The boreal forest holds the world's largest soil carbon (C) reservoir. A large portion of it is contained in a thick organic layer originating from the slow decay of bryophytes. Because a thick organic layer slows down tree growth, reduces forest productivity, and thereby reduces the potential wood supply, silvicultural treatments that aim to maintain or restore forest productivity after harvesting often involve mechanical site preparation. However, while these treatments can increase growth and C storage in trees, they can also lead to accelerated decomposition of the soil organic matter, reducing C storage. In this study, we assessed the nine-years effect of two silvicultural treatments on soil C dynamics in forested peatlands of northwestern Quebec, compared to unharvested controls: (1) cut with protection of regeneration and soils (CPRS; low soil disturbance, also called careful logging around advanced growth (CLAAG)), (2) CPRS followed by mechanical site preparation (CPRS + MSP, plowing; severe soil disturbance). The mass loss rate of three bryophytes (Pleurozium schreberi, Sphagnum capillifolium, and Sphagnum fuscum) was measured over two growing seasons together with soil organic carbon (SOC) stocks. We also studied the different effects of temperature, water table level, depth, and type of soil layer on mosses decomposition.We observed a significant influence of silvicultural treatments, bryophyte species, and soil layer type (fibric, mesic, humic and mineral) on bryophyte mass loss, which was higher in the CPRS + MSP treatment (21.6 ± 0.13 % standard error) than in control sites (9.5 ± 0.21 %); CPRS alone resulted in an intermediate mass loss of 11.6 ± 0.23 %, for Sphagnum mosses. Bryophyte mass loss was significantly higher in fibric than humic layer. SOC stocks in the uppermost organic soil layer (fibric) were lower in the CPRS + MSP group than in the control group, while the CPRS group was intermediate; however, differences were not statistically significant for the other soil layer and for total SOC. We conclude that while CPRS + MSP accelerates Sphagnum moss decomposition in the topsoil layer, it has limited impact on total soil C stocks that are detectable with stock change methods.

Understanding biodiversity patterns and its environmental drivers is crucial to meet conservation targets and develop effective monitoring tools. Inconspicuous species such as lichens require special attention since they are ecologically important but sensitive species that are often overlooked in conservation planning. Remote sensing (RS) can be particularly beneficial for these species as in combination with modelling techniques it allows planners to assess and better understand biodiversity patterns. This study aims to model the lichen α-diversity (species richness) and β-diversity (species turnover) biodiversity components using high resolution RS variables across a subarctic region in Northern Quebec (∼190.25 km2). Two sensors, one commercial (WorldView-3, WV3) and another freely accessible (Sentinel-2, S2), at different resolutions (1.2 m and 10 m, respectively) were tested separately to develop our variables and feed the models. Lichens were sampled in 45 plots across different habitat types, ranging from forested habitats (coniferous, deciduous) to wetlands (bogs, fens) and rocky outcrops. Two sets of uncorrelated variables (Red and NIR; EVI2) from each sensor were parallelly used to build the α- and β-diversity models (8 models in total) through Poisson regressions and generalized dissimilarity modelling (GDM), respectively. Red and NIR variables were useful for modeling the two biodiversity components at both resolutions, providing information on stand canopy closure and structure, respectively. EVI2, especially from WV3, was only informative for assessing β-diversity, providing similar information than Red. Poisson models explained up to 32 % of the variation in lichen α-diversity, with Red, NIR and EVI2, either from WV3 or S2, showing negative relationships with lichen richness. GDMs described well the relationship between β-diversity and spectral dissimilarity (R2 from 0.25 to 0.30), except for the S2 EVI2 model (R2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation. This study contributes to improve our knowledge and to inform on the use of RS to understand biodiversity patterns of inconspicuous species, which we consider to be an essential step to enhance their representation in conservation planning.

Linear feature networks are the roads, trails, pipelines, and seismic lines developed throughout many commercial boreal forests. These linear features, while providing access for industrial, recreational, silvicultural, and fire management operations, also have environmental implications which involve both the active and non-active portions of the network. Management of the existing linear feature networks across boreal forests would lead to the optimization of maintenance and construction costs as well as the minimization of the cumulative environmental effects of the anthropogenic linear footprint. Remote sensing data and predictive modelling are valuable support tools for the multi-level management of this network by providing accurate and detailed quantitative information aiming to assess linear feature conditions (e.g., deterioration and vegetation characteristic dynamics). However, the potential of remote sensing datasets to improve knowledge of fine-scale vegetation characteristic dynamics within forest roads has not been fully explored. This study investigated the use of high-spatial resolution (1 m), airborne LiDAR, terrain, climatic, and field survey data, aiming to provide information on vegetation characteristic dynamics within forest roads by (i) developing a predictive model for the characterization of the LiDAR-CHM vegetation cover dynamic (response metric) and (ii) investigating causal factors driving the vegetation cover dynamic using LiDAR (topography: slope, TWI, hillshade, and orientation), Sentinel-2 optical imagery (NDVI), climate databases (sunlight and wind speed), and field inventory (clearing width and years post-clearing). For these purposes, we evaluated and compared the performance of ordinary least squares (OLS) and machine learning (ML) regression approaches commonly used in ecological modelling—multiple linear regression (mlr), multivariate adaptive regression splines (mars), generalized additive model (gam), k-nearest neighbors (knn), gradient boosting machines (gbm), and random forests (rf). We validated our models’ results using an error metric—root mean square error (RMSE)—and a goodness-of-fit metric—coefficient of determination (R2). The predictions were tested using stratified cross-validation and were validated against an independent dataset. Our findings revealed that the rf model showed the most accurate results (cross-validation: R2 = 0.69, RMSE = 18.69%, validation against an independent dataset: R2 = 0.62, RMSE = 20.29%). The most informative factors were clearing width, which had the strongest negative effect, suggesting the underlying influence of disturbance legacies, and years post-clearing, which had a positive effect on the vegetation cover dynamic. Our long-term predictions suggest that a timeframe of no less than 20 years is expected for both wide- and narrow-width roads to exhibit ~50% and ~80% vegetation cover, respectively. This study has improved our understanding of fine-scale vegetation dynamics around forest roads, both qualitatively and quantitatively. The information from the predictive model is useful for both the short- and long-term management of the existing network. Furthermore, the study demonstrates that spatially explicit models using LiDAR data are reliable tools for assessing vegetation dynamics around forest roads. It provides avenues for further research and the potential to integrate this quantitative approach with other linear feature studies. An improved knowledge of vegetation dynamic patterns on linear features can help support sustainable forest management.

The reproductive ecology of the semi-serotinous species black spruce (Picea mariana (Mill.) BSP) in northern boreal forests remains poorly understood. There is a general lack of data on cone/seed production and viability as a function of biotic tree-level characteristics and abiotic variables. No studies currently exist to quantify these differences over a large gradient in temperature, elevation, and precipitation. Extensive physical, ecological, dendrometric, and reproductive data were collected from young to very old black spruce stands in northern Quebec. ANOVA and general linear mixed models were used to examine interannual cone production, and the relative importance of the biotic and abiotic explanatory factors in determining total cone production; length of the cone-bearing zone; filled seeds per cone; proportion of filled seeds; and seed viability. The results illustrate that the reproductive ecology of black spruce in northern cold forests is mainly explained by biotic variables such as age and diameter at breast height, and by abiotic variables related to temperature such as elevation, length of the growing season, and growing degree-days. Black spruce exhibits a lower reproductive potential in northern cold forests, making it possibly less resilient to increased fire frequency, particularly in unproductive and very young or very old stands.