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1. Temperature Gradients and Inversions in a Forested Cascade Range Basin: Synoptic‐ to Local‐Scale Controls

   https://doi.org/10.1029/2020JD032686  

   Rupp, David E.; Shafer, Sarah L.; Daly, Christopher; Jones, Julia A.; Frey, Sarah J. K. (November 2020, Journal of Geophysical Research: Atmospheres)

   Abstract Cold‐air pooling and associated air temperature inversions are important features of mountain landscapes, but incomplete understanding of their controlling factors hinders prediction of how they may mediate potential future climate changes at local scales. We evaluated how topographic and forest canopy effects on insolation and local winds altered the expression of synoptic‐scale meteorological forcing on near‐surface air temperature inversions and how these effects varied by time of day, season, and spatial scale. Using ~13 years of hourly temperature measurements in forest canopy openings and under the forest canopy at the H.J. Andrews Experimental Forest in the western Cascade Range of Oregon (USA), we calculated air temperature gradients at the basin scale (high vs. low elevation) and at the cross‐valley scale for two transects that differed in topography and forest canopy cover. ERA5 and NCEP NCAR R1 reanalysis data were used to evaluate regional‐scale conditions. Basin and cross‐valley temperature inversions were frequent, particularly in winter and often persisted for several days. Nighttime inversions were more frequent at the cross‐valley scale but displayed the same intra‐annual pattern at the basin and regional scales, becoming most frequent in summer. Nighttime temperature gradients at basin and cross‐valley scales responded similarly to regional‐scale controls, particularly free‐air temperature gradients, despite differences in topography and forest cover. In contrast, the intra‐annual pattern of daytime inversions differed between the basin and cross‐valley scales and between the two cross‐valley transects, implying that topographic and canopy effects on insolation and local winds were key controls at these scales. 

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2. Bryophytes in fir waves: Forest canopy indicator species and functional diversity decline in canopy gaps

   https://doi.org/10.1111/jvs.12718  

   Berdugo, Monica B.; Dovciak, Martin; Nakashizuka, ed., Tohru (March 2019, Journal of Vegetation Science)

   Abstract AimsBryophytes can cover three quarters of the ground surface, play key ecological functions, and increase biodiversity in mesic high‐elevation conifer forests of the temperate zone. Forest gaps affect species coexistence (and ecosystem functions) as suggested by the gap and gap‐size partitioning hypotheses (GPH,G_SPH). Here we test these hypotheses in the context of high‐elevation forest bryophyte communities and their functional attributes. Study SiteSpruce–fir forests on Whiteface Mountain, NY,USA. MethodsWe characterized canopy openness, microclimate, forest floor substrates, vascular vegetation cover, and moss layer (cover, common species, and functional attributes) in three canopy openness environments (gap, gap edge, forest canopy) across 20 gaps (fir waves) (n = 60); the functional attributes were based on 16 morphologic, reproductive, and ecological bryophyte plant functional traits (PFTs). We testedGPHandG_SPHrelative to bryophyte community metrics (cover, composition), traits, and trait functional sensitivity (functional dispersion;FDis) using indicator species analysis, ordination, and regression. ResultsCanopy openness drove gradients in ground‐level temperature, substrate abundance and heterogeneity (beta diversity), and understory vascular vegetation cover. TheGPHwas consistent with (a) the abundance patterns of forest canopy indicator species (Dicranum fuscescens,Hypnum imponens, andTetraphis pellucida), and (b)FDisbased on threePFTs (growth form, fertility, and acidity), both increasing with canopy cover. We did not find support forGPHin the remaining species or traits, or forG_SPHin general; gap width (12–44 m) was not related to environmental or bryophyte community gradients. ConclusionsThe observed lack of variation in most bryophyte metrics across canopy environments suggests high resistance of the bryophyte layer to natural canopy gaps in high‐elevation forests. However, responses of forest canopy indicator species suggest that canopy mortality, potentially increased by changing climate or insect pests, may cause declines in some forest canopy species and consequently in the functional diversity of bryophyte communities. 

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3. High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery

   https://doi.org/10.3390/rs14143409  

   John, Aji; Cannistra, Anthony F.; Yang, Kehan; Tan, Amanda; Shean, David; Hille Ris Lambers, Janneke; Cristea, Nicoleta (July 2022, Remote Sensing)

   Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins. 

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4. High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning

   https://doi.org/10.3389/frwa.2023.1128758  

   Yang, Kehan; John, Aji; Shean, David; Lundquist, Jessica D.; Sun, Ziheng; Yao, Fangfang; Todoran, Stefan; Cristea, Nicoleta (June 2023, Frontiers in Water)

   Mountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing snow cover datasets have a coarse resolution. To advance our observation capabilities of snow cover in meadows and forests, we developed a machine learning model to generate snow-covered area (SCA) maps from PlanetScope imagery at about 3-m spatial resolution. The model achieves a median F1 score of 0.75 for 103 cloud-free images across four different sites in the Western United States and Switzerland. It is more accurate (F1 score = 0.82) when forest areas are excluded from the evaluation. We further tested the model performance across 7,741 mountain meadows at the two study sites in the Sierra Nevada, California. It achieved a median F1 score of 0.83, with higher accuracy for larger and simpler geometry meadows than for smaller and more complexly shaped meadows. While mapping SCA in regions close to or under forest canopy is still challenging, the model can accurately identify SCA for relatively large forest gaps (i.e., 15m < DCE < 27m), with a median F1 score of 0.87 across the four study sites, and shows promising accuracy for areas very close (>10m) to forest edges. Our study highlights the potential of high-resolution satellite imagery for mapping mountain snow cover in forested areas and meadows, with implications for advancing ecohydrological research in a world expecting significant changes in snow. 

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5. Press-pulse interactions and long-term community dynamics in a Chihuahuan Desert grassland

   Scott L Collins, Y. Anny (October 2020, Journal of vegetation science)

   Questions: Reordering of dominant species is an important mechanism of community response to global environmental change. We asked how wildfire (a pulse event) interacts with directional changes in climate (environmental presses) to affect plant community dynamics in a Chihuahuan Desert grassland. Location: Sevilleta National Wildlife Refuge, Socorro County, New Mexico, USA Methods: Vegetation cover by species was measured twice each year from 1989 to 2019 along two permanently located 400 m long line intercept transects, one in Chihuahuan Desert grassland, and the second in the ecotone between Chihuahuan Desert and Great Plains grasslands. Trends in community structure were plotted over time, and climate sensitivity functions were used to predict how changes in the Pacific Decadal Oscillation (PDO) affected vegetation dynamics. Results: Community composition was undergoing gradual change in the absence of disturbance in the ecotone and desert grassland. These changes were related to the reordering of abundances between two foundation grasses, Bouteloua eriopoda and B. gracilis, that together account for >80% of aboveground primary production. However, reordering varied over time in response to wildfire (a pulse) and changes in the PDO (a press). Community dynamics were initially related to the warm and cool phases of the PDO, but in the ecotone these relationships changed following wildfire, which reset the system. Conclusions: Species reordering is an important component of community dynamics in response to ecological presses. However, reordering is a complex, non-linear process in response to ecological presses that may change over time and interact with pulse disturbances. 

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