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Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short‐interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire‐fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5‐Land for each day of a fire's duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short‐interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long‐interval fires; however, all Canadian boreal ecozones experienced short‐interval reburning (1981–2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short‐interval fire spread days was more extreme than the conditions during long‐interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short‐interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981–2021). Our results suggest an ongoing degradation of fire‐fuel feedbacks, which is likely to continue with climatic warming and drying. [ABSTRACT FROM AUTHOR]

Although broadleaf tree species of the boreal biome have a lower flammability compared to conifers, there is a period following snow melt and prior to leaf flush (i.e., greenup), termed the "spring window" by fire managers, when these forests are relatively conducive to wildfire ignition and spread. The goal of this study was to characterize the duration, timing, and fire proneness of the spring window across boreal Canada and assess the link between these phenological variables and the incidence of springtime wildfires. We used remotely sensed snow cover and greenup data to identify the annual spring window for five boreal ecozones from 2001 to 2021 and then compared seasonality of wildfire starts (by cause) and fire‐conducive weather in relation to this window, averaged over the 21‐year period. We conducted a path analysis to concomitantly evaluate the influence of the spring window's duration, the timing of greenup, and fire‐conducive weather on the annual number and the seasonality of spring wildfires. Results show that the characteristics of spring windows vary substantially from year to year and among geographic zones, with the interior west of Canada having the longest and most fire‐conducive spread window and, accordingly, the greatest springtime wildfire activity. We also provide support for the belief that springtime weather generally promotes wind‐driven, rather than drought‐driven wildfires. The path analyses show idiosyncratic behavior among ecozones, but, in general, the seasonality of the wildfire season is mainly driven by the timing of the greenup, whereas the number of spring wildfires mostly responds to the duration of the spring window and the frequency of fire‐conducive weather. The results of this study allows us to better understand and anticipate the biome‐wide changes projected for the northern forests of North America. [ABSTRACT FROM AUTHOR]

Non‐renewable resource extraction contributes greatly to degradation of wildlife habitats in boreal landscapes. In western Canada, oil and gas exploration and extraction have left a dense network of linear disturbances (seismic lines) and abandoned well pads that have fragmented boreal forest. Among multiple ecological effects, these disturbances have increased predator access to the preferred habitat of some wildlife taxa, most notably boreal woodland caribou, resulting in population declines. Restoration of seismic lines and abandoned well pads is a critical activity to improve the recovery of woodland caribou populations. We present a linear programming model that optimally allocates restoration efforts to maximize the access of caribou to nearby undisturbed habitat in a fragmented landscape. We applied the model to examine restoration scenarios in the Cold Lake First Nations area in northeastern Alberta, Canada, which includes caribou habitat but also areas of active oil and gas extraction. The model depicts the landscape as a network of interconnected habitat patches and combines three network flow sub‐problems. The first sub‐problem enforces the spatial connectivity of the remaining network of unrestored sites. The second sub‐problem maximizes access to suitable habitat from the restored locations and the third sub‐problem ensures the allocation of restoration activities in as few spatially contiguous restoration projects as possible. The approach is generalizable and applicable to assist restoration planning in other resource extraction regions and for other taxa. [ABSTRACT FROM AUTHOR]

Nitrate contamination of alluvial aquifers in agricultural areas is a typical and major problem around the world. Nitrogen applied to crops, in the form of synthetic fertilizers or manure, in excess of plant uptake, largely leaches to groundwater in the form of nitrate, which is stable and highly mobile in oxygen-rich groundwaters. Increased awareness of the impact that excess nitrogen has had on groundwater and major health concerns about nitrate are prompting new regulations for farmers, e.g., in Europe and California, USA. This study is focused in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Though nitrogen loading rates for several crop and landuse types in the Central Valley have been estimated or measured in a handful of studies, nitrogen loading rates for specific crop or landuse types and their impact to groundwater quality remain largely unknown. Knowledge of crop or other landuse specific groundwater nitrate impact may aid future regulatory decisions. Nitrogen loading rates for specific crop or landuse types are expected to vary depending on individual landuse practices; and interactions with hydrogeologic parameters that may promote or inhibit nitrate leaching. In this study, we developed a novel Bayesian regression model that allowed us to estimate crop or other landuse-specific groundwater nitrogen loading rate probability distributions from surveys of private wells, each of which is likely impacted by more than one landuse. We used recent nitrate measurements from 2149 wells in the Central Valley. We estimated nitrogen loading rate distributions for 15 crop and landuse groups. These were shown to compare favorably with prior mass-balance estimates of loading rates based on agronomic estimates of nitrogen loading. [ABSTRACT FROM AUTHOR]

The velocity of climate change is an elegant analytical concept that can be used to evaluate the exposure of organisms to climate change. In essence, one divides the rate of climate change by the rate of spatial climate variability to obtain a speed at which species must migrate over the surface of the earth to maintain constant climate conditions. However, to apply the algorithm for conservation and management purposes, additional information is needed to improve realism at local scales. For example, destination information is needed to ensure that vectors describing speed and direction of required migration do not point toward a climatic cul-de-sac by pointing beyond mountain tops. Here, we present an analytical approach that conforms to standard velocity algorithms if climate equivalents are nearby. Otherwise, the algorithm extends the search for climate refugia, which can be expanded to search for multivariate climate matches. With source and destination information available, forward and backward velocities can be calculated allowing useful inferences about conservation of species (present-to-future velocities) and management of species populations (future-to-present velocities). [ABSTRACT FROM AUTHOR]

Drought is usually the precursor to large wildfires in northwestern boreal Canada, a region with both large wildfire potential and extensive peatland cover. Fire is a contagious process, and given weather conducive to burning, wildfires may be naturally limited by the connectivity of fuels and the connectivity of landscapes such as peatlands. Boreal peatlands fragment landscapes when wet and connect them when dry. The aim of this paper is to construct a framework by which the hydrological dynamics of boreal peatlands can be incorporated into standard wildfire likelihood models, in this case the Canadian Burn-P3 model. We computed hydrologically dynamic vegetation cover for peatlands (37% of the study area) on a real landscape in the Canadian boreal plain, corresponding to varying water table levels representing wet, moderate, and severely dry fuel moisture and hydrological conditions. Despite constant atmospheric drivers of fire spread (air temperature, humidity, and wind speed) between drought scenarios, fire activity increased 6-fold in moderate drought relative to a low drought baseline; severe (1 in 40 years) drought scenarios drove fires into previously fire-restrictive environments. Fire size increased 5-fold during moderate drought conditions and a further 20–25% during severe drought. Future climate change is projected to lead to an increase in the incidence of severe drought in boreal forests, leading to increases in burned area due to increasing fire frequency and size where peatlands are most abundant. Future climate change in regions where peatlands have historically acted as important barriers to fire spread may amplify ongoing increases in fire activity already observed in Western North American forests. [ABSTRACT FROM AUTHOR]

Persistent fire refugia, which are forest stands that have survived multiple fires, play an important ecological role in the resilience of mountainous forest ecosystems following disturbances. The loss of numerous refugia patches to large, high-severity fires in recent years is prompting the need to better understand drivers of fire refugia endurance. We investigate the role of topographic features on fire refugia survivorship based on pre-1950 fire regime conditions. Mapped refugia patches (n = 557) covering 28% of the forested landscape were used to develop three predictive models based on patch size (all sizes, <30 ha, <10 ha), as a function of explanatory variables describing several components of topography. Five topographic variables consistently favoured persistent fire refugia occurrence, though the ranking of explanatory variable importance varied among patch-size models. For the all-refugia model, elevation (23.7%), proportion of non-fuel at a 5000-m scale (20.3%), solar radiation (14.6%), Topographic Position Index at a 2000-m scale (10.1%), and distance from rivers (10.1%) were the top variables. The models’ predictive abilities were high, but decreased with larger patch sizes. We conclude that many suitable areas are currently unoccupied by fire refugia; that random elements affect their survivorship; and that additional environmental factors not considered in this study may contribute to their persistence. With changing climate and fire-regime conditions, careful fire and forest management considerations will be needed to limit future losses of persistent fire refugia forests. [ABSTRACT FROM AUTHOR]