Spatial data replacing temporal data in population viability analyses: An empirical investigation for plants

Abstract

In conservation management, there is an urgent need for estimates of population viability and for knowledge of the contributions of different life-history stages to population growth rates. Collection of long-term demographic data from a study population is time-consuming and may considerably delay the start of proper management actions. We examined the possibility of replacing a long-term temporal data set (demographic data from several years within a population) with a short-term spatial data set (demographic data from different populations for the same subset of two continuous years) for stochastic estimates of population viability. Using matrix population models for ten perennial plant species, we found that the matrix elements of spatial data sets often deviated from those of temporal data sets and that matrix elements generally varied more spatially than temporally. The appropriateness of replacing temporal data with spatial data depended on the subset of years and populations used to estimate stochastic population growth rates (log λs). Still, the precision of log λs estimates measured as variation in the yearly change of logarithmic population size rarely differed significantly between the spatial and temporal data sets. Since a spatiotemporal comparison of matrix elements and their variation cannot be used to assess whether spatial and temporal data sets are interchangeable, we recommend further research on the topic.