Temporal correlations among demographic parameters are ubiquitous but highly variable across species

Abstract

Temporal correlations among demographic parameters can strongly influence population dynamics. Our empirical knowledge, however, is very limited regarding the direction and the magnitude of these correlations and how they vary among demographic parameters and species’ life histories. Here, we use long‐term demographic data from 15 bird and mammal species with contrasting pace of life to quantify correlation patterns among five key demographic parameters: juvenile and adult survival, reproductive probability, reproductive success and productivity. Correlations among demographic parameters were ubiquitous, more frequently positive than negative, but strongly differed across species. Correlations did not markedly change along the slow‐fast continuum of life histories, suggesting that they were more strongly driven by ecological than evolutionary factors. As positive temporal demographic correlations decrease the mean of the long‐run population growth rate, the common practice of ignoring temporal correlations in population models could lead to the underestimation of extinction risks in most species.

Life history adaptations to fluctuating environments: Combined effects of demographic buffering and lability of demographic parameters

“…Interestingly, there is no general consensus on the degree to which positive or negative covariance in demographic parameters are more common in the wild, nor if the sign, magnitude or type of (st)age-specific demographic parameters involved correlate with the position of a species along the fast–slow continuum (but see a recent comparative study, Fay et al ., 2022). From empirical studies, positive covariances have been reported predominantly in long-lived species (e.g., Dahlgren et al ., 2016; Rotella et al ., 2012; van de Pol et al ., 2010) with substantial (e.g., Coulson et al ., 2005) or weak (e.g., Altwegg et al ., 2007; Compagnoni et al ., 2016; Johnson et al ., 2010) effects on fitness.…”

“…From empirical studies, positive covariances have been reported predominantly in long-lived species (e.g., Dahlgren et al ., 2016; Rotella et al ., 2012; van de Pol et al ., 2010) with substantial (e.g., Coulson et al ., 2005) or weak (e.g., Altwegg et al ., 2007; Compagnoni et al ., 2016; Johnson et al ., 2010) effects on fitness. In contrast, negative covariances were less often detected (Fay et al ., 2022), with often small effects on ln(λ s ). To our knowledge, relatively few studies have specifically addressed this question among species towards the fast-end of the continuum.…”

Consequences of cross-season demographic correlations for population viability

“…Here, we test the hypothesis, proposed by Fay et al (2022), that cross-season correlations in survival and reproduction are predominately a result of environmental conditions, and thus their magnitude and direction should differ among geographically separate populations. Furthermore, we quantify the proportion of variation in realised population growth rates that is attributable to the underlying demographic correlations using a transient-LTRE.…”

“…Positive correlations increase population size fluctuations, reducing the long-term population growth rate (Tuljapurkar & Orzack 1980;Compagnoni et al 2016), with particular implications for endangered populations (Tuljapurkar & Orzack 1980;Boyce et al2006). Positive correlations among demographic rates also appear to be more common than negative correlations, particularly in iteroparous species (Yoccoz et al 2002;Fay et al 2022). Despite their potential importance as a secondary driver, correlations in demographic rates are still rarely considered in population viability analyses.…”

“…A recent study (Fay et al 2022) investigated temporal correlations in demographic rates in relation to species' life histories (generation times) along the slow-fast continuum of life-history variation and proposed that environmental factors (e.g., climate conditions) were major drivers of demographic correlations. We therefore expect differences in the strength, and potentially direction, of seasonal demographic correlations between populations of the same species because of differences in the conditions they experience.…”