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Understanding how maximum canopy height is related to forest community assembly is essential yet largely unexplored. Maximum canopy height is affected by competition and abiotic environmental factors through different ecological processes (e.g. niche differentiation and environmental filtering), as well as historical or stochastic factors. However, little has been done to empirically examine the ecological processes that influence maximum canopy height. We set out to examine the relationship between maximum canopy height and community phylogenic structure. We surveyed maximum canopy heights from a regional dataset of forest plots (466 sites of 50 m × 50 m) from the boreal forest of northeastern Alberta, Canada. We then explored the relationships between maximum canopy height as measured by airborne LiDAR (Light Detection and Ranging) and the phylogenetic structure of seed plants, represented by net relatedness index and nearest taxa index. We found stronger phylogenetic clustering among major evolutionary clades for communities with higher maximum canopy height, which implied that environmental filtering by abiotic factors is a driving factor for boreal forests. However, we also found stronger phylogenetic overdispersion within each clade for communities with higher maximum height, indicating more intense niche differentiation. Our results suggest that communities with higher maximum canopy height may have experienced more intense historical abiotic environmental filtering and recent niche differentiation in boreal forests. These findings will contribute to the monitoring and management of forest biodiversity. [ABSTRACT FROM AUTHOR]

In the past decade, a large volume of peer‐reviewed papers has examined the potential impacts of oil and gas resource extraction in the Canadian oil sands (OS). A large proportion focuses on terrestrial biology: wildlife, birds, and vegetation. We provide a qualitative synthesis of the condition of the environment in the oil sands region (OSR) from 2009 to 2020 to identify gaps and progress cumulative effects assessments. Our objectives were to (1) qualitatively synthesize and critically review knowledge from the OSR; (2) identify consistent trends and generalizable conclusions; and (3) pinpoint gaps in need of greater monitoring or research effort. We visualize knowledge and terrestrial monitoring foci by allocating papers to a conceptual model for the OS. Despite a recent increase in publications, focus has remained concentrated on a few key stressors, especially landscape disturbance, and a few taxa of interest. Stressor and response monitoring is well represented, but direct monitoring of pathways (linkages between stressors and responses) is limited. Important knowledge gaps include understanding effects at multiple spatial scales, mammal health effects monitoring, focused monitoring of local resources important to Indigenous communities, and geospatial coverage and availability, including higher attribute resolution in human footprint, comprehensive land cover mapping, and up‐to‐date LiDAR coverage. Causal attribution based on spatial proximity to operations or spatial orientation of monitoring in the region is common but may be limited in the strength of inference that it provides. Integr Environ Assess Manag 2022;18:388–406. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS: We provide a qualitative synthesis of the condition of the environment in the Canadian oil sands region (OSR) in northeastern Alberta from 2009 to 2020 to identify gaps and progress cumulative effects assessments. Despite a recent increase in publications, focus has remained concentrated on a few key stressors and a few taxa of interest, for which monitoring is well represented, though direct monitoring of pathways (linkages between stressors and responses) is limited. Important gaps include a lack of understanding of effects at multiple spatial scales, a lack of focused monitoring of local resources important to Indigenous communities, and geospatial data resolution and availability. Causal attribution based on spatial proximity to oil sands operations or spatial orientation of monitoring in the OSR is common but may be limited in the strength of inference that it provides. [ABSTRACT FROM AUTHOR]