ABSTRACT

Forest fragmentation has a relevant impact on biodiversity. An interesting alternative to estimate these indices is to use sampling data. This study aims to estimate aggregation index (AI) and the degree of clumping of forested landscape based on AI. The assessment was conducted using different point distances, inventory regions and cardinal directions. For this purpose, a dataset from one five-year periods (2007–2011) of the Swedish National Forest Inventory (NFI) was used. The estimation of AI from field-based inventory can give us a general picture of the current status of forest landscape. The results also show that the estimated AI is a distance dependent function. The corresponding estimated variance of the index is smaller for longer distances. The obtained results indicate that the estimated variance depends on both sample size and pair point distances. Estimated AI showed different values in different cardinal directions. To compare two regions or a given region over time, a given point distance should be used. The main advantage of the applied procedure is that a range of AI values can be produced rather than a single number. Furthermore, in field-based inventory, the obtained results are more reliable, because one works implicitly with a single forest definition only.

摘要

森林破碎化对生物多样性有相关影响。估计这些指数的一个有趣的替代方法是使用抽样数据。本研究旨在基于AI估计聚集指数（AI）和森林景观的丛集程度。评估是使用不同的点距离、库存区域和基本方向进行的。为此，使用了瑞典国家森林清单 (NFI) 一个五年期 (2007-2011) 的数据集。从基于实地的清单中估计 AI 可以让我们大致了解森林景观的当前状况。结果还表明，估计的 AI 是一个距离相关的函数。距离越远，对应的指数估计方差越小。获得的结果表明估计方差取决于样本大小和对点距离。估计的 AI 在不同的主要方向上显示出不同的值。要随时间比较两个区域或给定区域，应使用给定的点距离。应用程序的主要优点是可以生成一系列 AI 值而不是单个数字。此外，在基于实地的清单中，获得的结果更可靠，因为一个人只隐含地使用单个森林定义。