Long‐term datasets are particularly important in the ecological sciences because they allow for the integration of data collected across variable biotic and abiotic conditions. The overall objective was to investigate factors affecting population persistence of long‐lived plant species using a long‐term monitoring dataset. Specifically, the project team investigated the effects of fire frequency, canopy cover, metapopulation structure, and functional traits on the persistence of 1,396 populations of 41 rare plant species at Fort Bragg over 23 years.

Technical Approach

The project team used the Fort Bragg Monitoring and Assessment of Rare Plant Species (hereafter MARPS) database, which contains over 32,000 population‐years of data collected over four surveys from 1991‐93, 1998‐99, 2005‐06, and 2012‐14. The project team modeled population persistence using a Cormack‐Jolly‐Seber (CJS) mark‐recapture model in a Bayesian framework which took into account imperfect detection. They modeled the fire history of each population by classifying atmospherically corrected Landsat images for the time period 1991‐2014 with the mid‐infrared burn index (MIRBI) and a Random Forest (RF) algorithm. They modeled canopy cover for the years 1991‐2014 in a manner similar to the approach to modeling the fire history, using Landsat imagery, LiDAR data collected in 2012, and a RF algorithm. They quantified metapopulation structure for each species with ≥10 populations using the connectivity parameter (S) from Hanksi’s incidence function model. For a subset of species, the project team acquired data for seven functional traits that have been associated with plant performance in the study system. The project team analyzed the relationship between population persistence and fire frequency, canopy cover, metapopulation structure, area, and functional traits using Generalized Linear Mixed Models (GLMMs) with a binomial error structure and a logit link function that was embedded in a CJS model within a Bayesian framework. Finally, the project team explored the additional insights gained via the long‐term dataset by comparing results from 1991‐2014 to data collected from 1991‐1999.


The results confirm that long‐term persistence of rare plant populations in the longleaf pine ecosystem is influenced by fire frequency, canopy cover, population area, population connectivity, and hydrologic position. Across all study species, populations have higher probabilities of persistence if they experience more frequent fires, occur under lower canopy cover, are more highly connected, occupy a larger area, or occur in upland habitats. While no single functional trait was found to be correlated with population persistence, there were interactions between fire frequency and specific leaf area, and canopy cover and onset of flowering.


Fire frequency, canopy cover, and metapopulation structure, three factors that influence population persistence on Fort Bragg, represent a continuum of management options in terms of resource investment. Burning management units according to a schedule is likely the least costly in terms of resource investment and would likely yield the greatest overall benefits. If management units are not burned on a frequent (i.e. three‐year) interval and woody vegetation is allowed to escape the fire trap and enter the midstory and canopy, mechanical thinning is the second option. Enhancing metapopulation structure by targeted population reintroduction and augmentation represents the third and most costly management option. While overall trends in the data were strong, the results also identified substantial diversity in species response to fire and canopy cover, necessitating species specific management where adequate knowledge is available and additional detailed studies where knowledge is still insufficient to inform management. This is especially relevant for federally listed species. Additionally, the project team has confirmed the value and benefit of long‐term data for elucidating important drivers of rare plant population persistence in our study system.