Life history mediates the trade-offs among different components of demographic resilience

Abstract

Accelerating rates of biodiversity loss underscore the need to understand how species achieve resilience-the ability to resist and recover from a/biotic disturbances. Yet, the factors determining the resilience of species remain poorly understood, due to disagreements on its definition and the lack of large-scale analyses. Here, we investigate how the life history of 910 natural populations of animals and plants predicts their intrinsic ability to be resilient. We show that demographic resilience can be achieved through different combinations of compensation, resistance and recovery after a disturbance. We demonstrate that these resilience components are highly correlated with life history traits related to the species' pace of life and reproductive strategy. Species with longer generation times require longer recovery times post-disturbance, whilst those with greater reproductive capacity have greater resistance and compensation. Our findings highlight the key role of life history traits to understand species resilience, improving our ability to predict how natural populations cope with disturbance regimes.

Keywords: comparative biology; conservation; disturbance; fast-slow continuum; pace of life; population collapse; recovery; resistance; stability; traits.

FIGURE 1

The resilience of a wild population can be quantified via three components: compensation, resistance and recovery time. (a) Decomposition of the demographic resilience components of a population affected by a punctual disturbance (lightning bolt). After a disturbance, the size and growth rate of a population may change differently according to how the population structure (i.e. the proportion of individuals at different age/stage/size in the population) is affected by the disturbance. (b) Resistance, the ability to prevent a decline in population size following a disturbance, is measured as the inverse of a population's decrease following a disturbance relative to its undisturbed conditions (i.e. with stable population structure (Stott et al., 2011)). Low resistance describes high population declines relative to a stable population. (c) Compensation, the ability to increase relative to the population size following a disturbance, is quantified as the population's increase with respect to an undisturbed population. Large increases in population size indicate high compensation, whilst small increases indicate low compensation. (d) Recovery time, the period that a population needs to reattain a stable structure after a disturbance

FIGURE 2

Evolutionary history explains a higher degree of variability of the demographic resilience in animals than in plants. Patterns of variation of demographic compensation, resistance and recovery time (Figure 1) for the examined 162 populations of 69 animal species and 748 populations of 232 plant species. The inward ring represents resistance, middle ring compensation and outer ring recovery time. Evolutionary history explains a greater amount of variability of demographic resilience in animals (a) than in plants (b). Values showed in each panel represent the mean values of compensation, resistance and recovery time per species. (a) In animals, the phylogenetic signal was stronger for compensation (0.63 ± 0.18, mean ± SE), than for resistance (0.48 ± 0.26) and recovery time (0.41 ± 0.21). Silhouettes represent, from the top in a clockwise direction, chimpanzee (Pan troglodytes), red grouper (Epinephelus morio), peregrine falcon (Falco peregrinus), common tern (Sterna hirundo), green sea turtle (Chelonia mydas), California sea lion (Zalophus californianus), polar bear (Ursus maritimus) and red deer (Cervus elaphus). (b) In plants, compensation (0.04 ± 0.05) and resistance (0.02 ± 0.04) show a weak phylogenetic signal, whilst recovery time had a stronger phylogenetic signal (0.66 ± 0.08). Silhouettes represent, from the top in a clockwise direction, woodland geranium (Geranium sylvaticum), wild plantain (Heliconia acuminata), white Cypress‐pine (Callitris columellaris), alpine sea holly (Eryngium alpinum), purple pitcher plant (Sarracenia purpurea), Douglas's catchfly (Silene douglasii) and grey alder (Alnus incana). Silhouettes’ source: phylopic.org

FIGURE 3

The components of demographic resilience correlate differently for plants than for animals. Correlations between the components of resilience, (a, d) resistance vs. recovery time, (b, e) resistance vs. compensation and (c, f) recovery time vs. compensation for 162 populations of 69 animal species (a–c) and 748 populations of 232 plant species (d–f). Insets show the distribution of the residual correlations between the components of resilience, where ρ represents the mean value of the distribution. Positive values of ρ indicate a positive correlation between components, and negative values represent a trade‐off. The correlation between resistance and recovery time is (a) positive for animals but (d) negative for plants. Resistance and compensation are positively correlated in both (b) animals and (e) plants. Recovery time and compensation are (c) slightly positively correlated in animals and (f) slight negatively correlated in plants. The residual correlations were estimated by fitting a multivariate multilevel Bayesian model using compensation, resistance and recovery time as the response variable and with no predictors (see Methods)

FIGURE 4

The three components of demographic resilience—resistance, compensation and recovery time—strongly correlate with two key species life history traits: generation time and mean reproductive output. (a–c) display the correlations of (a) compensation, (b) resistance and (c) recovery time, with generation time. (d–f) display the correlations of (d) compensation, (e) resistance and (f) recovery time, with mean reproductive output. Here, we show the correlations between the scaled values of the demographic resilience components of resistance, compensation and recovery time with the scaled values of generation time and reproductive output of 162 populations of 69 animal species (blue) and 748 populations of 232 plant species (orange). Lines represent the predictions from the multilevel Bayesian models (Table S2), where thin lines correspond to the predictions drawn from each of the 250 posterior samples of the model, and the thick line represents the mean outcome of the model