Varying congruence among spatial patterns of vascular plants and vertebrates based on habitat groups

Abstract

Proxies are adopted to represent biodiversity patterns due to inadequate information for all taxa. Despite the wide use of proxies, their efficacy remains unclear. Previous analyses focused on overall species richness for fewer groups, affecting the generality and depth of inference. Biological taxa often exhibit very different habitat preferences. Habitat groupings may be an appropriate approach to advancing the study of richness patterns. Diverse geographical patterns of species richness and their potential mechanisms were then examined for habitat groups. We used a database of the spatial distribution of 32,824 species of mammals, birds, reptiles, amphibians and plants from 2,376 counties across China, divided the five taxa into 30 habitat groups, calculated Spearman correlations of species richness among taxa and habitat groups, and tested five hypotheses about richness patterns using multivariate models. We identified one major group [i.e., forest‐ and shrub‐dependent (FS) groups], and some minor groups such as grassland‐dependent vertebrates and desert‐dependent vertebrates. There were mostly high or moderate correlations among FS groups, but mostly low or moderate correlations among other habitat groups. The prominent variables differed among habitat groups of the same taxon, such as birds and reptiles. The sets of predictors were also different within the same habitat, such as forests, grasslands, and deserts. Average correlations among the same habitat groups of vertebrates and among habitat groups of a single taxon were low or moderate, except correlations among FS groups. The sets of prominent variables of species richness differed strongly among habitat groups, although elevation range was the most important variable for most FS groups. The ecological and evolutionary processes that underpin richness patterns might be disparate among different habitat groups. Appropriate groupings based on habitats could reveal important patterns of richness gradients and valuable biodiversity components.

Effects of the Climatic Region on Richness Correlations between Vascular Plants and Vertebrates in Nature Reserves of China

“…The RC is considered strong when the between-taxon correlation coefficient exceeds 0.5 [50]. Our results were consistent with previous studies and supported significant positive RCs between vascular plants and vertebrates in most cases [23,24,51,52], indicating that vascular plants have application value to be indicator taxa for vertebrates. Note that the effectiveness of vascular plants as indicator taxa for vertebrates varied with climatic regions and taxonomic groups (Figure 2).…”

“…However, vascular plants were considered indicator taxa because they were easy to survey, widely distributed, and stable in taxonomy [15,54]. For example, at China's regional and local scales, there are significant strong RCs between vascular plants and a few taxonomic groups (vertebrates, insects), and vascular plants have been considered potential indicator taxa [23,24,39,51]. Brunbjerg et al [15] also confirmed that vascular plants are suitable indicator taxa for many taxonomic groups in Denmark (e.g., bryophytes, lichens, macro-fungi).…”

“…That variation might have been caused by differences in research conditions such as research Diversity 2022, 14, 499 2 of 13 scale, habitat type, and climatic regions [18,19]. Although some empirical studies have confirmed the effect of scale and habitat type on RCs among taxonomic groups [20][21][22][23][24], only a few studies have compared RCs between tropical and temperate regions. Further, they did not focus on the drivers [18,19].…”

Homomorphic Integral and Dual Rectified Convolutional Grading for Salinity Prediction During Seedling in Rice

“…This is because of the reason with the application of preprocessing unwanted or irrelevant areas can be eliminated and obtaining significant region of interest. Table 1 list the performance analysis results of accuracy, precision and recall using the proposed HFI-DRLC method and existing methods, optimization strategy for salinity stress at seedling in rice [1], GWAS and linkage mapping [2] respectively (both without and with preprocessing). All the three performance metrics are measured in terms of percentage (%).…”

“…In this section, the performance analysis in terms of MAE and prediction time using the proposed HFI-DRLC method and existing methods, optimization strategy for salinity stress at seedling in rice [1] and GWAS and linkage mapping [2] are detailed. Table 2 list the performance analysis results of prediction time and MAE using the proposed HFI-DRLC method and existing methods, optimization strategy for salinity stress at seedling in rice [1], GWAS and linkage mapping [2] respectively. The prediction time performance metric is measured in terms of milliseconds (ms) whereas the MAE is evaluated in terms of percentage (%).…”