Endemic plant conservation is important for maintaining regional diversity and in reducing homogenization. Research on rare endemics often focuses on genetic variability and pollen limitation. Less well studied is how other life history traits impact reproduction. We examined the effects of plant size on demographics of a threatened species, smooth coneflower (Echinacea laevigata), which is a narrow endemic in forest glade habitats in the southeastern USA. We tagged and recorded the size and reproductive status of plants in five plots (e.g., subpopulations) in Montgomery County, Virginia, USA, from 2014 to 2017. We used integral projection modeling to assess key aspects of the life cycle, estimate how changes in size and reproductive status impacted population growth, survival, and future reproduction. We found that very few plants reproduced each year (range: 1.2–18.2%). Plant size was a strong predictor of survival, reproduction, and growth probabilities the next year. We identified a population level cost of reproduction that reduced lambda by 2%. Reproductive plants experienced additional costs including a 5.7% decline in survival, 16% decline in growth, and a 10% lower chance of flowering in the next year compared to plants that were not reproductive. Overall, lambdas were above 1 indicating the population was growing in all years. This work demonstrated plant size had strong impacts on reproductive dynamics and reproduction can incur a cost on future population dynamics. Therefore, management of this species should focus on preserving environmental conditions that promote plant growth such as preventing woody encroachment.
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8 July 2024
Plant Size and Cost of Flowering Interact to Govern Population Growth Rate in a Rare Endemic Plant Species, Smooth Coneflower
Rachel J. Collins,
M. Henry H. Stevens,
Sam Truslow,
Ryan Klopf,
Ryan D. Huish
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Natural Areas Journal
Vol. 44 • No. 3
July 2024
Vol. 44 • No. 3
July 2024
Appalachian glade habitat
Echinacea laevigata
endemic species conservation
integral projection modeling
size-structured population modeling