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ABSTRACT Experiments have long been the gold standard for causal inference in Ecology. As Ecology tackles progressively larger problems, however, we are moving beyond the scales at which randomised controlled experiments are feasible. To answer causal questions at scale, we need to also use observational data —something Ecologists tend to view with great scepticism. The major challenge using observational data for causal inference is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders—known or unknown—lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this omitted variable bias, other disciplines have developed rigorous approaches for causal inference from observational data that flexibly control for broad suites of confounding variables. We show how ecologists can harness some of these methods—causal diagrams to identify confounders coupled with nested sampling and statistical designs—to reduce risks of omitted variable bias. Using an example of estimating warming effects on snails, we show how current methods in Ecology (e.g., mixed models) produce incorrect inferences due to omitted variable bias and how alternative methods can eliminate it, improving causal inferences with weaker assumptions. Our goal is to expand tools for causal inference using observational and imperfect experimental data in Ecology. 

Abstract Wildfires have increased in size, frequency, and intensity in arid regions of the western United States because of human activity, changing land use, and rising temperature. Fire can degrade water quality, reshape aquatic habitat, and increase the risk of high discharge and erosion. Drawing from patterns in montane dry forest, chaparral, and desert ecosystems, we developed a conceptual framework describing how interactions and feedbacks among material accumulation, combustion of fuels, and hydrologic transport influence the effects of fire on streams. Accumulation and flammability of fuels shift in opposition along gradients of aridity, influencing the materials available for transport. Hydrologic transport of combustion products and materials accumulated after fire can propagate the effects of fire to unburned stream–riparian corridors, and episodic precipitation characteristic of arid lands can cause lags, spatial heterogeneity, and feedbacks in response. Resolving uncertainty in fire effects on arid catchments will require monitoring across hydroclimatic gradients and episodic precipitation. 

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 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. 

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable, and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose-decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose-decomposition rates, when combined with genus-level litter-quality attributes, explain published leaf-litter-decomposition rates with impressive accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth, and reveals rapid decomposition across continental-scale areas dominated by human activities. v1.0 first data release includes all code for models, analyses, and figures. v1.1 addition of code for a new supplemental figure (Figure S1) v1.2 includes new color schemes for all figures, and new title 

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities. 

Climate change is increasing the frequency and severity of short-term (~1 y) drought events—the most common duration of drought—globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function—aboveground net primary production (ANPP)—was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.