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Data from: Water the odds? Spring rainfall and emergence-related seed traits drive plant recruitment


Larson, Julie; Ebinger, Kathleen; Suding, Katharine (2021), Data from: Water the odds? Spring rainfall and emergence-related seed traits drive plant recruitment, Dryad, Dataset,


Recruitment of new individuals from seed is a critical component of plant community assembly and reassembly, especially in the context of ecosystem disturbance and recovery. While frameworks typically aim to predict how communities will be filtered on the basis of traits influencing established plant responses to the environment, assembly from seed is more complex: the responses of seeds (affected by dormancy and germination function) and establishing plants (affected by root and leaf function) can both influence outcomes within a single growing season. This creates a potential role for a more diverse set seed and seedling traits, and for environmental variability on shorter timescales (e.g., seasonal versus annual dynamics), than are typically considered. We followed thousands of individual seeds comprising eleven herbaceous grassland species through the first growing season, seeking to uncover critical environmental (precipitation amount and timing) and trait-based filters on seedling emergence and survival in assembling communities. We saw the biggest recruitment limitation when seeds failed to emerge, driven independently by a dry spring and interspecific variation in seed mass (positive effect) and seed dormancy (negative effect). Seedling survival rates were higher than emergence, with weaker predictive roles for traits like seedling root mass allocation (positive effect) and seed mass (positive under spring drought), and lesser impacts of summer rainfall on soil moisture and survival. Interestingly, most trait relationships were not conditional on rainfall, suggesting water-independent mechanisms of their respective advantages. Although recruitment is a complex process, our findings suggest that trait-based assembly frameworks can be a useful way to anticipate outcomes, particularly if dynamic early-stage conditions (e.g., spring rainfall) and attributes (e.g., seed dormancy) receive greater attention. Given the importance of recruitment for community turnover in the context of global change and land management efforts, this is an area ripe for continued expansion in trait-based and applied ecology.


We collected seed emergence and recruitment data from a set of 17 native and exotic herbaceous species sown into a common garden experiment in Boulder, CO, USA. We manipulated rainfall as part of the experiment to understand how recruitment patterns differed under ambient conditions (control) and relatively dry or wet conditions in the spring or summer. Seeds were sown in late winter and montiored on an individual basis weekly through late summer. Our data reflect the germination/emergence success, survivorship, and successful recruitment for each planted seed, as well as the timing of these events. We used different types of mixed models to relate recruitment outcomes to rainfall treatment, and also explored whether species-level functional traits held predictive power (including seed mass, dormancy, and seedling root traits).

Please see the published article and supporting information for further details (Larson et al. Water the odds? Spring rainfall and emergence-related seed traits drive plant recruitment. Oikos)

Usage Notes

There are several CSV files containing data related to the implementation of the rainfall treatment, impacts of the rainfall treatment on soil moisture, the summary and temporal responses of individual seeds, and species-level traits. Please see the metadata file (metadata_Larson_Ebinger_Suding_2021.csv) for a description of each spreadsheet and variables within. Missing values are generally denoted with NA.

Code for analyses reported in the manuscript is available upon request from the authors.


University of Colorado Undergraduate Research Opportunities Program, Award: NA

University of Colorado Undergraduate Research Opportunities Program