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

空间生态学研究:时空动力学的数学研究

基本信息

批准号：
14540580
负责人：
IWASA Yoh
金额：
$ 2.62万
依托单位：
KYUSHU UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14540580/
关键词：
BCI 50ha plot land use change MCBC lattice model spatial ecology gap dynamics deforestation spatial Markov chain 土地利用空間マルコフモデル種の共存森林動態空間時系列解析モンテカルロバイアス除去法

项目摘要

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.

最近，空间生态数据已从长期的生态研究地点或远程饲养技术中积累。作为一系列地图给出的土地使用和土地覆盖的空间模式的变化。在该研究项目中，我开发了一种新的数学方法来估计基于空间马尔可夫模型的生态系统过程和人类对时空数据的影响。通过这种方式，我们可能会获得生态系统变化的基本理论框架。[1]自1980年代初以来，已经进行了新热带森林的大区域人口普查，从地图数据估算生态过程。与史蒂夫·哈伯尔（Steve Hubbell）及其同事合作，我们在2年内分析了Times系列的植被高度空间数据。将50公顷的地块分为大约2000个地点，每个地点具有5x5m。根据最大植被高度，我将地点分类为间隙和非间隙。然后开发了马尔可夫过渡模型，描述了空间模式的动力学。我们观察到从树冠位点到间隙位点的过渡速率，而回收率（相反的过渡）在很大程度上取决于邻居。结果，空间模式将与彼此汇总的间隙位点变为非随机。我们还认识到，估计过渡速率参数的简单方法包括较大的偏见。这是因为站点的过渡会改变其邻居的过渡率。为了消除偏见，我们使用了一种名为NCBC的方法，该方法是由Hakoyama博士和我本人开发的。[2]基于土地利用变化的建模。站点分类为森林，农业土地，道路等，然后对这些离散状态之间的过渡过程进行建模。从空间数据中，我们可以知道邻居修改过渡速率的效果。我们开发了马尔可夫空间模型，该模型可以预测基于土地使用变化的森林丧失的未来变化。