where β 0 is the intercept andβ i is
the coefficient of the i th environmental
variable vi (x , y) to be estimated
(Waagepetersen, 2007), with v 1 being flooding
height, v 2 soil moisture,v 3 terrain slope, v 4elevation, v 5 litterfall height,v 6 canopy opening in 2017, andv 7 canopy opening in 2008.
We used the Z value to evaluate the significance and direction of the
effect of the different environmental variables on each species’
pattern. For a significance level of α = 0.05, we have a significant and
positive association with a given variable if Z >
1.96 (and a negative if z < 1.96) and the larger the
absolute value of Z , the stronger the association. We considered
habitat effects to be strong when Z ≥ |4|. We
fitted λh (x, y ) using maximum likelihood
estimation to determine the values of the coefficientsβ i (Shen et al. , 2009; Wang et al. ,
2011) in the package “spatstat” (Baddeley et al. , 2015) of the
software R (R Core Team, 2018).