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1. Caribou-Poker Creeks Research Watershed (Alaska) Pulse-Amplitude Modulation Fluorometry Needle-Scale Observations (March 2022 - July 2024)

   https://doi.org/10.18739/a2ww77233  

   Dawson_III, Wayne; Jablonski, Andrew; Kim, Jongmin; Watts, Jennifer; Epstein, Howard; Yang, Xi (April 2026, NSF Arctic Data Center)

   This dataset consists of pulse-amplitude modulation (PAM) fluorometry data collected using six Micro-PAM (Walz GmbH, Effeltrich, Germany) measurement heads installed on two black spruce trees at the Caribou-Poker Creeks Research Watershed National Ecological Observatory Network (NEON) site (site abbreviation: BONA) near Fairbanks, Alaska. These measurements provide information on the physiological status of photosystem II and photosynthesis more broadly for the needles and trees observed. Three PAM measurement heads were installed on each tree at low, intermediate, and high heights qualitatively within the canopy. Five measurement heads (second intermediate position measurement excluded) were installed in March 2022, with the sixth measurement head being installed in July 2022. Data was collected semi-continuously, including the harsh winter season, until July 2024. PAM fluorescence measurements were collected in a ramped scheme centered around noon to maximize measurement frequency while still allowing for adequate dark acclimation for the nighttime measurement. Saturation pulses were performed by each measuring head every fifteen minutes from 9:00AM – 5:00PM, every thirty minutes from 7:00AM-9:00AM and 5:00PM-7:00PM, and every hour from 2:00AM-7:00AM and 7:00PM-9:00PM. 

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2. Combining Open-Source Machine Learning and Publicly Available Aerial Data (NAIP and NEON) to Achieve High-Resolution High-Accuracy Remote Sensing of Grass–Shrub–Tree Mosaics

   https://doi.org/10.3390/rs17132224  

   Noble, Brynn; Ratajczak, Zak (July 2025, Remote Sensing)

   Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. 

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3. Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

   https://doi.org/10.1111/gcb.16916  

   Wang, Ran; Springer, Kyle R.; Gamon, John A. (November 2023, Global Change Biology)

   Abstract Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance‐based remotely sensed optical signals at northern latitude or high‐altitude regions are readily confounded by snow coverage, hampering applications of satellite‐based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness‐based indices and ecosystem productivity of many evergreen‐dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global‐scale photosynthesis monitoring efforts using remotely sensed vegetation indices. 

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4. Postfire deciduous canopies drive patterns in snowshoe hare herbivory of regenerating black spruce

   https://doi.org/10.1139/cjfr-2019-0150  

   Olnes, Justin; Kielland, Knut; Genet, Hélène; Ruess, Roger (November 2019, Canadian Journal of Forest Research)

   The future of boreal forests in Alaska, United States, will likely consist of more deciduous-dominant stands because larger and more severe fires facilitate the establishment of deciduous species such as trembling aspen (Populus tremuloides Michx.) and Alaska birch (Betula neoalaskana Sarg.). Whether stands transition to a deciduous-dominant system or mixed-wood forest or return to being dominated by black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) depends on the capacity of regenerating black spruce to grow and produce seed before the next fire. We hypothesized that winter herbivory by snowshoe hares (Lepus americanus Erxleben, 1777) can suppress black spruce under deciduous canopies. We addressed this question by measuring changes in spruce height and herbivory across 54 plots in Interior Alaska that burned 8–88 years ago and related these data to plot-level data collected by the Bonanza Creek Long-Term Ecological Research program. Spruce were more likely browsed at deciduous-dominant sites with dense canopies, and this browsing likely reduced their height growth. Although we found more subtle effects of browsing on height at the individual level, browsing was an important variable in a confirmatory path analysis at the plot level. These observations affirm our broader hypothesis of the selectivity of hare browsing, in that snowshoe hares prefer to browse spruce that are taller and faster growing, effectively “leveling” regenerating seedlings and saplings so that browsed and unbrowsed individuals within a site are the same height. 

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5. Material Legacies and Environmental Constraints Underlie Fire Resilience of a Dominant Boreal Forest Type

   https://doi.org/10.1007/s10021-022-00772-7  

   Day, Nicola J.; Johnstone, Jill F.; Reid, Kirsten A.; Cumming, Steven G.; Mack, Michelle C.; Turetsky, Merritt R.; Walker, Xanthe J.; Baltzer, Jennifer L. (June 2022, Ecosystems)

   Abstract Resilience of plant communities to disturbance is supported by multiple mechanisms, including ecological legacies affecting propagule availability, species’ environmental tolerances, and biotic interactions. Understanding the relative importance of these mechanisms for plant community resilience supports predictions of where and how resilience will be altered with disturbance. We tested mechanisms underlying resilience of forests dominated by black spruce ( Picea mariana ) to fire disturbance across a heterogeneous forest landscape in the Northwest Territories, Canada. We combined surveys of naturally regenerating seedlings at 219 burned plots with experimental manipulations of ecological legacies via seed addition of four tree species and vertebrate exclosures to limit granivory and herbivory at 30 plots varying in moisture and fire severity. Black spruce recovery was greatest where it dominated pre-fire, at wet sites with deep residual soil organic layers, and fire conditions of low soil or canopy combustion and longer return intervals. Experimental addition of seed indicated all species were seed-limited, emphasizing the importance of propagule legacies. Black spruce and birch ( Betula papyrifera ) recruitment were enhanced with vertebrate exclusion. Our combination of observational and experimental studies demonstrates black spruce is vulnerable to effects of increased fire activity that erode ecological legacies. Moreover, black spruce relies on wet areas with deep soil organic layers where other species are less competitive. However, other species can colonize these areas if enough seed is available or soil moisture is altered by climate change. Testing mechanisms underlying species’ resilience to disturbance aids predictions of where vegetation will transform with effects of climate change. 

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