Research in Spatial Ecology : Mathematical Studies of Spatio-Temporal Dynamics

近年来，从长期的生态研究点或遥感技术中积累了空间生态数据。土地利用和土地覆盖的空间格局的变化，以一系列地图的形式给出。在这个研究项目中，我发展了一种新的数学方法，基于空间马尔科夫模型，从时空数据中估计生态系统过程及其对人类的影响。[1]从地图数据估算生态过程。自20世纪80年代初S以来，我们对一片新热带森林进行了50公顷样地的大面积普查。我们与史蒂夫·哈贝尔等人合作，分析了两年多的植被高度空间数据的时间序列。这块50公顷的地块被分成大约2000个地块，每个地块有5x5米。我根据最大植被高度将立地分为林隙和非林隙。然后提出了描述空间格局动态变化的马尔可夫过渡模型。我们观察到从树冠位置到林隙位置的转移率和恢复率(相反的转移率)强烈依赖于邻居。因此，随着林隙位置相互聚集，空间格局将变得非随机。我们也认识到，简单地估计跃迁速率参数的方法存在较大的偏差。这是因为站点的过渡会改变其邻居的过渡速率。为了消除这种偏差，我们使用了一种名为NCBC的方法，该方法是由博山博士和我自己开发的。[2]基于土地利用变化的建模。将站点分为森林、农业用地、道路等，然后对这些离散状态之间的转换过程进行建模。从空间数据中，我们可以知道邻居对过渡率的影响。我们开发了一个马尔科夫空间模型，该模型可以根据土地利用的变化来预测未来森林损失的变化。

Recently, spatial ecological data have been accumulated from long-term ecological research sites or from remote censing techniques. The change in the spatial pattern of land use and land cover given as a series of maps. In this research project, I develop a new mathematical method to estimate the ecosystem process and the human impact from spatio-temporal data, based on spatial Markov model. By this way we might obtain a basic theoretical framework of ecosystem change.[1] Estimating ecological process from map data.BCI 50ha plot, large-area census of a neotropical forest has been carried out since early 1980's. In collaboration with Steve Hubbell and his colleagues, we analyzed times series of vegetation height spatial data over 2 years. The 50ha plot was separated into about 2000 sites each having 5x5m. I classified sites to gaps and non-gaps according to the maximum vegetation height. Then developed Markovian transition model describing the dynamics of spatial patterns. We observed that the transition rate from a canopy site to a gap site and the recovery rate (the opposite transition) depend strongly on the neighbors. As a consequence, the spatial pattern would become non-random with gap sites aggregated with each other.We also recognized that the simple way of estimating transition rate parameter includes a large bias. This is because the transition at a site would change the rate of transition in its neighbors. To remove the bias we used a method named NCBC, which was developed by Dr Hakoyama and myself.[2] Modeling based on land use change.Sites are classified to forest, agricultural land, road etc., and then model the process of transition between these discrete states. From spatial data, we can know the effect of neighbors to modify the transition rate. We developed a Markovian spatial model that can predict future change in the loss of forests based on land use change.