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31.
Istem Fer Anthony K. Gardella Alexey N. Shiklomanov Eleanor E. Campbell Elizabeth M. Cowdery Martin G. De Kauwe Ankur Desai Matthew J. Duveneck Joshua B. Fisher Katherine D. Haynes Forrest M. Hoffman Miriam R. Johnston Rob Kooper David S. LeBauer Joshua Mantooth William J. Parton Benjamin Poulter Tristan Quaife Ann Raiho Kevin Schaefer Shawn P. Serbin James Simkins Kevin R. Wilcox Toni Viskari Michael C. Dietze 《Global Change Biology》2021,27(1):13-26
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century. 相似文献
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Motivated by both analytical tractability and empirical practicality, community ecologists have long treated the species pair as the fundamental unit of study. This notwithstanding, the challenge of understanding more complex systems has repeatedly generated interest in the role of so‐called higher‐order interactions (HOIs) imposed by species beyond the focal pair. Here we argue that HOIs – defined as non‐additive effects of density on per capita growth – are best interpreted as emergent properties of phenomenological models (e.g. Lotka–Volterra competition) rather than as distinct ‘ecological processes’ in their own right. Using simulations of consumer‐resource models, we explore the mechanisms and system properties that give rise to HOIs in observational data. We demonstrate that HOIs emerge under all but the most restrictive of assumptions, and that incorporating non‐additivity into phenomenological models improves the quantitative and qualitative accuracy of model predictions. Notably, we also observe that HOIs derive primarily from mechanisms and system properties that apply equally to single‐species or pairwise systems as they do to more diverse communities. Consequently, there exists a strong mandate for further recognition of non‐additive effects in both theoretical and empirical research. 相似文献
34.
Andrew Ferguson Thomas E. Reed Tom F. Cross Philip McGinnity Paulo A. Prodöhl 《Journal of fish biology》2019,95(3):692-718
Brown trout Salmo trutta is endemic to Europe, western Asia and north-western Africa; it is a prominent member of freshwater and coastal marine fish faunas. The species shows two resident (river-resident, lake-resident) and three main facultative migratory life histories (downstream–upstream within a river system, fluvial–adfluvial potamodromous; to and from a lake, lacustrine–adfluvial (inlet) or allacustrine (outlet) potamodromous; to and from the sea, anadromous). River-residency v. migration is a balance between enhanced feeding and thus growth advantages of migration to a particular habitat v. the costs of potentially greater mortality and energy expenditure. Fluvial–adfluvial migration usually has less feeding improvement, but less mortality risk, than lacustrine–adfluvial or allacustrine and anadromous, but the latter vary among catchments as to which is favoured. Indirect evidence suggests that around 50% of the variability in S. trutta migration v. residency, among individuals within a population, is due to genetic variance. This dichotomous decision can best be explained by the threshold-trait model of quantitative genetics. Thus, an individual's physiological condition (e.g., energy status) as regulated by environmental factors, genes and non-genetic parental effects, acts as the cue. The magnitude of this cue relative to a genetically predetermined individual threshold, governs whether it will migrate or sexually mature as a river-resident. This decision threshold occurs early in life and, if the choice is to migrate, a second threshold probably follows determining the age and timing of migration. Migration destination (mainstem river, lake, or sea) also appears to be genetically programmed. Decisions to migrate and ultimate destination result in a number of subsequent consequential changes such as parr–smolt transformation, sexual maturity and return migration. Strong associations with one or a few genes have been found for most aspects of the migratory syndrome and indirect evidence supports genetic involvement in all parts. Thus, migratory and resident life histories potentially evolve as a result of natural and anthropogenic environmental changes, which alter relative survival and reproduction. Knowledge of genetic determinants of the various components of migration in S. trutta lags substantially behind that of Oncorhynchus mykiss and other salmonines. Identification of genetic markers linked to migration components and especially to the migration–residency decision, is a prerequisite for facilitating detailed empirical studies. In order to predict effectively, through modelling, the effects of environmental changes, quantification of the relative fitness of different migratory traits and of their heritabilities, across a range of environmental conditions, is also urgently required in the face of the increasing pace of such changes. 相似文献
35.
Karim Arafah Sbastien Nicolas Voisin Victor Masson Cdric Alaux Yves Le Conte Michel Bocquet Philippe Bulet 《Proteomics》2019,19(23)
Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix‐assisted laser desorption ionization–mass spectrometry (MALDI–MS) profiling has emerged within this decade as a powerful tool to identify in routine micro‐organisms and is currently used in real‐time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram‐positive and ‐negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees’ systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high‐throughput strategy that addresses honey bee health on an individual scale is introduced. 相似文献
36.
Peter S. Thuy-Boun Ana Y. Wang Ana Crissien-Martinez Janice H. Xu Sandip Chatterjee Gregory S. Stupp Andrew I. Su Walter J. Coyle Dennis W. Wolan 《Molecular & cellular proteomics : MCP》2022,21(3):100197
The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC. 相似文献
37.
Athanasios Paschalis Simone Fatichi Jakob Zscheischler Philippe Ciais Michael Bahn Lena Boysen Jinfeng Chang Martin De Kauwe Marc Estiarte Daniel Goll Paul J. Hanson Anna B. Harper Enqing Hou Jaime Kigel Alan K. Knapp Klaus S. Larsen Wei Li Sebastian Lienert Yiqi Luo Patrick Meir Julia E. M. S. Nabel Rom Ogaya Anthony J. Parolari Changhui Peng Josep Peuelas Julia Pongratz Serge Rambal Inger K. Schmidt Hao Shi Marcelo Sternberg Hanqin Tian Elisabeth Tschumi Anna Ukkola Sara Vicca Nicolas Viovy Ying‐Ping Wang Zhuonan Wang Karina Williams Donghai Wu Qiuan Zhu 《Global Change Biology》2020,26(6):3336-3355
Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models. 相似文献
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Rolando De la Cruz Guillermo Marshall Fernando A. Quintana 《Biometrical journal. Biometrische Zeitschrift》2011,53(5):735-749
In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual‐specific random effects in a non‐linear mixed‐effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two‐stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation. 相似文献