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83 results for “trait integration”
Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry
<p>Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry. </p> <p>Dataset includes 1) model and analysis, and 2) supplemental data. </p> <p>In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file. </p> <p><strong>Acknowledgments</strong></p> <p>We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory. </p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). </p> <p><strong>Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations</strong></p> <p>Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9<br> 1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA.<br>2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA.<br>3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106<br>4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA<br>5Department of Natural Resources and the Environment, University of New Hampshire;<br>Durham, NH, USA 03824<br>6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ,<br>85721, USA<br>7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France<br>8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210<br>9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA.<br>10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA.<br>11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA.<br>12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University<br>of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia<br>13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA</p>
Relyea, R. A., and K. L. Yurewicz. 2002. Predicting community outcomes from pairwise interactions: Integrating density- and trait-mediated effects. Oecologia 131:569-579.
Understanding how species interactions shape the structure of ecological communities based on pairwise comparisons has been a difficult undertaking for ecologists because effects in reassembled communities can be different than simple density-mediated interactions would suggest. Part of this complexity occurs because many species change their behavior and morphology with different predators and competitors and, thus, change their per-capita interaction rates (i.e. trait-mediated interactions). Our objective was to use a simple experimental community of two predators (larval dragonflies, Anax longipes, and larval salamanders, Ambystoma tigrinum), two prey (larval green frogs, Rana clamitans, and larval bullfrogs, R. catesbeiana), and a shared prey resource to determine whether we can predict interactions in a reassembled community by combining our knowledge of density- and trait-mediated interactions,. We combined pairwise laboratory experiments on predation rates and predator-induced behaviors with a mesocosm experiment to examine density- and trait-mediated effects. We used a factorial combination of no predators, caged Anax (to induce anti-predator traits without changing prey density), and lethal Anax crossed with no predators, caged Ambystoma, and lethal Ambystoma. The species interactions in the reassembled community were qualitatively predictable based on the pairwise experiments. Lethal Anax preyed upon Ambystoma and green frogs while lethal Ambystoma only preyed upon green frogs. Anax also reduced the activity of the green frogs; this caused a decrease in salamander predation on green frogs, a decrease in green frog acquisition of resources, and an increase in bullfrog acquisition of resources. Ambystoma had no effect on green frog activity, no effect on resource acquisition by green frogs, and no effect on resource acquisition by bullfrogs. These results suggest that we can better understand how species interact in natural communities if we have a more deta
Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
<p><strong>Aim</strong>: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends.<br><strong>Location</strong>: Tanzania, East Africa.<br><strong>Time period</strong>: 1980-1999 and 2000-2020.<br><strong>Major taxa studied</strong>: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders.<br><strong>Methods</strong>: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits.<br><strong>Results</strong>: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results.<br><strong>Main conclusions</strong>: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.</p>
Data from: Integrating deep learning derived morphological traits and molecular data for total-evidence phylogenetics: lessons from digitized collections
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Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
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Integrating floral trait and flowering time distribution patterns help reveal a more dynamic nature of co-flowering community assembly processes
<p>Species' floral traits and flowering times are known to be the major drivers of pollinator-mediated plant-plant interactions in diverse co-flowering communities. However, their simultaneous role in mediating plant community assembly and plant-pollinator interactions is still poorly understood. Since not all species flower at the same time, inference of facilitative and competitive interactions based on floral trait distribution patterns should account for fine phenological structure (intensity of flowering overlap) within co-flowering communities. Such an approach may also help reveal the simultaneous action of competitive and facilitative interactions in structuring co-flowering communities.</p> <p>Here we used modularity within a co-flowering network context, as a novel approach to detect convergent and/or over-dispersed patterns in floral trait distribution and pollinator sharing. Specifically, we evaluate differences in floral trait and pollinator distribution patterns within (high temporal flowering overlap) and among co-flowering modules (low temporal flowering overlap). We further evaluate the consistency of observed floral trait and pollinator sharing distribution patterns across space (three geographic regions) and time (dry and rainy seasons).</p> <p>We found that floral trait similarity was significantly higher in plant species within co-flowering modules than in species among them. This suggests pollinator facilitation may lead to floral trait convergence, but only within co-flowering modules. However, our results also revealed seasonal and spatial shifts in the underlying interactions (facilitation or competition) driving co-flowering assembly, suggesting that the prevalent dominant interactions are not static.</p> <p>Synthesis: Overall, we provide strong evidence showing that the use of flowering time and floral trait distribution alone may be insufficient to fully uncover the role of pollinator-mediated interactions in community assembly. Integrating this information along with patterns of pollinator sharing will greatly help reveal the simultaneous action of facilitative and competitive pollinator-mediated interactions in co-flowering communities. The spatial and temporal variation in flowering and trait distribution patterns observed further emphasize the importance of adopting a more dynamic view of community assembly processes.</p>
Data from: Defining a spectrum of integrative trait-based vegetation canopy structural types
Vegetation canopy structure is a fundamental characteristic of terrestrial ecosystems that defines vegetation types and drives ecosystem functioning. We use the multivariate structural trait composition of vegetation canopies to classify ecosystems within a global canopy structure spectrum. Across the temperate forest subset of this spectrum we assess gradients in canopy structural traits, characterize canopy structural types (CST), and evaluate drivers and functional consequences of canopy structural variation. We derive CSTs from multivariate canopy structure data, illustrating variation along three primary structural axes and resolution into six largely distinct and functionally relevant CSTs. Our results illustrate that within-ecosystem successional processes and disturbance legacies can produce variation in canopy structure similar to that associated with sub-continental variation in forest types and ecoclimatic zones. The potential to classify ecosystems into CSTs based on suites of structural traits represents an important advance in understanding and modeling structure-function relationships in vegetated ecosystems.
Data from: Integration and harmonization of trait data from plant individuals across heterogeneous sources
<p>Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.</p>
Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data
<p>The growing availability of genetic datasets, in combination with machine learning frameworks, offer great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we analyzed repurposed data using machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 species using open-access scripts and various databases. After identifying important predictors of nucleotide diversity with random forest regression, we conducted follow-up analyses to examine the roles of phylogenetic history, geography, and demographic processes on intraspecific diversity. Although life history traits were not important predictors for this dataset, we found significant phylogenetic signal in genetic diversity within amphibians. We also found that salamander species at northern latitudes contain lower genetic diversity. Data repurposing and machine learning provide valuable tools for detecting patterns with relevance for conservation, but concerted efforts are needed to compile meaningful datasets with greater utility for understanding global biodiversity.</p>
Weak genetic signal for phenotypic integration implicates developmental processes as major regulators of trait covariation
<p>Phenotypic integration is an important metric that describes the degree of covariation among traits in a population, and is hypothesized to arise due to selection for shared functional processes. Our ability to identify the genetic and/or developmental underpinnings of integration is marred by temporally overlapping cell-, tissue-, and structure-level processes that serve to continually 'overwrite' the structure of covariation among traits through ontogeny. Here we examine whether traits that are integrated at the phenotypic level, also exhibit a shared genetic basis (e.g., pleiotropy). We micro-CT scanned two hard tissue traits, and two soft tissue traits (mandible, pectoral girdle, atrium, and ventricle respectively) from an F<sub>5</sub> hybrid population of Lake Malawi cichlids, and used geometric morphometrics to extract 3D shape information from each trait. Given the large degree of asymmetric variation that may reflect developmental instability, we separated symmetric- from asymmetric-components of shape variation. We then performed quantitative trait loci (QTL) analysis to determine the degree of genetic overlap between shapes. While we found ubiquitous associations among traits at the phenotypic level, except for a handful of notable exceptions, our QTL analysis revealed few overlapping genetic regions. Taken together, this indicates developmental interactions can play a large role in determining the degree of phenotypic integration among traits, and likely obfuscate the genotype to phenotype map, limiting our ability to gain a comprehensive picture of the genetic contributors responsible for phenotypic divergence.</p>
Data from: Phenotypic integration in an extended phenotype: among‐individual variation in nest‐building traits of the alfalfa leafcutting bee (Megachile rotundata)
Structures such as nests and burrows are an essential component of many organisms' life-cycle and requires a complex sequence of behaviors. Because behaviors can vary consistently among individuals and be correlated with one another, we hypothesized that these structures would 1) show evidence of among-individual variation, 2) be organized into distinct functional modules, and 3) show evidence of trade-offs among functional modules due to limits on energy budgets. We tested these hypotheses using the alfalfa leafcutting bee, Megachile rotundata, a solitary bee and important crop pollinator. M. rotundata constructs complex nests by gathering leaf materials to form a linear series of cells in pre-existing cavities. In this study, we examined variation in the following nest construction traits: reproduction (number of cells per nest and nest length), nest protection (cap length and number of leaves per cap), cell construction (cell size and number of leaves per cell), and cell provisioning (cell mass) from 60 nests. We found a general decline in investment in cell construction and provisioning with each new cell built. In addition, we found evidence for both repeatability and plasticity in cell provisioning with little evidence for trade-offs among traits. Instead, most traits were positively, albeit weakly, correlated (r ~ 0.15), and traits were loosely organized into covarying modules. Our results show that individual differences in nest construction are detectable at a level similar to that of other behavioral traits and that these traits are only weakly integrated. This suggests that nest components are capable of independent evolutionary trajectories.
Data from: The demographic effects of functional traits: an integral projection model approach reveals population-level consequences of reproduction-defense tradeoffs
Quantitatively linking individual variation in functional traits to demography is a necessary step to advance our understanding of trait-based ecological processes. We constructed a population model for Asclepias syriaca to identify how functional traits affect vital rates and population growth and whether tradeoffs in chemical defense and demography alter population growth. Plants with higher foliar cardenolides had lower fiber, cellulose, and lignin levels, as well as decreased sexual and clonal reproduction. Average cardenolide concentrations had the strongest effect on population growth. In both the sexual and clonal pathway, the tradeoff between reproduction and defense affected population growth. We found that both increasing the mean of the distribution of individual plant values for cardenolides and herbivory decreased population growth. However, increasing the variance in both defense and herbivory increased population growth. Functional traits can impact population growth and quantifying individual-level variation in traits should be included in assessments of population-level processes.
Multiple traits and multifarious environments: integrated divergence of morphology and life history
<p><span><span><span><span><span><span><span><span><span><span><span>Understanding complex responses of multiple character suites (e.g., behaviour, life history, morphology) to multifarious environments is a challenging task. Here we use a multivariate approach (partial least squares structural equation modelling) to disentangle drivers (i.e., predation, resource availability, and population demographics) of phenotypic divergence among populations of Bahamas mosquitofish (<i>Gambusia hubbsi</i>) inhabiting blue holes. We further employ a two-block partial least squares analysis in a novel approach to uncovering integrated and independent aspects of divergence in correlated character suites. Results suggest that phenotypic divergence mainly resulted from differences in predation regimes, with population demography and resource availability also influencing particular aspects of divergence. We uncovered statistically significant covariation of life histories and morphologies, and revealed that phenotypic divergence between predation regimes involved both integrated and independent responses. For instance, female life histories diverged mostly independently of morphology, although some morphological shifts (abdominal distension) resulted from changes in fecundity. In contrast, males showed strong morphological divergence independent of life history, but much of their life-history shifts reflected joint morphological changes (lean weight and body shape). Our study illustrates the utility of gathering disparate data types from multiple populations/species to understand the causes and nature of phenotypic divergence in the wild.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Integrating life history traits into predictive phylogeography
Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine-learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest (PNW) and the Southwestern Aridlands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine-learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies.
Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data
Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible, and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30,040 hectares) along a 3,322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient, and cost-effective manner.
Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone
Hybrid zones provide unique opportunities to examine reproductive isolation and introgression in nature. We utilized 45,384 Single Nucleotide Polymorphism (SNP) loci to perform association mapping of 14 floral, vegetative, and ecological traits that differ between Iris hexagona and Iris fulva, and to investigate, using a Bayesian Genomic Cline (BGC) framework, patterns of genomic introgression in a large and phenotypically diverse hybrid zone in southern Louisiana. Many loci of small effect-size were consistently found to be associated with phenotypic variation across all traits, and several individual loci were revealed to influence phenotypic variation across multiple traits. Patterns of genomic introgression were quite heterogeneous throughout the Louisiana Iris genome, with I. hexagona alleles tending to be favored over those of I. fulva. Loci that were found to have exceptional patterns of introgression were also found to be significantly associated with phenotypic variation in a small number of morphological traits. However, this was the exception rather than the rule, as most loci that were associated with morphological trait variation were not significantly associated with excess ancestry. These findings provide insights into the complexity of the genomic architecture of phenotypic differences and are a first step towards identifying loci that are associated with both trait variation and reproductive isolation in nature.
Data from: Match and mismatch: integrating consumptive effects of predators, prey traits, and habitat selection in colonizing aquatic insects
<p>Predators are a particularly critical component of habitat quality, as they affect survival, morphology, behavior, population size, and community structure through both consumptive and non-consumptive effects. Non-consumptive effects can often exceed consumptive effects, but their relative importance is undetermined in many systems. Our objective was to determine the consumptive and non-consumptive effects of a predaceous aquatic insect, <em>Notonecta irrorata</em>, on colonizing aquatic beetles. We tested how <em>N. irrorata </em>affected survival and habitat selection of colonizing aquatic beetles, how beetle traits contributed to their vulnerability to predation by <em>N. irrorata,</em> and how combined consumptive and non-consumptive effects affected populations and community structure. Predation vulnerabilities ranged from 0–95% mortality, with size, swimming, and exoskeleton traits generating species-specific vulnerabilities. Habitat selection ranged from predator avoidance to preferentially colonizing predator patches. Attraction of Dytiscidae to <em>N. irrorata</em> may be a natural ecological trap given similar cues produced by these taxa. Hence, species-specific habitat selection by prey can be either predator-avoidance responses that reduce consumptive effects, or responses that magnify predator effects. <em>Notonecta irrorata</em> had both strong consumptive and non-consumptive effects on populations and communities, while combined effects predicted even more distinct communities and populations across patches with or without predators. Our results illustrate that an aquatic invertebrate predator can have functionally-unique consumptive effects on prey, attracting and repelling prey, while prey have functionally-unique responses to predators. Determining species-specific consumptive and non-consumptive effects is important to understand patterns of species diversity across landscapes.</p>
Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data
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Data from: Phenotypic integration between claw and toepad traits promotes microhabitat specialization in the Anolis adaptive radiation
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Integrating forest inventory and LiDAR observations to uncover the role of plant traits on cooling and humidifying effects in urban areas
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