Bayesian estimates of transition probabilities in seven small lithophytic orchid populations: maximizing data availability from many small samples

PLoS One. 2014 Jul 28;9(7):e102859. doi: 10.1371/journal.pone.0102859. eCollection 2014.

Abstract

Predicting population dynamics for rare species is of paramount importance in order to evaluate the likelihood of extinction and planning conservation strategies. However, evaluating and predicting population viability can be hindered from a lack of data. Rare species frequently have small populations, so estimates of vital rates are often very uncertain due to lack of data. We evaluated the vital rates of seven small populations from two watersheds with varying light environment of a common epiphytic orchid using Bayesian methods of parameter estimation. From the Lefkovitch matrices we predicted the deterministic population growth rates, elasticities, stable stage distributions and the credible intervals of the statistics. Populations were surveyed on a monthly basis between 18-34 months. In some of the populations few or no transitions in some of the vital rates were observed throughout the sampling period, however, we were able to predict the most likely vital rates using a Bayesian model that incorporated the transitions rates from the other populations. Asymptotic population growth rate varied among the seven orchid populations. There was little difference in population growth rate among watersheds even though it was expected because of physical differences as a result of differing canopy cover and watershed width. Elasticity analyses of Lepanthes rupestris suggest that growth rate is more sensitive to survival followed by growth, shrinking and the reproductive rates. The Bayesian approach helped to estimate transition probabilities that were uncommon or variable in some populations. Moreover, it increased the precision of the parameter estimates as compared to traditional approaches.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Bayes Theorem*
  • Orchidaceae*
  • Population Dynamics
  • Puerto Rico

Grants and funding

This research was partly funded by a FoPI grant from the University of Puerto Rico – Humacao campus, and by the National Science Foundation, HRD #026200, the Australian Research Centre for Urban Ecology, the Australian Research Council (DP0346165), and the Applied Environmental Decision Analysis hub. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.