A major goal in ecology is to make generalizable predictions of organism responses to environmental variation based on their traits. However, straightforward relationships between traits and fitness are rare and likely to vary with environmental context. Characterizing how traits mediate demographic responses to the environment may enhance the predictions of organism responses to global change. We synthesized 15 years of demographic data and species-level traits in a shortgrass steppe to determine whether the effects of leaf and root traits on growth and survival depended on seasonal water availability. We predicted that (1) species with drought-tolerant traits, such as lower leaf turgor loss point (TLP) and higher leaf and root dry matter content (LDMC and RDMC), would be more likely to survive and grow in drier years due to higher wilting resistance, (2) these traits would not predict fitness in wetter years, and (3) traits that more directly measure physiological mechanisms of water use such as TLP would best predict demographic responses. We found that graminoids with more negative TLP and higher LDMC and RDMC had higher survival rates in drier years. Forbs demonstrated similar yet more variable responses. Graminoids grew larger in wetter years, regardless of traits.However, in both wet and dry years, graminoids with more negative TLP and higher LDMC and RDMC grew larger than less negative TLP and low LDMC and RDMC species. Traits significantly mediated the impact of drought on survival, but not growth, suggesting that survival could be a stronger driver of species' drought response in this system. TLP predicted survival in drier years, but easier to measure LDMC and RDMC were equal or better predictors.These results advance our understanding of the mechanisms by which drought drives population dynamics, and show that abiotic context determines how traits drive fitness.

Long‐term demographic data are rare yet invaluable for conservation, management, and basic research on the underlying mechanisms of population and community dynamics. Historical and contemporary mapped datasets of plant location and basal area present a relatively untapped source of demographic records that, in some cases, span over 20 years of sequential data collection. However, these maps do not uniquely mark individual plants, making the process of collecting growth, survival, and recruitment data difficult. Recent efforts to translate historical maps of plant occurrence into shapefiles make it possible to use computer algorithms to track individuals through time and determine individual growth and survival. We summarize the plantTracker R package, which contains user‐friendly functions to extract neighbourhood density, growth, and survival data from repeatedly‐sampled maps of plant location and basal area. These functions can be used with data derived from quadrat maps, aerial photography, and remote sensing, and while designed for use with perennial plants, can be applied to any repeatedly mapped sessile organism. This package contains two primary functions: trackSpp(), which tracks individuals through time and assigns demographic data, as well as getNeighbors(), which calculates both within and between‐species neighbourhood occupancy around each mapped individual. plantTracker also contains functions to estimate plot‐level recruitment, calculate plot‐level population growth rate, and create quadrat maps. We tested the accuracy of the trackSpp() function on two spatial demographic datasets. The function was nearly perfect at assigning individual identities and survival status when tested on maps of tree basal area and perennial forb point locations. In both cases, the function correctly assigned survival and recruitment with 99% accuracy. These accurate and precise functions will expand the amount of data available to investigate demographic processes, which are fundamental drivers of population, community, and ecosystem processes.