Data from: Relating species richness to the structure of continuous landscapes: alternative methodological approaches

Numerous studies have focused on the relationship between landscape structure and plant diversity based on the patch-mosaic landscape paradigm, by deriving structural data from classified images. Since the use of discrete classes poses limitations for predicting biodiversity patterns in complex, low human-impacted ecosystems, two alternative methods have been used to analyze changes of landscape attributes in a continuum: moving-window metrics and surface metrics (image texture). Here we compare these two approaches for predicting richness of all plant species, legume species, legume trees, legume shrubs, legume forbs and legume climbers across a tropical landscape in Mexico, based on records of vascular plants in 250 10 × 10 m-plots. Multiple regression and variation partitioning methods were used to analyze the effects of the two landscape descriptors (moving-window and surface metrics), scale (400 and 200 m moving window sides) and space (based on the extraction of principal coordinates of neighbor matrices’ vectors) on species richness. The predictive power of all metrics was relatively small for total species richness, but generally higher for legume species. For legume forbs, surface metrics-based models indicated a direct association between species richness and landscape homogeneity. Moving-window metrics were highly sensitive to the biological group and to spatial scale, likely due to a leftover effect of image classification procedures. Conversely, surface metrics were more independent from scale and taxonomy. Attempts to predict species richness in highly diverse, low human-impacted tropical ecosystems more rapidly and accurately should better rely on surface metrics rather than on moving-window metrics, in line with the continuous landscape paradigm.

诸多研究依托斑块镶嵌景观范式，通过分类影像提取结构数据，围绕景观结构与植物多样性的关联展开了大量探索。鉴于离散类别在复杂低人类干扰生态系统的生物多样性格局预测中存在局限，学界已采用两种替代方法分析连续体中的景观属性变化：移动窗口度量（moving-window metrics）与表面度量（surface metrics，即图像纹理）。本研究基于墨西哥一处热带景观内250个10米×10米维管植物样地的调查记录，对比了这两种方法对各类物种丰富度的预测效果，涵盖全部植物物种、豆科植物、豆科乔木、豆科灌木、豆科草本以及豆科藤本。研究采用多元回归与变异划分方法，分析了两种景观描述因子（移动窗口度量与表面度量）、尺度（400米与200米移动窗口边长）以及空间因子（基于邻域矩阵向量主坐标提取）对物种丰富度的影响。所有度量方法对总物种丰富度的预测能力相对较弱，但对豆科物种的预测能力整体更强。针对豆科草本，基于表面度量的模型显示物种丰富度与景观同质性呈直接正相关。移动窗口度量对生物类群与空间尺度均具有较高敏感性，这可能源于影像分类流程遗留的效应。与之相反，表面度量则更少受尺度与分类学类群的影响。若要更快速精准地预测高多样性低人类干扰热带生态系统的物种丰富度，结合连续景观范式，应优先选用表面度量而非移动窗口度量。