Landscape Ecology
Neutral models of landscape heterogeneity
Terms/people:
Model
Stauffer
neutral model
neighborhood (movement) rules
Caswell
NLM
Purposes and Uses of Ecological Models
Ecological models are developed and used for several purposes:
-to explore real or hypothetical scenarios, especially in cases where experiments are not
easy to conduct for ethical or logistical reasons;
-to interpolate or extrapolate understanding across scales where direct empirical work
may be difficult or impossible;
-to provide a framework for comparison across systems, providing the equivalent of
experimental design and control;
-to provide a formal organizing framework for ideas or data (i.e., a framework for
synthesis and integration);
-to make predictions about specific scenarios.
Why are models used so frequently in landscape ecology?
Neutral Models in Landscape Ecology
Neutral landscape models (NLMs) are a special class of models that is particularly
useful in a discipline where replication and manipulation are logistically problematic. The
objective of using neutral landscape models is to provide a neutral benchmark (i.e., a
standard of reference, a standard for comparison, a yardstick) that will serve as a
framework for the interpretation of real, observed landscape patterns. This neutral model
is one that is generated under a neutral (null) scenario (usually randomness). That is, a
neutral model is used to generate a pattern in the absence of specific processes that affect
the landscape (e.g. topography, disturbance, etc.). Owing to the logistical impossibility of
conducting experiments over large areas that may lack true replicates, neutral models
provide a very good alternative. These models are used to determine how real landscape
properties differ from a theoretical (random) distribution. They can also be used to
determine how certain patterns can affect ecological processes such as organism
dispersal, disturbance spread, etc. by acting as a null model.
So why the emphasis on having a null (a.k.a. neutral) model? Recall from an earlier
lecture that null models in science are extremely important. Science is characterized as a
way of gaining knowledge, but unlike intuition, non-science, and pseudo-science (other
ways of gaining knowledge), true science asks falsifiable questions (i.e., the questions
have "yes" and "no" as possible outcomes). One of those answers will change the status
quo whereas the other answer will not (the null). In order to be able to tell the difference
in the answers, you need a null.
Examples:
Fahrig (1997)
Fahrig (2001)
Origin of NLMs:
Caswell 1976 - models of community assembly and diversity structure vs. a model that
was neutral with respect to species interactions
Stauffer 1985 - use of randomly generated landscape patterns to compare to real patterns
How to construct an NLM:
The approach begins with developing methods for generating expected values for
landscape pattern, generated on a grid of n x n cells. You first designate values
representing each land cover type of interest (e.g. 1=habitat, 0=nonhabitat; or 1=habitat
A, 2=habitat B, 3=habitat C, etc.). Land cover types are assigned with given (or random)
probabilities (p) in a distribution (e.g. if you want a landscape that is 60% forest and 40%
grassland, pforest=0.6, pgrassland=0.4). Random number tables or random number generators
(computer algorithms) are used to generate the assignments. This distribution may be
perfectly random or it may be semi-random (e.g. with degrees of contagion, with
hierarchical assignments, etc.).
A single NLM is not particularly useful. Why do you need to generate multiple NLMs
for a given value of p?
When p of the habitat of interest is = 0 or 1, the NLM is (obviously) the same as the real
landscape pattern. The closer p gets to 0 or 1, the more similar an NLM is to a real
pattern.
The size of the grid cells obviously has an effect on landscape structure as a whole.
Having more but smaller cells takes more computer effort but represents the landscape at
a finer grain.
Change over time (e.g. simulating land cover change over time to represent habitat loss)
is represented by changing the distribution of cell values.
You can also represent habitat growth outward from a source (e.g. seedling regeneration
in a field surrounding a forest patch). Neighborhood rules (a.k.a. movement rules) are
important for this effort (e.g. 4-cell rule vs. 8-cell diagonal rule vs. 12-cell leapfrog rule,
etc.).
NLMs can also be built to reflect hierarchical structure, which represents changes in
pattern with changes in scale. Hierarchical structure has the same or similar pattern at
different scales (e.g. fractals). Hierarchical structure is built into an NLM using a
recursive procedure (one that references itself at different levels of resolution). Click
here for an example of a hierarchical NLM, and here for three examples of different
NLMs.
References:
Botkin, D.B. 1993. Forest Dynamics: An Ecological Model. Oxford University Press,
Oxford.
Cale, W.G., R.V. O'Neill, and H.H. Shugart. 1983. Development and application of
desirable ecological models. Ecol. Modelling 18:171-186.
Caswell, H. 1976. Community structure: a neutral model analysis. Ecol. Monogr. 46:327354.
Costanza, R. 1989. Model goodness of fit: a multiple resolution procedure. Ecol.
Modelling 47:199-215.
Fahrig, L. 1997. Relative effects of habitat loss and fragmentation on species extinction.
Journal of Wildlife Management 61:603-610.
Fahrig, L. 2001. How much habitat is enough? Biological Conservation 100:65-74.
Gardner, R.H., B.T. Milne, M.G. Turner, and R.V. O'Neill. 1987. Neutral models for the
analysis of broad-scale landscape pattern. Landscape Ecol. 1:19-28.
Gardner, R. H. and D. L. Urban. 2007. Neutral models for testing landscape hypotheses.
Landscape Ecology 22:15-29.
Haefner, J.W. 1996. Modeling Biological Systems: Principles and Applications.
Chapman and Hall, New York, NY.
Mankin, J.B., R.V. O'Neill, H.H. Shugart, and B.W. Rust. 1975. The importance of
validation in ecosystems analysis. In: New Directions in the Analysis of Ecological
Systems, Part 1 (G.S. Innis, ed.). Simulation Councils Proceedings Series, vol. 5.
Simulation Councils, La Jolla, CA.
Oreskes, N., K. Shrader-Frechette, and K. Belitz. 1994. Verification, validation, and
confirmation of numerical models in the earth sciences. Science 263:641-646.
Power, M. 1993. The predictive validation of ecological and environmental models. Ecol.
Modelling 68:33-50.
Stauffer, D. 1985. Introduction to Percolation Theory. Taylor and Francis, London.
With, K.A., and A.W. King. 1997. The use and misuse of neutral landscape models in
ecology. Oikos 79:219-229.