Assessing the impact of climate variation on survival in vertebrate populations

Abstract

The impact of the ongoing rapid climate change on natural systems is a major issue for human societies. An important challenge for ecologists is to identify the climatic factors that drive temporal variation in demographic parameters, and, ultimately, the dynamics of natural populations. The analysis of long-term monitoring data at the individual scale is often the only available approach to estimate reliably demographic parameters of vertebrate populations. We review statistical procedures used in these analyses to study links between climatic factors and survival variation in vertebrate populations. We evaluated the efficiency of various statistical procedures from an analysis of survival in a population of white stork, Ciconia ciconia, a simulation study and a critical review of 78 papers published in the ecological literature. We identified six potential methodological problems: (i) the use of statistical models that are not well-suited to the analysis of long-term monitoring data collected at the individual scale; (ii) low ratios of number of statistical units to number of candidate climatic covariates; (iii) collinearity among candidate climatic covariates; (iv) the use of statistics, to assess statistical support for climatic covariates effects, that deal poorly with unexplained variation in survival; (v) spurious detection of effects due to the co-occurrence of trends in survival and the climatic covariate time series; and (vi) assessment of the magnitude of climatic effects on survival using measures that cannot be compared across case studies. The critical review of the ecological literature revealed that five of these six methodological problems were often poorly tackled. As a consequence we concluded that many of these studies generated hypotheses but only few provided solid evidence for impacts of climatic factors on survival or reliable measures of the magnitude of such impacts. We provide practical advice to solve efficiently most of the methodological problems identified. The only frequent issue that still lacks a straightforward solution was the low ratio of the number of statistical units to the number of candidate climatic covariates. In the perspective of increasing this ratio and therefore of producing more robust analyses of the links between climate and demography, we suggest leads to improve the procedures for designing field protocols and selecting a set of candidate climatic covariates. Finally, we present recent statistical methods with potential interest for assessing the impact of climatic factors on demographic parameters.

Quantifying drivers of population dynamics for a migratory bird throughout the annual cycle

“…An important consideration for understanding long-term population declines is that these declines can only be driven by covariates that also display a linear trend over the same time period [25]. However, the widespread decline in forest cover and habitat quality across all regions makes it difficult to conclusively demonstrate that wood thrush declines were driven by habitat degradation within any specific region.…”

“…However, the widespread decline in forest cover and habitat quality across all regions makes it difficult to conclusively demonstrate that wood thrush declines were driven by habitat degradation within any specific region. Therefore, we followed Grosbois et al [25] and removed temporal trends from all covariates and used the detrended estimates as the annual covariates in our analysis. Thus, important covariates must account for a significant fraction of the variation in annual abundance after accounting for the linear decline of the covariate.…”

Demographic consequences of heavy metals and persistent organic pollutants in a vulnerable long-lived bird, the wandering albatross

“…The 95% confidence interval (CI) of the slope parameters was used, as well as Akaike's information criterion corrected (AICc) for small sample size [28] for inference. We considered a contaminant's effect to be statistically supported when 0 was outside the 95% CI of the mean of the slope of the relationship [29]. A composite model was then constructed by combining all the covariates that were detected to have an effect on demographic parameters.…”

Adélie penguin (Pygoscelis adeliae) survival rates and their relationship to environmental indices in the South Shetland Islands, Antarctica

“…This procedure allowed an analysis of deviance to estimate the proportion of deviance in the full CMR model explained by each covariate (a deviance R 2 ; Grosbois et al 2008) in isolation from the other indices that have been identified as important drivers of population dynamics. We compared the fit of the generic time-varying model and each environmental covariate model with respect to a constant model.…”

“…We ask whether the CMR data provide support for declines in juvenile survival rates, declines in adult survival rates, or increased variability in survival rates. We also examine the ability of environmental indices to explain variation in estimated survival rates through an integrated CMR modeling approach (Lebreton et al 1992;Grosbois et al 2008). …”