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S W Pacala

Publications and source records attributed to S W Pacala.

4 recordsLinked to original sources
PubMed1991

Effects of competitive asymmetry on a local density model of plant interference.

Although competition between plants is usually asymmetric (i.e. larger plants have a disproportionate effect on smaller plants) almost all models of plant competition at the local level have assumed symmetric competition. We add a simple version of competitive asymmetry to the local density neighborhood models of plant interference and population dynamics developed by Pacala & Silander (1985, Am. Nat. 125, 385-411; 1987, Oikos 48, 217-224) by assuming that plants within a neighborhood can be put in a linear dominance hierarchy based upon their initial size. The size of a focal plant is a function of the number of dominant and the number of subordinate neighbors within its neighborhood, with subordinate neighbors having less of an effect than dominant ones. Asymmetry prevents precipitous changes in focal plant size with changes in local density, making the relationship between focal plant size and local density hyperbolic, even if the symmetric model is not hyperbolic. Thus, asymmetry makes the model conform to the law of constant final yield, irrespective of the form of the relationship between plant size and local crowding. Asymmetry also prevents population dynamic oscillations in the model in cases in which it would occur in the absence of asymmetry. The results show that asymmetry has major effects on a model of local interference in plants, and point to the importance of including it in such models.

Ecology↗
PubMed1990

Host-parasitoid associations in patchy environments.

Studies of insect host-parasitoid interactions have contributed much to the consensus that spatial patchiness is important in the regulation of natural populations. A variety of theoretical models predict that host and parasitoid populations, although unstable in the absence of environmental heterogeneity, may persist at roughly steady overall densities in a patchy environment owing to variation in levels of parasitism from patch to patch. Observed patterns of parasitism, however, have a variety of forms (with variation in attack rates among patches depending directly or indirectly on host density, or showing variation uncorrelated with host density). There is some confusion about the dynamical consequences of these different forms. Here we first show how the dynamical effects of all these forms of environmental heterogeneity can be assessed by a common criterion. This 'CV2 greater than 1 rule' states that the overall population densities will remain roughly steady from generation to generation if the coefficient of variation squared (CV2) of the density of searching parasitoids in the vicinity of each host exceeds approximately unity. By partitioning CV2 into components, we show that both direct and inverse patterns of dependence on host density, and density-independent patterns, all contribute to population regulation in the same way. Second, we show how a maximum-likelihood method can be applied to the kind of field data that are usually available (that is, percentage parasitism versus local host density) to estimate the components of CV2. This analysis indicates that heterogeneity is large enough to stabilize dynamics in 9 of 34 published studies, and that density-independent heterogeneity is the main factor in most cases.

Algorithms↗
PubMed1988

The relation between the number of parasites/host and host age: population dynamic causes and maximum likelihood estimation.

We examined dynamical factors that shape the distribution of the number of parasites/host in constant or temporally varying environments, and with or without host-age dependent variation in host susceptibility and parasite mortality. We predict properties of the parasite distribution in the absence of density-dependent factors such as density-dependent mortality of recruitment and parasite-induced host mortality. These properties provide a criterion for the detection of density dependence in temporally variable systems with host-age dependent interactions. We have then introduced methods to estimate and statistically evaluate the effects of host age or size on the distribution of parasites/host. The methods are based on a maximum likelihood protocol for linear and non-linear regression when data are negatively binomially distributed. We have illustrated the use of the theoretical results and statistical methods by re-analysing the data of Halvorsen & Andersen (1984) on cestode infections in Norwegian arctic charr and by analysing new data on nematode infections in Caribbean Anolis lizards.

Age Factors↗