Antecedent Effect Models as an Exploratory Tool to Link Climate Drivers to Herbaceous Perennial Population Dynamics...

Area of Science:

• Ecology
• Population Dynamics
• Climate Change Research

Background:

• Predicting natural population responses to climate is crucial in ecology.
• Studies linking climate to population dynamics are limited.
• Antecedent effect models use climate and population data to identify relevant time windows for ecological responses.

Purpose of the Study:

• Compare the predictive performance of antecedent effect models against simpler statistical models.
• Showcase the exploratory potential of antecedent effect models using a case study.
• Investigate climate's influence on population growth rates and vital rates.

Main Methods:

• Fitted three antecedent effect models: weighted mean models (WMM), stochastic antecedent models (SAM), and Finnish horseshoe models (FHM).
• Compared these models against a linear model with a yearly climate predictor and a null model.
• Utilized demographic data from 77 natural plant populations (34 species) over 7-36 years.
• Modeled asymptotic population growth rate (λ) and vital rates (survival, development, reproduction).

Main Results:

• Climate-inclusive models did not consistently outperform null models in predictive power.
• Hypothesized that yearly climate effects are complex, weak, or confounded, hindering simple prediction.
• Antecedent effect models revealed biologically sensible correlations between precipitation anomalies and vital rates in a case study.

Conclusions:

• Antecedent effect models are more suitable as exploratory tools for hypothesis generation with limited temporal data.
• These models can identify potential climate-driven mechanisms influencing population dynamics.
• Further research can refine antecedent effect models for improved ecological predictions.