RT Journal Article
SR Electronic
T1 Antecedent effect models as an exploratory tool to link climate drivers to plant population dynamics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.03.11.484031
DO 10.1101/2022.03.11.484031
A1 Aldo Compagnoni
A1 Tiffany M. Knight
A1 Dylan Childs
A1 Roberto Salguero-Gómez
YR 2022
UL http://biorxiv.org/content/early/2022/08/05/2022.03.11.484031.abstract
AB Understanding mechanisms and predicting trends in natural population responses to climate is a key goal of ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction).We compare the predictive performance of antecedent effect models against simpler models. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish Horseshoe prior (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between 7 and 36 years of length. We fit models to the overall asymptotic population growth rate (λ) and its underlying vital rates: survival, development, and reproduction.Antecedent effect models do not consistently outperform the linear or null models. Average differences in performance are small. Surprisingly, ranked in order of decreasing predictive performance, the best performing approaches are: null model > linear model > FHM > SAM > WMM. Models that include climate as a predictor have a higher ability to predict development than survival and reproduction and a higher ability to predict vital rates than population growth rate.Synthesis: In temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools. These models can help identify which single monthly anomalies drive population dynamics while controlling the effect of noise on estimates. As such, antecedent effect models can be a valuable tool for hypothesis generation, and to increase our understanding of the links between climate and plant population dynamics.Competing Interest StatementThe authors have declared no competing interest.