weibull parameters for vascular plant species abundance distributions (sads) (Alborz Darou)
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Weibull Parameters For Vascular Plant Species Abundance Distributions (Sads), supplied by Alborz Darou, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Use of cover data to model species abundance distributions through continuous probability functions"
Article Title: Use of cover data to model species abundance distributions through continuous probability functions
Journal: Scientific Reports
doi: 10.1038/s41598-025-94587-w
Figure Legend Snippet: Model fit evaluation for plant species abundance distributions (SADs) along an elevational gradient in the Alborz Mountains (Iran). (a) Log-transformed χ 2 -test values for different parametric models (exponential, gamma, lognormal, Pareto, and Weibull), representing the goodness-of-fit between observed species richness in each cover class and expected values from the models. Lower Chi² values indicates a better fit. (b) ΔAICc values for the same five models, where lower values indicate a better fit. Three data points from the exponential model were removed for clarity as they had ΔAICc values below − 100.
Techniques Used: Transformation Assay
Figure Legend Snippet: Number of times each species abundance distribution (SAD) model was selected among the equally best supported models (AICc ≤ 2) for the species abundance distributions of vascular plants along an elevational gradient in the Alborz Mountains (Iran). Values of mean species richness and total vegetation cover are also shown.
Techniques Used:
Figure Legend Snippet: Changes of Weibull parameters for vascular plant species abundance distributions (SADs) along a transect in the Alborz Mountains (Iran): (a) relationship between φ (shape) and elevation; (b) relationship between λ (scale) and elevation. Dashed lines represent the loess smoother. Grey areas show the standard error of the smoother. Dot size is proportional to the weighted regression by the inverse of the standard errors of the estimations.
Techniques Used: