where β 0 is the intercept andβ 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).