosaic of patches, and these must be defined relative to
the phenomenon under consideration. Patches represent relatively discrete areas of
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homogeneous environmental conditions, where the patch boundaries are distinguished by
environmental character states different enough from their surroundings that they may be
perceived by or are relevant to the organism or process of interest.
Patches are dynamic and occur on a variety of spatial and temporal scales. A patch at any
scale includes patchiness at finer scales – and this mosaic of patches is defined by
patchiness at broader scales. Thus any landscape contains a hierarchy of patch mosaics
across a range of scales.
2.1.1 Patch definitions
In any case, methods are required for defining patches out of patchiness, only after which
we can quantify and summarize various aspects of their pattern on the landscape. Patch
boundaries are artificially imposed and meaningful only when referenced to a particular
scale, and patches may be more distinct at some scales than others.
First, we need to define the two ways that spatial (patch) data are stored: vector and
raster data.
Vector Data Data are stored in irregularly shaped polygons. In ArcGIS, these are called
shape files or coverages.
Raster Data. Data are stored in homogenous rectangles, typically squares. Also called
lattice data, pixels, grains, grid cells. In ArcGIS, these are grids files.
Both data types assume that the area within each unit (polygon or grid cell) is
homogenous. 35mm photographs contain continuous data (limited only by the size of the
molecule of the recording medium), however they must be converted to vector or raster
data in order for us to use them in any meaningful way.
There are four general approaches to defining patches.
Simple aggregation of like-valued regions
An easy method of defining patches is simply to combine all adjacent areas that have the
same (or similar) value for the attribute of interest. A common methods is to cluster
adjacent cells in a raster grid if the cells have the same value. All GIS packages have a
utility to do this, and more complicated approaches can be devised for particular
applications.
Moving- or split-window methods
An alternative approach is to define patches by defining their edges – that is, where the
measured value changes abruptly. There are two general approaches:
A. Local variance methods - Variance is computed within a moving window on a
raster grid, with the variance saved as the value of the center cell of the window for a new
data grid. Regions where the values change will have high local variance.
B. Local rate-of-change - Similarly, one could calculate a local regression on X and Y
coordinates and then consider the slope of this regression as an indication of the rate-ofchange;
areas of steep slope indicate edges of patches.
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In either case, a surface plot of this local variance or slope will "outline" patches of
comparative homogeneity. In a GIS, one could compute a local variance, then threshold
this value to create vectors of values above the threshold, ultimately joining these into
polygons. A useful feature of this approach is that it provides a measure of edge width
and contrast (e.g., whether the edges are abrupt or "fuzzy"), which might be useful in
some applications. This method also requires a fairly extensive initial landscape map.
Global zonation or Iterative Splitting
Another alternative is to use a divisive approach, beginning with a single patch (the entire
study area or data set) and then successively dividing this into regions that are statistically
similar. The basic approach is to iteratively divide the region into all possible pairs of
sub-regions, and then compare the variances between sub-regions relative to the internal
variances for each sub-regions (an F test). The data split that maximizes the between-subregion
variance finds the most homogeneous sub-regions. This procedure is then repeated
recursively. This can be computationally complex for large data sets.
Spatially constrained clustering
A final method to create patches is to cluster them hierarchically, but with a constraint of
spatial adjacency. In this, a multivariate agglomerative clustering algorithm is extended
to enforce the condition that groups are joined only if spatially adjacent (with adjacency
defined for the application).
2.1.2 Patch Characteristics
Patches can be characterized compositionally in terms of the variables measured within
them. This may include the mean (mode, central, or max) value and internal
heterogeneity (variance, range). In spatial applications, we want additional information
about the patch's shape or spatial configuration. A patch can be described by its:
A. Area. The size of the patch, in units of map scale or as a proportion of the total
map area. Area may also be subdivided into edge versus interior (core) area, with edges
defined in terms of some buffering distance.
B. Perimeter (edge length). The linear measure of patch circumference.
C. Shape complexity A measure of how irregularly a patch is shaped. For example,
the shape of Alaska is more complex than Kansas. Shape complexity is often
summarized in terms of the edge to area ratio.
Most methods that define patches (especially GIS) provide these indices simply or even
automatically. Virtually all GIS packages calculate the area and perimeter (edge length)
of each patch. During our second lab, we will be using a simpler program (IAN) that
calculates these (and many other) metrics automatically.
2.2 Corridors and Edges
Corridors are essentially a unique sub-set of patches. Corridors are defined as narrow,
linear patches of a patch type that differ from those on either side. Corridors are
important in that they may also function as habitat, dispersal vectors, or barriers as a
consequence of their structure. Corridors may be either temporary or permanent for the
process or organism of interest.
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Three types of structural corridors exist:
A. Line corridors. The width of the corridor is too narrow to allow for interior
environmental conditions to develop
B. Strip corridors – the width of the corridor is wide enough to allow for interior
conditions to develop
C. Stream corridors – totally different, special category
Functionally, corridors may be categorized differently. Several functions have been
documented:
A. Habitat corridor – Linear landscape element that provides habitat for
survivorship, natality, and movement. Habitat corridors tend to increase the connectivity
of a landscape.
B. Facilitated movement corridor – Linear landscape element that provides for
survivorship and movement, but not necessarily natality, between other habitat patches.
Facilitated movement corridors also tend to increase connectivity.
Most attention has been on facilitated movement corridors. Some have argued that we
can only demonstrate this corridor function when the rate of immigration to the target
patch increases above what it would be without the corridor. As in many aspects of
landscape ecology and ecology in general, this has not been experimentally tested.
C. Barrier or filter corridor – Linear landscape element that prohibits or
differentially impedes (filters) the flow of materials or organisms. Barrier/filter corridors
decrease matrix connectivity.
D. Modifying Corridor - The corridor or edge modifies the effect of abiotic or
biotic effects on the surrounding matrix. The edge modifies the inputs materials and/or
organisms between two patches or between a patch and the surrounding matrix.
Edges are a unique type of corridor. Between every two patches lies an edge. Edges are
rarely as abrupt as depicted in any GIS. Rather, edges will be structurally complex and
may contain unique elements of both neighbors. The measurement of edges was
discussed alongside patch definitions. In general, edges are much more difficult to define
than patches. Edges most often function as barrier/filter corridors (C) or produce unique
effects on the patch or matrix (D).
An example is the forest edge: next to a clearcut, the forest edge will experience greater
mortality than the interior. There are important and significant effects on light
transmittance, moisture, and temperature both within the edge and on the neighboring,
more interior forest. The species richness of understory plants will also be greater near
the forest edge (but may be less unique).
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2.3 Matrix
The matrix is considered the most common or connected landscape element type, and
thus plays the dominant role in landscape function. The matrix is obvious in many cases,
but not always (thus leading to the landscape continuum model, below), and it may not be
appropriate to consider any specific landscape element the matrix. The matrix should be
defined based on the object or process under consideration and the scale of investigation.
What may seen to be a “fragmented” matrix or landscape at one scale may only be an odd
inclusion at a different scale, depending on the abundance and configuration of the
patches in question.
2.4 Limitations of the patch-corridor-matrix model
Other than the fact that the matrix is not always easily defined or distinguished, the P-C-
M model has been criticized for being over-simplistic. Classifying landscapes into
patches and corridors that contrast with the matrix are essentially human constructs that
are facilitated by mapping tools (GIS). Humans and other organisms may perceive and
(or) perceive such landscape elements in very different ways.
In addition, the P-C-M model sometimes suffers from the binary classification of habitat
into suitable vs. unsuitable habitat when habitats for species most often occur on a
gradient. Notably, the patches and corridors defined by the P-C-M model themselves
contain interior heterogeneity that may affect species and/or processes that occur with the
elements.
3. The Landscape Continuum (LC) Model
In landscapes where patches are not discrete or obviously defined, patches may not be
easily differentiated from the matrix. The landscape continuum model was developed in
response to this issue, originally for semi-cleared grazing and agricultural landscapes in
Australia that had small fragments of woodlands and isolated, scattered native trees.
Patches and corridors are too difficult to define in this setting and others (like grasslands).
Although single trees may not provide habitat for some taxa, a set of isolated trees may
collectively provide habitat – so these small elements should be considered rather than
absorbed into the background matrix. The LC model was developed as a improvement
over the P-C-M model, which often inaccurately assumes the matrix to be “unsuitable”
and patches to be “habitat islands”.
As one example, the model can contain four broad cover classes:
A. Intact cover – containing >90% cover and having low levels of modification.
B. Variegated cover – containing 60-90% cover and variable levels of modification
– matrix still consists of suitable habitat.
C. Fragmented cover – containing 10-60% cover and variable levels of
modification – but matrix is now unsuitable or “destroyed” habitat.
D. Relictual cover – containing < 10% cover and usually high levels of
modification.
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The LC model recognized that landscapes are more complex than described by the patchcorridor-matrix
model, which contains only very discrete landscape conditions, where
patches and corridors are distinct from the matrix. Recognition that a landscape may be
highly variegated rather than a series of discrete patches may provide additional
information in landscapes where strict ‘patches’ do not exist.
3.1.1 Limitations of the landscape continuum model
The landscape continuum model is subject to issues of scale as is the P-C-M model –
what is variegated to one organism may not be to another, and what is fragmented at one
scale may not be at another. Likewise, the LC model also considers only binary measures
of habitat – suitable vs. non-suitable – rather than gradients, and can also be
anthropocentric.
3.2 Overlap between P-C-M-E and LC Models
In as much as the two models differ, they also contain similar components. The primary
difference is the level of focus – the PCM model focuses more on the elements of the
landscapes - the size and shape of patches and corridors – while the LC model tends to
focus more on whole landscapes. However, both consider the matrix to be the dominant
element on the landscape, both are subject to similar issues of scale, and both suffer from
similar limitations.
So which is better? Ecologists – and scientists in general – like to simplify and
generalize. This is extraordinarily difficult to do on landscapes because they are so
exceedingly complex. This complexity has attracted people more towards the PCM
model for its simplicity, at least when patches are easily defined – although the use of this
model has also guided the way we study landscapes.
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