Bibliography on Modeling issues

Beissinger, S. R.  1998.  On the use of demographic models of population viability in endangered species management.  Journal of Wildlife Management 62:821-841.

Overview of simple to complex population simulation models; focus is on the extreme data requirements for complex models, so don't build a model for which you do not have the data to support.

Brook, B. W., L. Lim, R. Harden, and R. Frankham.  1997.  Does population viability analysis software predict the behaviour of real populations? A retrospective study on the Lord Howe Island Woodhen Tricholimnas sylvestris (Sclater).  Biological Conservation 82:119-128.

Population size predictions from the models were severely biased unless they incorporated census (i.e., carrying capacity) data that were not available when the original PVA would have been conducted.

Bucci, G., and M. Borghetti. 1997. Understory vegetation as a useful predictor of natural regeneration and canopy dynamics in Pinus sylvestris forests in Italy. Acta Oecologia International Journal of Ecology 18(4):485-501.

Clarke, Keith C. and Leonard J. Gaydos. 1998. Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographic Information Science. 12 (7): 699-714.

This article describes the use of cellular automaton to model urban growth. The "loose-coupling" means that the GIS and the model don't run in concert--the model is written in C and the output is viewed in a GIS. They use historical urban extents as a "seed" for the model and to calibrate various model settings. Other model inputs include: digital elevation models, transportation layers, and exclusion areas (lakes, national parks, etc.). The resulting model can be used to predict urban growth with statistical confidence intervals on the predictions. They show examples for San Francisco and the Washington-Baltimore area.

This paper is a good example of a spatial-temporal model being applied to a geographic problem. It also shows how simple model inputs lead to complex behaviour and applicability in a real-world, seemingling complex setting.

Cropper, W.P. 2000. SPM2: A simulation model for slash pine (Pinus elliottii) forests. Forest Ecology and Management 126(2):201-212.

Dibble, A.C., J.C. Brissette, and M.L. Hunter. 1999. Putting community data to work: some understory plants indicate red spruce regeneration habitat. Forest Ecology and Management 114(2-3):275-291.

Edwards, T.C., Jr., E.T. Deshler, D. Foster, and G.G. Moisen. 1996. Adequacy of Wildlife Habitat Relation Models for Estimating Spatial Distributions of Terrestrial Vertebrates. Conservation Biology 10:263-270.

Evaluates adequacy of the wildlife habitat relations data generated by gap analysis in predicting distributions of terrestrial vertebrates at landscape scales. Concludes that the modeling process provides a "reasonably high" level of accuracy for use in conservation planning at the ecoregion level.

Fielding, A.H. and P.F. Haworth. 1995. Testing the Generality of Bird-Habitat Models. Conservation Biology 9:1466-1481.

Assessed the use of a variety of bird-habitat models to predict species distributions. Models showed a wide range of predictive success. Therefore questioned value of both distributional and habitat-change predictive models in conservation-based studies.

Homer, C.G., Ramsey, D.R., Edwards, T.C., and A. Falconer, 1997. Landscape cover-Type modeling using a multi-scene thematic mapper mosaic, Photogrammetric Engineering & Remote Sensing, 63:59-67.

A good article about a GIS based modeling approach to classify vegetation cover utilized landform variables.

Howard, A., A. E. Irish, and C. S. Reynolds. (1996) A new simulation of cyanobacterial underwater movement (SCUM '96). Journal of Plankton Research 18:1375-1385.

This model simulates the movement of one genera of algae(Microcystis) in the water column based on such factors as colony density and lake mixing. The model has proved realistic when compared with field observations.

Hilton, J., A. E. Irish, and C. S. Reynolds. (1992) Active reservoir management: a model solution. In:  Sutcliffe, D. W., and Jones, J. G., eds. Eutrophication: research and application to water supply. Freshwater Biological Association, Cumbria, UK, pp. 185-196.

This model involves both algal growth and algal species succession. The model includes both physical and chemical parameters, including pulses of phosphorus from the sediments. This model resulted in good predictions of field observations.

Keane, Robert E. Morgan, Penelope and White, Joseph D. 1999. Temporal patterns of ecosystem processes on simulated landscapes in Glacier National Park, Montana, USA. Landscape Ecology. 14(3):311-329.

Liou, Lily W., & Trevor D. Price, 1994. Speciation by reinforcement of premating isolation. Evolution, 48(5): 1451-1459.

This one is a good sample of using a model to generate and test hypotheses, and a very interesting article if one is interested in hybridization or species barriers.

McKenzie, D., and C.B. Halpern. 1999. Modeling the distributions of shrub species in Pacific northwest forests. Forest Ecology and Management 114(2-3):293-307.

Mickelson, J.G., Civco, and J.A. Silander Jr. 1998. Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery, Photogrammetric Engineering & Remote Sensing, 64:891-904.

Good article about a model developed to classify canopy species but didn't give much detail about how they created the model.

Mills, L.S, S.G. Hayes, C. Baldwin, M.J. Wisdom, J. Citta, D.J. Mattson, and K. Murphy. 1996. Factors leading to different viability predictions for a Grizzly bear data set. Conservation Biology 10: 863-873.

This paper illustrates how models can be used in conservation biology. In addition to presenting a real life example, it also shows the importance of using modeling as only a tool and not something to base important managment decisions on.

Phillys, Patrick C. & Stevan J. Arnold, 1989. Visualizing multivariate selection. Evolution 43(6): 1209-22.

This is an interesting article to expand the views of selection most models are on single or very few genes; this expands it to show selection on several traits at once.

Post, W.M., and J. Pastor. 1996. Linkages - an individual-based forest ecosystem model. Climatic Change 34(2):253-261.

Starfield, A.M. 1997. A pragmatic approach to modeling for wildlife management. J. Wildl. Manage. 61(2):261-270.

This paper contrasts 2 views of modeling: the model as a representation of "truth" and the model as a problem-solving tool. It outlines how to use a model usefully and efficiently. The purpose of the paper is to encourage wildlife managers and scientists to view models as a problem-solving tools to be used as routinely as they collect data and analyze it, not as representations of reality.

This paper is a good overview of how computer modeling can be a useful tool in wildlife management. It also discusses the dangers of "black box'' modeling and the importance of viewing modeling as a tool, not the absolute truth.

Schaffer, W.M. 1981. Ecological abstraction: the consequences of reduced dimensionality in ecological models. Ecological Monographs 51(4): 383-410.

This paper calls attention to some of the problems and consequences associated with trying to describe the dynamics of ecosystems while considering fewer than the total number of interacting species.

Van Horssen, P.W., P.P. Schott, and A. Barendregt. 1999. A GIS-based plant prediction model for wetland ecosystems. Landscape Ecology 14(3):253-265.

Veldkamp, A. and L.O. Fresco. 1996. CLUE: a conceptual model to study the Conversion of Land Use and its Effects. Ecological Modelling 85: 253-270.

This article presents a broad, multi-input conceptual model for land-use change. It analagous to a "sim-city" approach where most of what they deem as the major inputs to land-use changes are considered. The conceptual model is geared towards argicultural regions and seems very complex. They illustrate the model on an imaginary landscape and state that in the output "plausible patterns emerge." I think that in actual practice, this model might be overly complex and difficult to validate.

Willmott, C.J. and G. L. Gaile. 1992. "Modeling" in R.F. Abler, M.G. Marcus and J.M. Olson. Geography's Inner Worlds. New Brunswick, N.J. Rutgers University Press. pp. 163-186.

Yaussy, D.A. 2000. Comparison of an empirical forest growth and yield simulator and a forest gap simulator using actual 30-year growth from two even-aged forests in Kentucky. Forest Ecology and Management 126 (3) 385-398.