Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.

“Weather whiplash” is a colloquial phrase for describing an extreme event that includes shifts between two opposing weather conditions. Prior media coverage and research on these types of extremes have largely ignored winter weather events. However, rapid swings in winter weather can result in crossing from frozen to unfrozen conditions, or vice versa; thus, the potential impact of these types of events on coupled human and natural systems may be large. Given rapidly changing winter conditions in seasonally snow‐covered regions, there is a pressing need for a deeper understanding of such events and the extent of their impacts to minimize their risks. Here we introduce the concept of winter weather whiplash, defined as a class of extreme event in which a collision of unexpected conditions produces a forceful, rapid, back‐and‐forth change in winter weather that induces an outsized impact on coupled human and natural systems. Using a series of case studies, we demonstrate that the effects of winter weather whiplash events depend on the natural and human context in which they occur, and discuss how these events may result in the restructuring of social and ecological systems. We use the long‐term hydrometeorological record at the Hubbard Brook Experimental Forest in New Hampshire, USA to demonstrate quantitative methods for delineating winter weather whiplash events and their biophysical impacts. Ultimately, we argue that robust conceptual and quantitative frameworks for understanding winter weather whiplash events will contribute to the ways in which we mitigate and adapt to winter climate change in vulnerable regions.

Monitoring changes within the land surface and open water bodies is critical for natural resource management, conservation, and environmental policy. While the use of satellite imagery for these purposes is common, fine-scale change detection can be a technical challenge. Difficulties arise from variable atmospheric conditions and the problem of assigning pixels to individual objects. We examined the degree to which two machine learning approaches can better characterize change detection in the context of a current conservation challenge, artisanal small-scale gold mining (ASGM). We obtained Sentinel-2 imagery and consulted with domain experts to construct an open-source labeled land-cover change dataset. The focus of this dataset is the Madre de Dios (MDD) region in Peru, a hotspot of ASGM activity. We also generated datasets of active ASGM areas in other countries (Venezuela, Indonesia, and Myanmar) for out-of-sample testing. With these labeled data, we utilized a supervised (E-ReCNN) and semi-supervised (SVM-STV) approach to study binary and multi-class change within mining ponds in the MDD region. Additionally, we tested how the inclusion of multiple channels, histogram matching, and La*b* color metrics improved the performance of the models and reduced the influence of atmospheric effects. Empirical results show that the supervised E-ReCNN method on 6-Channel histogram-matched images generated the most accurate detection of change not only in the focal region (Kappa: 0.92 (± 0.04), Jaccard: 0.88 (± 0.07), F1: 0.88 (± 0.05)) but also in the out-of-sample prediction regions (Kappa: 0.90 (± 0.03), Jaccard: 0.84 (± 0.04), and F1: 0.77 (± 0.04)). While semi-supervised methods did not perform as accurately on 6- or 10-channel imagery, histogram matching and the inclusion of La*b* metrics generated accurate results with low memory and resource costs. These results show that E-ReCNN is capable of accurately detecting specific and object-oriented environmental changes related to ASGM. E-ReCNN is scalable to areas outside the focal area and is a method of change detection that can be extended to other forms of land-use modification.

Rapid 21st-century climate change may lead to large population decreases and extinction in tropical montane cloud forest species in the Andes. While prior research has focused on species migrations per se, ecotones may respond to different environmental factors than species. Even if species can migrate in response to climate change, if ecotones do not they can function as hard barriers to species migrations, making ecotone migrations central to understanding species persistence under scenarios of climate change. We examined a 42-year span of aerial photographs and high resolution satellite imagery to calculate migration rates of timberline–the grassland-forest ecotone–inside and outside of protected areas in the high Peruvian Andes. We found that timberline in protected areas was more likely to migrate upward in elevation than in areas with frequent cattle grazing and fire. However, rates in both protected (0.24 m yr⁻¹) and unprotected (0.05 m yr⁻¹) areas are only 0.5–2.3% of the rates needed to stay in equilibrium with projected climate by 2100. These ecotone migration rates are 12.5 to 110 times slower than the observed species migration rates within the same forest, suggesting a barrier to migration for mid- and high-elevation species. We anticipate that the ecotone will be a hard barrier to migration under future climate change, leading to drastic population and biodiversity losses in the region unless intensive management steps are taken.

The water balance of the Arctic tundra is shifting as permafrost stability, seasonality, and the ratio of precipitation to evaporation respond to amplified Arctic warming. While in some northern tundra locations there has been a notable increase in the number of water bodies, generally, the tundra landscape has experienced a decline in the number and area of lakes. We analyzed changes in small lake count (10,000 m²), and lake surface area across the periglacial tundra of western Greenland, using historical satellite and aerial imagery and weather data from the late 1960s to present. Overall, we found a decrease in lake count (21%) and surface area (2%) across our study region. Specifically, smaller ponds were particularly prone to change, with decreases of 28% in count and 15% in surface area. Shrinking lakes often became revegetated by both emergent aquatic and terrestrial vegetation, which captures potential successional trajectories following Arctic lake drying. Additionally, while annual precipitation may be increasing, it occurred primarily during the winter months in the form of snow, which may or may not contribute to the overall growing season water budget. Conversely, the peak growing season months of June, July, and August all have experienced significant increases in potential evaporation rates, thus likely creating a water deficit for much of the growing season. These results suggest that a large section of deglaciated Greenland appears likely to become drier in the summer months, which may result in widespread ecological consequences.