Data from: Relating species richness to the structure of continuous landscapes: alternative methodological approaches
Cite this dataset
Gallardo-Cruz, J. Alberto et al. (2019). Data from: Relating species richness to the structure of continuous landscapes: alternative methodological approaches [Dataset]. Dryad. https://doi.org/10.5061/dryad.vc51n47
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.