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The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km²shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. 

Over the past several decades, forests worldwide have experienced increases in biotic disturbances caused by insects and plant pathogens – a trend that is expected to continue with climate warming. Whereas the causes and effects of individual biotic disturbances are well studied, spatiotemporal interactions among multiple biotic disturbances are less so, despite their importance to ecosystem function and resilience. Here, we highlight an emerging phenomenon of “hotspots” of biotic disturbances (that is, two or more biotic disturbances that overlap in space and time), documenting trends in recent decades in temperate conifer forests of the western US. We also explore potential mechanisms behind and effects of biotic disturbance hotspots, with particular focus on how altered post‐disturbance recovery (successional pathways) can have profound consequences for ecosystem resilience and biodiversity conservation. Finally, we propose research directions that can elucidate drivers of biotic disturbance hotspots and their ecological effects at various spatial scales, and provide insight into this new knowledge frontier. 

Burn severity, which can be reliably estimated by validated spectral indices, is a key element for understanding ecosystem dynamics and informing management strategies. However, in North Patagonian forests, where wildfires are a major disturbance agent, studies aimed at the field validation of spectral indices of burn severity are scarce. The aim of this work was to develop a field validated methodology for burn-severity mapping by studying two large fires that burned in the summer of 2013–2014 in forests of Araucaria araucana and other tree species. We explored the relation between widely used spectral indices and a field burn-severity index, and we evaluated index performance by examining index sensitivity in discriminating burn-severity classes in different vegetation types. For those indices that proved to be suitable, we adjusted the class thresholds and constructed confusion matrices to assess their accuracy. Burn severity maps of the studied fires were generated using the two most accurate methods and were compared to evaluate their level of agreement. Our results confirm that reliable burn severity estimates can be derived from spectral indices for these forests. Two severity indices, the delta normalized burn ratio (dNBR) and delta normalized difference vegetation index (dNDVI), were highly related to the fire-induced changes observed in the field, but the strength of these associations varied across the five different vegetation types defined by tree heights and tree and tall shrub species regeneration strategies. The thresholds proposed in this study for these indices generated classifications with global accuracies of 82% and Kappa indices of 70%. Both the dNBR and dNDVI classification approaches were more accurate in detecting high severity, but to a lesser degree for detecting low severity burns. Moderate severity was poorly classified, with producer and user errors reaching 50%. These constraints, along with detected differences in separability, need to be considered when interpreting burn severity maps generated using these methods. 

1. Amplified by warming temperatures and drought, recent outbreaks of native bark beetles (Curculionidae: Scolytinae) have caused extensive tree mortality throughout Europe and North America. Despite their ubiquitous nature and important effects on ecosystems, forest recovery following such disturbances is poorly understood, particularly across regions with varying abiotic conditions and outbreak effects. 2. To better understand post-outbreak recovery across a topographically complex region, we synthesized data from 16 field studies spanning subalpine forests in the Southern Rocky Mountains, USA. From 1997 to 2019, these forests were heavily affected by outbreaks of three native bark beetle species (Dendroctonus ponderosae, Dendroctonus rufipennis and Dryocoetes confusus). We compared pre- and post-outbreak forest conditions and developed region-wide predictive maps of post-outbreak (1) live basal areas, (2) juvenile densities and (3) height growth rates for the most abundant tree species – aspen (Populus tremuloides), Engelmann spruce (Picea engelmannii), lodgepole pine (Pinus contorta) and subalpine fir (Abies lasiocarpa). 3. Beetle-caused tree mortality reduced the average diameter of live trees by 28.4% (5.6 cm), and species dominance was altered on 27.8% of field plots with shifts away from pine and spruce. However, most plots (82.1%) were likely to recover towards pre-outbreak tree densities without additional regeneration. Region-wide maps indicated that fir and aspen, non-host species for bark beetle species with the most severe effects (i.e. Dendroctonus spp.), will benefit from outbreaks through increased compositional dominance. After accounting for individual size, height growth for all conifer species was more rapid in sites with low winter precipitation, high winter temperatures and severe outbreaks. 4. Synthesis. In subalpine forests of the US Rocky Mountains, recent bark beetle outbreaks have reduced tree size and altered species composition. While eventual recovery of the pre-outbreak forest structure is likely in most places, changes in species composition may persist for decades. Still, forest communities following bark beetle outbreaks are widely variable due to differences in pre-outbreak conditions, outbreak severity and abiotic gradients. This regional variability has critical implications for ecosystem services and susceptibility to future disturbances. 

Since the late 1990s, extensive outbreaks of native bark beetles (Curculionidae: Scolytinae) have affected coniferous forests throughout Europe and North America, driving changes in carbon storage, wildlife habitat, nutrient cycling, and water resource provisioning. Remote sensing is a crucial tool for quantifying the effects of these disturbances across broad landscapes. In particular, Landsat time series (LTS) are increasingly used to characterize outbreak dynamics, including the presence and severity of bark beetle-caused tree mortality, though broad-scale LTS-based maps are rarely informed by detailed field validation. Here we used spatial and temporal information from LTS products, in combination with extensive field data and Random Forest (RF) models, to develop 30-m maps of the presence (i.e., any occurrence) and severity (i.e., cumulative percent basal area mortality) of beetle-caused tree mortality 1997–2019 in subalpine forests throughout the Southern Rocky Mountains, USA. Using resultant maps, we also quantified spatial patterns of cumulative tree mortality throughout the region, an important yet poorly understood concept in beetle-affected forests. RF models using LTS products to predict presence and severity performed well, with 80.3% correctly classified (Kappa = 0.61) and R2 = 0.68 (RMSE = 17.3), respectively. We found that ≥10,256 km2 of subalpine forest area (39.5% of the study area) was affected by bark beetles and 19.3% of the study area experienced ≥70% tree mortality over the twenty-three year period. Variograms indicated that severity was autocorrelated at scales < 250 km. Interestingly, cumulative patch-size distributions showed that areas with a near-total loss of the overstory canopy (i.e., ≥90% mortality) were relatively small (<0.24 km2) and isolated throughout the study area. Our findings help to inform an understanding of the variable effects of bark beetle outbreaks across complex forested regions and provide insight into patterns of disturbance legacies, landscape connectivity, and susceptibility to future disturbance.