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Abstract Pigment dynamics in temperate evergreen forests remain poorly characterized, despite their year-round photosynthetic activity and importance for carbon cycling. Developing rapid, nondestructive methods to estimate pigment composition enables high-throughput assessment of plant acclimation states. In this study, we investigate the seasonality of eight chlorophyll and carotenoid pigments and hyperspectral reflectance data collected at both the needle (400–2400 nm) and canopy (420–850 nm) scales in Pinus palustris (longleaf pine) at the Ordway Swisher Biological Station in north-central Florida, USA. Needle spectra were obtained at three distinct times throughout the year, while tower-based spectra were collected continuously over a nine-month period. Seasonal trends in photoprotective pigments (e.g., lutein and xanthophylls) and photosynthetic pigments (e.g., chlorophylls) aligned closely with seasonal changes in photosynthetically active radiation and gross primary productivity. To track inter-tree and seasonal variability in pigment pools with hyperspectral reflectance data, we used correlation analyses and ridge regression models. Ridge regression models using the full hyperspectral range outperformed predictions using standard linear regression with specific wavelengths in a normalized difference index fashion. Ridge regression successfully predicted all pigment pools (R2 > 0.5) with comparable accuracy at both the needle and canopy scales. The models performed best for lutein, neoxanthin, antheraxanthin, and chlorophyll a and b - which had greater inter-tree and seasonal variation - and achieved moderate accuracy for violaxanthin, alpha-carotene, and beta-carotene. These results provide a foundation for scaling biochemical traits from ground-based sensors to airborne and satellite platforms, particularly in ecosystems with subtle changes in pigment dynamics. 

Abstract The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive “greenness” of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIF_relative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r² = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUE_Pand SIF_relativewhich were well correlated across all sites (r² = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. 

PREMISEBiological invasions increasingly threaten native biodiversity and ecosystem services. One notable example is the common reed,Phragmites australis, which aggressively invades North American salt marshes. Elevated atmospheric CO₂and nitrogen pollution enhance its growth and facilitate invasion becauseP. australisresponds more strongly to these enrichments than do native species. We investigated how modifications to stomatal features contribute to strong photosynthetic responses to CO₂and nitrogen enrichment inP. australisby evaluating stomatal shifts under experimental conditions and relating them to maximal stomatal conductance (g_wmax) and photosynthetic rates. METHODSPlants were grownin situin open‐top chambers under ambient and elevated atmospheric CO₂(eCO₂) and porewater nitrogen (N_enr) in a Chesapeake Bay tidal marsh. We measured light‐saturated carbon assimilation rates (A_sat) and stomatal characteristics, from which we calculatedg_wmaxand determined whether CO₂and N_enraltered the relationship betweeng_wmaxandA_sat. RESULTSeCO₂and N_enrenhanced bothg_wmaxandA_sat, but to differing degrees;g_wmaxwas more strongly influenced by N_enrthrough increases in stomatal density whileA_satwas more strongly stimulated by eCO₂. There was a positive relationship betweeng_wmaxandA_satthat was not modified by eCO₂or N_enr, individually or in combination. CONCLUSIONSChanges in stomatal features co‐occur with previously described responses ofP. australisto eCO₂and N_enr. Complementary responses of stomatal length and density to these global change factors may facilitate greater stomatal conductance and carbon gain, contributing to the invasiveness of the introduced lineage. 

Summary Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO₂flux measurements, and a predictive model to simulate seasonal canopy color dynamics.We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.