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Abstract This manuscript shares the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data.When providing scientific computing support, focus is first placed on how to foster the collaborative aspects of multidisciplinary projects on the technological side by providing virtual spaces to communicate and share documents. Then insights on data management planning and how to implement a centralized data management workflow for data‐driven projects are provided.Developing reproducible workflows requires the development of code. We describe tools and practices that have been successful in fostering collaborative coding and scaling on remote servers, enabling teams to iterate more efficiently. We have found short training sessions combined with on‐demand specialized support to be the most impactful combination in helping scientists develop their technical skills.Here we share our experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long‐lasting effects on the way researchers conduct science. We hope that other groups supporting team‐ and data‐driven science (in environmental science and beyond) will benefit from the lessons we have learned over the years through trial and error. 

ABSTRACT Mast seeding, the synchronous and highly variable production of seed crops by perennial plants, is a population‐level phenomenon and has cascading effects in ecosystems. Mast seeding studies are typically conducted at the population/species level. Much less is known about synchrony in mast seeding between species because the necessary long‐term data are rarely available. To investigate synchrony between species within communities, we used long‐term data from seven forest communities in the U.S. Long‐Term Ecological Research (LTER) network, ranging from tropical rainforest to boreal forest. We focus on cross‐species synchrony and (i) quantify synchrony in reproduction overall and within LTER sites, (ii) test for relationships between synchrony with trait and phylogenetic similarity and (iii) investigate how climate conditions at sites are related to levels of synchrony. Overall, reproductive synchrony between woody plant species was greater than expected by chance, but spanned a wide range of values between species. Based on 11 functional and reproductive traits for 103 species (plus phylogenetic relatedness), cross‐species synchrony in reproduction was driven primarily by trait similarity with phylogeny being largely unimportant, and synchrony was higher in sites with greater climatic water deficit. Community‐level synchrony in masting has consequences for understanding forest regeneration dynamics and consumer‐resource interactions. 

Abstract The seasonal behavior of fluvial dissolved silica (DSi) concentrations, termedDSi regime, mediates the timing of DSi delivery to downstream waters and thus governs river biogeochemical function and aquatic community condition. Previous work identified five distinct DSi regimes across rivers spanning the Northern Hemisphere, with many rivers exhibiting multiple DSi regimes over time. Several potential drivers of DSi regime behavior have been identified at small scales, including climate, land cover, and lithology, and yet the large‐scale spatiotemporal controls on DSi regimes have not been identified. We evaluate the role of environmental variables on the behavior of DSi regimes in nearly 200 rivers across the Northern Hemisphere using random forest models. Our models aim to elucidate the controls that give rise to (a) average DSi regime behavior, (b) interannual variability in DSi regime behavior (i.e., Annual DSi regime), and (c) controls on DSi regime shape (i.e., minimum and maximum DSi concentrations). Average DSi regime behavior across the period of record was classified accurately 59% of the time, whereas Annual DSi regime behavior was classified accurately 80% of the time. Climate and primary productivity variables were important in predicting Average DSi regime behavior, whereas climate and hydrologic variables were important in predicting Annual DSi regime behavior. Median nitrogen and phosphorus concentrations were important drivers of minimum and maximum DSi concentrations, indicating that these macronutrients may be important for seasonal DSi drawdown and rebound. Our findings demonstrate that fluctuations in climate, hydrology, and nutrient availability of rivers shape the temporal availability of fluvial DSi. 

Abstract Increased occurrence, size, and intensity of fire result in significant but variable changes to hydrology and material retention in watersheds with concomitant effects on stream biogeochemistry. In arid regions, seasonal and episodic precipitation results in intermittency in flows connecting watersheds to recipient streams that can delay the effects of fire on stream chemistry. We investigated how the spatial extent of fire within watersheds interacts with variability in amount and timing of precipitation to influence stream chemistry of three forested, montane watersheds in a monsoonal climate and four coastal, chaparral watersheds in a Mediterranean climate. We applied state-space models to estimate effects of precipitation, fire, and their interaction on stream chemistry up to five years following fire using 15 + years of monthly observations. Precipitation alone diluted specific conductance and flushed nitrate and phosphate to Mediterranean streams. Fire had positive and negative effects on specific conductance in both climates, whereas ammonium and nitrate concentrations increased following fire in Mediterranean streams. Fire and precipitation had positive interactive effects on specific conductance in monsoonal streams and on ammonium in Mediterranean streams. In most cases, the effects of fire and its interaction with precipitation persisted or were lagged 2–5 years. These results suggest that precipitation influences the timing and intensity of the effects of fire on stream solute dynamics in aridland watersheds, but these responses vary by climate, solute, and watershed characteristics. Time series models were applied to data from long-term monitoring that included observations before and after fire, yielding estimated effects of fire on aridland stream chemistry. This statistical approach captured effects of local-scale temporal variation, including delayed responses to fire, and may be used to reduce uncertainty in predicted responses of water quality under changing fire and precipitation regimes of arid lands. 

Abstract Plants display a range of temporal patterns of inter‐annual reproduction, from relatively constant seed production to “mast seeding,” the synchronized and highly variable interannual seed production of plants within a population. Previous efforts have compiled global records of seed production in long‐lived plants to gain insight into seed production, forest and animal population dynamics, and the effects of global change on masting. Existing datasets focus on seed production dynamics at the population scale but are limited in their ability to examine community‐level mast seeding dynamics across different plant species at the continental scale. We harmonized decades of plant reproduction data for 141 woody plant species across nine Long‐Term Ecological Research (LTER) or long‐term ecological monitoring sites from a wide range of habitats across the United States. Plant reproduction data are reported annually between 1957 and 2021 and based on either seed traps or seed and/or cone counts on individual trees. A wide range of woody plant species including trees, shrubs, and lianas are represented within sites allowing for direct community‐level comparisons among species. We share code for filtering of data that enables the comparison of plot and individual tree data across sites. For each species, we compiled relevant life history attributes (e.g., seed mass, dispersal syndrome, seed longevity, sexual system) that may serve as important predictors of mast seeding in future analyses. To aid in phylogenetically informed analyses, we also share a phylogeny and phylogenetic distance matrix for all species in the dataset. These data can be used to investigate continent‐scale ecological properties of seed production, including individual and population variability, synchrony within and across species, and how these properties of seed production vary in relation to plant species traits and environmental conditions. In addition, these data can be used to assess how annual variability in seed production is associated with climate conditions and how that varies across populations, species, and regions. The dataset is released under a CC0 1.0 Universal public domain license. 

Abstract Fluvial silicon (Si) plays a critical role in controlling primary production, water quality, and carbon sequestration through supporting freshwater and marine diatom communities. Geological, biogeochemical, and hydrological processes, as well as climate and land use, dictate the amount of Si exported by streams. Understanding Si regimes—the seasonal patterns of Si concentrations—can help identify processes driving Si export. We analyzed Si concentrations from over 200 stream sites across the Northern Hemisphere to establish distinct Si regimes and evaluated how often sites moved among regimes over their period of record. We observed five distinct regimes across diverse stream sites, with nearly 60% of sites exhibiting multiple regime types over time. Our results indicate greater spatial and interannual variability in Si seasonality than previously recognized and highlight the need to characterize the watershed and climate variables that affect Si cycling across diverse ecosystems. 

Abstract Riverine exports of silicon (Si) influence global carbon cycling through the growth of marine diatoms, which account for ∼25% of global primary production. Climate change will likely alter river Si exports in biome‐specific ways due to interacting shifts in chemical weathering rates, hydrologic connectivity, and metabolic processes in aquatic and terrestrial systems. Nonetheless, factors driving long‐term changes in Si exports remain unexplored at local, regional, and global scales. We evaluated how concentrations and yields of dissolved Si (DSi) changed over the last several decades of rapid climate warming using long‐term data sets from 60 rivers and streams spanning the globe (e.g., Antarctic, tropical, temperate, boreal, alpine, Arctic systems). We show that widespread changes in river DSi concentration and yield have occurred, with the most substantial shifts occurring in alpine and polar regions. The magnitude and direction of trends varied within and among biomes, were most strongly associated with differences in land cover, and were often independent of changes in river discharge. These findings indicate that there are likely diverse mechanisms driving change in river Si biogeochemistry that span the land‐water interface, which may include glacial melt, changes in terrestrial vegetation, and river productivity. Finally, trends were often stronger in months outside of the growing season, particularly in temperate and boreal systems, demonstrating a potentially important role of shifting seasonality for the flux of Si from rivers. Our results have implications for the timing and magnitude of silica processing in rivers and its delivery to global oceans. 

Abstract Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet. 

Abstract Synchronous dynamics (fluctuations that occur in unison) are universal phenomena with widespread implications for ecological stability. Synchronous dynamics can amplify the destabilizing effect of environmental variability on ecosystem functions such as productivity, whereas the inverse, compensatory dynamics, can stabilize function. Here we combine simulation and empirical analyses to elucidate mechanisms that underlie patterns of synchronous versus compensatory dynamics. In both simulated and empirical communities, we show that synchronous and compensatory dynamics are not mutually exclusive but instead can vary by timescale. Our simulations identify multiple mechanisms that can generate timescale‐specific patterns, including different environmental drivers, diverse life histories, dispersal, and non‐stationary dynamics. We find that traditional metrics for quantifying synchronous dynamics are often biased toward long‐term drivers and may miss the importance of short‐term drivers. Our findings indicate key mechanisms to consider when assessing synchronous versus compensatory dynamics and our approach provides a pathway for disentangling these dynamics in natural systems. 

Abstract The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationshipsa priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.