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.

翻译：雨林生态系统的动态极为复杂，涉及树木的出生、死亡与生长，以及树木、动物、气候和环境之间的复杂交互作用。本研究关注两次雨林普查之间新生树木与死亡树木的空间分布模式。基于当前树木分布与空间协变量，我们为新生树木的条件强度与树木死亡的条件概率分别建立了回归模型。利用仅涉及数据条件一阶性质的复合条件似然函数，对回归参数进行估计。在构建参数估计协方差矩阵的弱假设估计量时，我们仅需空间条件相关性衰减的温和假设，同时通过复合似然得分函数的条件中心化方法避免对时间相关性的假设。雨林普查得到的点模式时间序列长度有限，但每个点模式覆盖相当大的空间区域。为获取渐近结果，我们采用固定时间跨度、递增空间域的极限定理。该方法还能有效处理基于历史点模式构建的随机协变量带来的挑战。特别地，只需对时空过程的创新项施加弱相依性假设即可满足要求。通过模拟研究与雨林数据应用对所提出的方法进行了验证。