Bryophytes represent an essential component of global biodiversity and play a significant role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial exploitation of natural resources threatens bryophyte species and the ecological processes and services they support. However, the consideration of bryophytes in conservation issues is limited by current knowledge gaps on their distribution and diversity patterns. This is mainly due to the ineffectiveness of traditional field surveys to acquire information over large areas. Using remote sensing data in combination with species distribution models (SDMs), we aim to predict and map diversity patterns (in terms of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) in 28,436 km2 of boreal forests of Quebec (Canada). A bryophyte presence/absence database was used to develop four response variables: total bryophyte richness, moss richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental predictors including climate, topography, soil moisture and drainage as well as vegetation. Then a final set of predictors was selected individually for each response variable through a two-step selection procedure. The Random Forest (RF) algorithm was used to develop spatially explicit regression models and to generate predictive cartography at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics were provided. Our models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze–thaw events) consistently appeared among the most important predictors for all bryophyte groups modeled. However, guild-level models identified differences in important factors determining the richness of each of the guilds and, therefore, in their predicted richness patterns. For example, the predictor number of days > 30 °C was especially relevant for liverworts, while drainage class, topographic position index and PALSAR HH-polarized L-band were identified among the most important predictors for sphagna. These differences have important implications for management and conservation strategies for bryophytes. This study provides evidence of the potential of remote sensing for assessing and making predictions on bryophyte diversity across the landscape. © 2020 Elsevier Ltd

Soil data and soil mapping are indispensable tools in sustainable forest management. In northern boreal ecosystems, paludification is defined as the accumulation of partially decomposed organic matter over saturated mineral soils, a process that reduces tree regeneration and forest growth. Given this negative effect on forest productivity, spatial prediction of paludification in black spruce stands is important in forest management. This paper provides a description of the soil database to predict organic layer thickness (OLT) as a proxy of paludification in northeastern Canada. The database contains 13,944 OLT measurements (in cm) and their respective GPS coordinates. We collected OLT measurements from georeferenced ground plots and transects from several previous projects. Despite the variety of sources, the sampling design for each dataset was similar, consisting of manual measurements of OLT with a hand probe. OLT measurements were variable across the study area, with a mean ± standard deviation of 21 ± 24?cm (ranging from a minimum of 0?cm to a maximum of 150?cm), and the distribution tended toward positive skewing, with a large number of low OLT values and fewer high OLT values. The dataset has been used to perform OLT mapping at 30-m resolution and predict the risk of paludification in northeastern Canada (Mansuy et al., 2018) [1]. The spatially explicit and continuous database is also available to support national and international efforts in digital soil mapping.

In northern boreal ecosystems, paludification is defined as the accumulation of partially decomposed organic matter over saturated mineral soils, a process that reduces tree regeneration and forest growth. Given this negative effect on forest productivity, spatial prediction of paludification in black spruce stands is important in forest management. Here, we used the Random Forest approach to predict organic layer thickness (OLT) as a proxy of paludification in northeastern Canada, where forests tend to paludify naturally. The RF approach involved regression and classification models using a suite of 20 environmental predictors derived from multiple sources. The performance of each model was evaluated using cross-validation and an independent dataset based on conventional ecological survey maps from a provincial forest inventory. Importance measures of the predictors indicated that slope, topographic position index, spectral bands 4 and 5 from Landsat, latitude, and PALSAR_HH were the most important variables explaining the spatial distribution of OLT for both models. Cross-validated relative root mean square error (± standard deviation) for the regression model was estimated at 20.66%?±?0.576, with R2 of 0.41?±?0.020, whereas the average out-of-bag error for the classification model was estimated at 44.75%. However, both models performed better in predicting high risk of paludification (OLT values >40?cm). With predicted OLT values averaging 44.07?±?16.80?cm (range 4.25–104.58?cm), the spatial patterns were in accordance with the results of previous studies at the national and landscape scale. These results highlight that ecological types such as black spruce–sphagnum on thin-to-thick organic deposit, with ombrotrophic drainage, are particularly prone to paludification (OLT depth?>?40?cm) throughout the study area. Limitations of the models and applications for decision-making in forest management are discussed.

Digital soil mapping (DSM) involves the use of georeferenced information and statistical models to map predictions and uncertainties related to soil properties. Many remote regions of the globe, such as boreal forest ecosystems, are characterized by low sampling efforts and limited availability of field soil data. Although DSM is an expanding topic in soil science, little guidance currently exists to select the appropriate combination of statistical methods and model formulation in the context of limited data availability. Using the Canadian managed forest as a case study, the main objective of this study was to investigate to which extent the choice of statistical method and model specification could improve the spatial prediction of soil properties with limited data. More specifically, we compared the cross-product performance of eight statistical approaches (linear, additive and geostatistical models, and four machine-learning techniques) and three model formulations (“covariates only”: a suite of environmental covariates only; “spatial only”: a function of geographic coordinates only; and “covariates + spatial”: a combination of both covariates and spatial functions) to predict five key forest soil properties in the organic layer (thickness and C:N ratio) and in the top 15 cm of the mineral horizon (carbon concentration, percentage of sand, and bulk density). Our results show that 1) although strong differences in predictive performance occurred across all statistical approaches and model formulations, spatially explicit models consistently had higher R2 and lower RMSE values than non-spatial models for all soil properties, except for the C:N ratio; 2) Bayesian geostatistical models were among the best methods, followed by ordinary kriging and machine-learning methods; and 3) comparative analyses made it possible to identify the more performant models and statistical methods to predict specific soil properties. We make modeling tools and code available (e.g., Bayesian geostastical models) that increase DSM capabilities and support existing efforts toward the production of improved digital soil products with limited data.

Strategies for increasing the mobilization of forest biomass supply chains for bioenergy production require continuous assessments of the spatial and temporal availability of biomass feedstock. Using remote sensing products at a 250-m pixel resolution, estimates of theoretical biomass availability from harvest residues and fire-killed trees were computed by combining Canada-wide maps of forest attributes (2001) and of yearly (2002–2011) fires and harvests. At the national scale, biomass availability was estimated at 47 ± 18 M ODT year?1 from fire-killed trees and at 14 ± 2 M ODT year?1 from harvest residues. Mean biomass densities in burned and harvested pixels were estimated at 34 ± 3.0 ODT ha?1 and at 24 ± 1.2 ODT ha?1, respectively. Mean biomass densities also varied dramatically among ecozones, from 14 ODT ha?1 to 206 ODT ha?1 and from 6 ODT ha?1 to 63 ODT ha?1 for burned and harvested pixels, respectively. Spatial averaging with a 100-km radius window shows distinct hotspots of biomass availability across Canada. The largest hotspots from fire-killed trees reached 3.6 M ODT year?1 in the Boreal Shield and the Boreal Plains ecozones of northern Alberta and Saskatchewan, where fires are large and frequent. The largest hotspots from harvest residues reached 1.2 M ODT year?1 in the Montane Cordillera ecozone of British Columbia. The use of spatially explicit remote sensing products yields estimates of theoretical biomass availability that are methodologically consistent across Canada. Future development should include validations with on-the-ground forest inventories as well as the factoring in of environmental, technical and economic considerations to implement operational biomass supply chains.

In the Lower Athabasca region of Alberta (Canada), surface mining for bitumen from oil sands creates highly disturbed environments, which need to be restored, after mine closing, to equivalent land capability in terms of biodiversity and ecosystem services. We demonstrate a method to characterize ecosystem diversity and conditions using biophysical indicators of the Lower Athabasca meant for informing land reclamation planning and monitoring by identifying and creating a typology of the main assemblages of topography, soil and forest vegetation at the watershed, landform and ecosite scales, and analysing the relationships among land units of various scales. Our results showed that watersheds could be classified into distinct groups with specific features, even for a region with a generally flat or gently rolling topography, with slope, surficial deposits and aspect as key drivers of differences. Despite the subtle topography, the moisture regime, which is linked to large-scale cycles that are dependent on the surrounding matrix, was of primary importance for driving vegetation assemblages. There was no unique and homogeneous association between topography and vegetation; the specific landforms each displayed a range of ecosites, and the same ecosites were found in different landforms. This suggests that landscapes cannot be defined in a qualitative manner but rather with quantitative indicators that express the proportion occupied by each class of ecological units within the coarser units, therefore requiring during land reclamation that sufficient care is given to create heterogeneity within a given landform in terms of soil texture and drainage so that a mosaic of ecosite conditions is created.

Severe crown fires are determining disturbances for the composition and structure of boreal forests in North America. Fire cycle (FC) associations with continental climate gradients are well known, but smaller scale controls remain poorly documented. Using a time since fire map (time scale of 300 years), the study aims to assess the relative contributions of local and regional controls on FC and to describe the relationship between FC heterogeneity and vegetation patterns. The study area, located in boreal eastern North America, was partitioned into watersheds according to five scales going from local (3 km2) to landscape (2800 km2) scales. Using survival analysis, we observed that dry surficial deposits and hydrography density better predict FC when measured at the local scale, while terrain complexity and slope position perform better when measured at the middle and landscape scales. The most parsimonious model was selected according to the Akaike information criterion to predict FC throughout the study area. We detected two FC zones, one short (159 years) and one long (303 years), with specific age structures and tree compositions. We argue that the local heterogeneity of the fire regime contributes to ecosystem diversity and must be considered in ecosystem management.

Il est reconnu que les feux de forêt d’origine naturelle ne peuvent pas et même ne doivent pas être éliminés de la forêt boréale nord-américaine. Les feux de forêt occasionnent des pertes immédiates de volume de bois, perturbent la conversion de la structure courante d’âge de la forêt vers une structure cible et empêchent l’approvisionnement planifié en bois (APB) d’être atteint de manière constante. Dans cet article, nous explorons dans quelle mesure les déficits périodiques en bois disponible causés par divers risques de feux peuvent être atténués par la coupe de récupération et par le degré de tolérance des gestionnaires forestiers face à ces déficits, et ceci en fonction de la structure d’âge des forêts. Les simulations sont faites en utilisant une représentation temporelle déterministe et stochastique des feux. Les résultats montrent que la fréquence des périodes en déficit de bois peut être réduite par la coupe de récupération et par l’introduction de mesures de tolérance à ces déficits, et que ce potentiel d’atténuation est influencé par la structure d’âge de la forêt initiale et par le niveau de pertes par le feu. Les résultats montrent également que même un taux de coupe de récupération à 100 % ne peut pas compenser entièrement les pertes de bois par le feu et éliminer les déficits périodiques qui en résultent. En outre, l’ajout de la variabilité interannuelle des feux réduit l’efficacité des deux mesures d’atténuation. Enfin, puisque l’APB n’est en fait jamais réalisé dans les forêts sujettes aux feux, le coût réel d’une réduction l’APB doit être estimé non pas par la différence l’APB, mais plutôt par la différence plus réaliste de récolte de bois réalisée.q

The characterization of the fire regime in the boreal forest rarely considers spatial attributes other than fire size. This study investigates the spatial attributes of fires using the physiography of the landscape as a spatial constraint at a regional scale. Using the Canadian National Fire Database, the size, shape, orientation and eccentricity were assessed for 1,136 fires between 1970 and 2010 in Quebec's boreal forest and were summarized by ecodistrict. These spatial metrics were used to cluster 33 ecodistricts into homogeneous fire zones and then to determine which environmental variables (climate, topography, hydrography, and surficial deposits) influence the spatial attributes of fires. Analyses showed that 28 out of 33 ecodistricts belonging to a given fire zone were spatially contiguous, suggesting that factors driving the spatial attributes of fire are acting at a regional scale. Indeed, the orientation and size of fires vary significantly among the zones and are driven by the spatial orientation of the landscape and the seasonal regional climate. In some zones, prevailing winds during periods conducive to fire events parallel to the orientation of the landscape may favour the occurrence of very large fires (>100,000 ha). Conversely, an orientation of the landscape opposite to the prevailing winds may act as a natural firebreak and limit the fire size and orientation. This study highlights the need to consider the synergistic relationship between the landscape spatial patterns and the climate regime over the spatial attributes of fire at supra-regional scale. Further scale-dependant studies are needed to improve our understanding of the spatial factors controlling the spatial attributes of fire. © 2014 Her Majesty the Queen in Right of Canada.