  The dynamics of a rain forest is extremely complex involving births, deaths
and growth of trees with complex interactions between trees, animals, climate,
and environment. We consider the patterns of recruits (new trees) and dead
trees between rain forest censuses. For a current census we specify regression
models for the conditional intensity of recruits and the conditional
probabilities of death given the current trees and spatial covariates. We
estimate regression parameters using conditional composite likelihood functions
that only involve the conditional first order properties of the data. When
constructing assumption lean estimators of covariance matrices of parameter
estimates we only need mild assumptions of decaying conditional correlations in
space while assumptions regarding correlations over time are avoided by
exploiting conditional centering of composite likelihood score functions. Time
series of point patterns from rain forest censuses are quite short while each
point pattern covers a fairly big spatial region. To obtain asymptotic results
we therefore use a central limit theorem for the fixed timespan - increasing
spatial domain asymptotic setting. This also allows us to handle the challenge
of using stochastic covariates constructed from past point patterns.
Conveniently, it suffices to impose weak dependence assumptions on the
innovations of the space-time process. We investigate the proposed methodology
by simulation studies and applications to rain forest data.
