Structural equation models linking aboveground biomass and species richness in the primary <i>Pinus kesiya</i> forest.

<p>(a) Effects of species richness, soil nutrient regime and stand age on aboveground biomass. (b) Effects of species richness, soil nutrient regime, stand age and climate moisture index on aboveground biomass. (c) and (d) The model with climate moisture index as the linking mechanism. The coefficients are standardized prediction coefficients indicate each path. Solid lines represent significant paths (<i>P</i><0.05) and dash lines indicate non-significant paths (<i>P</i>≥0.05).</p

Direct, indirect and total standardized effects on AGB based on structural equation models.

<p>Direct, indirect and total standardized effects on AGB based on structural equation models.</p

Tailored Synthesis of Two Metal Borates KSrM₃B₂O₉ (M = Al and Ga) Exhibiting Wide Ultraviolet Transparency

Borate materials continue to command considerable attention due to their remarkable capacity for applications in deep ultraviolet (UV) wavelengths. Herein, two new metal borates KSrM3B2O9 (M = Al and Ga) were extracted via the application of flux techniques. These two crystals adopt a centrosymmetric space group P21/c (no. 14), showcasing a layered structural configuration composed of isolated [BO3] plane triangles and [AlO4]/[GaO4] tetrahedra. Thermal analysis revealed that KSrM3B2O9 (M = Al and Ga) exhibits an incongruent nature and possesses good thermal stability up to 1083 and 983 °C, respectively. Notably, these compounds display a short UV-transmission cutoff edge, approximately around 194 and 200 nm, accompanied by band gaps of 5.47 and 4.83 eV, respectively. Furthermore, KSrM3B2O9 (M = Al and Ga) demonstrates a moderate optical birefringence of 0.026 and 0.025, respectively. Additionally, first-principles calculations were employed to shed light on the intricate interplay between the structure and properties of these compounds

Summary of the general linear models (GLMs) for the relationships between the endogenous variables and predictor variables, each variable separately analyzed.

<p>Summary of the general linear models (GLMs) for the relationships between the endogenous variables and predictor variables, each variable separately analyzed.</p

The distribution of 112 plots inventoried in the <i>Pinus kesiya</i> primary forest by using ArcGIS 9.3(ESRI,Redlands,CA,USA;http://www.esri.com).

<p>The distribution of 112 plots inventoried in the <i>Pinus kesiya</i> primary forest by using ArcGIS 9.3(ESRI,Redlands,CA,USA;<a href="http://www.esri.com/" target="_blank">http://www.esri.com</a>).</p

Relationship between species richness and aboveground biomass in a primary <i>Pinus kesiya</i> forest.

<p>The red solid line is from multiple OLS regression by adding the cubic term. Gray shaded areas show 95% confidence interval of the fit.</p

Biomass allometric equations of each component of <i>Pinus kesiya</i> and other broadleaf species.

<p>Biomass allometric equations of each component of <i>Pinus kesiya</i> and other broadleaf species.</p

Data_Sheet_2_Functional traits and phylogeny jointly regulate the effects of environmental filtering and dispersal limitation on species spatial distribution.csv

IntroductionRevealing the spatial distribution pattern and formation mechanism of species in a community can provide important clues for community renewal, succession, and diversity maintenance mechanisms.MethodsIn this study, we employed spatial point process modeling to identify and quantify the processes contributing to the spatial distribution of species. Simultaneously, we explored the relationship between functional traits and species spatial distribution characteristics in conjunction with phylogenetic studies.ResultsThe results revealed that the LGCP model effectively described all species, indicating that the spatial pattern of species may be influenced by a combination of environmental filtering and dispersal limitation. Disparities in species spatial distribution were elucidated by characterizing functional traits, such as body size and resource conservation. Incorporating phylogenetic information enhanced the predictive capacity of functional traits in explaining species spatial distribution.DiscussionThis study underscores the significance of the joint effects of environmental filtering and dispersal limitation in generating species spatial distribution patterns. Integrating spatial point process models with considerations of functional traits and phylogeny proves to be an effective approach for comprehending the mechanisms governing species combinations.</p

Data_Sheet_1_Functional traits and phylogeny jointly regulate the effects of environmental filtering and dispersal limitation on species spatial distribution.ZIP

IntroductionRevealing the spatial distribution pattern and formation mechanism of species in a community can provide important clues for community renewal, succession, and diversity maintenance mechanisms.MethodsIn this study, we employed spatial point process modeling to identify and quantify the processes contributing to the spatial distribution of species. Simultaneously, we explored the relationship between functional traits and species spatial distribution characteristics in conjunction with phylogenetic studies.ResultsThe results revealed that the LGCP model effectively described all species, indicating that the spatial pattern of species may be influenced by a combination of environmental filtering and dispersal limitation. Disparities in species spatial distribution were elucidated by characterizing functional traits, such as body size and resource conservation. Incorporating phylogenetic information enhanced the predictive capacity of functional traits in explaining species spatial distribution.DiscussionThis study underscores the significance of the joint effects of environmental filtering and dispersal limitation in generating species spatial distribution patterns. Integrating spatial point process models with considerations of functional traits and phylogeny proves to be an effective approach for comprehending the mechanisms governing species combinations.</p

Recurrence quantification analysis.

The recurrence rate (RR), determinism (DET), longest diagonal line (LMAX), entropy (ENT), laminarity (LAM) and trapping time (TT) for polyp A, polyp B and polyp C are shown in (a), (b), (c), (d), (e), (f), respectively. The means with error bars calculated by the standard deviation of corresponding quantities are also displayed. Corresponding quantities are shown in brown for the situation before the drastic morphology change and in orange for the situation after the drastic morphology change.</p