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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 (r 2  = 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 P and SIF relative which were well correlated across all sites (r 2  = 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., (© 2024 The Author(s). Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

Hurricane María, a catastrophic natural event in 2017, left an indelible mark on the Puerto Rican landscape. This study examines the hurricane's ecological repercussions by analyzing tree cover in the municipality of Ponce, southern Puerto Rico. Our primary focus is on the changes in tree cover, encompassing urban, coastal, and rural areas, from before to after the hurricane. Utilizing highresolution spatial imagery and remote sensing techniques, we map and compare vegetation cover in 2016 (pre-Hurricane María) and 2018 (post-Hurricane María). The study covers three landscapes of Ponce, from the north, center, and south of the municipality, offering a comprehensive understanding of the hurricane's impact on different landscapes. The findings reveal profound alterations in tree cover, with specific attention to the urban, coastal, and rural zones. In the aftermath of Hurricane María, the North/ Rural zone witnessed substantial decreases of 14.73% in tree cover. The Center/Urban zone displayed a mix of trends, highlighting the complex interaction between urbanization and hurricane impacts. Coastal areas in the South/Coast zone exhibited diverse impacts, emphasizing the need for conservation and restoration strategies. Detailed insights into specific neighborhoods underscore the localized social impacts of Hurricane María. Located in the northern region of the municipality, Guaraguao's negative change in tree cover may translate into reduced biodiversity and ecosystem services. Urban neighborhoods, such as San Antón, experienced considerable negative changes, indicating challenges such as increased heat stress and decreased aesthetic value. This research not only enriches our understanding of the ecological shifts induced by Hurricane María, but also informs for future resilience planning and conservation efforts. By emphasizing the interconnected nature of social and environmental impacts, the study serves as a tool for adaptive public policies that integrate ecological resilience with community well-being. [ABSTRACT FROM AUTHOR]

We measured light‐related patterns of primary productivity within a topographically complex Oregon watershed over a 30‐year period. Second‐growth conifer densities were experimentally altered in 1981. Plots receiving at least 3434 MJ m−2 over a 6‐month growing season averaged 40% greater aboveground net primary productivity (ANPP) than those receiving less light (p = 0.000). Unthinned stands potentially built enough LAI to compensate for low light, but risked mortality that exceeded resilience. The two light levels acted as basins of attraction for other physiological and ecological processes, including size–density relationships and limiting foliar nutrients. Initial (1981) LAI and the irradiation step (above or below 3434 MJ m−2) explained 60% of variation in a 30‐year ANPP. Irradiation within each light group did not affect ANPP. At high irradiation, foliar N/Ca and slope steepness (both negative) explained 58% of the variation in residuals from the initial models, while at low irradiation on north, east, and west aspects, 83% of residual variation was explained by foliar Mg (+), understory cover (+), and 30‐year mortality (−). Light use efficiency (LUE) of fully stocked stands correlated with LAI and foliar N/K. Results suggest that understory influence on tree foliar N (+ or −) enhances ANPP by regulating critical nutrient ratios. Mortality reduced or eliminated differences among thinning levels, which did not vary at low light and only between unthinned and heavily thinned at high light. Values associated with relatively open forests (biodiversity, resilience) may be attained without large sacrifice of long‐term carbon sinks. In our study, light interacts with topography to produce nonlinear dynamics in which small changes in irradiation can have large consequences. Reduced sunlight has been suggested as a geoengineering option to combat global warming. Ecological changes out of proportion to lowered irradiation are a distinct possibility, including sharp reductions in terrestrial carbon sinks. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of soil respiration (R s ) and its environmental drivers is crucial for accurately modeling terrestrial carbon fluxes. However, current methodologies often lead to divergent estimates and rely on annual predictions that may overlook critical interactions occurring at seasonal scales. A critical knowledge gap lies in understanding how temporal resolution affects both R s predictions and their environmental drivers. Here, we employ deep learning models to predict global R s at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting R s , including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global R s estimation of 79.4 ± 5.7 Pg C year -1 for the MRM and 78.3 ± 7.5 Pg C year -1 for ARM (mean ± SD). While the difference in global estimations between both models is small, there are notable disparities in the spatial contribution of dominant drivers. The MRM highlights an influence of both temperature and LAI, while the ARM emphasizes a dominant role of precipitation. These findings underscore the critical role of temporal resolution in capturing seasonal variations and identifying key R s -environment relationships that annual models may obscure. High temporal resolution R s predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between R s and its drivers, refining carbon flux models, detecting critical seasonal thresholds, and enhancing the reliability of future Earth system predictions. This work highlights the need for further research into monthly and seasonal R s variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate., (© 2025 John Wiley & Sons Ltd.)

In the semi-arid grasslands of the southwest United States, annual precipitation is divided between warm-season (July-September) convective precipitation and cool-season (December-March) frontal storms. While evidence suggests shifts in precipitation seasonal distribution, there is a poor understanding of the ecosystem carbon flux responses to cool-season precipitation and the potential legacy effects on subsequent warm-season carbon fluxes. Results from a two-year experiment with three cool-season precipitation treatments (dry, received 5th percentile cool-season total precipitation; normal, 50th; wet, 95th) and constant warm-season precipitation illustrate the direct and legacy effects on carbon fluxes, but in opposing ways. In wet cool-season plots, gross primary productivity (GPP) and ecosystem respiration (ER) were 103% and 127% higher than in normal cool-season plots. In dry cool-season plots, GPP and ER were 47% and 85% lower compared to normal cool-season plots. Unexpectedly, we found a positive legacy effect of the dry cool-season treatment on warm-season carbon flux, resulting in a significant increase in both GPP and ER in the subsequent warm season, compared to normal cool-season plots. Our results reveal positive legacy effects of cool-season drought on warm-season carbon fluxes and highlight the importance of the relatively under-studied cool-growing season and its direct/indirect impact on the ecosystem carbon budget., (© 2024 John Wiley & Sons Ltd.)

Despite decades of progress, much remains unknown about successional trajectories of carbon (C) cycling in north temperate forests. Drivers and mechanisms of these changes, including the role of different types of disturbances, are particularly elusive. To address this gap, we synthesized decades of data from experimental chronosequences and long-term monitoring at a well-studied, regionally representative field site in northern Michigan, USA. Our study provides a comprehensive assessment of changes in above- and belowground ecosystem components over two centuries of succession, links temporal dynamics in C pools and fluxes with underlying drivers, and offers several conceptual insights to the field of forest ecology. Our first advance shows how temporal dynamics in some ecosystem components are consistent across severe disturbances that reset succession and partial disturbances that slightly modify it: both of these disturbance types increase soil N availability, alter fungal community composition, and alter growth and competitive interactions between short-lived pioneer and longer-lived tree taxa. These changes in turn affect soil C stocks, respiratory emissions, and other belowground processes. Second, we show that some other ecosystem components have effects on C cycling that are not consistent over the course of succession. For example, canopy structure does not influence C uptake early in succession but becomes important as stands develop, and the importance of individual structural properties changes over the course of two centuries of stand development. Third, we show that in recent decades, climate change is masking or overriding the influence of community composition on C uptake, while respiratory emissions are sensitive to both climatic and compositional change. In synthesis, we emphasize that time is not a driver of C cycling; it is a dimension within which ecosystem drivers such as canopy structure, tree and microbial community composition change. Changes in those drivers, not in forest age, are what control forest C trajectories, and those changes can happen quickly or slowly, through natural processes or deliberate intervention. Stemming from this view and a whole-ecosystem perspective on forest succession, we offer management applications from this work and assess its broader relevance to understanding long-term change in other north temperate forest ecosystems., (© 2025 The Ecological Society of America.)

The carbon sink function performed by the different vegetation types along the environmental gradient in coastal zones plays a vital role in mitigating climate change. However, inadequate understanding of its spatiotemporal variations across different vegetation types and associated regulatory mechanisms hampers determining its potential shifts in a changing climate. Here, we present long-term (2011-2022) eddy covariance measurements of the net ecosystem exchange (NEE) of CO 2 at three sites with different vegetation types (tidal wetland, nontidal wetland, and cropland) in a coastal zone to examine the role of vegetation type on annual carbon sink strength. We found that the three study sites are stable carbon sinks and are influenced by their distinct physiological and phenological factors. The annual NEE of the tidal wetland, nontidal wetland, and cropland were determined predominantly by the seasonal peaks of net CO 2 uptake, release, and duration of CO 2 uptake period. Furthermore, the changes in annual NEE were sensitive to climatic variables, as spring mean air temperature reduced the carbon sink strength in the tidal wetland, maximum daily precipitation in summer reduced it in the nontidal wetland, and summer mean global radiation elicited the same effect in the cropland. Finally, a worldwide database of the three vegetation types was compiled, using which we further validated the global consistency of the biological controls. Overall, these results emphasize the importance of considering the underlying mechanisms by which vegetation types influence NEE for the accurate forecasting of carbon sink dynamics across different coastal vegetation types under climate change., (© 2025 John Wiley & Sons Ltd.)

Tree-ring data has been widely used to inform about tree growth responses to drought at the individual scale, but less is known about how tree growth sensitivity to drought scales up driving changes in forest dynamics. Here, we related tree-ring growth chronologies and stand-level forest changes in basal area from two independent data sets to test if tree-ring responses to drought match stand forest dynamics (stand basal area growth, ingrowth, and mortality). We assessed if tree growth and changes in forest basal area covary as a function of spatial scale and tree taxa (gymnosperm or angiosperm). To this end, we compared a tree-ring network with stand data from the Spanish National Forest Inventory. We focused on the cumulative impact of drought on tree growth and demography in the period 1981-2005. Drought years were identified by the Standardized Precipitation Evapotranspiration Index, and their impacts on tree growth by quantifying tree-ring width reductions. We hypothesized that forests with greater drought impacts on tree growth will also show reduced stand basal area growth and ingrowth and enhanced mortality. This is expected to occur in forests dominated by gymnosperms on drought-prone regions. Cumulative growth reductions during dry years were higher in forests dominated by gymnosperms and presented a greater magnitude and spatial autocorrelation than for angiosperms. Cumulative drought-induced tree growth reductions and changes in forest basal area were related, but initial stand density and basal area were the main factors driving changes in basal area. In drought-prone gymnosperm forests, we observed that sites with greater growth reductions had lower stand basal area growth and greater mortality. Consequently, stand basal area, forest growth, and ingrowth in regions with large drought impacts was significantly lower than in regions less impacted by drought. Tree growth sensitivity to drought can be used as a predictor of gymnosperm demographic rates in terms of stand basal area growth and ingrowth at regional scales, but further studies may try to disentangle how initial stand density modulates such relationships. Drought-induced growth reductions and their cumulative impacts have strong potential to be used as early-warning indicators of regional forest vulnerability., (© 2022 The Ecological Society of America.)

The FLUXNET Meeting 2023 brought together scientists to discuss eddy covariance research on ecosystem functioning and plans for data sharing. FLUXNET is a global network of tower sites that measure exchanges between ecosystems and the atmosphere. The meeting focused on future priorities such as data sharing infrastructure, outreach, and support for early career scientists. The article also explores FLUXNET's current and historical activities, applications of its data, and future opportunities in carbon accounting, remote sensing, and land surface modeling. FLUXNET aims to collect and share long-term continuous datasets to study climate change's impact on ecosystems, with standardized data processing and distribution being important goals. Education, outreach, and community building are also emphasized, with efforts to support low- and medium-income countries. The FLUXNET Early Career Network plays a crucial role in promoting global collaboration. Overall, FLUXNET aims to strengthen connections and collaboration to address questions about ecosystem functioning in the face of climate change. [Extracted from the article]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

Marking its 50th year in 2024, the International Tree-Ring Databank (ITRDB) is a lasting and invaluable scientific resource, composed of over 6000 tree-ring chronology sites and more than 9000 publicly available measurement data files. It is the central global repository for tree-ring chronologies and associated measurements, providing the foundation for centennial to millennial length climate reconstructions, including large-scale spatially gridded datasets and hundreds of studies on earth systems, ecological processes, and societal responses to global change. As the stewards of the ITRDB, we report on significant progress made to ensure its vitality in an era of big data, with all sites and associated measurement files meeting FAIR data standards, including citable DOIs and the achievement of machine readability via computing languages such as R and other software. This progress is thanks in large part to the global dendrochronological community for their collaborations on data checking and software development. It is a time to celebrate the repository and the tree-ring community that had the foresight and generosity to create and contribute to it. We look forward to another 50 years of innovation and insight from the community, and to maintaining the ITRDB as an ever-growing network of tree-ring chronology sites. [ABSTRACT FROM AUTHOR]

In the Northern Hemisphere, anthropogenic nitrogen (N) deposition contributed to the enhancement of the global terrestrial carbon (C) sink, partially offsetting CO 2 emissions. Across several long-term field experiments, this ecosystem-level response was determined to be driven, in part, by the suppression of microbial activity associated with the breakdown of soil organic matter. However, since the implementation of emission abatement policies in the 1970s, atmospheric N deposition has declined globally, and the consequences of this decline are unknown. Here, we assessed the response of soil C storage and associated microbial activities, in a long-term field study that experimentally increased N deposition for 24 years. We measured soil C and N, microbial activity, and compared effect sizes of soil C in response to, and in recovery from, the N deposition treatment across the history of our experiment (1994-2022). Our results demonstrate that the accumulated C in the organic horizon has been lost and exhibits additional deficits 5 years post-termination of the N deposition treatment. These findings, in part, arise from mechanistic changes in microbial activity. Soil C in the mineral soil was less responsive thus far in recovery. If these organic horizon C dynamics are similar in other temperate forests, the Northern Hemisphere C sink will be reduced and climate warming will be enhanced., (© 2024 The Author(s). Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively. [ABSTRACT FROM AUTHOR]

Much of the previous research on total and heavy precipitation trends across the northeastern United States (herein, the Northeast) used daily precipitation totals over relatively short periods of record, which do not capture the full range of climate variability and change. Less well understood are the characteristics of long-term changes and synoptic patterns in longer-duration heavy precipitation events across the Northeast. A multiduration (1, 2, 3, 7, 14, and 30 days), multi-return-interval (2, 5, 10, and 50 years) precipitation dataset was used to diagnose changes in various types of precipitation events across the Northeast from 1895 to 2017. Increasing trends were found in all duration and return-interval event combinations with the rarest, longest duration events increasing at faster rates than more-frequent, shorter-duration ones. Daily 850-hPa geopotential height patterns associated with precipitation events were extracted from rotated principal component analysis and k-means clustering analysis, which allowed for the main synoptic types present as well as their structure and evolution to be analyzed. The daily synoptic patterns thus identified were found to be similar across all durations and return intervals and included coastal low (Northeasters, tropical cyclones, and predecessor rain events), deep trough, East Coast trough, zonal, and high pressure patterns. [ABSTRACT FROM AUTHOR]

In dryland soils, spatiotemporal variation in surface soils (0–10 cm) plays an important role in the function of the "critical zone" that extends from canopy to groundwater. Understanding connections between soil microbes and biogeochemical cycling in surface soils requires repeated multivariate measurements of nutrients, microbial abundance, and microbial function. We examined these processes in resource islands and interspaces over a two‐month period at a Chihuahuan Desert bajada shrubland site. We collected soil in Prosopis glandulosa (honey mesquite), Larrea tridentata (creosote bush), and unvegetated (interspace) areas to measure soil nutrient concentrations, microbial biomass, and potential soil enzyme activity. We monitored the dynamics of these belowground processes as soil conditions dried and then rewetted due to rainfall. Most measured variables, including inorganic nutrients, microbial biomass, and soil enzyme activities, were greater under shrubs during both wet and dry periods, with the highest magnitudes under mesquite followed by creosote bush and then interspace. One exception was nitrate, which was highly variable and did not show resource island patterns. Temporally, rainfall pulses were associated with substantial changes in soil nutrient concentrations, though resource island patterns remained consistent during all phases of the soil moisture pulse. Microbial biomass was more consistent than nutrients, decreasing only when soils were driest. Potential enzyme activities were even more consistent and did not decline in dry periods, potentially helping to stimulate observed pulses in CO2 efflux following rain events observed at a co‐located eddy flux tower. These results indicate a critical zone with organic matter cycling patterns consistently elevated in shrub resource islands (which varied by shrub species), high decomposition potential that limits soil organic matter accumulation across the landscape, and nitrate fluxes that are decoupled from the organic matter pathways. [ABSTRACT FROM AUTHOR]

The National Ecological Observatory Network Terrestrial Observation System (NEON TOS) produces open‐access data products that allow data users to investigate the impact of change drivers on key "sentinel" taxa and soils. The spatial and temporal sampling strategy that coordinates implementation of these protocols enables integration across TOS products and with products generated by NEON aquatic, remote sensing, and terrestrial instrument subsystems. Here, we illustrate the plots and sampling units that make up the physical foundation of a NEON TOS site, and we describe the scales (subplot, plot, airshed, and site) at which sampling is spatially colocated across protocols and subsystems. We also describe how moderate resolution imaging spectroradiometer‐enhanced vegetation index (MODIS‐EVI) phenology data are used to temporally coordinate TOS sampling within and across years at the continental scale of the observatory. Individually, TOS protocols produce data products that provide insight into populations, communities, and ecosystem processes. Within the spatial and temporal framework that guides cross‐protocol implementation, the ability to draw inference across data products is enhanced. To illustrate this point, we develop an example using R software that links two TOS data products collected with different temporal frequencies at both plot and site spatial scales. A thorough understanding of how TOS protocols are integrated with each other in space and time, and with other NEON subsystems, is necessary to leverage NEON data products to maximum effect. For example, a researcher must understand the spatial and temporal scales at which soil biogeochemistry data, soil microbe biomass data, and plant litter production and chemistry data may be combined to quantify soil nutrient stocks and fluxes across NEON sites. We present clear links among TOS protocols and across NEON subsystems that will enhance the utility of NEON TOS data products for the data user community. [ABSTRACT FROM AUTHOR]

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Periodic observations of vegetation index, such as the normalized difference vegetation index (NDVI), can be used for data assimilation in heterogenous ecosystems. Indeed, the new Sentinel 2 Multispectral instrument and Landsat 8 Operational Land Imager sensor data are available at such high temporal and spatial resolutions that can be used to detect the patches of the main vegetation components (grass and trees) of heterogenous ecosystems, and capture their dynamics. We demonstrate the possibility to merge grass and tree NDVI observations and models, to optimally provide robust predictions of grass and tree leaf area index. The proposed assimilation approach assimilates NDVI data through the Ensemble Kalman filter (EnKF) and dynamically calibrates a key vegetation dynamic model parameter, the maintenance respiration coefficient (ma). In the presence of large bias of the grass and tree ma base values, only the use of the proposed assimilation approach removes the large bias in the biomass balance, dynamically calibrating maintenance respiration coefficients, and corrects the model. The performance of a land surface – vegetation model was improved by assimilating observations of NDVI. The effective impact of the proposed assimilation approach on the evapotranspiration and CO2 uptake predictions in the heterogenous ecosystem is also demonstrated. [ABSTRACT FROM AUTHOR]

Previous studies have confirmed the time-lagged and cumulative effects of drought and anthropogenic activities on vegetation growth, but these studies focus on the time-lagged effect of drought and are poorly known how vegetation productivity responds to anthropogenic activities. Here, based on the reconstructed Normalized Difference Vegetation Index, the Standardized Precipitation Evapotranspiration Index and land use degree comprehensive index, we diagnosed the spatiotemporal pattern of vegetation and drought, investigated time-lagged and cumulative effects of drought and anthropogenic activities over China through the month where the maximum correlation coefficient occurred. It revealed that the browning trend of 32.21% of vegetated lands was covered by overall greening, especially in northwestern China. Drought intensified with a rate of 0.0014/year. 66.41% and 54.57% of the vegetated lands had time-lagged and cumulative response to drought, with a shorter timescales of 1–4 months, indicating the higher sensitivity of vegetation growth to drought. There was a U-shaped relationship between moisture conditions and vegetation response time. 49.9% of China's vegetation showed time-lagged effects to anthropogenic activities, with a longer timescales of 6–10 years, demonstrating that anthropogenic activities triggered ecological changes but vegetation ecosystems cannot keep pace. The accumulated and time-lagged years declined with increased land use intensity. [ABSTRACT FROM AUTHOR]

The timing of phenological events is highly sensitive to climate change, and may influence ecosystem structure and function. Although changes in flowering phenology among species under climate change have been reported widely, how species‐specific shifts will affect phenological synchrony and community‐level phenology patterns remains unclear. We conducted a manipulative experiment of warming and precipitation addition and reduction to explore how climate change affected flowering phenology at the species and community levels in an alpine meadow on the eastern Tibetan Plateau. We found that warming advanced the first and last flowering times differently and with no consistent shifts in flowering duration among species, resulting in the entire flowering period of species emerging earlier in the growing season. Early‐flowering species were more sensitive to warming than mid‐ and late‐flowering species, thereby reducing flowering synchrony among species and extending the community‐level flowering season. However, precipitation and its interactions with warming had no significant effects on flowering phenology. Our results suggest that temperature regulates flowering phenology from the species to community levels in this alpine meadow community, yet how species shifted their flowering timing and duration in response to warming varied. This species‐level divergence may reshape flowering phenology in this alpine plant community. Decreasing flowering synchrony among species and the extension of community‐level flowering seasons under warming may alter future trophic interactions, with cascading consequences to community and ecosystem function. [ABSTRACT FROM AUTHOR]

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change., (© 2024 The Authors. American Journal of Botany published by Wiley Periodicals LLC on behalf of Botanical Society of America.)

We use the Multiple Element Limitation (MEL) model to examine responses of 12 ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water‐use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C‐nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change. [ABSTRACT FROM AUTHOR]

Macrosystem‐scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a "network of networks" (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of "big data" ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade‐offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner. [ABSTRACT FROM AUTHOR]

Carbon (C) allocation and nonstructural carbon (NSC) dynamics play essential roles in plant growth and survival under stress and disturbance. However, quantitative understanding of these processes remains limited. Here we propose a framework where we connect commonly measured carbon cycle components (eddy covariance fluxes of canopy CO2 exchange, soil CO2 efflux, and allometry‐based biomass and net primary production) by a simple mass balance model to derive ecosystem‐level NSC dynamics (NSCi), C translocation (dCi), and the biomass production efficiency (BPEi) in above‐ and belowground plant (i = agp and bgp) compartments. We applied this framework to two long‐term monitored loblolly pine (Pinus taeda) plantations of different ages in North Carolina and characterized the variations of NSC and allocation in years under normal and drought conditions. The results indicated that the young stand did not have net NSC flux at the annual scale, whereas the mature stand stored a near‐constant proportion of new assimilates as NSC every year under normal conditions, which was comparable in magnitude to new structural growth. Roots consumed NSC in drought and stored a significant amount of NSC post drought. The above‐ and belowground dCi and BPEi varied more from year to year in the young stand and approached a relatively stable pattern in the mature stand. The belowground BPEbgp differed the most between the young and mature stands and was most responsive to drought. With the internal C dynamics quantified, this framework may also improve biomass production estimation, which reveals the variations resulting from droughts. Overall, these quantified ecosystem‐scale dynamics were consistent with existing evidence from tree‐based manipulative experiments and measurements and demonstrated that combining the continuous fluxes as proposed here can provide additional information about plant internal C dynamics. Given that it is based on broadly available flux data, the proposed framework is promising to improve the allocation algorithms in ecosystem C cycle models and offers new insights into observed variability in soil–plant–climate interactions. [ABSTRACT FROM AUTHOR]

It is widely accepted that global warming is affecting forests near the tree line by increasing tree growth in these cold‐limited environments. However, since about 1970, a reduction in tree growth near the tree line has been observed in response to warming and increased drought stress. This reduction in tree growth has been mainly reported in forests of the northern hemisphere but less studied in southern forests. In this study, we investigated tree populations of Nothofagus pumilio located near the arboreal altitudinal limit in the central Patagonian Andes (45–47° S, Aysén region, Chile). In this region, warming has been accompanied by increased drought conditions since the 2000s. We explored whether this climatic variability has promoted or reduced tree growth at the regional scale in tree lines of these broadleaved temperate forests of central Patagonia. We constructed tree‐ring chronologies and determined common growth patterns and trends, and then analyzed the influence of recent climate. We detected a significant change in the slope of regional growth trends between the periods 1955–1985 and 1985–2015. We found that positive growth trends in the period 1955–1985 were associated with warmer and drier springs. However, after 1985, we found a stabilization in N. pumilio growth associated with a steady increase in temperature in autumn. Our results support the idea that more frequent warm autumns, with very thin or no snow cover, have stabilized tree growth due to water deficit at the end of the growing season of N. pumilio. The predicted climate change scenario of increasing temperatures and drought in central Patagonia may increase competition among trees for water, particularly at the end of the growing season. Consequently, we could expect a decreasing forest growth trend in central Patagonia, potentially impacting forest dynamics of these southern forests. [ABSTRACT FROM AUTHOR]

Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems. [ABSTRACT FROM AUTHOR]

Determining when a disturbance has occurred, its severity, and when the system recovered, is important to numerous questions in the aquatic sciences. This problem can be conceptualized as the timing and degree of perturbation from a typical state, and when the system returns to that typical state. We present an algorithm for detecting disturbance and recovery designed for high‐frequency time series, e.g., data produced by automated sampling devices in instrumented buoys and flux towers. The algorithm quantifies differences in the empirical cumulative distribution functions of moving windows over reference and evaluation periods, and is sensitive to changes in the mean, variance, and higher statistical moments. Tests on simulated data show it accurately identifies disturbance and recovery. Three case studies illustrate the application of our algorithm in different empirical settings. A case study on dissolved oxygen in a Florida, USA estuary following a hurricane identified the disturbance and recovery 73 d later. A case study on air temperature and net ecosystem exchange in the Florida everglades identified cold snaps coinciding with periods of reduced carbon uptake. A case study on rotifer abundance following zebra mussel invasion in the Hudson River, NY showed rotifer collapse following invasion and recovery over a decade later. Methods such as ours can improve understanding response to disturbance and facilitate comparative and synthetic study of disturbance impacts across ecosystems. [ABSTRACT FROM AUTHOR]

Photoelectron spectroscopy is a powerful characterisation tool for semiconductor surfaces and interfaces, providing in principle a correlation between the electronic band structure and surface chemistry along with quantitative parameters such as the electron affinity, interface potential, band bending and band offsets. However, measurements are often limited to ultrahigh vacuum and only the top few atomic layers are probed. The technique is seldom applied as an in situ probe of surface processing; information is usually provided before and after processing in a separate environment, leading to a reduction in reproducibility. Advances in instrumentation, in particular electron detection has enabled these limitations to be addressed, for example allowing measurement at near-ambient pressures and the in situ, real-time monitoring of surface processing and interface formation. A further limitation is the influence of the measurement method through irreversible chemical effects such as radiation damage during X-ray exposure and reversible physical effects such as the charging of low conductivity materials. For wide-gap semiconductors such as oxides and carbon-based materials, these effects can be compounded and severe. Here we show how real-time and near-ambient pressure photoelectron spectroscopy can be applied to identify and quantify these effects, using a gold alloy, gallium oxide and semiconducting diamond as examples. A small binding energy change due to thermal expansion is followed in real-time for the alloy while the two semiconductors show larger temperature-induced changes in binding energy that, although superficially similar, are identified as having different and multiple origins, related to surface oxygen bonding, surface band-bending and a room-temperature surface photovoltage. The latter affects the p-type diamond at temperatures up to 400 °C when exposed to X-ray, UV and synchrotron radiation and under UHV and 1 mbar of O2. Real-time monitoring and near-ambient pressure measurement with different excitation sources has been used to identify the mechanisms behind the observed changes in spectral parameters that are different for each of the three materials. Corrected binding energy values aid the completion of the energy band diagrams for these wide-gap semiconductors and provide protocols for surface processing to engineer key surface and interface parameters. [ABSTRACT FROM AUTHOR]

We used a low-cost hobby drone to produce high resolution aerial photographs of a 12 ha mature longleaf pine (Pinus palustris) stand. The photos were combined into orthophoto mosaics and digital surface models to produce repeatable crown maps. Repeated flights allowed the use of tree phenology to separate longleaf from loblolly (Pinus taeda) and pond (Pinus serotina) pines, as well as some hardwood species. Careful ground control was necessary to produce aerial crown maps that matched field measured stems. However, average crown area/stem basal area ratio of 15 m radius plots produced correlation coefficients comparable to open single tree measures, ground control improved the relationship especially for loblolly and pond pine. With ground control height measurement was comparable to SfM (Surface from Motion) research results but had a positive bias greater than 1m. The most difficult problem was determining individual trees associated with a mapped crown area. Height measures were hampered by our inability to determine a correction for the true ellipsoid height of the camera. [ABSTRACT FROM AUTHOR]

The rhizosphere is a challenging ecosystem to study from a systems biology perspective due to its diverse chemical, physical, and biological characteristics. In the past decade, microfluidic platforms (e.g. plant-on-a-chip) have created an alternative way to study whole rhizosphere organisms, like plants and microorganisms, under reduced-complexity conditions. However, in reducing the complexity of the environment, it is possible to inadvertently alter organism phenotype, which biases laboratory data compared to in situ experiments. To build back some of the complexity of the rhizosphere in a fully-defined, parameterized approach we have developed a rhizosphere-on-a-chip platform that mimics the physical structure of soil. We demonstrate, through computational simulation, how this synthetic soil structure can influence the emergence of molecular "hotspots" and "hotmoments" that arise naturally from the plant's exudation of labile carbon compounds. We establish the amenability of the rhizosphere-on-a-chip for long-term culture of Brachypodium distachyon, and experimentally validate the presence of exudate hotspots within the rhizosphere-on-a-chip pore spaces using liquid microjunction surface sampling probe mass spectrometry. [ABSTRACT FROM AUTHOR]