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Population spread, Dispersal, Bayes, Migration, Invasion, Climate change

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Subject-Keyword: Population spread Dispersal Bayes Migration Invasion Climate change

Type of item: Journal Article Published

Language: English



Description: Recent literature on plant population spread advocates quantification of long-distance dispersal LDD. These estimates could provide insights into rates of migration in response to climate change and rates of alien invasions. LDD information is not available for parameterization of current models because it is hard to obtain. We combine a new stochastic model with a flexible framework that permits assimilation of evidence that might be derived from a range of sources. Results are consistent with the prediction of traditional diffusion that population spread has a finite asymptotic velocity. Unlike traditional diffusion, spread is not well described by the mean; it is erratic. In contrast with deterministic models, our results show that inherent uncertainty, rather than parameter sensitivity, thwarts informative forecasts of spread velocity. Analysis shows that, because LDD is inherently unpredictable, even full knowledge of LDD parameters might not provide informative estimates of velocity for populations characterized by LDD. Although predictive distributions are too broad to provide precise estimates of spread rate, they are valuable for comparing spread potential among species and for identifying potential for invasion. Using combinations of dispersal data and the estimates provided by dispersal biologists that derive from multiple sources, the model predicts spread rates that are much slower than those from traditional deterministic fat-tailed models and from simulation models of spread, but for different reasons. Deterministic fat-tailed models overestimate spread rate, because they assume that fractions of individuals can rapidly occupy distant sites. Stochastic models recognize that distant colonization is limited to discrete individuals. Stochastic simulations of plant migration overestimate migration of trees, because they typically assume values of R-0 that are too large.

Date created: 2003

DOI: doi:10.7939-R3222R71N

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Rights: © 2003 Ecological Society of America. This version of this article is open access and can be downloaded and shared. The original authors and source must be cited.

Autor: Clark, J. S. Lewis, M. A. McLachlan, J. S. HilleRisLambers, J.



Ecology, 84(8), 2003, pp.
1979–1988 q 2003 by the Ecological Society of America ESTIMATING POPULATION SPREAD: WHAT CAN WE FORECAST AND HOW WELL? JAMES S.
MCLACHLAN,1 AND JANNEKE HILLERISLAMBERS1 1Department 2 of Biology and University Program in Ecology, Duke University, Durham, North Carolina 27708 USA Department of Mathematical Sciences and Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1 Key words: Bayes; climate change; dispersal; invasion; migration; population spread. INTRODUCTION Increasing efforts to estimate long-distance dispersal (LDD) is an understandable reaction to growing realization that rare events can control the rate of population spread (Kot et al.
1996, Clark 1998, Higgens and Richardson 1999, Cain et al.
2000, Clark et al.
2001c). Scenarios of climate change and spread of exotic species are based on dispersal estimates and migration potential.
Models used to calculate spread require estimates of offspring production (e.g., net reproductive rate R0), a dispersal kernel, and a time scale.
The dispersal kernel describes the scatter of offspring about the parent plant in the form of a probability density function.
Estimation requires a full accounting of seed in terms of proportions of R0 offspring that travel various distances.
The time scale is controlled by schedules of fecundity, mortality, and growth and is often summarized in models by generation time T. Manuscript received 6 November 2001; revised 16 August 2002; accepted 3 September 2002.
Corresponding Editor: M.
L. Cain.
For reprints of this Special Feature, see footnote 1, p.
1943. 3 E-mail: Before investing heavily in the study of LDD, it is worth asking how well such data would allow us to anticipate spread.
Dispersal studies typically target a particular vector, such as extreme winds (Snow et al. 1995), vertebrates (Storm and Montgomery 1975, Johnson et al.
1997, Shilton et al. 1999)...

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