Variability in demographic rates on multiple scales: Heliconia acuminata in fragments of the Amazon

Background/Question/Methods

Demographers recognize that a population’s probability of extinction is affected by three main categories of stochasticity: environmental stochasticity, demographic stochasticity, and, more recently, demographic heterogeneity (i.e. among-individual variation in vital rates). Demographic heterogeneity increases the risk of extinction in density dependent populations more than environmental stochasticity does, but if it’s left out of the statistical model, variation due to demographic heterogeneity is erroneously lumped in with environmental variation.

Here, we quantify environmental stochasticity and demographic heterogeneity to improve predictions of population viability in a long term observational study of marked individuals of Heliconia acuminata in the Amazon. The number of shoots and number of inflorescences per plant were monitored yearly for 12 years in 10 plots, 4 of which were forest fragments.

Fragmentation alters forest hydrology and wind patterns in ways that we hypothesize will create more spatial and temporal environmental heterogeneity. To test this hypothesis, we used mixed models to quantify variability in vital rates on multiple scales and habitats: yearly, individual, and landscape scales, in fragmented and continuous forest. We quantified uncertainty in the variability using MCMC sampling. We also did simulations to estimate our ability to correctly quantify variability in vital rates on different scales.

Results/Conclusions

In models of shoots number, years were the most variable unit, with more among year variability in continuous forest than fragments (92% MCMC support). This could be due to El Nino events. Plots were the second most variable unit, with more among plot variability in fragments than in continuous forest (93% MCMC support). Estimates of individual variability were near zero; this matched bias observed in our simulations. Variation on the individual scale may be due to processes such as canopy openings that are too temporary to detect in this data.

In models of inflorescence number, plots were the most variable unit and variability was nearly equal in the two habitat types. The amount of variability among years and among individuals were nearly equal, with more among year variability in continuous forest (100% MCMC support) and more among individual variability in the fragments (100% MCMC support). There was no edge effect or clustering in the spatial distribution of residuals from the reproduction model.

In both models, continuous forest had more temporal variability. Plants in continuous forest may be more able to take advantage of good years.

We will also discuss how observed variation scales up to the population level.