Data for EMSL Project 60938 from February 2025
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To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 1.0 in 2017, followed by 2.0 in 2018, and 3.0 in 2021. This new release of FRED 4.0 now has 213,941 observations of 238 root traits, for a combined total of roughly 3.4 million data fields for root traits and ancillary data together. FRED 4.0 has 39.8% more root trait observations than FRED 3.0 and a 34.4% increase in unique data sources. This release of FRED 4.0 also includes significant increases in geographic regions that have long been underrepresented in global datasets, notably in the tropical low latitudes. Ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently with root trait observations. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. Despite this fine-root focus, FRED accepts data collected from roots of all sizes and contains observations of many root classes including coarse roots. Data collection will continue for the foreseeable future. The FRED4_Entire_Database_2026.csv file is the flat csv data file for FRED 4.0, and the FRED4_dd.csv file is the data dictionary of all columns available in FRED, including column IDs, column names, definitions, and unit (where applicable).
TropiRoot 1.0 is a new tropical root database with root characteristics across environment gradients. It has data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 includes root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology and root chemistry. This initiative represents an approximately 30% increase in the currently available data for tropical roots in the Fine Root Ecology Database (FRED). TropiRoot 1.0, contains root characteristics from 25 different countries where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data was available, including soil data, these data was either extracted and included in the database or their availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match the ones reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions, and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models.
Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.
Successful implementation of forest management as a nature-based climate solution is dependent on the durability of management-induced changes in forest carbon storage and sequestration. As forests face unprecedented stability risks in the face of ongoing climate change, much remains unknown regarding how management will impact forest stability, or how interactions with climate might shift the response of forests to management across spatiotemporal scales. Here, we used a process-based model to simulate multidecadal projections of forest dynamics in response to changes in management and climate. Simulations were conducted across gradients in forest type, edaphic factors, and management intensity under two alternate radiative forcing scenarios (RCP4.5 and RCP8.5). This allowed for the quantification of forest stability shifts in response to climate change, and the role of management in modulating that response, where ecosystem stability is characterized as the resilience and temporal stability of net primary production, aboveground biomass, and soil carbon. Our results indicate that forest structure is primarily shaped by management, but the same management strategy often produced divergent structures over time, due to interactions with regional climate change. We found that management can be used to increase stability and minimize the release of stored carbon by reducing mortality, but also highlight the regional dependency of management-induced changes in resilience to climate change.
An improved understanding of root vertical distribution is crucial for assessing plant-soil-atmosphere interactions and their influence on the land carbon sink. Here, we analyze a continental-scale dataset of fine roots reaching 2 meters depth, spanning from Alaskan tundra to Puerto Rican forests. Contrary to the expectation that fine root abundance decays exponentially with depth, we found root bimodality at ~20% of 44 sites, with secondary biomass peaks often below 1m. Root bimodality was more likely in areas with low total fine root biomass and was more frequent in shrublands than grasslands. Notably, secondary peaks coincided with high soil nitrogen content at depth. Our analyses suggest that deep soil nutrients tend to be underexploited, while root bimodality offers plants a mechanism to tap into deep soil resources. Our findings add to the growing recognition that deep soil dynamics are systematically overlooked, and calls for more research attention to this deep frontier in the face of global environmental change.
Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.
Turnover in species composition often lags behind the pace of climate change, resulting in mismatches between climate and communities. However, the impact of these community‐climate disequilibria on ecosystem functions is rarely considered, and current methods for measuring disequilibria assume that species ranges were, until recently, in equilibrium with climate. Here, in this work, we develop a simple theoretical model to address both of these problems by linking community‐climate disequilibrium with ecosystem functioning. We show how disequilibrium can impair functioning in the near‐term even when climate change is expected to enhance functioning in the long‐term. Responses are most likely to change over time in communities where turnover is slow, the impact of disequilibrium counteracts the direct effects of climate on ecosystem function, and pre‐existing disequilibrium is large. These findings emphasise the importance of precise and unbiased estimates of community‐climate disequilibria for improving ecological forecasts. By fitting our model to time series of both climate and ecosystem function from a metacommunity simulation, we show the potential for community‐climate disequilibrium to be inferred without direct knowledge about species' distributions or climatic tolerances. We end by outlining a research agenda to apply dynamic disequilibrium concepts and test novel hypotheses across diverse ecosystems.
Climate warming is predicted to strongly affect the functioning of terrestrial ecosystems. The plant root system is a critical component of these ecosystems, with fine roots, in particular, playing a key role in plant water and nutrient uptake and transport. In addition, root litter and exudation represent the dominant plant carbon inputs into soil. Consequently, understanding fine‐root responses to warming is essential for predicting how the growth, resilience, and carbon storage of terrestrial ecosystems will respond to future climate change. Despite the growing literature on fine‐root responses to warming, most studies have focused on topsoil (0–30 cm). However, a significant portion of the fine‐root mass occurs below this depth. For instance, c. 40% of fine‐root mass is found below 30 cm in temperate and tropical ecosystems. Due to the importance of fine roots for plants and belowground carbon cycling, focusing solely on surface soils overlooks the critical need for insights into how roots in deeper soil layers (e.g. below 30 cm) respond to warming. We argue that studying the entire soil profile is necessary to comprehensively understand fine‐root dynamics under climate warming and the implications for soil organic carbon cycling, water, and nutrient uptake.
Humans are driving biodiversity change, which also alters community functional traits. However, how changes in the functional traits of the community alter ecosystem functions—especially belowground—remains an important gap in our understanding of the consequences of biodiversity change. We test hypotheses for how the root traits of the root economics space (composed of the collaboration and conservation gradients) are associated with proxies for ecosystem functioning across grassland and forest ecosystems in both observational and experimental datasets from 810 plant communities. First, we assessed whether community-weighted means of the root economics space traits adhered to the same trade-offs as species-level root traits. Then, we examined the relationships between community-weighted mean root traits and aboveground biomass production, root standing biomass, soil fauna biomass, soil microbial biomass, decomposition of standard and plot-specific material, ammonification, nitrification, phosphatase activity, and drought resistance. We found evidence for a community collaboration gradient but not for a community conservation gradient. Yet, links between community root traits and ecosystem functions were more common than we expected, especially for aboveground biomass, microbial biomass, and decomposition. These findings suggest that changes in species composition, which alter root trait means, will in turn affect critical ecosystem functions.
This data package contains processed measurements of soil carbon dioxide (CO₂) efflux collected using LI-COR LI-8100 soil respiration chambers at the Tropical Responses to Altered Climate Experiment (TRACE) located at the Sabana Field Research Station near Luquillo, Puerto Rico. The TRACE site is a mature, closed-canopy tropical wet forest within the Luquillo Experimental Forest. These data quantify soil surface CO₂ fluxes from both ambient (control) and experimentally warmed plots to evaluate how long-term soil warming affects belowground carbon cycling in tropical ecosystems. The data files include time-series tables of CO₂ flux (µmol CO₂ m⁻² s⁻¹), soil temperature (°C), and ancillary environmental variables, stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement intervals, and processing steps. These data were generated to address the research question: How does sustained soil warming influence soil respiration and carbon flux dynamics in tropical wet forests?