Variation in and Correlation between Intrinsic Rate of Increase and Carrying Capacity

Abstract

Intrinsic population growth rate and density dependence are fundamental components of population dynamics. Theory suggests that variation in and correlations between these parameters among patches within a population can influence overall population size, but data on the degree of variation and correlation are rare. Replicate populations of a specialist aphid (Chaetosiphon fragaefolii) were followed on 11 genotypes of host plant (Fragaria chiloensis) in the greenhouse. Population models fit to these census data provide estimates of intrinsic growth rate and carrying capacity for aphid populations on each plant genotype. Growth rate and carrying capacity varied substantially among plant genotypes, and these two parameters were not significantly correlated. These results support the existence of spatial variation in population dynamic parameters; data on frequency distributions and correlations of these parameters in natural populations are needed for evaluation of the importance of variation in growth rate and density dependence for population dynamics in the field.

Host selection by an insect herbivore with spatially variable density dependence

“…Our results indicate that when density dependence varies among host-plant individuals, as has recently been shown to happen in a number of systems (Agrawal 2004;Underwood 2007;Miller 2007) including E. diana (Wetzel 2014), female oviposition behavior may be focused on finding plants that mediate weak density dependence, rather than finding plants with low densities of competitors. This means that resource selection behavior may depend not just on the presence or absence of density-dependent offspring performance but also on the details of how offspring density dependence varies in strength among sites within populations.…”

“…It varies in form and strength among species (Sibly et al 2005) as well as among sites within species. Recent work shows that the parameters of density dependence experienced by insect herbivores can vary substantially among plant individuals within a population because of differences in a variety of nutritive and defensive traits (Agrawal 2004;Underwood 2007;Miller 2007;Wetzel 2014). These findings suggest that ovipositing females should not only respond to the density of competitors but also to traits predictive of the strength of density dependence mediated by plants.…”

The soybean aphid, Aphis glycines (Hemiptera: Aphididae): Population dynamics on edamame soybeans in Nebraska, USA

“…These various summer movements are thought to be the likely source of infestations in Nebraska (Hunt, 2004). Colonization and build-up of aphid population are affected by different factors including planting date, predators, host plant resistance, insecticides temperature, rainfall, wind velocity and direction, and the amount of vegetation (Onstad, 2001;Underwood, 2004). Significant yield loss (8 to 25%) occurs when the soybean plants are heavily infested by the aphid during the early reproductive stage (DiFonzo and Hines, 2002).…”

Density‐dependent recruitment structures a heterogeneous distribution of herbivores among host plants

“…Recognition of this issue led to improved experimental methods for detecting density dependence and, perhaps more importantly, helped lead to an appreciation for the prevalence of spatial variation in parameters of population dynamics within populations (Harrison and Cappuccino 1995, Schmitt and Holbrook 2007, variation which theory suggests can have major consequences for population dynamics (Pulliam 1988, Underwood 2004. Recent empirical work has focused on finding factors that can cause spatial variation in the strength of density dependence (Forrester and Steele 2004, Miller 2007, Schmitt and Holbrook 2007, Underwood 2007), but less progress has been made in comparing the consequences of spatial variation in density-dependent and density-independent parameters on patterns of abundance in natural systems. These insights would improve our understanding of how local processes scale up to large-scale dynamics and improve predictive models of spatially heterogeneous systems.…”