Landscape Analysis and
Animal Movement
Analysis
RESM 575
Spring 2010
Lecture 14
Today
Part A
 Landscape analysis and metrics
Part B
 Animal movement analysis
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Putting landscape biodiversity in perspective
Stein et al., 2006. Precious Heritage: The Status of Biodiversity in the United States. The Nature Conservancy.
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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
resources.
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(Theiling, 2006)
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Landscape metrics


Goal is to study the pattern–process
relationships
This has resulted in the development of
literally hundreds of indices of landscape
patterns.
Spatial
pattern
Ecological
processes
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Landscape context
Landscapes
They
don’t exist in isolation
are nested within larger landscapes
Degree
of “openness of a system”
EX: from a geomorphological perspective, a watershed is a closed
system
EX: for a bird population, a watershed is an open system
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Landscape scale
Most
important consideration in an ecological
landscape investigation
Must
be explicitly defined
Describe
patterns or relationships relative to scale
Be
extremely cautious when attempting to
compare landscapes measured at different scales
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Classes of landscape pattern
Applied to four types of spatial data:
 Spatial point patterns
 Linear network patterns
 Surface patterns
 Categorical map patterns
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Spatial point patterns


The locations of the points are of primary
interest rather than any quantity or quality
Clustered, random, dispersed?
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Linear network patterns

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Map of streams or riparian areas and the goal
is to characterize the physical structure
Corridor density, connectivity, etc.
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Surface patterns

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No explicit boundaries, patches are not
delineated, looking for spatial dependencies
Elevation, precipitation, continuous data
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Categorical map patterns



Mosaic of discrete patches, land cover and
use
Goal is to characterize the composition and
spatial configuration
Most popular and the one we will focus on
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Categorical map patterns

Characterization falls under:

Composition

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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
landscape.
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Composition example metrics



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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.
Richness.
 Simply the number of different patch types.
Eveness
 The relative abundance of different patch types, typically
emphasizing either relative dominance or its complement,
equitability.
Diversity
 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
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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.
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MORE! Spatial configuration example
metrics

Characterization falls under






Isolation, proximity
Contrast
Dispersion
Contagion
Subdivision
Connectivity
See link to McGarigal (1999) on website for more info
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What makes a landscape metric
useful?



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
EX:
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?
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GIS role in measuring landscapes


Most GISs can calculate the basic metrics
All of the more sophisticated metrics use GIS
data as inputs



FRAGSTATS
http://www.umass.edu/landeco/research/fragstats/
fragstats.html
ATILLA Analytical Tools Interface for Landscape
Assessments http://www.epa.gov/nerlesd1/landsci/attila/
PATCH ANALYST
http://flash.lakeheadu.ca/~rrempel/patch/
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Limitations


Metrics are a snapshot in time
High degree of correlation among metrics
(patch size, area, core area, edge, etc)
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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?
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Typical landscape metrics

Fragmentation

Edge

Core area or interior forest
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Fragstats metrics
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References
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Boyce, M.S., and A. Haney. 1997. Ecosystem Management: Applications for Sustainable Forest and Wildlife
Resources. Yale University Press, New Haven & London. 361 pages
Forman, R.T. T., and M. Godron. 1986. Landscape Ecology. Wiley, New York.
Grumbine, R. E. 1994. What is Ecosystem Management. Conservation Biology8:27-38.
Hobbs, R. 1997. Future Landscapes and the Future of Landscape Ecology. Landscape and Urban Planning 37:19.
Jones, B.K, K.H. Ritters, J. D. Wickham, R.D. Tankersley, R.V. ONeill, D.J. Chaloud, E. R. Smith, and A.C. Neale.
1997 An Ecological Assessment of United States Mid-Atlantic Region: A Landscape Atlas.
Benedic, M. A. and E. T. McMahon. 2000. Green infrastructure: smart conservation for the 21 st century. The
Sprawl Watch Clearinghouse Monograph Series, The Conservation Fund.
Grayson, R. B., I. D. Moore, and T. A. McMahon. 1992. Physically based hydrologic modeling: 1. A terrain based
model for investigative purposes. Water Resources Research 28(10):2639-2658.
Loehle, C. 1999. Optimizing wildlife habitat mitigation with a habitat defragmentation algorithm. Forest Ecology and
Management 120 (1999) 245-251
Mitasova, H. J. Hofieka, M., Zlocha, L. R. Iverson. 1996. Modeling topographic potential for erosion and
deposition using GIS. International Journal of Geographic Information Systems 10:629-641.
Riters, K. H. 1995. A Factor Analysis of Landscape Pattern and Structure Metrics. Landscape Ecology 10:23-39.
Wickham, J. D. Jones, K. B. Ritters, K. H. O’Neill, R. V. Tankersley, R. D. Smith, E. R. Neale, A. C. and Chaloud,
D. J. 1999. An integrated environmental assessment of the US Mid-Atlantic Region. Environ Manag 24: 553-560.
Wickham, J. D., R. V. O’Neill, and K. B. Jones. 2000. Forest fragmentation as an economic indicator. Landscape
Ecology 15: 171-179.
Wiens, J. 1976. 1976. Population responses to patchy environments. Ann. Rev. Ecol Syst. 7:81-120
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Part B.
Animal Movement
Overview


Background on animal movement
Hawth’s tools
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Field studies of animals


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Commonly record the locations where
individuals are observed.
In many cases these point data, often
referred to as "fixes", are determined by radio
telemetry.
These data may be used in both "basic" and
"applied" contexts.
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Using the point data or “fixes”

Used to test basic hypotheses

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animal behavior
resource use
population distribution
interactions among individuals and populations.
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Other uses of the point data

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Location data may also be used in
conservation and management of species.
The problem for researchers is

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To determine which data points are relevant to
their needs
How to best summarize the information.
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Researchers using the point data

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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)
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Home range notes
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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"
activities.
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To maintain scientific integrity
(repeatability)


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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.
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Home range models
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Minimum convex polygons
Bivariate normal models
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Nonparametric models
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Jennrich-Turner estimator
weighted bivariate normal estimator,
multiple ellipses,
Dunn estimator
grid cell counts,
Fourier series smoothing,
harmonic mean
Contouring models
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peeled polygons,
kernel methods,
hierarchical incremental cluster analysis
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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).
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Hawth’s tools
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Hawth’s tools

Includes 2 home range analysis models:

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minimum convex polygons (MCPs) and
kernel methods.
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Minimum convex polygons

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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).

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"Percent" minimum convex polygons
"probability polygons" (Kenward 1987),
"restricted polygons" (Harris et al. 1990)
"mononuclear peeled polygons"
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Kernal methods
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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
distribution.
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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References
Burt, W. H. 1943. Territoriality and home range concepts as applied to mammals. J. Mammal. 24:346-352.
(Harris, S., W. J. Cresswell, P. G. Forde, W. J. Trewhella, T. Woollard, and S. Wray 1990. Home-range
analysis using radio-tracking data - a review of problems and techniques particularly as applied to the
study of mammals. Mammal Rev. 20:97-123.
Jones, M. C., J. S. Marron, and S. J. Sheather. 1996. A brief survey of bandwidth selection for density
estimation. J. Amer. Stat. Assoc. 91:401-407.
Kenward, R. 1987. Wildlife radio tagging. Academic Press, Inc., London, UK. 222 pp.
Kenward, R. E., and K. H. Hodder. 1996. RANGES V: an analysis system for biological location data. Inst.
Terrestrial Ecol., Furzebrook Res. Stn., Wareham, UK. 66 pp.
Larkin, R. P., and D. Halkin. 1994. A review of software packages for estimating animal home ranges. Wildl.
Soc. Bull. 22:274-287.
Lawson, E. J. G., and A. R. Rodgers. 1997. Differences in home-range size computed in commonly used
software programs. Wildl. Soc. Bull. 25:721-729.
Michener, G. R. 1979. Spatial relationships and social organization of adult Richardson's ground squirrels.
Can. J. Zool. 57:125-139..
Rodgers, A. R., R. S. Rempel, and K. F. Abraham. 1996. A GPS-based telemetry system. Wildl. Soc. Bull.
24:559-566.
Schoener, T. W. 1981. An empirically based estimate of home range. Theor. Pop. Biol. 20:281-325.
Seaman, D. E., and R. A. Powell. 1996. An evaluation of the accuracy of kernel density estimators for home
range analysis. Ecology 77:2075-2085.
Swihart, R. K., and N. A. Slade. 1985a. Testing for independence of observations in animal movements.
Ecology 66:1176-1184.
Swihart, R. K., and N. A. Slade. 1985b. Influence of sampling interval on estimates of home-range size. J.
Wildl. Manage. 49:1019-1025.
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology
70:164-168.
Worton, B. J. 1995. Using Monte Carlo simulation to evaluate kernel-based home range estimators. J. Wildl.
Manage. 59:794-800.
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