Patrick James Atherton Avila

Effective wildfire impact assessment and post-fire management rely on burn-severity maps that accurately characterize fire effects on vegetation and soils. These maps inform time-sensitive activities such as post-fire flood and debris-flow modeling, risk mitigation for homes and communities, and ecosystem recovery planning, but the satellite observations used to generate them are often obstructed or delayed by smoke, clouds, and snow. Across 9,129 large fires (≥ 404 ha) in Canada and the United States from 2016–2024, we quantified post-fire data obstruction and evaluated observational latency – defined as the time required to acquire the first usable scene – for four freely available moderate-resolution remote sensing datasets: Landsat, Sentinel-1, Sentinel-2, and the Harmonized Landsat Sentinel-2 (HLS) product. For multispectral datasets (Landsat, Sentinel-2, and HLS), cloud cover was the primary source of data obstruction, particularly in mesic and coastal regions, whereas Sentinel-1 (synthetic aperture radar) can penetrate clouds. Consequently, Sentinel-1 had the shortest observational latency (median = 4 days), followed by HLS (7 days), Sentinel-2 (10 days), and Landsat (17 days). Likewise, one-month composite imagery from Sentinel-1 (89.6%) and HLS (82.8%) achieved near-complete coverage for most fires, substantially better than Sentinel-2 (70.5%) and Landsat (56.1%). Despite its widespread use in operational burn-severity mapping, Landsat provided the slowest and least complete early post-fire coverage. For 77 fires in forested ecosystems, we also evaluated relationships between early post-fire imagery and field-derived burn-severity measurements collected 0–3 years after fire. All multispectral datasets corresponded well with field data and reliably identified areas of near-total canopy loss using imagery acquired within two months after fire (> 80% balanced accuracy), performing nearly as well as extended assessments using imagery from a full post-fire growing season. Sentinel-1 produced less accurate burn-severity estimates but may remain valuable where persistent cloud cover limits observations, including eastern Canada and the Pacific Northwest. Overall, HLS provided the best combination of latency, spatial coverage, and burn-severity mapping performance, making it a strong candidate for operational use and ecological monitoring across diverse North American ecosystems.

Background: Atmospheric moisture can either mitigate or exacerbate burn severity depending on fuel and environmental conditions. However, changes in fuel moisture typically lag behind shifts in atmospheric moisture associated with weather or climate changes. When atmospheric moisture decreases, lagged decreases in fuel moisture can affect fire hazard in two ways: they can increase fuel flammability and wildfire severity in the short term, or alternatively, they can decrease wildfire severity over longer periods by slowing the accumulation of combustible materials. Yet, the temporal scales at which lagged moisture changes most strongly influence burn severity are not well understood. Hence, we investigated how time-lagged moisture availability at both inter- and intra-annual timescales, forest composition, and drought conditions during the burn period contribute to wildfire burn severity across 1025 fires (1986–2024) in Ontario (Canada). Specifically, we quantified the relative effects of daily vapour pressure deficit (VPD), annual climate moisture index (CMI) anomalies, fuel composition, and fire‑season weather using distributed lag generalized additive models and hierarchical partitioning to compare the contributions of moisture at intra-annual lag intervals up to 30 days before fire (VPD) and interannual lag intervals up to 5 years before fire (CMI). Results: We found that intra-annual atmospheric dryness and interannual moisture availability jointly regulate burn severity, with effects varying by lag. Coniferous forest cover was the strongest predictor of both median and extreme severity. Interannual moisture availability (CMI) explained more variation than intra‑annual drying (VPD). Wetter‑than‑normal conditions 4–5 years before fire increased severity, while long‑lag dryness reduced it, likely through fuel limitations. At 1–2 year lags, dry conditions increased severity and wet conditions lowered it. When considering extreme burn severity, cumulative multi‑day VPD was more influential than individual lag effects. Conclusions: These results show that burn severity is driven by short‑term flammability and multi‑year fuel legacies, underscoring the need to integrate lagged moisture dynamics into fire‑severity modelling under climate change.

Context: Forest insect outbreaks are major landscape-scale disturbances that shape forest composition and dynamics. In eastern North America, the spruce budworm (SBW) is a key defoliator driving these patterns. Understanding how local forest management interacts with outbreak dynamics at broad spatial extents is crucial for designing sustainable management strategies under climate change. Objective: We evaluated whether forest management strategies can modify SBW outbreak development across broad spatial extents and over long time frames. Methods: We developed a spatially explicit forest landscape model to simulate SBW outbreaks in Quebec, Canada, over 80 years. Simulations were used to compare scenarios with different harvesting rates and regeneration strategies under current and future climate conditions. Results: Outbreaks affected from 60,000 to 160,000 km ² (11–31% of forest area), with climate change driving a gradual northward shift in maximum outbreak impacts. Annual harvesting of 1% of the forest and promoting non-host regeneration (e.g., trembling aspen) reduced SBW-induced forest mortality by up to 30%, particularly when combined with higher harvest intensities that accelerated species turnover and advanced dampening effects by one to two decades. In contrast, prioritizing host species (e.g., black spruce) had little effect on outbreak patterns. Conclusions: Local management strategies can substantially influence large-extent outbreak dynamics if applied consistently and intensively. However, our results highlight that even proactive measures will only partially offset future risks, as climate-driven northward shifts may reduce management effectiveness. Spatially explicit models provide valuable insights to design adaptive forest management that mitigates insect disturbances under climate change. Graphical

The spruce budworm (Choristoneura fumiferana; SBW) is a periodically outbreaking forest insect pest that affects the boreal forests of North America through extensive defoliation and tree mortality. Causes of widespread spatial synchrony of SBW outbreaks remain a key question in the ecology and management of this species. While the Moran effect (correlated favourable environmental conditions) and density‐dependent dispersal (from epicentres of demographic explosions) have been proposed and supported as drivers of synchronised outbreaks, the relative contribution of long‐distance dispersal is still poorly understood. In this study, we use a novel approach to distinguish resident from migrant moths and to assign migrants to likely source clusters with the goal of better characterising regional dispersal. First, we characterise the genetic diversity and structure of resident SBW larvae and three phenologically separated groups of moths over one flight season using Genotyping‐by‐Sequencing. Then, using a novel machine learning approach, we assign putative migrants to their likely source populations. We hypothesised that migrant moths and resident larvae would be genetically distinct and could be assigned to source populations. Our findings revealed complex patterns of moth dispersal and population differentiation within a single season, including two spatially overlapping genetic clusters. We observed subtle but significant genetic differences between resident larvae and migrant moths, supporting the hypothesis that long‐distance dispersal contributes to outbreak dynamics and synchrony. These insights enhance our understanding of SBW population dynamics and suggest that effective management strategies, such as the Early Intervention Strategy (EIS), must account for the role of dispersal in mitigating the detrimental effects of major outbreaks.

Population outbreaks are characterized by irruptive changes in population density and connectivity resulting in rapid demographic and spatial expansion, often at the landscape scale. Outbreaks are common across multiple taxa, many of which inhabit northern ecosystems. Outbreaks of Lepidopteran defoliators in forest ecosystems are a particularly compelling example of this phenomenon, given the massive spatial scales over which these outbreaks can occur, their frequency, and socioeconomic impacts. The eastern spruce budworm (SBW) is a native outbreaking Lepidopteran defoliator of North American boreal forests. Cyclic outbreaks of the SBW influence ecosystem functioning and resilience, as well as forest productivity, timber supply, and other socioeconomic values related to management and mitigation. Despite these significant impacts, the ecological and biological drivers and outcomes of these outbreaks remain poorly understood. Here, we present an extensive genotypic dataset for 1998 geo‐referenced SBW individuals collected between the years of 2012 and 2017, during the rising and peak phases of an outbreak that began approximately in 2006. Our sampling covers an unprecedented scope in the extent and number of individuals collected between 2012 and 2017 from Quebec, and in 2015 from New Brunswick (Canada) and from Maine (USA), from multiple SBW life stages, including early and late instar larvae (L2–L6), pupae, and adult moths. Genomic DNA extraction was followed by library preparation and high‐throughput sequencing using Genotyping‐by‐Sequencing (GBS). Samples were genotyped for single nucleotide polymorphisms (SNPs) and aligned to the bw6 version of the SBW genome. This dataset represents one of the most extensive genotypic datasets to date for a boreal insect and is unique as it includes multiple years during a developing (ongoing, at time of sampling) outbreak. Sampling effort covered areas close to the epicenter of the outbreak (Quebec/Canada) and adjacent areas affected by the outbreak progress. This dataset also provides genome‐wide characterization of SBW populations from Quebec, serving as a standard for the identification of future samples regarding their locality of origin, structure and connectivity. These data represent a valuable novel resource for further study of the spatial and temporal dynamics of SBW, and how spatial genetic diversity and gene flow are affected by population outbreaks. These data provide a temporal snapshot of SBW genetic diversity, which can serve as baseline for future studies regarding outbreaks, and the impact of human‐induced environmental changes on complex population dynamics. This genotype dataset comprises a unique representation of genomic‐level composition and variation observed in subsequent generations of an irruptive, cyclic outbreaking species and is of utmost importance for exploring and describing how accelerated demographic variation impacts the development of spatial genetic structure across heterogeneous landscapes. We believe this dataset is essential to management and conservation biology initiatives not only for SBW and boreal forests but also for providing a starting point for broader evolutionary and ecological studies of complex population dynamics. Furthermore, the knowledge, data collection, and curation framework we present here can be used to inform similar spatial temporal baseline studies of other outbreaking (e.g., mountain pine beetle, red‐backed voles) and invasive species (e.g., spongy moth and emerald ash borer). The data are released under a CC BY 4.0 license.

Fluid administration is almost universally accepted as a part of the treatment of any hospitalized patient, but this is based on old perspectives and an incomplete understanding of the intravenous fluid dynamics. A long time ago Shires et al described and defined a “third space” which is a nonfunctional fluid that can be considered a fluid loss and must be replaced; this understanding led to over reanimation1, 2. The surgical scheduled patient is a perfect example, most of those procedures don’t compromise the oral route, don’t imply large fluid loses, and don’t need large intravenous fluids even in complex cases like congenital heart defects

Context: The mountain pine beetle (MPB; Dendroctonus ponderosae) is a native bark beetle whose outbreaks leads to widespread conifer forest mortality. Of particular concern to forest and wildfire managers is the influence of MPB outbreaks on wildfire via spatial legacies left in impacted forest stands. There is, however, limited consensus in the literature regarding how MPB outbreaks affect wildfire across western North America. Objectives: This meta-analysis aims to (1) summarize available evidence regarding MPB-wildfire interactions, and (2) identify environmental and methodological indicators associated with various wildfire responses (i.e., amplified, neutral, or dampened) post-outbreak. Methods: We include peer-reviewed publications focusing on MPB outbreaks and subsequent wildfire activity in forests across western Canada and the USA between 2000 and 2021. A classification scheme was used to examine attributes of each publication to assess which indicators contribute most to their associated wildfire response. Results: We found that spatial scale, forest fuels, and weather are main drivers of variation in wildfire response post-outbreak. Metrics of forest fuels and inclusion of weather data on a stand-scale are related to amplified fire responses, whereas dampened responses correspond to landscape-scale analyses. Furthermore, red-stage stands are associated with amplified fire response, whereas other stages are associated with dampened response—supporting current conceptual models of the importance of outbreak stage on wildfire. Conclusions: Advancing our understanding regarding drivers of wildfire responses post-MPB outbreak is key to developing accurate, and comparative research studies. These findings provide crucial information for wildfire, and forest management agencies, especially in forests newly exposed to this disturbance interaction under climate change.

The Canadian forest fire weather index (FWI) system requires spatially continuous, gridded weather data for temperature, relative humidity, wind speed, and precipitation. Reliable estimates of the Canadian FWI system components are needed to ensure the safety of communities, resources, and ecosystems. The quality of the interpolated input weather variables are typically evaluated using error estimates from cross‐validation. These error estimates are used for selecting between spatial interpolation methods for generating the continuous weather surfaces. Leave‐one‐out cross‐validation (LOOCV) is the most commonly used method, but it is biased in spatially clustered weather station networks. Accurate error estimation is important for selecting the optimal interpolation method and evaluating how well an interpolated surface represents true patterns in a weather variable. Other cross‐validation methods may better account for bias relating to clustered weather station networks. We present a comparison of cross‐validation methods for evaluating spatial interpolation models of weather variables for generating the inputs to the Canadian FWI system with the objective of determining whether they identify the same spatial interpolation model as having the lowest error. We found that LOOCV, shuffle‐split, stratified shuffle‐split, and a modified buffered leave‐one‐out procedure generally identified the same spatial interpolation models as having the lowest error. Spatial k‐fold favored spatial interpolation models with extrapolation ability. Our findings indicate that the most computationally efficient cross‐validation approach can be used for automatically selecting spatial interpolation models for weather surface generation, which will improve the quality of historical daily FWI maps.

Context: Dispersal has a key role in the population dynamics of outbreaking species such as the spruce budworm (Choristoneura fumiferana) as it can synchronize the demography of distant populations and favor the transition from endemic to epidemic states. However, we know very little about how landscape structure influences dispersal in such systems while such knowledge is essential for better forecasting of spatially synchronous population dynamics and to guide management strategies.Objectives: We aimed to characterize the spatial environmental determinants of spruce budworm dispersal to determine how these features affect outbreak spread in Quebec (Canada). We then apply our findings to predict expected future landscape connectivity and explore its potential consequences on future outbreaks.Methods: We used a machine-learning landscape genetics approach on 447 larvae covering most of the outbreak area and genotyped at 3562 SNP loci to identify the main variables affecting connectivity.Results: We found that the connectivity between outbreak populations was driven by the combination of precipitation and host cover. Our forecasting suggests that between the current and next outbreaks, connectivity may increase between Ontario and Quebec, and might decrease in the eastern part, which could have the effect of limiting outbreak spread from Ontario and Quebec to the eastern provinces.Conclusions: Although we did not identify any discrete barriers, low connectivity areas might constrain dispersal in the current and future outbreaks and should in turn, be intensively monitored. However, continued sampling as the outbreak progresses is needed to confirm the temporal stability of the observed patterns.