Statistical analyses
We
performed
polynomial regression analyses to assess the form of the patterns of
species mean range size as a function of elevation along the gradient.
Best-fit models were selected based on
the corrected Akaike’s information
criterion (AICc). To choose the most
appropriate method (the Steven’s method, the midpoint method, and the
specimen method) to describe the mean elevational range size pattern, we
compared the Goodness of Fit (R ²) between the best models. Then
we chose the model with the highest R ² and used this method to
calculate the species mean elevational range size for subsequent
analyses.
Multiple regression analyses were conducted to explain species mean
elevational range size. The dependent variable was species mean
elevational range size of each 100-m elevational band (according to the
Steven’s method). ATR, NDVI, and HH were used as independent variables.
Based on the lowest AICc value, the best models (delta AICc<2)
were selected from the 7 models representing all possible combinations
of the 3 independent variables. OLS linear regression model was also
fitted to test the relationship between the area percentage of suitable
habitat types and HH and the relationship between inflow intensity and
species richness.
The polynomial regression analyses were performed in the PAST 3.0
(http://folk.uio.no/ohammer/past/) (Hammer, Harper, & Ryan,
2001). Correlation analysis, OLS regression models, multiple regression
analyses, and model selection were performed in the SAM 4.0 (http://www.
ecoevol.ufg.br/sam/) (Rangel, Dinizfilho, & Bini, 2010).