A Perfect Storm: The Combined Effects on Population Fluctuations of Autocorrelated Environmental Noise, Age Structure, and Density Dependence

Abstract

While it is widely appreciated that climate can affect the population dynamics of various species, a mechanistic understanding of how climate interacts with life-history traits to influence population fluctuations requires development. Here we build a general density-dependent age-structured model that accounts for differential responses in life-history traits to increasing population density. We show that as the temporal frequency of favorable environmental conditions increases, population fluctuations also increase provided that unfavorable environmental conditions still occur. As good years accumulate and the number of individuals in a population increases, successive life-history traits become vulnerable to density dependence once a return to unfavorable conditions prevails. The stronger this ratcheting of density dependence in life-history traits by autocorrelated climatic conditions, the larger the population fluctuations become. Highly fecund species, and those in which density dependence occurs in juvenile and adult vital rates at similar densities, are most sensitive to increases in the frequency of favorable conditions. Understanding the influence of global warming on temporal correlation in regional environmental conditions will be important in identifying those species liable to exhibit increased population fluctuations that could lead to their extinction.

The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management

“…Spatial autocorrelation can also influence patterns of yield loss, another influence to make loss similar across space and to buffer changes in loss over time (Margosian et al, 2009). In cases where there is increased variation in climate variables and associated increased variation in pathogen or pest populations, local extinction may also become more common (García-Carreras and Reuman, 2011;Ruokolainen et al, 2009;Wilmers et al, 2007), so that future disease loss at any given location will depend on the spatial structure of conditions suitable to reintroduce pathogens or pests. Similarly, decisionmaking may also be correlated in space, as farmers compare results for purposes of decisionmaking, or where a single farmer has responsibility for a number of different fields.…”

“…The color of an environmental time series and associated population time series may logically be related (García-Carreras and Reuman, 2011;Ruokolainen et al, 2009;Wilmers et al, 2007). García-Carreras and Reuman (2011) concluded that climate variables have become relatively bluer over the past century (on an annual basis), such that higher frequency oscillations may also be observed for populations affected by weather.…”

Correlative Changes in Life-History Variables in Response to Environmental Change in a Model Organism

“…The first group uses data from field populations to decompose variation in demographic rates into different ecological and/or evolutionary contributions (Coulson et al 2001;van de Pol et al 2010), and it also uses data from field populations to assess how populations respond to perturbation of demographic rates (e.g., Forcada et al 2008;Morris et al 2008;Dalgleish et al 2010; and to recurrent disturbances (e.g., Tuljapurkar et al 2003;Barrows et al 2010;Vincenzi et al 2012). The second group consists of theoretical studies that investigate the demography of populations and stylized life histories in stochastic environments (e.g., Wilmers et al 2007;Lande et al 2009;Tuljapurkar et al 2009). Results from the first group of studies indicate that the relative importance of specific demographic rates to the growth rate of populations can depend on the frequency of disturbances (Tuljapurkar et al 2003).…”

“…They also forecast that the population growth rate of longer-lived species will be less sensitive to increases in climate variability (Morris et al 2008;Dalgleish et al 2010) but that the population consequences of a change in climate variability can be outweighed by the consequences of changes in the mean environment (van de Pol et al 2010;Coulson et al 2011). The theoretical studies, in turn, indicate that a change in the serial correlation of demographic rates may increase or decrease population growth rate depending on the structure of the life history (Tuljapurkar et al 2009) and that a change in the magnitude or type of environmental stochasticity can induce a shift from fast to slow life histories (Lande et al 2009) or even lead to extinction due to increased population fluctuations (Wilmers et al 2007). These are notable insights but the question of how common concurrent change in life-history variables such as the population growth rate, generation time, and lifetime reproductive success is in response to changing environmental change, is still open.…”

“…Here we develop IPMs for the bulb mite to examine the likely life-history response of populations to changes in important properties of the environment: (i) the temporal frequency of favorable environment states and (ii) the serial correlation of environment states. Investigating population responses to these two types of changes is paramount to understanding the effects of climate change on the dynamics of structured populations (Boyce et al 2006;Wilmers et al 2007) and also informs on how stochastic change shapes the evolution of life histories (Tuljapurkar et al 2009). We use a scenario in which the environment is in one of two states, good or bad, and investigate the life-history response to changes in the environment in four steps.…”