Multiscale spatial patterns of species diversity and biomass together with their correlations along geographical gradients in subalpine meadows

Abstract

Researchers frequently discuss spatial distribution patterns of species diversity and biomass together with their correlations along geographical gradients. Typical subalpine meadows occur widely on the east of the Loess Plateau, China; here, we selected nine mountains belonging to four mountain systems from north to south on the east of the plateau. We analyzed five latitudinal and longitudinal gradients together with six elevational gradients to study the spatial distribution patterns of species diversity (including α , β , and γ diversity) and biomass plus with their relationships at various scales. Results showed that (1) for diversity, α -Diversity manifested unimodal variation patterns in horizontal spaces, peaking at high latitude and low longitude. However, α -diversity was not sensitive to elevation in vertical spaces and tended to decrease with increasing elevation. With increased latitude, longitude, and elevation, β -Diversity diminished; meanwhile, the rate of species turnover decreased and the similarity of community composition enlarged. γ -Diversity demonstrated quadratic function changes that were initially incremental and then decreased with increasing longitude, elevation, and latitude from 37.5° to 40°. In general, β- diversity had positive correlation with γ -diversity and negative correlation with α- diversity, which conformed to the function of β = γ / α . (2) For biomass, changes of aboveground biomass (AB) were more obvious along latitudinal gradients, whereas variations of belowground biomass (BB) had smaller differences along longitudinal and latitudinal gradients. More biomass was allocated to BB toward the north and east, whereas root-to-shoot ratio (R/S) was more evident at greater latitude than greater longitude. With increased elevation, more biomass was also allocated to BB, and the relationship of biomass to elevation was closer in AB. In short, the relation of biomass allocation tended to belowground plant parts with different geographical scales. (3) Species diversity had the strongest positive influence on AB. The Patrick and Shannon indices had correlations of power functions with AB and R/S, respectively, indicating that an allometric model could be used to model relationships between species diversity and biomass. In conclusion, the unique geomorphological structures with a series of basins between mountain systems on the east of the Loess Plateau, meant that subalpine meadows were mostly distributed along latitudinal directions, so the spatial distribution of species diversity and biomass was more evident along latitudinal gradients, and thus the response of aboveground biomass was more sensitive to variations of spatial gradients and species diversity.

The mid-domain effect of mountainous plants is determined by community life form and family flora on the Loess Plateau of China

“…With the purpose of further verifying the MDE hypothesis, α-diversity indices focused on the local and within-community vegetation characteristics were employed in this study to analyze patterns of species diversity along an altitudinal gradient in the tree, shrub, and herb communities of the Lvliang Mountains, including the Simpson index, Shannon index, and Pielou index 40 , 41 . These indices were calculated according to the following formulas 33 , 35 , 42 in Microsoft Office Excel 2010.…”

“…In order to obtain a better validation of the MDE hypothesis, measurements of species diversity are primarily conducted at three spatial scales known as α-, β-, and γ-diversity 33 . Among these types of diversity, α-diversity (within-habitat diversity) principally focuses on species number in a homogeneous habitat at local scales.…”

“…These factors are closely related to environmental resources and vegetation community structures, and thus α-diversity can reflect variations of plant species along environmental gradients 34 . As a result, α-diversity is preferentially used to explain the MDE hypothesis 33 , 35 . α-diversity elucidates two driving factors of species distribution in all kinds of environments.…”

Integrating Habitat Suitability and the Near-Nature Restoration Priorities into Revegetation Plans Based on Potential Vegetation Distribution

“…Secondly, temperature and moisture have large-scale, long-term, and stable observational data and, therefore, this method is adopted by most scholars. Thirdly, the topography of the Loess Plateau is complex and changeable, and it has the ability to redistribute temperature, light, and water [52,53]. Nevertheless, other factors also affect the distribution of the native species.…”

Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

“…The very encouraging results that were obtained here with FOTO indices also permit the use of other texture indices that are derived, for example, from a grey-scale co-occurrence matrix, wavelet decomposition or multiband texture analysis. The addition of auxiliary information, such as topographic, climatic or geomorphological data, could also be considered for improving estimates of AGB [13,57].…”