Extrapolating demography with climate, proximity and phylogeny: approach with caution

Area of Science:

• Ecology
• Population Biology
• Conservation Biology

Background:

• Understanding plant population responses is crucial for assessing threats like climate change and invasions.
• Demographic data are scarce for most species and often geographically aggregated.
• Extrapolating limited demographic data to predict population performance across species and spatial scales is challenging.

Purpose of the Study:

• To assess the extent to which existing demographic data can be extrapolated to predict plant population performance.
• To model how climate, geographic proximity, and phylogeny influence population performance metrics.
• To evaluate the limitations of data extrapolation for conservation and management.

Main Methods:

• Utilized 550 matrix models from the COMPADRE Plant Matrix Database, encompassing 210 species.
• Modeled the predictive power of geographic proximity and phylogeny on population performance metrics.
• Analyzed the variation explained by these factors across different population performance indicators.

Main Results:

• Models incorporating geographic proximity and phylogeny explained 5-40% of the variation in key population performance metrics.
• Extrapolation of demographic data between species showed poor predictive accuracy.
• The spatial scale of reliable extrapolation was smaller than the scales relevant to landscape-level threats.

Conclusions:

• Demographic data extrapolation for plant populations should be approached with caution due to limited predictive power.
• Current data aggregation and extrapolation methods are insufficient for predicting population responses to large-scale threats.
• Future research requires more geographically extensive demographic sampling to inform landscape-level conservation strategies.