Landscape metrics and Animal movement analysis

Landscape Analysis and
Animal Movement
RESM 575
Spring 2010
Lecture 14
Part A
 Landscape analysis and metrics
Part B
 Animal movement analysis
Putting landscape biodiversity in perspective
Stein et al., 2006. Precious Heritage: The Status of Biodiversity in the United States. The Nature Conservancy.
Landscape (ecologist, env scientist)
a conceptual unit for the study of spatial patterns
in the physical environment and the influence of
these patterns on important environmental
(Theiling, 2006)
Landscape metrics
Goal is to study the pattern–process
This has resulted in the development of
literally hundreds of indices of landscape
Landscape context
don’t exist in isolation
are nested within larger landscapes
of “openness of a system”
EX: from a geomorphological perspective, a watershed is a closed
EX: for a bird population, a watershed is an open system
Landscape scale
important consideration in an ecological
landscape investigation
be explicitly defined
patterns or relationships relative to scale
extremely cautious when attempting to
compare landscapes measured at different scales
Classes of landscape pattern
Applied to four types of spatial data:
 Spatial point patterns
 Linear network patterns
 Surface patterns
 Categorical map patterns
Spatial point patterns
The locations of the points are of primary
interest rather than any quantity or quality
Clustered, random, dispersed?
Linear network patterns
Map of streams or riparian areas and the goal
is to characterize the physical structure
Corridor density, connectivity, etc.
Surface patterns
No explicit boundaries, patches are not
delineated, looking for spatial dependencies
Elevation, precipitation, continuous data
Categorical map patterns
Mosaic of discrete patches, land cover and
Goal is to characterize the composition and
spatial configuration
Most popular and the one we will focus on
Categorical map patterns
Characterization falls under:
Features associated with the variety and abundance
of patch types but not considering the placement,
attributes, or location of the patches in the mosaic
Spatial Configuration
refers to the spatial character and arrangement,
position, or orientation of patches within the class or
Composition example metrics
Proportional abundance of each-class.
 One of the simplest and perhaps most useful pieces of
information that can be derived is the proportion of each class
relative to the entire map.
 Simply the number of different patch types.
 The relative abundance of different patch types, typically
emphasizing either relative dominance or its complement,
 Diversity is a composite measure of richness and evenness and
can be computed in a variety of forms (e.g. Shannon’s,
Simpson’s), depending on the relative emphasis placed on these
two components
Spatial configuration example metrics
Characterization falls under
Patch size distribution and density
Patch shape complexity
Mean, median, max, variance
Simple and compact or irregular and convoluted,
perimeter per area unit
Core areas
Interior area of patches, integrates patch size, shape,
and edge effect distance into a single measure.
All other things being equal, smaller patches with
greater shape complexity have less core area.
MORE! Spatial configuration example
Characterization falls under
Isolation, proximity
See link to McGarigal (1999) on website for more info
What makes a landscape metric
A strong relationship between metric and
functional response
The metric must pick up changes in the
landscape that are important to a species or
ecological process
The black bear requires large intact forest patches of 125 acres or greater
where does this habitat currently exist or where is that threshold of 125
acres being approached?
GIS role in measuring landscapes
Most GISs can calculate the basic metrics
All of the more sophisticated metrics use GIS
data as inputs
ATILLA Analytical Tools Interface for Landscape
Metrics are a snapshot in time
High degree of correlation among metrics
(patch size, area, core area, edge, etc)
McGarigal (1999) suggests…
Before selecting a metric:
1. Does it represent landscape composition or configuration, or both?
2. What aspect of composition or configuration does it represent?
3. Is it spatially explicit and, if so, at the patch-, class-, or landscapelevel?
4. How is it affected by the designation of a matrix element?
5. Does it reflect an island biogeographical or landscape mosaic
perspective of landscape pattern?
6. How does it behave or respond to variation in landscape pattern?
7. What is the range of variation in the metric under an appropriate
spatiotemporal reference framework?
Typical landscape metrics
Core area or interior forest
Fragstats metrics
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Wickham, J. D. Jones, K. B. Ritters, K. H. O’Neill, R. V. Tankersley, R. D. Smith, E. R. Neale, A. C. and Chaloud,
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Part B.
Animal Movement
Background on animal movement
Hawth’s tools
Field studies of animals
Commonly record the locations where
individuals are observed.
In many cases these point data, often
referred to as "fixes", are determined by radio
These data may be used in both "basic" and
"applied" contexts.
Using the point data or “fixes”
Used to test basic hypotheses
animal behavior
resource use
population distribution
interactions among individuals and populations.
Other uses of the point data
Location data may also be used in
conservation and management of species.
The problem for researchers is
To determine which data points are relevant to
their needs
How to best summarize the information.
Researchers using the point data
Rarely interested in every point that is visited,
or the entire area used by an animal during
its lifetime.
Focus on the animal's "home range“
"…that area traversed by the individual in its
normal activities of food gathering, mating, and
caring for young. (Rogers and Carr, 1998)
Home range notes
Occasional sallies outside the area, perhaps
exploratory in nature, should not be
considered as in part of the “home range."
(Burt 1943).
Thus, in its simplest form, "home range
analysis" involves the delineation of the area
in which an animal conducts its "normal"
To maintain scientific integrity
Objective criteria must be used to select
movements that are "normal" (White and
Garrott 1990).
The obvious difficulty is in the definition of
what should be considered "normal".
Because of this difficulty, there has been a
proliferation of home range analysis models.
Home range models
Minimum convex polygons
Bivariate normal models
Nonparametric models
Jennrich-Turner estimator
weighted bivariate normal estimator,
multiple ellipses,
Dunn estimator
grid cell counts,
Fourier series smoothing,
harmonic mean
Contouring models
peeled polygons,
kernel methods,
hierarchical incremental cluster analysis
Of note:
• However, home range analysis may involve more than just
estimating the characteristics of areas occupied by animals.
• Researchers often want to know about the distances, headings,
times and speed of animal movements between locations.
• They may also want to assess interactions of animals based on
areas of overlap among home ranges or distances between
individuals at a particular point in time.
Most of these methods and their limitations have been reviewed by
Harris et al. (1990) and White and Garrott (1990).
Hawth’s tools
Hawth’s tools
Includes 2 home range analysis models:
minimum convex polygons (MCPs) and
kernel methods.
Minimum convex polygons
MCPs do not indicate how intensively
different parts of an animal's range are used
Constructed by connecting the peripheral
points of a group of points, such that external
angles are greater than 180° (Mohr 1947).
"Percent" minimum convex polygons
"probability polygons" (Kenward 1987),
"restricted polygons" (Harris et al. 1990)
"mononuclear peeled polygons"
Kernal methods
Allow determination of centers of activity
Kernel analysis is a nonparametric statistical method for
estimating probability densities from a set of points.
In the context of home range analysis these methods describe
the probability of finding an animal in any one place.
Home range estimates are derived by drawing contour lines (i.e.,
isopleths) based on the volume of the curve under the utilization
Alternatively, isopleths can be drawn that connect regions of
equal kernel density. In either case, the isopleths define home
range polygons whose areas can be calculated.
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