Landscape Ecology
Part 1 - Individual responses to landscape structure: habitat selection
Terms/people:
Habitat
Fretwell & Lucas
Bea Van Horne
use vs. availability
ideal free distribution
despotic distribution
But first...what is habitat?
Habitat selection • habitat use vs. habitat availability (but more commonly is habitat occupation vs.
availability)
• habitat use may be assessed directly (e.g. observations or counts) or indirectly
(e.g. tracks, scats)
• habitat availability (p) quantified with less certainty
• assumption: preferred (and hence selected) habitat will be of higher quality (suitability)
than non-selected habitat ("non-habitat")
• null hypothesis (no selection, habitats used in proportion to their availability): most time
spent will be in most abundant/widespread habitat type
• selection usually assessed statistically via a chi-square test with 95% confidence
intervals calculated using a Bonferroni z statistic (see Neu et al. 1974, Sparks et al. 1994):
if O and E overlap within the 95% CI, then no selection (click here for an example)
Requires independent observations (usually individuals are the replicates, but this
may not be good if your animals are social and travel in packs, going where the pack
leader dictates: in this case the pack and not the individuals is the replicate).
Best statistical power if number of observations per category is > 5 and there are
> 5 categories.
But there are lots of alternative analytical/statistical methods, none of which
is the clear best to use. The two papers by Alldredge and Ratti give excellent
overviews.
• is a hierarchical process in space and time (see Johnson 1980)
• selection criteria may differ at different scales, making prediction difficult/impossible
across scales: e.g. McIntyre 1997 (discussed in class)
Two alternative mechanisms of how organisms select habitat: Ideal Free Distribution
and Despotic Distribution
Ideal Free Distribution (IFD) • Fretwell & Lucas (1970, Acta Biotheoretica 19:16-36)
• organisms are "ideal" in their judgement of habitat quality
• organisms are "free" to move from habitat to habitat in their quest for the best (click
here)
• therefore, organism density is a good predictor of habitat quality (best habitat = where
more organisms are)
But note the assumptions of the IFD:
• organisms are omniscient
• organisms do not exclude each other from high-quality habitats
Alternative to the IFD: Despotic Distribution (DD) • Fretwell (1972, Populations in a Seasonal Environment, Princeton University Press)
• Van Horne (1983, Journal of Wildlife Management 47:893-901)
• dominant organisms ("despots") are able to exclude subordinates, resulting in a negative
correlation (inverse relationship) between organism density and habitat quality
The bottom line
To assess habitat selection, you need data on habitat use and habitat availability. Given
that it is difficult to translate among scales, one needs to collect data at multiple scales (or
confine one’s conclusions to only the scale measured).
Even with these data, without having information about the social system of a species,
you will not know whether density is a good indicator of habitat quality and therefore
will not be able to assess habitat selection accurately.
References:
Alldredge, J.R., and J.T. Ratti. 1986. Comparison of some statistical techniques for
analysis of resource selection. J. Wildl. Manage. 50:157-165.
Alldredge, J.R., and J.T. Ratti. 1992. Further comparison of some statistical techniques
for analysis of resource selection. J. Wildl. Manage. 56:1-9.
Fretwell, S.D. 1972. Populations in a Seasonal Environment. Princeton University Press,
Princeton, NJ.
Fretwell, S.D., and H.L. Lucas. 1970. On territorial behaviour and other factors
influencing habitat distribution in birds. Acta Biotheoretica 19:16-36.
Garshelis, D.L. 2000. Delusions in habitat evaluation: measuring use, selection, and
importance. Pp. 111-164 in: Research Techniques in Animal Ecology: Controversies and
Consequences (L. Boitani and T.K. Fuller, eds.). Columbia University Press, New York,
NY.
Johnson, D.H. 1980. The comparison of usage and availability measurements for
evaluating resource preference. Ecology 61:65-71.
McClean, S.A., M.A. Rumble, R.M. King, and W.L. Baker. 1998. Evaluation of resource
selection methods with different definitions of availability. J. Wildl. Manage. 62:793801.
McIntyre, N.E. 1997. Scale-dependent habitat selection by the darkling beetle Eleodes
hispilabris (Coleoptera: Tenebrionidae). American Midland Naturalist 138:230-235.
Neu, C.W., C.R. Byers, and J.M. Peek. 1974. A technique for analysis of utilizationavailability data. J. Wildl. Manage. 38:541-545.
Sparks, E.J., J.R. Belthoff, and G. Ritchison. 1994. Habitat use by Eastern Screech-Owls
in central Kentucky. J. Field Ornith. 65:83-95.
Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of
Wildlife Management 47:893-901.
Part 2 - Individual responses to landscape structure: movement patterns
Terms/people:
movement (vs. dispersal vs. migration)
random walk/correlated random walk
cluster)
movement rules (rules of adjacency)
percolation
spanning cluster (percolating
pcrit
Why is studying movement important to landscape ecology?
Movement is a critically important phenomenon. It is what allows habitat selection to
occur, it helps prevent extinction, and it permits gene flow. It therefore determines the
abundance and distribution of organisms. A movement path is also a physical record of
how an organism interacts with its environment. Therefore, studying movement allows us
to quantify how spatial environmental patterns affect organism behaviors. Movement
paths provide us with insights about organism-environment relationships.
Terminology
"movement," "dispersal," and "migration" are not true synonyms
How do we quantify and study movement?
In landscape ecology, we are most interested in how spatial environmental patterns affect
movement behaviors. We therefore need some way of assessing how organisms move
through a landscape (and we also need a neutral model as a frame of reference).
“scale matching”
There are many ways to study movement. One common way is to develop a null model
that consists of a random walk. In a random walk, movement follows an algorithm for
passive diffusion (whereby an organism behaves as though it were an atom floating
through the environment, without any directionality). For slightly more realism, a
correlated random walk may be adopted (which allows more directionality). Both of
these null models, however, assume that the environment is basically a porous medium
without structure. This is obviously unrealistic. One way we can improve on this is by
using percolation models, which are forms of random walks planted atop a landscape of
a given pattern.
Percolation
-developed by mathematicians in the 1950s to describe movement of particles (see
Broadbent and Hammersley (1956), Proc. Camb. Phil. Soc. 53:629-641)
-applied in the physical sciences to study flow in porous media (see R. Zallen (1983), The
Physics of Amorphous Solids. Wiley, New York)
-applied to LE by Gardner and others in 1980s (see Gardner et al. (1987), Landscape
Ecol. 1:19-28)
How to make a percolation map (a type of neutral landscape model):
-start with a regular grid of cells (e.g. pixels, raster-based GIS)
-this array is sometimes called a lattice (esp. in math/physics)
-each cell (pixel) has a probability, p, of being "occupied" by a given habitat type
-probability of being unoccupied is 1-p
-define clusters of occupied cells
-different rules of adjacency (e.g. whether to include diagonals, leapfrogging)
-if a cluster of occupied cells reaches from one side of the map to another, it is called a
spanning cluster and the map (actually the organism on the map) is said to percolate
Properties of percolation maps:
for large maps (lots of cells), there is a sharp transition from a fragmented (nonpercolating) to a connected (percolating) landscape
-this is an example of a critical threshold (pcrit)
-for a square grid with only 4 nearest neighbors (ignore diagonals), the threshold is
0.5928
-value of pcrit varies depending on the lattice and adjacency rules ( 8- and 12-cell rules
have pcrit < 0.5928)
-mean patch size and size of largest patch increase dramatically around the critical
threshold
-amount of edge is max at p=0.5
-as p increases, the path thru the landscape becomes more linear
Implications for movement (of animals, materials, contagious catastrophes, etc.)
Recall that larger animals or animals with greater vagility interact with the landscape at
a different grain than do smaller/less mobile organisms. Therefore, an organism-centered
viewpoint is crucial.
References:
Crist, T.O., D.S. Guertin, J.A. Wiens, and B.T. Milne. 1992. Animal movement in
heterogeneous landscapes: An experiment with Eleodes beetles in shortgrass prairie.
Funct. Ecol. 6:536-544.
Gardner, R.H., R.V. O’Neill, M.G. Turner, and V.H. Dale. 1989. Quantifying scaledependent effects of animal movements with simple percolation models. Landscape Ecol.
3:217-227.
Johnson, A.R., J.A. Wiens, B.T. Milne, and T.O. Crist. 1992. Animal movements and
population dynamics in heterogeneous landscapes. Landscape Ecology 7:63-75.
McIntyre, N.E. 1997. Scale-dependent habitat selection by the darkling beetle Eleodes
hispilabris (Coleoptera: Tenebrionidae). American Midland Naturalist 138:230-235.
Turchin, P. 1998. Quantitative Analysis of Movement: Measuring and Modeling
Population Redistribution in Animals and Plants. Sinauer, Sunderland, MA.
Schultz, C.B., and E.E. Crone. 2001. Edge-mediated dispersal behavior in a prairie
butterfly. Ecology 82:1879-1892.
Stauffer, D. 1985. Introduction to Percolation Theory. Taylor and Francis, London.
Szacki, J., and A. Liro. 1991. Movements of small mammals in the heterogeneous
landscape. Landscape Ecology 5:219-224.
Wiens, J.A., and B.T. Milne. 1989. Scaling of 'landscapes' in landscape ecology, or,
landscape ecology from a beetle's perspective. Landscape Ecology 3:87-96.
With, K.A. 1994. Using fractal analysis to assess how species perceive landscape
structure. Landscape Ecology 9:25-36.