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，空间生态数据是从长期生态研究地点或遥感技术中积累的。以一系列地图形式给出的土地利用和土地覆盖空间格局的变化。在这个研究项目中，我开发了一种新的数学方法，基于空间马尔可夫模型来估计时空数据的生态系统过程和人类影响。这样我们就可以得到一个生态系统变化的基本理论框架。[1]从地图数据估计生态过程。BCI 50公顷样地，自1980年代初期以来一直对新热带森林进行大面积普查。我们与 Steve Hubbell 及其同事合作，分析了两年多的植被高度空间数据的时间序列。 50 公顷的地块被分成约 2000 个地点，每个地点的面积为 5x5 米。我根据最大植被高度将场地分为间隙和无间隙。然后开发了描述空间模式动态的马尔可夫转移模型。我们观察到，从冠层站点到间隙站点的转换率和恢复率（相反的转换）在很大程度上取决于邻居。结果，空间模式将变得非随机，间隙位点彼此聚集。我们还认识到，估计转移率参数的简单方法包含很大的偏差。这是因为一个站点的转变会改变其邻近站点的转变速率。为了消除偏差，我们使用了一种名为 NCBC 的方法，该方法是由 Hakoyama 博士和我自己开发的。[2]基于土地利用变化的建模。将场地分为森林、农田、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，我们可以知道邻居对改变转移率的影响。我们开发了一个马尔可夫空间模型，可以根据土地利用变化预测森林消失的未来变化。