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The role of transient dynamics in stochastic population growth for nine perennial plants
Author(s) -
Ellis Martha M.,
Crone Elizabeth E.
Publication year - 2013
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/13-0028.1
Subject(s) - transient (computer programming) , perennial plant , vital rates , population , variation (astronomy) , population growth , ecology , dynamics (music) , biology , environmental science , demography , computer science , physics , sociology , astrophysics , acoustics , operating system
Most populations exist in variable environments. Two sets of theory have been developed to address this variability. Stochastic dynamics focus on variation in population growth rates based on random differences in vital rates such as growth, survival, and reproduction. Transient dynamics focus on short‐term, deterministic responses to changes in the stage distribution of individuals. These processes are related: demographic variation shifts stage structures, producing transient responses, which then contribute to the overall variability of population growth rate. The relative contributions of vital rates vs. transient responses to stochastic dynamics, and the implications for transient analyses, are unclear. This study explores the role of transient responses in stochastic dynamics of nine perennial plant species. Across the species, transient responses contributed more on average to variability in annual population growth rates than did variation in vital rates alone. Transient potential of an average matrix was indicative of the contribution of transient dynamics, although these metrics varied greatly across years. Transient responses were often in the opposite direction as demographic variation, suggesting that transient dynamics may at times have a buffering effect on populations. Overall, transient dynamics had an important role in modulating environmental variation, with implications for both processes in understanding stochastic dynamics.