Accounting for variability when resurrecting dormant propagules substantiates their use in eco-evolutionary studies

There has been a steady rise in the use of dormant propagules to study biotic responses to environmental change over time. This is particularly important for organisms that strongly mediate ecosystem processes, as changes in their traits over time can provide a unique snapshot into the structure and function of ecosystems from decades to millennia in the past. Understanding sources of bias and variation is a challenge in the field of resurrection ecology, including those that arise because often-used measurements like seed germination success are imperfect indicators of propagule viability. Using a Bayesian statistical framework, we evaluated sources of variability and tested for zero-inflation and overdispersion in data from 13 germination trials of soil-stored seeds of Schoenoplectus americanus, an ecosystem engineer in coastal salt marshes in the Chesapeake Bay. We hypothesized that these two model structures align with an ecological understanding of dormancy and revival: zero-inflation could arise due to failed germinations resulting from inviability or failed attempts to break dormancy, and overdispersion could arise by failing to measure important seed traits. A model that accounted for overdispersion, but not zero-inflation, was the best fit to our data. Tetrazolium viability tests corroborated this result: most seeds that failed to germinate did so because they were inviable, not because experimental methods failed to break their dormancy. Seed viability declined exponentially with seed age and was mediated by seed provenance and experimental conditions. Our results provide a framework for accounting for and explaining variability when estimating propagule viability from soil-stored natural archives which is a key aspect of using dormant propagules in eco-evolutionary studies

Figure 3

Code to produce figure 3

Predicted means from ordinal regression (JH)

Predicted means from ordinal regression for juvenile density by habitat quality treatment combinations

Predicted means from LMM (standard deviation)

Predicted means and CIs for linear mixed model of standard deviation of dispersal kernels of experimental and standardized beetles

Figure S4

Code to produce figure S4

Dispersal kernel parameters predicted means (standard deviation and maximum)

Calculates standard deviation and maximum for random samples of 6 individuals per status in each array. Fits linear mixed model to these data and extracts predicted means for some treatment combinations

Figure S2

Plots correlation between mean distance dispersed of experimental and standardized beetles within an array for each treatment combination (Fig S2)

Figure S3

Code for producing figure S3

Figure S1

Code to calculate estimated carrying capacities using data from Stewart et al. 2017

Data from: The importance of growing up: juvenile environment influences dispersal of individuals and their neighbours

Dispersal is a key ecological process that is strongly influenced by both phenotype and environment. Here, we show that juvenile environment influences dispersal not only by shaping individual phenotypes, but also by changing the phenotypes of neighbouring conspecifics, which influence how individuals disperse. We used a model system (Tribolium castaneum, red flour beetles) to test how the past environment of dispersing individuals and their neighbours influences how they disperse in their current environment. We found that individuals dispersed especially far when exposed to a poor environment as adults if their phenotype, or even one‐third of their neighbours’ phenotypes, were shaped by a poor environment as juveniles. Juvenile environment therefore shapes dispersal both directly, by influencing phenotype, as well as indirectly, by influencing the external social environment. Thus, the juvenile environment of even a minority of individuals in a group can influence the dispersal of the entire group