Abstract Quantifying the structure and dynamics of species interactions in ecological communities is fundamental to studying ecology and evolution. While there are numerous approaches to analysing ecological networks, there is not yet an approach that can (1) quantify dissimilarity in the global structure of ecological networks that range from identical species and interaction composition to zero shared species or interactions and (2) map species between such networks while incorporating additional ecological information, such as species traits or abundances.To address these challenges, we introduce the use of optimal transport distances to quantify ecological network dissimilarity and functionally equivalent species between networks. Specifically, we describe the Gromov–Wasserstein (GW) and Fused Gromov–Wasserstein (FGW) distances. We apply these optimal transport methods to synthetic and empirical data, using mammal food webs throughout sub‐Saharan Africa for illustration. We showcase the application of GW and FGW distances to identify the most functionally similar species between food webs, incorporate additional trait information into network comparisons and quantify food web dissimilarity among geographic regions.Our results demonstrate that GW and FGW distances can effectively differentiate ecological networks based on their topological structure while identifying functionally equivalent species, even when networks have different species. The FGW distance further improves node mapping for basal species by incorporating node‐level traits. We show that these methods allow for a more nuanced understanding of the topological similarities in food web networks among geographic regions compared to an alternative measure of network dissimilarity based on species identities.Optimal transport distances offer a new approach for quantifying functional equivalence between networks and a measure of network dissimilarity suitable for a broader range of uses than existing approaches. OT methods can be harnessed to analyse ecological networks at large spatial scales and compare networks among ecosystems, realms or taxa. Optimal transport‐based distances, therefore, provide a powerful tool for analysing ecological networks with great potential to advance our understanding of ecological community structure and dynamics in a changing world.
more »
« less
A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems
Abstract Accurately quantifying rates and patterns of biological nitrogen fixation (BNF) in terrestrial ecosystems is essential to characterize ecological and biogeochemical interactions, identify mechanistic controls, improve BNF representation in conceptual and numerical modelling, and forecast nitrogen limitation constraints on future carbon (C) cycling.While many resources address the technical advantages and limitations of different methods for measuring BNF, less systematic consideration has been given to the broader decisions involved in planning studies, interpreting data, and extrapolating results. Here, we present a conceptual and practical road map to study design, study execution, data analysis and scaling, outlining key considerations at each step.We address issues including defining N‐fixing niches of interest, identifying important sources of temporal and spatial heterogeneity, designing a sampling scheme (including method selection, measurement conditions, replication, and consideration of hotspots and hot moments), and approaches to analysing, scaling and reporting BNF. We also review the comparability of estimates derived using different approaches in the literature, and provide sample R code for simulating symbiotic BNF data frames and upscaling.Improving and standardizing study design at each of these stages will improve the accuracy and interpretability of data, define limits of extrapolation, and facilitate broader use of BNF data for downstream applications. We highlight aspects—such as quantifying scales of heterogeneity, statistical approaches for dealing with non‐normality, and consideration of rates versus ecological significance—that are ripe for further development.
more »
« less
- Award ID(s):
- 1754126
- PAR ID:
- 10451097
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 6
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 1122-1137
- Size(s):
- p. 1122-1137
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Summary Coarse roots represent a globally important belowground carbon pool, but the factors controlling coarse root decomposition rates remain poorly understood relative to other plant biomass components. We compiled the most comprehensive dataset of coarse root decomposition data including 148 observations from 60 woody species, and linked coarse root decomposition rates to plant traits, phylogeny and climate to address questions of the dominant controls on coarse root decomposition.We found that decomposition rates increased with mean annual temperature, root nitrogen and phosphorus concentrations. Coarse root decomposition was slower for ectomycorrhizal than arbuscular mycorrhizal associated species, and angiosperm species decomposed faster than gymnosperms. Coarse root decomposition rates and calcium concentrations showed a strong phylogenetic signal.Our findings suggest that categorical traits like mycorrhizal association and phylogenetic group, in conjunction with root quality and climate, collectively serve as the optimal predictors of coarse root decomposition rates.Our findings propose a paradigm of the dominant controls on coarse decomposition, with mycorrhizal association and phylogeny acting as critical roles on coarse root decomposition, necessitating their explicit consideration in Earth‐system models and ultimately improving confidence in projected carbon cycle–climate feedbacks.more » « less
-
Abstract The predicted intensification of the North American Monsoon is expected to alter growing season rainfall patterns in the southwestern United States. These patterns, which have historically been characterized by frequent small rain events, are anticipated to shift towards a more extreme precipitation regime consisting of fewer, but larger rain events. Furthermore, human activities are contributing to increased atmospheric nitrogen deposition throughout this dryland region.Alterations in rainfall size and frequency, along with changes in nitrogen availability, are likely to have significant consequences for above‐ground net primary production (ANPP) and plant community dynamics in drylands. The conceptual bucket model predicts that a shift towards fewer, but larger rain events could promote greater rates of ANPP in these regions by maintaining soil moisture availability above drought stress thresholds for longer periods during the growing season. However, only a few short‐term studies have tested this hypothesis, and none have explored the interaction between altered rainfall patterns and nitrogen enrichment.To address this knowledge gap, we conducted a 14‐year rainfall addition and nitrogen fertilization experiment in a northern Chihuahuan Desert grassland to explore the long‐term impacts of changes in monsoon rainfall size and frequency, along with chronic nitrogen enrichment, on ANPP (measured as peak biomass) and plant community dynamics.Contrary to bucket model predictions, small frequent rain events promoted comparable rates of ANPP to large infrequent rain events in the absence of nitrogen enrichment. It was only when nitrogen limitation was alleviated that large infrequent rain events resulted in the greatest ANPP. Furthermore, we found that nitrogen enrichment had the greatest impact on plant community composition under the small frequent rainfall regime.Synthesis. Our long‐term field experiment highlights limitations of the bucket model by demonstrating that water and nitrogen availability sequentially limit dryland ecological processes. Specifically, our findings suggest that while water availability is the primary limiting factor for above‐ground net primary production in these ecosystems, nitrogen limitation becomes increasingly important when water is not limiting. Moreover, our findings reveal that small frequent rain events play an important but underappreciated role in driving dryland ecosystem dynamics.more » « less
-
Abstract The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated.We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling.To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster of species where vocalization rates declined as ambient noise increased and another cluster where vocalization rates declined over the nesting season. In some common species, the number of vocalizations produced per day was correlated at scales of up to 15 km. Rarefaction analyses showed that adding sampling sites increased species detections more than adding sampling days.Although our analyses used hand‐annotated data, the methods will extend readily to large‐scale automated detection of vocalization events. Such data are likely to become increasingly available as autonomous recording units become more advanced, affordable, and power efficient. Passive acoustic monitoring with human or automated identification at the species level offers growing potential to complement observer‐based studies of avian ecology.more » « less
-
Abstract Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters.We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals.We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with <20 camera sites needed for precise estimates of common (ψ > 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration.We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites.more » « less
An official website of the United States government
