Estimating local interaction from spatiotemporal forest data, and Monte Carlo bias correction.

Area of Science:

• Ecology
• Spatial modeling
• Forestry

Background:

• Continuous time spatially explicit models are crucial for understanding ecological processes.
• Fitting these models to discrete temporal data presents significant challenges.
• Forest gap dynamics, influenced by local interactions, are a key area of study.

Purpose of the Study:

• To identify and address a general problem in fitting continuous time spatially explicit models to discrete temporal data.
• To evaluate the performance of approximate maximum likelihood estimation versus a computer-intensive method for parameter estimation in forest gap models.
• To assess the impact of parameter estimation bias on predictions of spatial aggregation in forest gaps.

Main Methods:

• Development and application of a continuous time Markov model for forest gap dynamics on a square lattice.
• Fitting the model to spatiotemporal canopy height data from Barro Colorado Island (BCI) using approximate maximum likelihood estimation.
• Implementation and evaluation of Monte Carlo bias correction (MCBC) to improve parameter estimates.

Main Results:

• Approximate maximum likelihood estimation resulted in significant bias, underestimating the strength of interaction between nearby sites.
• The assumption of independent transitions between observation times led to this bias.
• Monte Carlo bias correction (MCBC) effectively removed bias in parameter estimates.
• MCBC estimates improved the model's ability to predict local gap density, indicating stronger site interactions.

Conclusions:

• Standard fitting methods for continuous time spatially explicit models can be biased when applied to discrete data.
• Monte Carlo bias correction (MCBC) offers a robust solution for accurate parameter estimation in such models.
• Accurate parameterization is essential for reliable predictions of spatial processes like forest gap dynamics.