Life Histories and Elasticity Patterns: Perturbation Analysis for Species with Minimal Demographic Data

Abstract

Elasticity analysis is a useful tool in conservation biology. The relative impacts of proportional changes in fertility, juvenile survival, and adult survival on asymptotic population growth λ (where ln(λ) = r, the intrinsic rate of increase) are determined by vital rates (survival, growth, and fertility), which also define the life history characteristics of a species or population. Because we do not have good demographic information for most threatened populations, it is useful to categorize species according to their life history characteristics and related elasticity patterns. To do this, we compared the elasticity patterns generated by the life tables of 50 mammal populations. In age-classified models, the sum of the fertility elasticities and the survival elasticity for each juvenile age-class are equal; thus, age at maturity has a large impact on the contribution of juvenile survival to λ. Mammals that mature early and have large litters (“fast” mammals, such as rodents and smaller carnivores) also generally have short lifespans; these populations had relatively high fertility elasticities and lower adult survival elasticities. “Slow” mammals (those that mature late), having few offspring and higher adult survival rates (such as ungulates and marine mammals), had much lower fertility elasticities and high adult or juvenile survival elasticities. Although certain life history characteristics are phylogenetically constrained, we found that elasticity patterns within an order or family can be quite diverse, while similar elasticity patterns can occur in distantly related taxa. We extended our generalizations by developing a simple age-classified model parameterized by juvenile survival, mean adult survival, age at maturity, and mean annual fertility. The elasticity patterns of this model are determined by age at maturity, mean adult survival, and λ, and they compare favorably with the summed elasticities of full Leslie matrices. Thus, elasticity patterns can be predicted, even when complete life table information is unavailable. In addition to classifying species for management purposes, the results generated by this simplified model show how elasticity patterns may change if the vital rate information is uncertain. Elasticity analysis can be a qualitative guide for research and management, particularly for poorly known species, and a useful first step in a larger modeling effort to determine population viability.