Living on the edge 231


mean number of detected species from all locations at each distance from the edge. We determined the relationship be- tween distance from the forest edge, environmental condi- tions and detections using generalized linear mixed models (GLMMs) fitted with the R package glmmTMB (Brooks et al., 2017). As our data collection followed a nested sam- pling design, we included location and transect ID as random intercepts in all models to account for non- independence between samples. We summarized environmental predictors for each loca-


tion.Wecalculated mean temperature and light intensity for the entire sampling period (including night-time and day- time temperatures). We also calculated the daily mean, maximum and minimum temperature and light intensity at each sampling location. We summarized forest structure for each plot into the following variables: mean tree height, mean bole height, mean diameter at breast height, mean crown area, mean connectivity and number of trees per plot. The environmental variables used in the models are provided in Supplementary Table 1, and R-code used to run the models is provided in Supplementary Material 1. We first tested for the effect of distance from the edge on microclimate and forest structure. The GLMMs for micro- climate variables also included the day of the year and hour of the day as random intercepts. We next tested for signifi- cant associations between detection events and distance from the forest edge using Pearson’s χ2 tests. We then test- ed the effects of microclimate and forest structure variables on overall mammal detections using a Poisson generaliz- ed linear model with a log link function fitted with the R package MASS (Venables & Ripley, 2002). We used a correlation matrix to check for co-linearity amongst pre- dictor variables. For those with a correlation coefficient .0.7, we selected only one to be included in the model. We determined which combination of variables produced the best model performance based on the Akaike infor- mation criterion (AIC) using automated model selection with the R package MuMIn (Bartoń, 2020). Finally, we tested for the effects of microclimate and for-


est structure variables on the occurrence of each mam- mal order by fitting a negative binomial GLMM with glmmTMB. Order was included as a random intercept. As in the GLMM, we first selected independent variables using a correlation matrix and used automated model selection with MuMIn to select the best combination of variables based on the model AIC.


Results Edge effects on forest structure and microclimate


Forest structure Tree height increased significantly with increased distance from the forest edge (P,0.05; Table 1,


Oryx, 2024, 58(2), 228–239 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323000212


TABLE 1 Coefficient estimates for the generalized linear mixed model with a Gaussian distribution testing the effects of distance fromthe forest edge on forest structure variables (N = 217 trees) at Aras Napal, Indonesia (Fig. 1). AIC, Akaike information criterion.


Dependent variables


Tree height ± SE (m)


Distance from edge (m)


Intercept


Random effects1 σ2


τ00 AIC


Bole height ± SE (m)


Diameter at breast height ± SE (cm)


0.002** ± 0.00 0.002*** ± 0.00 −0.001 ± 0.00 17.72*** ± 1.05


65.93 1.13


1,544.51


33.38 0.20


8.45*** ± 0.68 33.25*** ± 1.50 184.94


0.00 1,397.02


*P,0.1; **P,0.05; ***P,0.01. 1σ2, residual variance; τ00, random intercept variance.


1,764.32


Tree basal area ± SE (cm2)


0.00 ± 0.00


0.10*** ± 0.01 0.01


0.00 −382.04


Tree height:diameter at breast height ratio ± SE


0.006*** ± 0.00 55.53*** ± 1.83 267.97


0.00 1,844.04


Crown area ± SE (m2)


−0.0005 ± 0.00 Canopy


connectivity ± SE (%) 0.001 ± 0.00


45.29*** ± 4.12 43.41*** ± 3.03 1,356.24 2,192.69


0.00


538.29 9.87


1,996.05


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