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Detecting and understanding disturbance is a challenge in ecology that has grown more critical with global environmental change and the emergence of research on social–ecological systems. We identify three areas of research need: developing a flexible framework that incorporates feedback loops between social and ecological systems, anticipating whether a disturbance will change vulnerability to other environmental drivers, and incorporating changes in system sensitivity to disturbance in the face of global changes in environmental drivers. In the present article, we review how discoveries from the US Long Term Ecological Research (LTER) Network have influenced theoretical paradigms in disturbance ecology, and we refine a framework for describing social–ecological disturbance that addresses these three challenges. By operationalizing this framework for seven LTER sites spanning distinct biomes, we show how disturbance can maintain or alter ecosystem state, drive spatial patterns at landscape scales, influence social–ecological interactions, and cause divergent outcomes depending on other environmental changes.

Long-term research in ecology and evolution (LTREE) is considered fundamental for understanding complex ecological and evolutionary dynamics. However, others have argued for revision of LTREE efforts given perceived limitations in current research priorities and approaches. Yet most arguments about the benefits and failings of LTREE could be argued to reflect the views of only the limited number of scientists who have authored reports on the field, and not the wider community of ecological and evolutionary scientists. To more systematically and quantitatively assess the views of the community on LTREE contributions and future activities, we conducted and here report the results of a survey of ecological and evolutionary scientists at primarily U.S.-based institutions, completed by 1,179 respondents. The survey objectives were to (1) identify and prioritize research questions that are important to address through long-term, ecological field experiments and (2) understand the role that these experiments might play in generating and applying ecological and evolutionary knowledge. Almost 80% (𝑛 = 936) of respondents said that long-term experiments had contributed \"a great deal\" to ecological understanding. Compared to other research approaches (e.g., short-term, single-site, modeling, or lab), there was overwhelming support that multi-site, long-term research was very important for advancing theory, and that both observational and experimental approaches were required. Respondents identified a wide range of research questions for LTREE to address. The most common topic was the impact of global change (𝑛 = 1,352), likely because these processes play out over many years, requiring LTREE approaches to fully understand. Another recurrent theme was the potential of LTREE approaches to build evolutionary understanding across all levels of ecological organization. Critical obstacles preventing some scientists from engaging in LTREE included short-term funding mechanisms and fewer publications, whereas the longer-term value for advancing knowledge and an individual's career were widely recognized. Substantive advances in understanding ecological and evolutionary dynamics then seem likely to be made through engagement in long-term observational and experimental research. However, wider engagement seems dependent on a more supportive research environment and funding structure, through increased institutional acknowledgment of the contributions of long-term research, and greater program support during the establishment and maintenance of research.

How, where, and why carbon (C) moves into and out of an ecosystem through time are long-standing questions in biogeochemistry. Here, we bring together hundreds of thousands of C-cycle observations at the Harvard Forest in central Massachusetts, USA, a mid-latitude landscape dominated by 80–120-yr-old closed-canopy forests. These data answered four questions: (1) where and how much C is presently stored in dominant forest types; (2) what are current rates of C accrual and loss; (3) what biotic and abiotic factors contribute to variability in these rates; and (4) how has climate change affected the forest's C cycle? Harvard Forest is an active C sink resulting from forest regrowth following land abandonment. Soil and tree biomass comprise nearly equal portions of existing C stocks. Net primary production (NPP) averaged 680–750 g C.m⁻².yr⁻¹; belowground NPP contributed 38–47% of the total, but with large uncertainty. Mineral soil C measured in the same inventory plots in 1992 and 2013 was too heterogeneous to detect change in soil-C pools; however, radiocarbon data suggest a small but persistent sink of 10–30 g C.m⁻².yr⁻¹. Net ecosystem production (NEP) in hardwood stands averaged ~300 g C.m⁻².yr⁻¹. NEP in hemlock-dominated forests averaged ~450 g C.m⁻².yr⁻¹ until infestation by the hemlock woolly adelgid turned these stands into a net C source. Since 2000, NPP has increased by 26%. For the period 1992–2015, NEP increased 93%. The increase in mean annual temperature and growing season length alone accounted for ~30% of the increase in productivity. Interannual variations in GPP and NEP were also correlated with increases in red oak biomass, forest leaf area, and canopy-scale light-use efficiency. Compared to long-term global change experiments at the Harvard Forest, the C sink in regrowing biomass equaled or exceeded C cycle modifications imposed by soil warming, N saturation, and hemlock removal. Results of this synthesis and comparison to simulation models suggest that forests across the region are likely to accrue C for decades to come but may be disrupted if the frequency or severity of biotic and abiotic disturbances increases.

Disturbances often cause the disproportionate loss of foundation species but understanding how the frequency and severity of disturbance to such organisms influence biological communities remains unresolved. This gap in knowledge exists in part because of the rarity of ecologically meaningful studies capable of disentangling different elements of disturbance. Hence, we carried out a long-term (9 yr), large-scale (2,000 m² plots), spatially replicated (4 sites) field experiment in which we manipulated disturbance to a globally distributed marine foundation species, the giant kelp Macrocystis pyrifera, and tracked community responses over time. To distinguish the effects of disturbance frequency and severity on the biodiversity and composition of temperate rocky reef communities, we simulated the repeated loss of giant kelp from destructive winter waves across a background of natural variation in disturbance. By following the response of over 200 taxa from the surrounding community, we discovered that the frequency of disturbance to giant kelp changed the biomass, diversity, and composition of community guilds in a manner commensurate with their dependence on the physical (i.e., benthic light and space), trophic (i.e., living and detrital biomass), and habitat (i.e., biogenic structure) resources mediated by this foundation species. Annual winter disturbance to giant kelp reduced living and detrital giant kelp biomass by 57% and 40%, respectively, enhanced bottom light by 22%, and halved the seafloor area covered by giant kelp holdfasts. Concomitantly, the biomass of understory algae and epilithic sessile invertebrates more than doubled, while the biomass of rock-boring clams, mobile invertebrates, and fishes decreased 30–61%. Frequent loss of giant kelp boosted understory algal richness by 82% and lowered sessile invertebrate richness by 13% but did not affect the biodiversity of mobile fauna. In contrast to changes driven by disturbance frequency, interannual variation in the severity of disturbance to giant kelp had weaker, less consistent effects, causing only modest changes in assemblages of sessile invertebrates, mobile invertebrate herbivores, and fishes. Our results broaden the foundation species concept by demonstrating that repeated disturbance to a dominant habitat-forming species can outweigh the influence of less frequent but severe disturbances for the surrounding community.

Many scientists around the world became interested in the U.S. Long Term Ecological Research (U.S. LTER) Network's research model during the 1990s and began to develop LTER and Long Term Socio‐ecological Research networks in their own countries. These local networks, including the U.S. LTER Network, were loosely federated in 1993 to form the International Long Term Ecological Research (ILTER) Network, a “network of networks.” Although the first 10 yr of ILTER Network activities were largely supported by funds from the U.S. National Science Foundation, the ILTER Network had transformed into a robust, self‐sustaining entity by 2006 following a two‐year strategic planning process. The goal of the ILTER Network is to improve understanding of how pressures such as climate change and land use affect global ecosystems in order to inform solutions to current and future environmental problems. To fulfill this mission, the ILTER Network fosters collaborations among member scientists to extend the scope of their research across disciplinary boundaries and across more of the ILTER's 600+ research sites. The ILTER Network also has many long‐term data sets that are freely available for use by students, scientists, and policymakers all over the world. In this collection of papers, we consider how the ILTER Network has been, and will be, leveraged by U.S. researchers to advance understanding of ecological and socio‐ecological systems around the globe.

Long-term ecological research can resolve effects of disturbance on ecosystem dynamics by capturing the scale of disturbance and interactions with environmental changes. To quantify how disturbances interact with long-term directional changes (sea-level rise, freshwater restoration), we studied 17 yr of monthly dissolved organic carbon (DOC), total nitrogen (TN), and phosphorus (TP) concentrations and bacterioplankton productivity across freshwater-to-marine estuary gradients exposed to multiple disturbance events (e.g., droughts, fire, hurricanes, and low-temperature anomalies) and long-term increases in water levels. By studying two neighboring drainages that differ in hydrologic connectivity, we additionally tested how disturbance legacies are shaped by hydrologic connectivity. We predicted that disturbance events would interact with long-term increases in water levels in freshwater and marine ecosystems to increase spatiotemporal similarity (i.e., synchrony) of organic matter, nutrients, and microbial activities. Wetlands along the larger, deeper, and tidally influenced Shark River Slough (SRS) drainage had higher and more variable DOC, TN, and TP concentrations than wetlands along the smaller, shallower, tidally restricted Taylor River Slough/Panhandle (TS/Ph) drainage. Along SRS, DOC concentrations declined with proximity to coast, and increased in magnitude and variability following drought and flooding in 2015 and a hurricane in 2017. Along TS/Ph, DOC concentrations varied by site (higher in marine than freshwater wetlands) but not year. In both drainages, increases in TN from upstream freshwater marshes occurred following fire in 2008 and droughts in 2010 and 2015, whereas downstream increases in TP occurred with coastal storm surge from hurricanes in 2005 and 2017. Decreases in DOC:TN and DOC:TP were explained by increased TN and TP. Increases in bacterioplankton productivity occurred throughout both drainages following low-temperature events (2010 and 2011) and a hurricane (2017). Long-term TN and TP concentrations and bacterioplankton productivity were correlated (r > 0.5) across a range of sampling distances (1–50 km), indicating spatiotemporal synchrony. DOC concentrations were not synchronized across space or time. Our study advances disturbance ecology theory by illustrating how disturbance events interact with long-term environmental changes and hydrologic connectivity to determine the magnitude and extent of disturbance legacies. Understanding disturbance legacies will enhance prediction and enable more effective management of rapidly changing ecosystems.

Patterns of plant biomass partitioning are fundamental to estimates of primary productivity and ecosystem process rates. Allometric relationships between above‐ground plant biomass and non‐destructive measures of plant size, such as cover, volume or stem density are widely used in plant ecology. Such size‐biomass allometry is often assumed to be invariant for a given plant species, plant functional group or ecosystem type. Allometric adjustment may be an important component of the short‐ or long‐term response of plants to abiotic conditions. We used 18 years of size‐biomass data describing of 85 plant species to investigate the sensitivity of allometry to precipitation, temperature or drought across two seasons and four ecosystems in central New Mexico, USA. Size‐biomass allometry varied with climate in 65%–70% of plant species. Closely related plant species had similar sensitivities of allometry to natural spatiotemporal variation in precipitation, temperature or drought. Annuals were less sensitive than perennials, and forbs were less sensitive than grasses or shrubs. However, the differences associated with plant life history or functional group were not independent of plant evolutionary history, as supported by the application of phylogenetically independent contrasts. Our results demonstrate that many plant species adjust patterns in the partitioning of above‐ground biomass under different climates and highlight the importance of long‐term data for understanding functional differences among plant species. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.

The integration of the social sciences into long-term ecological research is an urgent priority. To address this need, a group of social, earth, and life scientists associated with the National Science Foundation's (NSF) Long-Term Ecological Research (LTER) Network have articulated a conceptual framework for understanding the human dimensions of ecological change for the LTER Network. This framework explicitly advocates that what is often divided into \"natural\" and human systems be considered a single, complex social-ecological system (SES). In this paper, we propose a list of core social science research areas, concepts, and questions; identify the need for multiscale investigatory frameworks crucial for implementing integrated research; and suggest practical approaches for integration. In sum, this paper is a general outline for empirical and cross-site research projects where investigators agree that bringing together social, biological, and earth scientists can lead to synthetic approaches and a unified understanding of the mechanisms regulating SES. Although the motivation for this goal is specific to the LTER Network and similar projects, we believe that the issues and ideas presented here are widely applicable to other interdisciplinary SES studies.

Plant functional diversity and soil microbial community composition are tightly coupled. Changes in these interactions may influence ecosystem functioning. Links between plant functional diversity, soil microbial communities and ecosystem functioning have been demonstrated in experiments using plant monocultures and mixtures, using broad plant and microbial functional groups, but have not been examined in diverse natural plant communities. We quantified the relative effects of plant and microbial functional properties on key ecosystem functions. We measured plant functional diversity, soil microbial community composition and parameters associated with nitrogen (N) cycling and key nutrient cycling processes at three grassland sites in different parts of Europe. Because plant structure and function strongly influence soil microbial communities, we determined relationships between ecosystem properties, plant traits and soil community characteristics following a sequential approach in which plant traits were fitted first, followed by the additional effects of soil microorganisms. We identified a continuum from standing green biomass and standing litter, linked mostly with plant traits, to potential N mineralization and potential leaching of soil inorganic N, linked mostly with microbial properties. Plant and microbial functional parameters were equally important in explaining % organic matter content in soil. A parallel continuum ran from plant height, linked with above-ground biomass, to plant quality effects captured by the leaf economics spectrum, which were linked with the recycling of carbon (C) and N. More exploitative species (higher specific leaf area, leaf N concentrations and lower leaf dry matter content) and taller swards, along with soil microbial communities dominated by bacteria, with rapid microbial activities, were linked with greater fodder production, but poor C and N retention. Conversely, dominance by conservative species (with opposite traits) and soil microbial communities dominated by fungi, and bacteria with slow activities, were usually linked with low production, but greater soil C storage and N retention. Synthesis – Grassland production, C sequestration and soil N retention are jointly related to plant and microbial functional traits. Managing grasslands for selected, or multiple, ecosystem services will thus require a consideration of the joint effects of plant and soil communities. Further understanding of the mechanisms that link plant and microbial functional traits is essential to achieve this.

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science USA National Phenology Network (USA-NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both USA-NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for USA-NPN data had the lowest error when using models built from the USA-NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross-scale model comparison is likely due to variation in phenological requirements within species. Models using the USA-NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if USA-NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale USA-NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.