山地草原蝴蝶物种丰富度与丰度的制图——生物多样性指标的空间应用

IF 4.6 2区 环境科学与生态学 Q1 BIODIVERSITY CONSERVATION
Friederike Barkmann, Erich Tasser, Ulrike Tappeiner, Peter Huemer, Benjamin Schattanek-Wiesmair, Kurt Lechner, Alois Ortner, Johannes Rüdisser
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引用次数: 0

摘要

目的将高质量的野外数据与高分辨率遥感数据相结合,可以深入了解生物多样性的空间分布,为生物多样性保护提供有价值的信息。基于遥感数据和野外调查,建立了高空间分辨率蝴蝶物种丰富度和丰度模型框架,以了解蝴蝶物种丰富度和丰度的空间分布,并分析其驱动因素和景观因子的影响尺度。地点:奥地利西部。方法将奥地利西部175个草地的结构化蝴蝶调查与遥感变量相结合,这些遥感变量描述了地形、草地特征以及一个地点周围不同半径的景观组成和配置。对于蝴蝶物种丰富度和丰度的空间预测,采用弹性网正则化的广义线性模型,并与逐步变量选择进行了比较。为了分析所选变量的影响及其效应尺度,应用了包含站点周围不同半径的景观变量和描述地形的变量的模型。结果物种丰富度预测值与实测值的Spearman秩相关系数为0.62。对于丰度,预测能力较低,相关性为0.52。较小半径(125和250 m)的模型通常比较大半径(500和1000 m)的模型具有更好的预测性能。在大多数模型中,海拔高度、最大草地生产力、北纬度和森林交错带密度都有影响。将遥感数据与空间建模技术相结合,大大提高了我们在高空间分辨率下理解蝴蝶物种丰富度格局和识别关键驱动因素的能力。森林边缘、小木本特征和适度草地生产力对蝴蝶物种丰富度和丰度有积极影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mapping Butterfly Species Richness and Abundance in Mountain Grasslands—Spatial Application of a Biodiversity Indicator

Mapping Butterfly Species Richness and Abundance in Mountain Grasslands—Spatial Application of a Biodiversity Indicator

Aim

The integration of high-quality field data with high-resolution remote sensing data can give detailed insights into the spatial distribution of biodiversity and provide valuable information for biodiversity conservation at a scale relevant for management action. We developed a framework based on remote sensing data and field surveys for modelling species richness and abundance of butterflies at high spatial resolution to inform about the spatial distribution of butterfly species richness and abundance and analyse their drivers and the scale of effect of landscape factors.

Location

Western Austria.

Methods

We combined structured butterfly surveys at 175 grassland sites in western Austria with remote sensing variables describing topography, grassland characteristics, and the landscape composition and configuration at different radii around a site. For spatial predictions of butterfly species richness and abundance, generalised linear models with elastic net regularisation were used and compared with stepwise variable selection. To analyse the influence of selected variables and their scale of effect, models with landscape variables in different radii around the sites and variables describing topography were applied.

Results

For species richness, the Spearman rank correlation between predicted and measured values was 0.62. For abundance, predictive power was lower with a correlation of 0.52. Models with variables from smaller radii (125 and 250 m) generally showed better predictive performance than those at larger radii (500 and 1000 m). We found an effect of elevation, maximum grassland productivity, northness, and forest ecotone density in most models.

Main Conclusions

Integrating remote sensing data with spatial modelling techniques substantially enhances our ability to understand patterns and identify key drivers of butterfly species richness at high spatial resolution. Our study highlights the positive influence of forest edges, small woody features, and moderate grassland productivity on butterfly species richness and abundance.

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来源期刊
Diversity and Distributions
Diversity and Distributions 环境科学-生态学
CiteScore
8.90
自引率
4.30%
发文量
195
审稿时长
8-16 weeks
期刊介绍: Diversity and Distributions is a journal of conservation biogeography. We publish papers that deal with the application of biogeographical principles, theories, and analyses (being those concerned with the distributional dynamics of taxa and assemblages) to problems concerning the conservation of biodiversity. We no longer consider papers the sole aim of which is to describe or analyze patterns of biodiversity or to elucidate processes that generate biodiversity.
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