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

Abstract

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 the records of vascular plants in 250 10 × 10 m plots. Multiple regression and variance 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.

La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia

“…La riqueza de especies es un atributo común en los estudios de modelado espacial con sensores remotos (Gallardo-Cruz et al, 2018;Hakkenberg, Gillet, & Legendre, 2018b), y es considerada una variable relevante para distinguir parches de vegetación sucesionalmente distintos (Granados-Victorino et al, 2017). De hecho, en el sito de estudio, ya se ha estudiado la riqueza en relación con la biomasa aérea (Hernández-Stefanoni et al, 2014).…”

Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

“…Another limitation is related to the assumption that the image is composed of patches of pure pixels with sharp boundaries, whereas images acquired over natural landscapes are usually characterized by mixed pixels and continuous gradients or fuzzy boundaries. Therefore, patch-mosaic metrics may fail to capture landscape heterogeneity, which is crucial information for habitat condition assessment [34]. Those limitations make the characterization of pattern changes difficult [35] and may lead to misinterpretation [36].…”

“…Continuous approaches do not require preliminary image classification, avoiding problems caused by subjectivity and misclassification as aforementioned [34]. Indeed, these continuous approaches are based on textural analysis depicting gradients and spatial patterns from data sources considered as continuous, such as remote sensing images [37].…”

“…Indeed, these continuous approaches are based on textural analysis depicting gradients and spatial patterns from data sources considered as continuous, such as remote sensing images [37]. Different methods focusing on image texture analysis exist and the main challenge will be to derive accurate indices depicting heterogeneity of vegetation structure from remotely sensed spectral and spatial information [34]. For instance, heterogeneity in vegetation structure can be characterized by analyzing spatial autocorrelation using variograms [38][39][40].…”

“…For instance, heterogeneity in vegetation structure can be characterized by analyzing spatial autocorrelation using variograms [38][39][40]. Textural indices such as Haralick indices [34,41], Gabor filters [42] and indices derived from wavelet transform [43] have been used to characterize the structure of semi open landscapes. However, their application requires expert parametrization and their ecological interpretation remains challenging.…”