Craig M. Pease

A Model of Population Growth, Dispersal and Evolution in a Changing Environment

The climatic and biotic conditions at any geographic location will change through time, for example, because of the advance of glaciers. If it is to avoid extinction, a species adapted to a moving habitat must either track its habitat spatially, or adapt genetically to the new environmental conditions. These processes of migration and evolution are important in determining continental biogeographic patterns. We develop a model to explore the relative contributions of adaptation and dispersal as alternative mechanisms whereby a population can respond to changing environmental conditions. In our model the environment to which the species is adapted moves across the landscape at a constant velocity, and a quantitative trait determines each individuals fitness as a function of the local environmental conditions. Local populations are allowed to adapt genetically to the environmental conditions at each point in space, so that a cline develops in the quantitative character. We find that if the rate of environmental movement is slow, the species will track its environment across space, otherwise it will go extinct. Additionally, the higher the genetic variance in the character, the easier it is for the species to maintain itself in a moving environment. Our results generalize previous models that predict a critical patch size of suitable habitat necessary for population persistence.

A critique of methods for measuring life history trade‐offs

Constraints have important effects on the evolution of life history strategies, but several difficulties have been encountered in determining constraints empirically. Here we investigate methods for measuring a specific type of constraint known as a trade‐off. A trade‐off between two traits implies a perfect negative correlation between the traits. Trade‐offs may involve more than two traits, however, and pairs of traits involved in such a higher‐dimensional trade‐off may be positively correlated. If some of the traits involved in a trade‐off are omitted from the experimental design, the trade‐off may not even be detectable. Direct measures of trade‐offs are thus complicated, and indirect means of identifying trade‐offs may often provide the only feasible measures.

DEMOGRAPHY OF THE YELLOWSTONE GRIZZLY BEARS

We undertook a demographic analysis of the Yellowstone grizzly bears (Ursus arctos) to identify critical environmental factors controlling grizzly bear vital rates, and thereby to help evaluate the effectiveness of past management and to identify future conservation issues. We concluded that, within the limits of uncertainty implied by the available data and our methods of data analysis, the size of the Yellowstone grizzly bear population changed little from 1975 to 1995. We found that grizzly bear mortality rates are about double in years when the whitebark pine crop fails than in mast years, and that the population probably declines when the crop fails and increases in mast years. Our model suggests that natural variation in whitebark pine crop size over the last two decades explains more of the perceived fluctuations in Yellowstone grizzly population size than do other variables. Our analysis used demographic data from 202 radio-telemetered bears followed between 1975 and 1992 and accounted for whiteba...

RENESTING DETERMINES SEASONAL FECUNDITY IN SONGBIRDS: WHAT DO WE KNOW? WHAT SHOULD WE ASSUME?

Abstract Because of the difficulty of following female songbirds through an entire breeding season, field ornithologists are seldom able to directly measure seasonal fecundity (defined as number of offspring produced per female during an entire breeding season). Instead, it is more commonly inferred from some measure of nest-productivity data (e.g. average number of offspring fledged per nesting attempt) using algorithms that make assumptions about the propensity of females to renest after a nest failure or after successfully fledging a brood. Recent analyses have often assumed set maximum numbers of nesting attempts and successful broods, and that all females breed up to those maxima. However, whereas data from songbirds intensively followed for an entire breeding season show that they are capable of up to 4–8 nesting attempts, many authors, in estimating seasonal fecundity, assume a maximum of only 1–4 nesting attempts. We applied a model to a Prairie Warbler (Dendroica discolor) data set (Nolan 1978) that allowed direct comparisons of (1) seasonal-fecundity estimates obtained assuming fixed maximum numbers of renestings and broods with (2) estimates obtained assuming that numbers of renesting attempts and successful nests are constrained only indirectly by length of breeding season. Although results under the latter assumption are concordant with Nolans (1978) direct empirical measure of Prairie Warbler seasonal fecundity, estimates under assumptions of fixed maxima of renestings or broods are in serious error for many parameter choices. As such, our analyses disclose that essentially all estimates of seasonal fecundity in the literature derived by assuming a limited maximum number of nesting attempts or of successful broods are biased. Most commonly, when nest mortality is high, seasonal fecundity is underestimated; in some cases where nest mortality is low, seasonal fecundity is overestimated. We recommend that researchers estimating seasonal fecundity from nest-productivity data use a model that explicitly sets breeding-season length and thereby only indirectly constrains the possible number of nesting attempts and successful broods. La Nidificación Repetida Determina la Fecundidad Estacional en las Aves Canoras: ¿Qué Sabemos? ¿Qué Deberíamos Suponer?

DETECTION OF DENSITY DEPENDENCE REQUIRES DENSITY MANIPULATIONS AND CALCULATION OF λ

To investigate density-dependent population regulation in the perennial bunchgrass Bouteloua rigidiseta, we experimentally manipulated density by removing adults or adding seeds to replicate quadrats in a natural population for three annual intervals. We monitored the adjacent control quadrats for 14 annual intervals. We constructed a population projection matrix for each quadrat in each interval, calculated lambda, and did a life table response experiment (LTRE) analysis. We tested the effects of density upon lambda by comparing experimental and control quadrats, and by an analysis of the 15-year observational data set. As measured by effects on lambda and on N(t+1/Nt in the experimental treatments, negative density dependence was strong: the population was being effectively regulated. The relative contributions of different matrix elements to treatment effect on lambda differed among years and treatments; overall the pattern was one of small contributions by many different life cycle stages. In contrast, density dependence could not be detected using only the observational (control quadrats) data, even though this data set covered a much longer time span. Nor did experimental effects on separate matrix elements reach statistical significance. These results suggest that ecologists may fail to detect density dependence when it is present if they have only descriptive, not experimental, data, do not have data for the entire life cycle, or analyze life cycle components separately.

A model of the dynamics of cowbirds and their host communities

Few studies have examined the potential consequences of cowbird (Molothrus spp.) parasitism on entire avian communities. Because cowbirds are host generalists, an obvious opportunity exists for such community-level effects. We developed a model to predict how cowbird abundance affects and is affected by the relative abundances of different types of host species. Toward this end, we divided the passerine hosts of cowbirds into three categories, each having different population dynamic properties: (1) ejectors of cowbird eggs; (2) extinction-prone acceptors of cowbird eggs (species that decline in abundance in response to high levels of cowbird parasitism); and (3) insensitive acceptors of cowbird eggs (species that maintain their abundance even at high levels of cowbird parasitism). Ejectors are sinks for cowbird eggs and thus indirectly benefit extinction-prone hosts. Conversely, insensitive acceptors can raise cowbird young without a concomitant decrease in their own abundance; as such, they indirectly harm extinction-prone species. Although cowbird abundance is determined by the abundance of both ejectors and insensitive acceptors, the reverse is generally not true (i.e. their abundance is independent of cowbird abundance). The mathematical model of cowbird/host community dynamics we present consists of two ordinary differential equations that incorporate the above assumptions about the different classes of hosts and the manner in which they interact with cowbirds. The model predicts that extinction-prone species will have a higher potential to persist when one or more of the following exist: (1) ejectors are relatively more abundant than insensitive acceptors; (2) ejectors are abundant relative to extinction-prone carrying capacity; (3) maximum potential cowbird per-capita population growth rate is small; and (4) the potential effect of cowbirds on extinction-prone population growth rate is small. Extinction-prone species will decline or go extinct in reverse situations.

On the evolutionary reversal of competitive dominance

The relationship between ecology and genetics is investigated, in part, by coevolutionary biology. One aspect of this relationship is the effect that genetic variability and natural selection have on determining the outcome of competitive interactions. This paper investigates the evolutionary reversal of competitive dominance, a particular mechanism whereby genetic considerations may influence competitive outcomes and population abundances. The formulation of these models was motivated by the laboratory experiments of Pimentel et al. (1965) with houseflies and blowflies and by the experiments of Ayala (1966a, 1969) and Moore (195 2a) with Drosophila. Arthur (1982) critically reviews these experiments. Pimentel et al. (1965) give a description of the dynamic interplay between the intensity of selection for competitive ability and the relative abundances of two competing species that forms the basis of the reversal of dominance. Suppose a rare and an abundant species are engaged in a competitive interaction. Members of the rare species will encounter the abundant species more often than they will encounter individuals of their own kind, hence superior interspecific competitive ability will be selected for. Conversely, individuals of the abundant species will encounter their own type more frequently, hence superior intraspecific competitive ability will be selected for. As the interspecific competitive ability of the originally rare species increases, the density of that species will increase and eventually overtake that of the originally abundant species, thereby producing a reversal of dominance. The most common experimental result of the authors cited above was for only one, or sometimes no, reversals of dominance to be observed, after which either the experiment was terminated, or one of the competitors went extinct. For example, in the experiment of Pimentel et al. (1965) houseflies and blowflies competed in a multicell laboratory cage. In nature, the houseflies and blowflies coexist on refuse and dung, while the laboratory populations oviposited on the same mixture of liver, yeast, agar and dried milk. During the first 50 weeks, or about 25 generations, houseflies comprised approximately 92% of the total fly population, and blowflies 8%. At about the twenty-fifth generation the blowfly abundance increased rapidly, with a concurrent decrease in the housefly population, which went extinct shortly thereafter. In single cell cages the blowfly usually went extinct, presumably before it could evolve increased interspecific competitive ability. Pimentel et al. therefore postulated that the spatial structure of the multicell cage allowed the blowfly to coexist sufficiently long for evolution, and hence a reversal of dominance, to occur. In this paper, I will use a quantitative genetical approach to modeling the evolution of species interactions and the reversal of dominance. The outline of the remainder of the paper is as follows: Three related mathematical models will be derived and discussed. In these models a distinction is made between the constant, nonevolutionary component of the competition coefficients, with a genetic variance of zero, and the part of the competition coefficients which is subject to evolutionary change, and therefore has a positive genetic variance. The first model is neutrally stable, and is used as a foundation on which the remaining two more

Temporal Variation in the Carrying Capacity of a Perennial Grass Population

Density dependence and, therefore, K (carrying capacity, equilibrium population size) are central to understanding and predicting changes in population size (N). Although resource levels certainly fluctuate, K has almost always been treated as constant in both theoretical and empirical studies. We quantified temporal variation in K by fitting extensions of standard population dynamic models to 16 annual censuses of a population of the perennial bunchgrass Bouteloua rigidiseta. Variable‐K models provided substantially better fits to the data than did models that varied the potential rate of population increase. The distribution of estimated values of K was skewed, with a long right tail (i.e., a few “jackpot” years). The population did not track K closely. Relatively slow responses to changes in K combined with large, rapid changes in K sometimes caused N to be far from K. In 13%–20% of annual intervals, K was so much larger than N that the population’s dynamics were best described by geometric growth and the population was, in effect, unregulated. Explicitly incorporating temporal variation in K substantially improved the realism of models with little increase in model complexity and provided novel information about this population’s dynamics. Similar methods would be applicable to many other data sets.

Paramyxovirus phylogeny: tissue tropism evolves slower than host specificity

In this paper, we compare the relative rates of evolution ofhost specificity and tissue tropism often paramyxoviruses, a group of medically and economically important parasites. We do this by reconstructing the phylogeny of these viruses using nucleotide sequence data and then mapping the host and tissue specificities of the various viruses onto this phylogeny. We show that, although the host specificities of these viruses are quite labile, tissue tropism evolves much more slowly. The most familiar paramyxoviruses are the mumps and measles viruses; the parainfluenza viruses, though less familiar, are an important cause of respiratory disease in young children. Complications of these diseases can be serious; for example, measles is an important cause of childhood mortality in undeveloped countries (Modlin, 1984), and subacute sclerosing panencephalitis is a fatal brain disorder resulting from persistent measles infection (Modlin, 1984; Wright, 1984; Tolpin and Schauf, 1984). Additionally, several of the paramyxoviruses, including rinderpest and Newcastle disease virus, infect domestic animals (cattle and poultry, respectively), with severe economic consequences.

Estimating relative parental investment in sons versus daughters

Fisher proposed that natural selection would adjust the population sex ratio so that parental expenditure on sons equals expenditure on daughters. Thus if two daughters can be produced for every son, the Fisherian equilibrium is ⅓ sons and ⅔ daughters. The relative cost of a son versus a daughter is necessarily manifested in the trade‐off between family size and sex ratio, and we offer a method to estimate this trade‐off from data on family compositions. Simulation studies indicate that the method works well in some cases but not others. Application of the method to data on a polychaete suggests that sons are much costlier than daughters; the observed sex ratio in fact significantly favored daughters, but not to the extreme predicted by our measure of differential cost.