Managed forests contribute to both economic and non-timber values, but the ecological role of managed, including planted, forests to biodiversity objectives at the landscape scale needs to be better understood. In this project in collaboration with J.D. Irving, Limited, we: 1) used airborne LiDAR and field data to identify terrestrial habitats; 2) monitored selected taxa by 18 stand type/seral stage habitat types in intensively and extensively managed forests and reserves; 3) assessed effects of management intensity on water quality and aquatic habitat; and 4) projected forest and wildlife habitat under planned management and natural disturbance scenarios. Taxa studied included songbirds, bryophytes and beetle species associated with mature-overmature forests, and several listed ground vegetation species. LiDAR-based enhanced forest inventory provided forest structure variables that improved bird habitat models and spatial predictions of bird habitat, metrics explaining bryophyte composition and richness, and variability in beetle abundance and richness. There was no evidence of negative landscape-level effects of increasing management intensity on bird communities in mature forest stands, suggesting that managed spruce-fir-tolerant hardwood landscapes provide habitat for bird species that need old forest. Richness, diversity, and composition of bryophyte guilds in reference stands in Mount Carleton Provincial Park unmanaged reserve did not differ from stands in the intensively managed District. The landscape focus and stratification into stand type/seral stages were important to understand habitat requirements. Catchments with greater forest management did not show any consistent signs of biological impairment from smaller to larger scales, and all sites had good or very good biological water quality based on the aquatic insect communities. This study helped to evaluate forest management effects on habitat areas, detected with airborne LiDAR data, that need to be addressed to enhance decision making processes.

Partial cutting has lower canopy removal intensities than clearcutting and has been proposed as an alternative harvesting approach to enhance ecosystem services, including carbon sequestration and storage. However, the ideal partial cutting/clearcutting proportion that should be applied to managed areas of the eastern Canadian boreal forest to enhance long-term carbon sequestration and storage at the landscape scale remains uncertain. Our study projected carbon dynamics over 100 years (2010–2110) under a portfolio of management strategies and future climate scenarios within three boreal forest management units in Quebec, Canada, distributed along an east–west gradient. To model future carbon dynamics, we used LANDIS-II, its Forest Carbon Succession extension, and several extensions that account for natural disturbances in the boreal forest (wind, fire, spruce budworm). We simulated the effects of several management strategies on carbon dynamics, including a business-as-usual strategy (clearcutting applied to more than 95 % of the annually managed area), and compared these projections against a no-harvest natural dynamics scenario. We projected an overall increase in total ecosystem carbon storage, mostly because of increased productivity and broadleaf presence under limited climate change. The drier Western region under climate scenario RCP8.5 was an exception, as stocks decreased after 2090 because of the direct negative effects of extreme climate change on coniferous species’ productivity. Under the natural dynamic scenario, our simulations suggest that the Quebec Forest in the Central and Western regions may act as a carbon sink, despite high fire-related carbon emissions, particularly under RCP4.5 and RCP8.5 Conversely, the eastern region periodically switched from carbon sink to source following SBW outbreaks, thus being a weak sink over the simulation period. Applying partial cutting to over 50 % of the managed forest area effectively mitigated the negative impacts of climate change on carbon balance, reducing differences in stand composition and carbon storage between naturally dynamic forests and those managed for timber. In contrast, clearcutting-based scenarios, including the business-as-usual approach, substantially reduced total ecosystem carbon storage— by approximately double (10 tC ha−1 yr−1) compared to partial cutting scenarios (<5 tC ha−1 yr−1). Clearcutting led to higher heterotrophic respiration due to the proliferation of fast-decomposing broadleaves, resulting in lower carbon accumulation compared to partial cuts. Our findings underscore the importance of balancing canopy removal intensities to increase carbon sequestration and storage while preserving other ecosystem qualities under climate change.

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.