Trees must allocate resources to core functions, like growth, defense, and reproduction. These allocation patterns have profound effects on forest health, yet little is known about how core functions trade off over time, and even less is known about how a changing climate will impact tradeoffs. We conducted a 21-year survey of growth, defense, and reproduction in 80 ponderosa pine individuals spanning 8 populations across environmental gradients along the Colorado Front Range, USA. We used linear mixed models to describe tradeoffs among these functions and to characterize variability among and within individuals over time. Growth and defense were lower in years of high cone production and local drought conditions amplified year-to-year tradeoffs between reproduction and growth, where trees located at sites with hotter and drier climates showed stronger tradeoffs between reproduction and growth. Our results support the environmental stress hypothesis of masting, which predicts that greater interannual variation in tree functions is associated with more marginal environments, such as those that are prone to drought. With warming temperatures and increased exposure to drought stress, trees will be faced with stronger interannual tradeoffs, which could lead to further decreases in growth and defensive efforts, ultimately increasing risks of mortality.

Data includes R code and datasets to reproduce figures and results from the manuscript titled: "Increased aridity is associated with stronger tradeoffs in ponderosa pine vital functions"

Data are counts of annual cone production from Pinus ponderosa across the Colorado Front Range, USA, from 80 trees and 8 sites, reconstructed using cone scars from the time period 2000-2020. Data also includes xylem growth measurements (taken from 12mm and 5mm cores), resin duct (total resin area, relative duct area, mean duct size), average stable carbon isotope data of each tree, and local site climate. See the readme file for full description of the files. 

We place no restrictions on the use of these data but encourage those interested in working with them to contact Angela Gonzalez at angiedgonzalez@gmail.com

R scripts (.r) can be executed using R software and viewed in standard text editors.

Tabular data (.csv, .xlsx) can be opened in MS Excel or open-source alternatives.