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The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape is challenging, in part due to extent and data scarcity. Species abundance models for ducks in the ABZ commonly use static (time invariant) habitat covariates to inform predictions, such as wetland type and extent maps. For the first time in this region, we developed species abundance models using high-resolution, time-varying wetland inundation data produced using satellite remote sensing methods. This data captured metrics of surface water extent and inundated vegetation in the Peace Athabasca Delta, Canada, which is within the NASA Arctic Boreal Vulnerability Experiment core domain. We used generalized additive mixed models to demonstrate the improved predictive value of this novel data set over time-invariant data. Our findings highlight both the potential complementarity and efficacy of dynamic wetland inundation information for improving estimation of duck abundance and distribution at high latitudes. Further, these data can be an asset to spatial targeting of biodiversity conservation efforts and developing model-based metrics of their success under rapidly changing climatic conditions. [ABSTRACT FROM AUTHOR]

The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape is challenging, in part due to extent and data scarcity. Species abundance models for ducks in the ABZ commonly use static (time invariant) habitat covariates to inform predictions, such as wetland type and extent maps. For the first time in this region, we developed species abundance models using high-resolution, time-varying wetland inundation data produced using satellite remote sensing methods. This data captured metrics of surface water extent and inundated vegetation in the Peace Athabasca Delta, Canada, which is within the NASA Arctic Boreal Vulnerability Experiment core domain. We used generalized additive mixed models to demonstrate the improved predictive value of this novel data set over time-invariant data. Our findings highlight both the potential complementarity and efficacy of dynamic wetland inundation information for improving estimation of duck abundance and distribution at high latitudes. Further, these data can be an asset to spatial targeting of biodiversity conservation efforts and developing model-based metrics of their success under rapidly changing climatic conditions.

Accurate and frequent mapping of transient wetland inundation in the boreal region is critical for monitoring the ecological and societal functions of wetlands. Satellite Synthetic Aperture Radar (SAR) has long been used to map wetlands due to its sensitivity to surface inundation and ability to penetrate clouds, darkness, and certain vegetation canopies. Here, we track boreal wetland inundation by developing a two-step modified decision-tree algorithm implemented in Google Earth Engine using Sentinel-1 C-band SAR and Sentinel-2 Multispectral Instrument (MSI) time-series data as inputs. This approach incorporates temporal as well as spatial characteristics of SAR backscatter and is evaluated for the Peace-Athabasca Delta, Alberta (PAD), and Yukon Flats, Alaska (YF) from May 2017 to October 2019. Within these two boreal study areas, we map spatiotemporal patterns in wetland inundation classes of Open Water (OW), Floating Plants (FP), Emergent Plants (EP), and Flooded Vegetation (FV). Temporal variability, frequency, and maximum extents of transient wetland inundation are quantified. Retrieved inundation estimates are compared with in-situ field mapping obtained during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE), and a multi-temporal Landsat-derived surface water map. Over the 2017–2019 study period, we find that fractional inundation area ranged from 18.0% to 19.0% in the PAD, and from 10.7% to 12.1% in the YF. Transient wetland inundation covered ~595 km2 of the PAD, comprising ~9.1% of its landscape, and ~102 km2 of the YF, comprising ~3.6%. The implications of these findings for wetland function monitoring, and estimating landscape-scale methane emissions are discussed, together with limitations and uncertainties of our approach. We conclude that time series of Sentinel-1 C-band SAR backscatter, screened with Sentinel-2 MSI optical imagery and validated by field measurements, offer a valuable tool for tracking transient boreal wetland inundation.

Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). While ESMs currently project large warming in the high northern latitudes, the magnitude and sign of the future carbon balance of Arctic-Boreal ecosystems are highly uncertain. The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is intended to improve future climate projections. Here, we benchmark the Coupled Model Intercomparison Project (CMIP) 5 and 6 (8 CMIP5 members and 12 CMIP6 members) with the International Land Model Benchmarking (ILAMB) tool over the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America. We show that the projected average net biome production (NBP) in 2100 from CMIP6 is higher than that from CMIP5 in the ABoVE domain, despite the model spread being slightly narrower. Overall, CMIP6 shows better agreement with contemporary observed carbon cycle variables (photosynthesis, respiration, biomass) than CMIP5, except for soil carbon and turnover time. Although both CMIP ensemble members project the ABoVE domain will remain a carbon sink by the end of the 21st century, the sink strength in CMIP6 increases with CO _2 emissions. CMIP5 and CMIP6 ensembles indicate a tipping point defined here as a negative inflection point in the NBP curve by 2050–2080 independently of the shared socioeconomic pathway (SSP) for CMIP6 or representative concentration pathway (RCP) for CMIP5. The model ensembles therefore suggest that, if the carbon sink strength keeps declining throughout the 21st century, the Arctic-Boreal ecosystems in North America may become a carbon source over the next century.

Remote sensing analyses of boreal forest regions have found widespread decreasing or increasing trends in normalized difference vegetation index (NDVI). Initially, these trends were attributed to climate change induced shifts in primary productivity. It is emerging, however, that fire disturbance and subsequent succession also strongly impact the optical properties of boreal forests. Here we use NDVI time series data from Landsat (1999–2018) paired with surveys of 102 forest stands with known recent fire history to investigate the relationship between NDVI and forest structure during succession. We found that NDVI varies systematically with stand age as a result of successional changes in forest structure and composition and that the proportion of deciduous (broad-leaved) trees in the upper canopy is a better predictor of NDVI than leaf area index. Recent fire disturbance led to strong NDVI decreases and early post-fire recovery of herbaceous and deciduous vegetation to strong NDVI increases. The mid-succession transition from deciduous to evergreen (needle-leaved) stands led to weak NDVI decreases, while mid-to-late succession thinning of evergreen canopies led to weak NDVI increases. Thus, both increasing and decreasing NDVI stands occur naturally across the landscape, and do not necessarily reflect a large-scale shift in boreal forest productivity.