Unsustainable hunting of bushmeat has dramatic impacts on ecological processes and people's livelihoods. Unfortunately, there is often a strong controversy regarding the sustainability of duiker hunting due to their continued presence in bushmeat markets, on the one hand, and the predictions of sustainable harvest models for duiker abundance, on the other. This apparent contradiction is largely due to biased low abundance estimates from dung surveys. We present results from a 52.4 km line transect dung survey using site decay rates to estimate duiker densities. In addition, camera trapping (14,995 camera trap-days) was used to provide detection rates and a baseline for the ratio of blue to red duikers as an index of hunting pressure from Nouabalé-Ndoki National Park, with almost zero levels of hunting. Dung surveys revealed high overall duiker densities (totalling 107.4 duikers per km 2 ) and quick dung decay rate. Camera trapping revealed high-duiker detection rates and a high ratio of red to blue duikers. Pristine protected areas and no-hunting zones continue to act as source habitats for high recruitment of harvested species. We discuss future options for monitoring duikers and applying the ratio of red to blue duikers as an index of the level of duiker hunting. K E Y W O R D Sbiomonitoring, camera trapping, Congo Basin, duikers, dung survey, line transect RésuméMalheureusement, il y a souvent une forte controverse autour de la durabilité de la chasse au céphalophe par sa présence continue sur les marchés de viande en brousse, d'une part, et les prévisions de modèles durables des récoltes liés à l'abondance des céphalophes, d'autre part. Cette contradiction apparente est en grande partie due aux estimations incorrectes faites par le faible nombre des prélèvements des excréments. Nous présentons les résultats d'un relevé des excréments en transects linéaires de 52,4 km en utilisant les taux de décomposition du site pour estimer la densité des céphalophes. En outre, le piégeage photographique (14,995 jours-pièges photographiques) a été utilisé pour fournir des taux de détection et une base de référence pour le rapport entre les céphalophes bleus et les rouges comme indice de la

Recent increases in forest diseases have produced significant mortality in boreal forests. These disturbances influence merchantable volume predictions as they affect the distribution of live and dead trees. In this study, we assessed the use of lidar, alone or combined with multispectral imagery, to classify trees and predict the merchantable volumes of 61 balsam fir plots in a boreal forest in eastern Canada. We delineated single trees on a canopy height model. The number of detected trees represented 92% of field trees. Using lidar intensity and image pixel metrics, trees were classified as live or dead with an overall accuracy of 89% and a kappa coefficient of 0.78. Plots were classified according to their class of mortality (low/high) using a 10.5% threshold. Lidar returns associated with dead trees were clipped. Before clipping, the root mean square errors were of 22.7 m 3 ha −1 in the low mortality plots and of 39 m 3 ha −1 in the high mortality plots. After clipping, they decreased to 20.9 m 3 ha −1 and 32.3 m 3 ha −1 respectively. Our study suggests that lidar and multispectral imagery can be used to accurately filter dead balsam fir trees and decrease the merchantable volume prediction error by 17.2% in high mortality plots and by 7.9% in low mortality plots.

Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.) Mill.) in eastern Canada. We used lidar point clouds to compute predictor variables of the merchantable volume in a nonlinear model. The best model included the mean height of first returns, the proportion of first returns below 2 m and the canopy surface roughness index. Our analysis shows a high correlation between lidar and field data of 119 plots (pseudo-R 2 = 0.91), however, residuals were heteroscedastic. More precise parameter estimates were obtained by adding to the model a variance function of variables describing the mean height of returns and the skewness of the area distribution of triangulated lidar returns. The residual standard deviation was better estimated (3.7 m 3 ha −1 multiplied by the variance function versus 28.0 m 3 ha −1 ). We found no effect of density on the predictions (p-value = 0.74). This suggests that the height and the spatial pattern of returns, rather than the density, should be considered to better assess the uncertainty of merchantable volume estimates.

Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy between the field inventory and the lidar survey. In this study, we used lidar data to predict the timber merchantable volume (MV) of five sites located along a bioclimatic gradient of temperature and elevation. The temporal discrepancies were up to three years. We adjusted a random canopy height coefficient (accounting for the variability amongst sites), and a growth function (accounting for the growth during the temporal discrepancy), to the predictive model. The MV could be predicted with a pseudo-R 2 of 0.86 and a residual standard deviation of 24.3 m 3 ha −1. The average biases between the field-measured and the predicted MVs were small. The variability of MV predictions was related to the bioclimatic gradient. Fixed-effect models that included a bioclimatic variable provided similar prediction accuracies. This study suggests that the variability amongst sites, the occurrence of a bioclimatic gradient and temporal discrepancies are essential in building a generalized lidar-based model for timber volume.