Leveraging projection models to evaluate long‐term dynamics of scrub mint translocations

Abstract

Abstract Translocated populations often show vigorous initial dynamics but eventually collapse. Modeling tools that incorporate basic ecological knowledge and allow for propagation of uncertainty can help identify potential risks. Here, we use Bayesian Integral Projection Models to estimate population growth rates (λs), associated elasticities, and extinction risks for the endangered Dicerandra christmanii. Our study compared natural populations in gaps (open areas) within the shrub matrix and roadsides, unoccupied gaps augmented with transplants, and introduced populations. These populations experienced different management, including prescribed fires, and had different initial conditions. Augmented gaps showed lower means but similar variation in λs as natural gaps. Yet, simulations indicate that augmentations can delay quasi‐extinction (40% of simulations) by 4 years at the population level. Introduced populations showed higher means and variation in λs as wild gaps. While vital rate estimates suggested initial translocation success, time to quasi‐extinction was projected to be 7 years shorter for introductions in gaps than for natural gap populations. These contradictory results are partially explained by the lack of established seed banks in introduced populations, which affected the response of early life stage transitions to a prescribed fire. This study highlights the need to account for site‐specific information in models of population dynamics, including initial conditions and management history, and especially cryptic life stages such as dormant seeds.

Keywords

• augmentation
• Bayesian
• fire
• habitat management
• integral projection models
• introduction