Hofmann Sylvia, Everaars Jeroen, Schweiger Oliver, Frenzel Mark, Bannehr Lutz, Cord Anna F
Department of Conservation Biology, Helmholtz Centre for Environmental Research, Leipzig, Germany.
German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Leipzig, Germany.
PLoS One. 2017 Oct 3;12(10):e0185591. doi: 10.1371/journal.pone.0185591. eCollection 2017.
Assessing species richness and diversity on the basis of standardised field sampling effort represents a cost- and time-consuming method. Satellite remote sensing (RS) can help overcome these limitations because it facilitates the collection of larger amounts of spatial data using cost-effective techniques. RS information is hence increasingly analysed to model biodiversity across space and time. Here, we focus on image texture measures as a proxy for spatial habitat heterogeneity, which has been recognized as an important determinant of species distributions and diversity. Using bee monitoring data of four years (2010-2013) from six 4 × 4 km field sites across Central Germany and a multimodel inference approach we test the ability of texture features derived from Landsat-TM imagery to model local pollinator biodiversity. Textures were shown to reflect patterns of bee diversity and species richness to some extent, with the first-order entropy texture and terrain roughness being the most relevant indicators. However, the texture measurements accounted for only 3-5% of up to 60% of the variability that was explained by our final models, although the results are largely consistent across different species groups (bumble bees, solitary bees). While our findings provide indications in support of the applicability of satellite imagery textures for modeling patterns of bee biodiversity, they are inconsistent with the high predictive power of texture metrics reported in previous studies for avian biodiversity. We assume that our texture data captured mainly heterogeneity resulting from landscape configuration, which might be functionally less important for wild bees than compositional diversity of plant communities. Our study also highlights the substantial variability among taxa in the applicability of texture metrics for modelling biodiversity.
基于标准化的野外采样工作来评估物种丰富度和多样性是一种既耗费成本又耗时的方法。卫星遥感（RS）有助于克服这些限制，因为它能够利用经济高效的技术收集大量空间数据。因此，人们越来越多地分析RS信息，以便对不同时空的生物多样性进行建模。在此，我们聚焦于图像纹理测量，以此作为空间栖息地异质性的替代指标，而空间栖息地异质性已被公认为是物种分布和多样性的一个重要决定因素。我们利用德国中部六个4×4千米野外站点的四年（2010 - 2013年）蜜蜂监测数据以及多模型推断方法，来测试从陆地卫星专题制图仪（Landsat - TM）图像中提取的纹理特征对当地传粉者生物多样性进行建模的能力。结果表明，纹理在一定程度上反映了蜜蜂多样性和物种丰富度的模式，其中一阶熵纹理和地形粗糙度是最相关的指标。然而，尽管不同物种组（熊蜂、独居蜂）的结果在很大程度上是一致的，但纹理测量仅占我们最终模型所解释的高达60%的变异性中的3 - 5%。虽然我们的研究结果表明卫星图像纹理适用于对蜜蜂生物多样性模式进行建模，但这与先前研究中报道的纹理指标对鸟类生物多样性具有高预测能力的情况不一致。我们认为，我们的纹理数据主要捕捉到了由景观格局导致的异质性，而这种异质性对于野生蜜蜂来说，在功能上可能不如植物群落的组成多样性重要。我们的研究还强调了在使用纹理指标对生物多样性进行建模时，不同分类群之间存在很大的变异性。
