Fig. 6: The most parsimonious path model illustrating the direct and indirect effects of biotic and abiotic factors on ecosystem stability.
Partial least squares path models for alpine meadow (a), desert (b), shrub (c) and steppe (d) are shown. The model explores the effects of latitude, climatic conditions, soil properties, species richness, FRic (functional richness) and CWM fast–slow (also called CWM_PC2, the second principal component of the plant functional traits) on detrended temporal stability. Blue and red lines indicate positive and negative significant relationships, respectively, and gray lines indicate insignificant relationships; the thickness of the line represents the strength of the causal relationship, supplemented by a standardized path coefficient. Each number in parentheses indicates the loading value of the indicator to the latent variable. R2 indicates the total variation of a dependent variable is explained by independent variables; GOF indicates the goodness of fit of the entire model. MAP mean annual precipitation, MAP_SD interannual precipitation variability, PS intraannual precipitation variability, MAT mean annual temperature, MAT_SD, interannual temperature variability; TS, intraannual temperature variability; Sand, soil sand content; pH, soil pH. *P < 0.05; **P < 0.01; ***P < 0.001.
