Conceptual framework and uncertainty analysis for large-scale, species-agnostic modelling of landscape connectivity across Alberta, Canada.

Sustainable land-use planning should consider large-scale landscape connectivity. Commonly-used species-specific connectivity models are difficult to generalize for a wide range of taxa. In the context of multi-functional land-use planning, there is growing interest in species-agnostic approaches, modelling connectivity as a function of human landscape modification. We propose a conceptual framework, apply it to model connectivity as current density across Alberta, Canada, and assess map sensitivity to modelling decisions. We directly compared the uncertainty related to (1) the definition of the degree of human modification, (2) the decision whether water bodies are considered barriers to movement, and (3) the scaling function used to translate degree of human modification into resistance values. Connectivity maps were most sensitive to the consideration of water as barrier to movement, followed by the choice of scaling function, whereas maps were more robust to different conceptualizations of the degree of human modification. We observed higher concordance among cells with high (standardized) current density values than among cells with low values, which supports the identification of cells contributing to larger-scale connectivity based on a cut-off value. We conclude that every parameter in species-agnostic connectivity modelling requires attention, not only the definition of often-criticized expert-based degrees of human modification.

Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning.

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands-a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage-in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.

Assessing the provision of carbon-related ecosystem services across a range of temperate grassland systems in western Canada.

Reliable data on the provision of ecosystem services (ES) is essential to the design and implementation of policies that incorporate ES into grassland conservation and restoration. We developed and applied an innovative approach for regional parameterization, and calibration of the CENTURY ecosystem model. We quantified spatiotemporal variation of soil organic carbon stock (SOC) and aboveground plant biomass production (AGB) and examined their responses to the recent climate change across a diverse range of native grassland systems in Alberta, western Canada. The simultaneous integration of SOC and AGB into calibration and analysis accounted for most of the spatiotemporal variability in the SOC and AGB measurements and resulted in reduced simulation uncertainty across nine grassland regions. These findings suggest the importance of the systematic parameterization and calibration for the reliable assessment of carbon-related ES across a wide geographic area with heterogeneous ecological conditions. Simulation results showed a pronounced variation in the spatial distribution of SOC and AGB and their associated uncertainty across grassland regions. Under baseline grazing intensity regime, an overall negative effect of recent climatic changes on the SOC, and a less consistent effect on the AGB were found. While, an overall positive or slightly negative impact of recent climate change on the SOC and AGB was found under a proposed 10% lower grazing intensity regime. These heterogeneities in the magnitude and direction of climate change effects under different grazing regimes suggest needs for a range of climate change adaptation strategies to maintain carbon-related ES in Alberta's grasslands. The modeling framework developed in this study can be used to improve the spatially explicit assessment of carbon-related ES in other geographically vast grassland areas and examine the effectiveness of alternative management scenarios to ensure the long-term provision of carbon-related ES in grassland systems.