Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use-inspired science, (4) scale, (5) generality , (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill-building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science.
Streams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site-specific studies and global-scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO 2 ranged from 36.9 AE 0.88 to 404 AE 33 μmol L À1 , CH 4 from 0.003 AE 0.0003 to 4.99 AE 0.72 μmol L À1 , and N 2 O from 0.015 to 0.04 μmol L À1 and spanned ranges of previous global compilations. Both CO 2 and CH 4 were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N 2 O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in-stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low-gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeo-chemical drivers of GHG production.
The Earth's surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale‐dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower‐based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso‐β, meso‐γ) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid‐latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface‐atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller‐scale turbulent and larger‐scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single‐point tower measurements through traditional time‐domain half‐hourly Reynolds decomposition. We report scale‐resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface‐atmospheric interactions over unstructured heterogeneities and can help inform multi‐scale model‐data integration of weather and climate models at a sub‐grid scale.
The movement of plant species across the globe exposes native communities to new species introductions. While introductions are pervasive, two aspects of variability underlie patterns and processes of biological invasions at macroecological scales. First, only a portion of introduced species become invaders capable of substantially impacting ecosystems. Second, species that do become invasive at one location may not be invasive in others; impacts depend on invader abundance and recipient species and conditions. Accounting for these phenomena is essential to accurately understand patterns of plant invasion and explain the idiosyncratic results reflected in the literature on biological invasions. The lack of community‐level richness and abundance data spanning broad scales and environmental conditions has until now hindered our understanding of invasions at a macroecological scale. To address this limitation, we leveraged quantitative surveys of plant communities in the United States and integrated and harmonized nine datasets into the Standardized Plant Community with Introduced Status (SPCIS) database. The database contains 14,056 unique taxa identified within 83,391 sampling units, of which 52.6% have at least one introduced species. The SPCIS database includes comparable information of plant species occurrence, abundance and native status across the 50 U.S. states and Puerto Rico. SPCIS can be used to answer macro‐scale questions about native plant communities and interactions with invasive plants. There are no copyright restrictions on the data, and we ask the users of this dataset to cite this paper, the respective paper(s) corresponding to the dataset sampling design (all references are provided in Data S1: Metadata S1: Class II‐B‐2), and the references described in Data S1: Metadata S1: Class III‐B‐4 as applicable to the dataset being utilized. This article is protected by copyright. All rights reserved.
The measurement of biodiversity is an integral aspect of life science research. With the establishment of second‐ and third‐generation sequencing technologies, an increasing amount of metabarcoding data is being generated as we seek to describe the extent and patterns of biodiversity in multiple contexts. The reliability and accuracy of taxonomically assigning metabarcoding sequencing data has been shown to be critically influenced by the quality and completeness of reference databases. Custom, curated, eukaryotic reference databases, however, are scarce, as are the software programs for generating them. Here, we present CRABS (Creating Reference databases for Amplicon‐Based Sequencing), a software package to create custom reference databases for metabarcoding studies. CRABS includes tools to download sequences from multiple online repositories (i.e., NCBI, BOLD, EMBL, MitoFish), retrieve amplicon regions through in silico PCR analysis and pairwise global alignments, curate the database through multiple filtering parameters (e.g., dereplication, sequence length, sequence quality, unresolved taxonomy, inclusion/exclusion filter), export the reference database in multiple formats for the immediate use in taxonomy assignment software, and investigate the reference database through implemented visualizations for diversity, primer efficiency, reference sequence length, database completeness, and taxonomic resolution. CRABS is a versatile tool for generating curated reference databases of user‐specified genetic markers to aid taxonomy assignment from metabarcoding sequencing data. CRABS can be installed via Docker and is available for download as a conda package and via GitHub (https://github.com/gjeunen/reference_database_creator).
Vector-borne diseases are responsible for more than 17% of human cases of infectious diseases. In most situations, effective control of debilitating and deadly vector-bone diseases (VBDs), such as malaria, dengue, chikungunya, yellow fever, Zika and Chagas requires up-to-date, robust and comprehensive information on the presence, diversity, ecology, bionomics and geographic spread of the organisms that carry and transmit the infectious agents. Huge gaps exist in the information related to these vectors, creating an essential need for campaigns to mobilise and share data. The publication of data papers is an effective tool for overcoming this challenge. These peer-reviewed articles provide scholarly credit for researchers whose vital work of assembling and publishing well-described, properly-formatted datasets often fails to receive appropriate recognition. To address this, GigaScience's sister journal GigaByte partnered with the Global Biodiversity Information Facility (GBIF) to publish a series of data papers, with support from the Special Programme for Research and Training in Tropical Diseases (TDR), hosted by the World Health Organisation (WHO). Here we outline the initial results of this targeted approach to sharing data and describe its importance for controlling VBDs and improving public health.
Manganese (Mn) may play an outsized role in soil biogeochemical cycles relative to its abundance. The role of Mn-facilitated oxidation of biomacromolecules during litter decomposition is well-established, but the balance between Mn-promoted soil organic carbon (SOC) oxidation and long-term SOC protection in mineral soils is unknown, especially in subsoils. In this study, we used soils collected across the US National Ecological Observatory Network (NEON) to assess the distribution of Mn and relationships between Mn abundance and SOC concentration, potential mineralization, and persistence at a continental spatial scale. Total reducible Mn was not spatially correlated to site moisture (Spearman’s Rho = 0.24), highlighting that Mn abundance may influence SOC cycling independently from other moisture-driven soil chemical properties (e.g., reactive iron and aluminum). However, Mn effects on SOC cycling depended on depth, soil, or site-level properties. In particular, fungal:bacterial biomass ratio, proportion of SOC in the free light fraction, lignin abundance, and/or proportion of undegraded organic matter mediated the effect of Mn on SOC cycling metrics. For example, the effect of Mn on SOC concentration in subsoils shifted from positive (approximately +270 % relative to mean subsoil SOC) to negative (−125 %) with increasing fungal:bacterial ratio. We propose that convergence of high Mn, lignin-rich substrates, and fungal:bacterial ratio amplifies lignin mineralization in surface soils, but does not result in higher net SOC turnover due to fungal biomass stabilization. In contrast, we suggest that Mn abundance promotes smaller, but more persistent SOC stocks in subsoils by accelerating SOC transformation from particulate to microbial biomass pools.
The species–area relationship (SAR) has over a 150‐year long history in ecology, but how its shape and origins vary across scales and organisms is still not fully understood. This is the first subcontinental freshwater study to examine both properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish), differing in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis‐Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ‐diversity, and species abundance distribution), the third predictor level. We calculated the SAR as a sample‐based rarefaction curve using 60 streams within landscape windows (scales) in the US, ranging from 160,000 to 6,760,000 km2. First, we found that all models provided good fits (R2 ≥ 0.93), but the frequency of the best fitting model was strongly dependent on organism, scale, and metacommunity properties. Michaelis‐Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model was best fitting exclusively at the smallest scales and in species‐poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log‐log form of the power model using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale‐ and organism‐dependent metacommunity properties, particularly spatial aggregation, while the intercept responded most strongly to species group and γ‐diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change would alter the SAR. This article is protected by copyright. All rights reserved.
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.
Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types. An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.
Plain Language Summary Scale refers to the patterns in space and oscillations in time of features in our universe. The Earth system features a wide range of scales. Understanding the processes that explain the size, shape, regularity, and changes in those scales looms large in our science. Land‐atmosphere interaction refers to the ways that organisms and elements of the land surface influence the structure and evolution of the atmosphere and in turn, how weather and climate processes influence the ground. Numerous studies through coordinated field experiments and computer simulations have helped us advance understanding of how scale influences land‐atmosphere interaction. We introduce a special collection that documents many of those.
Tidal wetlands play an important role in global carbon cycling by storing carbon in sediment at millennial time scales, transporting dissolved carbon into coastal waters, and contributing significantly to global CH4 budgets. However, these ecosystems' greenhouse gas monitoring and predictions are challenging due to spatial heterogeneity and tidal flooding. We utilized eddy covariance and chamber measurements to quantify fluxes of CO2 and CH4 at a restored tidal saltmarsh across spatial and temporal scales. Eddy covariance data revealed that the site was a strong net sink for CO2 (−387 g C‐CO2 m⁻² yr⁻¹, SD = 46) and a small net source of CH4 (0.7 g C‐CH4 m⁻² yr⁻¹, SD = 0.4). After partitioning net ecosystem exchange of CO2 into gross primary production and ecosystem respiration, we found that high net uptake of CO2 was due to low respiration emissions rather than high photosynthetic rates. We also found that respiration rates varied between land covers with increased respiration in mudflats compared to vegetated areas. Daytime soil chamber measurements revealed that the greatest CO2 emission was from higher elevation mudflat soils (0.5 μmol m⁻²s⁻¹, SE = 1.3) and CH4 emission was greatest from lower elevation Spartina foliosa soils (1.6 nmol m⁻²s⁻¹, SD = 8.2). Overall, these results highlight the importance of the relationships between wetland plant community and elevation, and inundation for CO2 and CH4 fluxes. Future research should include the use of high‐resolution imagery, automated chambers, and a focus on quantifying carbon exported in tidal waters.
Biodiversity has widely been documented to enhance local community stability but whether such stabilizing effects of biodiversity extend to broader scales remains elusive. Here, we investigated the relationships between biodiversity and community stability in natural plant communities from quadrat (1 m2) to plot (400 m2) and regional (5−214 km2) scales and across broad climatic conditions, using an extensive plant community dataset from the National Ecological Observatory Network. We found that plant diversity provided consistent stabilizing effects on total community abundance across three nested spatial scales and climatic gradients. The strength of the stabilizing effects of biodiversity increased modestly with spatial scale and decreased as precipitation seasonality increased. Our findings illustrate the generality of diversity–stability theory across scales and climatic gradients, which provides a robust framework for understanding ecosystem responses to biodiversity and climate changes. Analysing >6,000 plant species from plots across the US National Ecological Observatory Network (NEON), the authors show that plant diversity consistently stabilizes community abundance across spatial scales and broad ecoclimatic domains, with the strength of the stabilizing effect increasing with scale.
Concentration‐discharge (C‐Q) relationships can provide insight into how catchments store and transport solutes, but analysis is often limited to long‐term behavior assessed from infrequent grab samples. Increasing availability of high‐frequency sensor data has shown that C‐Q relationships can vary substantially across temporal scales, and in response to different hydrologic drivers. Here, we present four years of dissolved organic carbon (DOC) and nitrate‐nitrogen (NO3‐N) sensor data from a snowmelt‐ dominated catchment in the Rocky Mountains of Colorado. We assessed both the direction (enrichment vs. dilution) and hysteresis in C‐Q relationships across a range of time scales, from interannual to sub‐daily. Both solutes exhibited a seasonal flushing response, with concentrations initially increasing as solute stores are mobilized by the melt pulse, but then declining as these stores are depleted. The high‐frequency data revealed that the seasonal melt pulse was composed of numerous individual daily melt pulses. The solute response to daily melt pulses was relatively chemostatic, suggesting mobilization and depletion to be progressive rather than episodic processes. In contrast, rainfall‐induced pulses produced short‐lived but substantial enrichment responses, suggesting they may activate alternative solute sources or transport pathways. Finally, we observed low‐level diel variation during summer baseflow following the melt pulse, likely driven by effects of daily evapotranspiration cycles. Additional contributions from in‐stream metabolic cycles, independent from but covarying with diel streamflow cycles, could not be ruled out. The results clearly demonstrate that solute responses to daily cycles and individual events may differ significantly from the longer‐term seasonal behavior they combine to generate. This article is protected by copyright. All rights reserved.
Livestock agriculture accounts for ∼15% of global anthropogenic greenhouse gas (GHG) emissions. Recently, natural climate solutions (NCS) have been identified to mitigate farm‐scale GHG emissions. Nevertheless, their impacts are difficult to quantify due to farm spatial heterogeneity and effort required to measure changes in carbon stocks. Remote sensing (RS) models are difficult to parameterize for heterogeneous agricultural landscapes. Eddy covariance (EC) in combination with novel techniques that quantitatively match source area variations could help update such vegetation‐specific parameters while accounting for pronounced heterogeneity. We evaluate a plant physiological parameter, the maximum quantum yield (MQY), which is commonly used to calculate gross and net primary productivity in RS applications. RS models often rely on spatially invariable MQY, which leads to inconsistencies between RS and EC models. We evaluate if EC data improve RS models by updating crop specific MQYs to quantify agricultural GHG mitigation potentials. We assessed how farm harvest compared to annual sums of (a) RS without improvements, (b) EC results, and (c) EC‐RS models. We then estimated emissions to calculate the annual GHG balance, including mitigation through plant carbon uptake. Our results indicate that EC‐RS models significantly improved the prediction of crop yields. The EC model captures diurnal variations in carbon dynamics in contrast to RS models based on input limitations. A net zero GHG balance indicated that perennial vegetation mitigated over 60% of emissions while comprising 40% of the landscape. We conclude that the combination of RS and EC can improve the quantification of NCS in agroecosystems.
Note: Please see pdf for full abstract with equations. Finding a direct solution to the widely used Green-Ampt (G-A) one dimensional infiltration model has been subject of efforts for more than half a century. We derived an accurate explicit formula that appears be so far the simplest proposed model in the literature. Our approach builds upon simulating the Valiantzas’s G-A infiltration model by a second order polynomial. The proposed equation is F = Kt (0.70635 + 0.32415 √1+9.43456(S²/K²t)), with F, K, S and t being cumulative infiltration, saturated hydraulic conductivity, sorptivity and time, respectively. Relative errors (ɛ) by the application of this equation generally do not exceed ±0.3% in most practical infiltration problems faced in water resources engineering. It was both numerically and mathematically shown that absolute errors > 0.3% could only occur if Kt/F > 0.904, a criteria that could practically be the case for light textured soils like sand and loamy sand if they are exposed to long infiltration times, i.e., 6 hr and 19 hr in our examples, respectively. A simple linear adjustment model is proposed as Fadj ≅ 0.9796F + 0.335 S²/K to account for these uncommon real-World conditions. The linear adjustment assures that ɛ remains within the ± 0.3% range even under imaginary infiltration timings. Due to its simplicity and accuracy, the proposed equation here should find application among hydrologists, natural resource scientists and engineers who desire to easily derive accurate estimations of the Green-Ampt infiltration model.
Analysis of environmental DNA (eDNA) has gained widespread usage for taxonomically based biodiversity assessment. While interest in applying noninvasive eDNA monitoring for population genetic assessments has grown, its usage in this sphere remains limited. One barrier to uptake is that the effectiveness of eDNA detection below the species level remains to be determined for multiple species and environments. Here, we test the utility of this emergent technology to obtain within-species haplotypic variation of New Zealand (NZ) blackfoot pāua (Haliotis iris). We compare mitochondrial haplotype diversity recovered from marine eDNA samples against traditional tissue samples of blackfoot pāua collected at the same NZ coastal site. Targeting the ATP8-ATP6 region, we recovered four mitochondrial haplotypes from eDNA versus six haplotypes from tissue samples. Three common haplotypes were recovered with both eDNA and tissue samples, while only one out of three rare hap-lotypes – represented in tissue samples by one individual each – was recovered with our eDNA methods. We demonstrate that eDNA monitoring is an effective tool for recovering common genetic diversity from pāua, although rare (<5%) haplotypes are seldom recovered. Our results show the potential of eDNA to identify population- level haplotypic diversity for gastropods in the marine environment below the species level. This work demonstrates that eDNA methods can be an effective, noninvasive tool for genetic monitoring. Noninvasive eDNA sampling could minimize target organ-ism stress and human interaction enabling population genetic research for hard-to- sample, delicate, or sensitive species.
Environmental DNA (eDNA) metabarcoding has shown great promise as an effective, non-invasive monitoring method for marine biomes. However, long filtration times and the need for state-of- the- art laboratories are restricting sample replication and in situ species detections. Methodological innovations, such as passive filtration and self-contained DNA extraction protocols, have the potential to alleviate these is-sues. We explored the implementation of passive sampling and a self-contained DNA extraction protocol by comparing fish diversity obtained from active filtration (1 L; 0.45 μm cellulose nitrate [CN] filters) to five passive substrates, including 0.45 μm CN filters, 5 μm nylon filters, 0.45 μm positively charged nylon filters, artificial sponges, and fishing net. Fish diversity was then compared between the PDQeX Nucleic Acid Extractor and the conventional Qiagen DNeasy Blood & Tissue protocol. Experiments were conducted in both a controlled mesocosm and in situ at the Portobello Marine Laboratory, New Zealand. No significant differences in fish diversity were observed among active filtration and more porous passive materials (artificial sponges and fishing net) for both the mesocosm and harbor waters. For the in situ comparison, all passive filter membranes detected a significantly lower number of fish species, resulting from partial sample drop-out. While no significant differences in fish eDNA signal diversity were observed between either DNA extraction methods in the mesocosm, the PDQeX system was less effective at detecting fish for the in situ comparison. Our results demonstrate that a passive sampling approach using porous substrates can be effectively implemented to capture eDNA from seawater, eliminating vacuum filtration processing. The large variation in efficiency observed among the five substrate types, however, warrants further optimization of the passive sampling approach for routine eDNA applications. The PDQeX system can extract high-abundance DNA in a mesocosm with further optimization to detect low-abundance eDNA from the marine environment.
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