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Chang CJ, Chang CW, Lu HP, Hsieh CH, Wu JH. Bioenergetically constrained dynamical microbial interactions govern the performance and stability of methane-producing bioreactors. NPJ Biofilms Microbiomes 2025; 11:31. [PMID: 39971951 PMCID: PMC11840090 DOI: 10.1038/s41522-025-00668-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2024] [Accepted: 02/10/2025] [Indexed: 02/21/2025] Open
Abstract
Biogas generation from organic waste by anaerobic bioreactors as renewable energy largely depends on microbial community and species interplays involved. This microbial networking is complex and time-dependent, influencing community succession and reactor performance, but remains unexplored due to the challenges in quantifying dynamics. We employed empirical dynamic modeling to analyze daily networking from a newly established bioreactor converting sucrose to biogas. Over time, microbial interactions within the three trophic (fermentative, syntrophic, and methanogenic) groups varied substantially more than between groups. Notably, versatile syntrophic bacteria like Syntrophorhabdus exhibited stronger interaction strength (0.14 ± 0.22) to hydrogen-dependent methylotrophic Methanomassiliicoccus than strictly syntrophic bacteria associated with butyrate (0.01 ± 0.01 for Syntrophomonas) and propionate (0.00 ± 0.01 for Syntrophobacter). The time-varying interaction networks were closely linked to the system performance dynamics, particularly concerning hydrogen concentrations. As community succession progressed, the stability of interaction network increased through time, accompanied by increased complexity and higher interaction strength. Causal analyses revealed intricate feedback involving catabolic energetics, community structure, and microbial interactions. These feedback mechanisms played a crucial role in regulating anaerobic degradation processes, thereby offering strategies for manipulating microbial interactions to enhance bioreactor stability and efficiency.
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Affiliation(s)
- Chao-Jui Chang
- Department of Environmental Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chun-Wei Chang
- Institute of Fishery Sciences and Department of Life Science, National Taiwan University, Taipei, Taiwan.
| | - Hsiao-Pei Lu
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan.
| | - Chih-Hao Hsieh
- Institute of Oceanography, National Taiwan University, Taipei, Taiwan.
- Institute of Ecology and Evolutionary Biology, Department of Life Science, National Taiwan University, Taipei, Taiwan.
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.
- National Center for Theoretical Sciences, Taipei, Taiwan.
| | - Jer-Horng Wu
- Department of Environmental Engineering, National Cheng Kung University, Tainan, Taiwan.
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2
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Yu Z, Gan Z, Tawfik A, Meng F. Exploring interspecific interaction variability in microbiota: A review. ENGINEERING MICROBIOLOGY 2024; 4:100178. [PMID: 40104221 PMCID: PMC11915528 DOI: 10.1016/j.engmic.2024.100178] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 11/04/2024] [Accepted: 11/04/2024] [Indexed: 03/20/2025]
Abstract
Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.
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Affiliation(s)
- Zhong Yu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Zhihao Gan
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Ahmed Tawfik
- National Research Centre, Water Pollution Research Department, Dokki, Giza 12622, Egypt
- Department of Environmental Sciences, College of Life Sciences, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
| | - Fangang Meng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
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3
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Hashimoto K, Hayasaka D, Eguchi Y, Seko Y, Cai J, Suzuki K, Goka K, Kadoya T. Multifaceted effects of variable biotic interactions on population stability in complex interaction webs. Commun Biol 2024; 7:1309. [PMID: 39438612 PMCID: PMC11496648 DOI: 10.1038/s42003-024-06948-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/24/2024] [Indexed: 10/25/2024] Open
Abstract
Recent studies have revealed that biotic interactions in ecological communities vary over time, possibly mediating community responses to anthropogenic disturbances. This study investigated the heterogeneity of such variability within a real community and its impact on population stability in the face of pesticide application, particularly focusing on density-dependence of the interaction effect. Using outdoor mesocosms with a freshwater community, we found considerable heterogeneity in density-dependent interaction variability among links in the same community. This variability mediated the stability of recipient populations, with negative density-dependent interaction variability stabilizing whereas positive density-dependence and density-independent interaction variability destabilizing populations. Unexpectedly, the mean interaction strength, which is typically considered crucial for stability, had no significant effect, suggesting that how organisms interact on average is insufficient to predict the ecological impacts of pesticides. Our findings emphasize the multifaceted role of interaction variability in predicting the ecological consequences of anthropogenic disturbances such as pesticide application.
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Affiliation(s)
- Koya Hashimoto
- Faculty of Agriculture, Kindai University, Nakamachi 3327-204, Nara, Nara, 631-8505, Japan.
- National Institute for Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki, 305-8506, Japan.
- Faculty of Agriculture and Life Science, Hirosaki University, 3 Bunkyo-cho, Hirosaki, Aomori, 036-8561, Japan.
| | - Daisuke Hayasaka
- Faculty of Agriculture, Kindai University, Nakamachi 3327-204, Nara, Nara, 631-8505, Japan
| | - Yuji Eguchi
- Graduate School of Agriculture, Kindai University, Nakamachi 3327-204, Nara, Nara, 631-8505, Japan
| | - Yugo Seko
- National Institute for Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki, 305-8506, Japan
- Graduate School of Agriculture, Kindai University, Nakamachi 3327-204, Nara, Nara, 631-8505, Japan
| | - Ji Cai
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, Shiga, 520-2113, Japan
| | - Kenta Suzuki
- BioResource Research Center, RIKEN, Takanodai 3-1-1, Tsukuba, Ibaraki, 305-0074, Japan
- Institute for Multidisciplinary Sciences, Yokohama National University, Tokiwadai 9-5, Hodogaya, Yokohama, Kanagawa, 240-8501, Japan
| | - Koichi Goka
- National Institute for Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki, 305-8506, Japan
| | - Taku Kadoya
- National Institute for Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki, 305-8506, Japan
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4
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Kawatsu K. Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series. ROYAL SOCIETY OPEN SCIENCE 2024; 11:231795. [PMID: 39086828 PMCID: PMC11288672 DOI: 10.1098/rsos.231795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/12/2024] [Accepted: 06/11/2024] [Indexed: 08/02/2024]
Abstract
Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.
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Affiliation(s)
- Kazutaka Kawatsu
- Graduate School of Life Sciences, Tohoku University, Sendai980-8578, Japan
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5
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Tal O, Ostrovsky I, Gal G. A framework for identifying factors controlling cyanobacterium Microcystis flos-aquae blooms by coupled CCM-ECCM Bayesian networks. Ecol Evol 2024; 14:e11475. [PMID: 38932972 PMCID: PMC11199127 DOI: 10.1002/ece3.11475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 06/28/2024] Open
Abstract
Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. In this study, we present a novel computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergent Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. The constructed CCM-ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer-reviewed publications. Our findings suggest that Microcystis flos-aquae levels are influenced not only by community structure but also by ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. We demonstrated a non-parametric computational framework for causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos-aquae blooms in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.
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Affiliation(s)
- O. Tal
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
| | - I. Ostrovsky
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
| | - G. Gal
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
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6
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Ushio M, Saito H, Tojo M, Nagano AJ. An ecological network approach for detecting and validating influential organisms for rice growth. eLife 2023; 12:RP87202. [PMID: 37702717 PMCID: PMC10499375 DOI: 10.7554/elife.87202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and influenced by surrounding ecological community members. How ecological communities influence the rice performance in the field has been underexplored despite the potential of ecological communities to establish an environment-friendly agricultural system. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously overlooked organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA metabarcoding in 2017 in Japan. We detected more than 1000 species (including microbes and macrobes such as insects) in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated under field conditions in 2019 by field manipulation experiments. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.
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Affiliation(s)
- Masayuki Ushio
- Hakubi Center, Kyoto UniversityKyotoJapan
- Center for Ecological Research, Kyoto UniversityOtsuJapan
- Department of Ocean Science, The Hong Kong University of Science and Technology, Clear Water Bay, KowloonHong Kong SARChina
| | - Hiroki Saito
- Tropical Agriculture Research Front, Japan International Research Center for Agricultural SciencesOkinawaJapan
| | - Motoaki Tojo
- Graduate School of Agriculture, Osaka Metropolitan UniversityOsakaJapan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku UniversityOtsuJapan
- Institute for Advanced Biosciences, Keio UniversityTsuruokaJapan
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7
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Gonzalez A, Chase JM, O'Connor MI. A framework for the detection and attribution of biodiversity change. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220182. [PMID: 37246383 DOI: 10.1098/rstb.2022.0182] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/31/2023] [Indexed: 05/30/2023] Open
Abstract
The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps-causal modelling, observation, estimation, detection and attribution-for robust attribution. This workflow provides evidence of biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.
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Affiliation(s)
- Andrew Gonzalez
- Department of Biology, McGill University, Montreal, Canada H3A 1B1
- Quebec Centre for Biodiversity Science, Montreal, Canada H3A 1B1
| | - Jonathan M Chase
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig 04103, Germany
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale) 06099, Germany
| | - Mary I O'Connor
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver V6T 1Z4, Canada
- Santa Fe Institute, Santa Fe, NM 87501, USA
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8
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Zhao Q, Van den Brink PJ, Xu C, Wang S, Clark AT, Karakoç C, Sugihara G, Widdicombe CE, Atkinson A, Matsuzaki SIS, Shinohara R, He S, Wang YXG, De Laender F. Relationships of temperature and biodiversity with stability of natural aquatic food webs. Nat Commun 2023; 14:3507. [PMID: 37316479 DOI: 10.1038/s41467-023-38977-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 05/22/2023] [Indexed: 06/16/2023] Open
Abstract
Temperature and biodiversity changes occur in concert, but their joint effects on ecological stability of natural food webs are unknown. Here, we assess these relationships in 19 planktonic food webs. We estimate stability as structural stability (using the volume contraction rate) and temporal stability (using the temporal variation of species abundances). Warmer temperatures were associated with lower structural and temporal stability, while biodiversity had no consistent effects on either stability property. While species richness was associated with lower structural stability and higher temporal stability, Simpson diversity was associated with higher temporal stability. The responses of structural stability were linked to disproportionate contributions from two trophic groups (predators and consumers), while the responses of temporal stability were linked both to synchrony of all species within the food web and distinctive contributions from three trophic groups (predators, consumers, and producers). Our results suggest that, in natural ecosystems, warmer temperatures can erode ecosystem stability, while biodiversity changes may not have consistent effects.
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Affiliation(s)
- Qinghua Zhao
- Aquatic Ecology and Water Quality Management Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, The Netherlands.
- Research Unit of Environmental and Evolutionary Biology (URBE), University of Namur, Namur, Belgium.
- Institute of Complex Systems (naXys), University of Namur, Namur, Belgium.
- Institute of Life, Earth and the Environment (ILEE), University of Namur, Namur, Belgium.
| | - Paul J Van den Brink
- Aquatic Ecology and Water Quality Management Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, The Netherlands
- Wageningen Environmental Research, P.O. Box 47, 6700 AA, Wageningen, The Netherlands
| | - Chi Xu
- School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Shaopeng Wang
- Institute of Ecology, College of Urban and Environmental Science, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, 100871, Beijing, China
| | - Adam T Clark
- Institute of Biology, University of Graz, Holteigasse 6, 8010, Graz, Austria
| | - Canan Karakoç
- Department of Biology, Indiana University, 1001 East Third Street, Bloomington, IN, 47405, USA
| | - George Sugihara
- Scripps Institution of Oceanography, University of California-San Diego, La Jolla, CA, USA
| | | | - Angus Atkinson
- Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL13DH, UK
| | | | | | - Shuiqing He
- Wildlife Ecology and Conservation Group, Wageningen University & Research, Wageningen, The Netherlands
| | - Yingying X G Wang
- Department of Biological and Environmental Science, University of Jyväskylä, FI-40014, Jyväskylä, Finland
| | - Frederik De Laender
- Research Unit of Environmental and Evolutionary Biology (URBE), University of Namur, Namur, Belgium
- Institute of Complex Systems (naXys), University of Namur, Namur, Belgium
- Institute of Life, Earth and the Environment (ILEE), University of Namur, Namur, Belgium
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9
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Fujita H, Ushio M, Suzuki K, Abe MS, Yamamichi M, Iwayama K, Canarini A, Hayashi I, Fukushima K, Fukuda S, Kiers ET, Toju H. Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics. MICROBIOME 2023; 11:63. [PMID: 36978146 PMCID: PMC10052866 DOI: 10.1186/s40168-023-01474-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as "dysbiosis" in human microbiomes. METHODS We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. RESULTS We confirmed that the abrupt community changes observed through the time-series could be described as shifts between "alternative stable states" or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the "energy landscape" analysis of statistical physics or that of a stability index of nonlinear mechanics. CONCLUSIONS The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. Video Abstract.
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Affiliation(s)
- Hiroaki Fujita
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
| | - Masayuki Ushio
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
- Department of Ocean Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Kenta Suzuki
- Integrated Bioresource Information Division, BioResource Research Center, RIKEN, Tsukuba, Ibaraki, 305-0074, Japan
| | - Masato S Abe
- Faculty of Culture and Information Science, Doshisha University, Kyotanabe, Kyoto, 610-0321, Japan
| | - Masato Yamamichi
- School of Biological Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
- Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, 852-8523, Japan
| | - Koji Iwayama
- Faculty of Data Science, Shiga University, Hikone, 522-8522, Japan
| | - Alberto Canarini
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
| | - Ibuki Hayashi
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan
| | - Keitaro Fukushima
- Faculty of Food and Agricultural Sciences, Fukushima University, Kanayagawa 1, Fukushima, Fukushima, 960-1296, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa, 210-0821, Japan
- Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan
| | - E Toby Kiers
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hirokazu Toju
- Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
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10
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Munch SB, Rogers TL, Sugihara G. Recent developments in empirical dynamic modelling. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.13983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Stephan B. Munch
- Southwest Fisheries Science Center, National Marine Fisheries Service National Oceanic and Atmospheric Administration Santa Cruz California USA
- Department of Applied Mathematics University of California Santa Cruz California USA
| | - Tanya L. Rogers
- Southwest Fisheries Science Center, National Marine Fisheries Service National Oceanic and Atmospheric Administration Santa Cruz California USA
| | - George Sugihara
- Scripps Institution of Oceanography University of California San Diego La Jolla California USA
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11
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Grziwotz F, Chang CW, Dakos V, van Nes EH, Schwarzländer M, Kamps O, Heßler M, Tokuda IT, Telschow A, Hsieh CH. Anticipating the occurrence and type of critical transitions. SCIENCE ADVANCES 2023; 9:eabq4558. [PMID: 36608135 PMCID: PMC9821862 DOI: 10.1126/sciadv.abq4558] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Critical transition can occur in many real-world systems. The ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWSs) have been developed to anticipate the coming critical transition or distinguish types of transition. However, no effective method allows to establish practical threshold indicating the condition when the critical transition is most likely to occur. Here, we introduce a powerful EWS, named dynamical eigenvalue (DEV), that is rooted in bifurcation theory of dynamical systems to estimate the dominant eigenvalue of the system. Theoretically, the absolute value of DEV approaches 1 when the system approaches bifurcation, while its position in the complex plane indicates the type of transition. We demonstrate the efficacy of the DEV approach in model systems with known bifurcation types and also test the DEV approach on various critical transitions in real-world systems.
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Affiliation(s)
- Florian Grziwotz
- Institute for Evolution and Biodiversity, Westphalian Wilhelms-University Münster, Münster 48149, Germany
| | - Chun-Wei Chang
- Institute of Fisheries Science, Department of Life Science, National Taiwan University, Taipei 10617, Taiwan
- National Center for Theoretical Sciences, Taipei 10617, Taiwan
| | - Vasilis Dakos
- ISEM, CNRS, University of Montpellier, IRD, EPHE, Montpellier, France
| | - Egbert H. van Nes
- Department of Environmental Science, Wageningen University, Wageningen P.O. Box 47, 6700 AA, Netherlands
| | - Markus Schwarzländer
- Institute of Plant Biology and Biotechnology, University of Münster, Münster 48143, Germany
| | - Oliver Kamps
- Center for Nonlinear Science, Westphalian Wilhelms-University Münster, Münster 48149, Germany
| | - Martin Heßler
- Center for Nonlinear Science, Westphalian Wilhelms-University Münster, Münster 48149, Germany
- Institute for Theoretical Physics, Westphalian Wilhelms-University Münster, Münster 48149, Germany
| | - Isao T. Tokuda
- Department of Mechanical Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan
| | - Arndt Telschow
- Institute for Evolution and Biodiversity, Westphalian Wilhelms-University Münster, Münster 48149, Germany
- Institute for Environmental Systems Science, University of Osnabrück, Osnabrück 49076, Germany
| | - Chih-hao Hsieh
- National Center for Theoretical Sciences, Taipei 10617, Taiwan
- Institute of Oceanography, National Taiwan University, Taipei 10617, Taiwan
- Institute of Ecology and Evolutionary Biology, Department of Life Science, National Taiwan University, Taipei 10617, Taiwan
- Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
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12
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Medeiros LP, Allesina S, Dakos V, Sugihara G, Saavedra S. Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics. Ecol Lett 2023; 26:170-183. [PMID: 36318189 PMCID: PMC10092288 DOI: 10.1111/ele.14131] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.
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Affiliation(s)
- Lucas P Medeiros
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Massachusetts, Cambridge, USA.,Institute of Marine Sciences, University of California Santa Cruz, California, Santa Cruz, USA
| | - Stefano Allesina
- Department of Ecology & Evolution, University of Chicago, Illinois, Chicago, USA.,Northwestern Institute on Complex Systems, Northwestern University, Illinois, Evanston, USA
| | - Vasilis Dakos
- Institut des Sciences de l'Evolution de Montpellier, Université de Montpellier, Montpellier, France
| | - George Sugihara
- Scripps Institution of Oceanography, University of California San Diego, California, La Jolla, USA
| | - Serguei Saavedra
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Massachusetts, Cambridge, USA
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13
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Effects of phytoplankton, viral communities, and warming on free-living and particle-associated marine prokaryotic community structure. Nat Commun 2022; 13:7905. [PMID: 36550140 PMCID: PMC9780322 DOI: 10.1038/s41467-022-35551-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Free-living and particle-associated marine prokaryotes have physiological, genomic, and phylogenetic differences, yet factors influencing their temporal dynamics remain poorly constrained. In this study, we quantify the entire microbial community composition monthly over several years, including viruses, prokaryotes, phytoplankton, and total protists, from the San-Pedro Ocean Time-series using ribosomal RNA sequencing and viral metagenomics. Canonical analyses show that in addition to physicochemical factors, the double-stranded DNA viral community is the strongest factor predicting free-living prokaryotes, explaining 28% of variability, whereas the phytoplankton (via chloroplast 16S rRNA) community is strongest with particle-associated prokaryotes, explaining 31% of variability. Unexpectedly, protist community explains little variability. Our findings suggest that biotic interactions are significant determinants of the temporal dynamics of prokaryotes, and the relative importance of specific interactions varies depending on lifestyles. Also, warming influenced the prokaryotic community, which largely remained oligotrophic summer-like throughout 2014-15, with cyanobacterial populations shifting from cold-water ecotypes to warm-water ecotypes.
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14
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Mühlbauer LK, Harpole WS, Clark AT. Differences in initial abundances reveal divergent dynamic structures in Gause's predator-prey experiments. Ecol Evol 2022; 12:e9638. [PMID: 36545367 PMCID: PMC9760897 DOI: 10.1002/ece3.9638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/25/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Improved understanding of complex dynamics has revealed insights across many facets of ecology, and has enabled improved forecasts and management of future ecosystem states. However, an enduring challenge in forecasting complex dynamics remains the differentiation between complexity and stochasticity, that is, to determine whether declines in predictability are caused by stochasticity, nonlinearity, or chaos. Here, we show how to quantify the relative contributions of these factors to prediction error using Georgii Gause's iconic predator-prey microcosm experiments, which, critically, include experimental replicates that differ from one another only in initial abundances. We show that these differences in initial abundances interact with stochasticity, nonlinearity, and chaos in unique ways, allowing us to identify the impacts of these factors on prediction error. Our results suggest that jointly analyzing replicate time series across multiple, distinct starting points may be necessary for understanding and predicting the wide range of potential dynamic types in complex ecological systems.
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Affiliation(s)
| | - William Stanley Harpole
- Department of Physiological DiversityHelmholtz Centre for Environmental Research (UFZ)LeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Institute of BiologyMartin Luther UniversityHalleGermany
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15
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Clark AT, Mühlbauer LK, Hillebrand H, Karakoç C. Measuring stability in ecological systems without static equilibria. Ecosphere 2022. [DOI: 10.1002/ecs2.4328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
| | | | - Helmut Hillebrand
- Institute for Chemistry and Biology of Marine Environments Carl‐von‐Ossietzky University Oldenburg Wilhelmshaven Germany
- Helmholtz‐Institute for Functional Marine Biodiversity at the University of Oldenburg Oldenburg Germany
- Alfred Wegener Institute, Helmholtz‐Centre for Polar and Marine Research Bremerhaven Germany
| | - Canan Karakoç
- Department of Biology Indiana University Bloomington Indiana USA
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16
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Decomposing predictability to identify dominant causal drivers in complex ecosystems. Proc Natl Acad Sci U S A 2022; 119:e2204405119. [PMID: 36215500 PMCID: PMC9586263 DOI: 10.1073/pnas.2204405119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.
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17
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Yu Z, Huang Y, Gan Z, Meng Y, Meng F. State-Space-Based Framework for Predicting Microbial Interaction Variability in Wastewater Treatment Plants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12765-12777. [PMID: 35943816 DOI: 10.1021/acs.est.2c02844] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Substantial attempts have been made to control microbial communities for environmental integrity, biosystem performance, and human health. However, it is difficult to manipulate microbial communities in practice due to the varying and nonlinear nature of interspecific interaction networks. Here, we develop a manifold-based framework to investigate the patterns of microbial interaction variability in wastewater treatment plants using manifold geometric properties and design a simple control strategy to manipulate the microbes in nonlinear communities. We validate our framework using the readily available and nonsequential microbiome profiles of wastewater treatment plants. Our results show that some microbes in the activated sludge and anammox communities display deterministic rival or cooperative relationships and constitute a stable subnetwork within the whole nonlinear community network. We further use a simulation to demonstrate that these microbes can be used to drive a microbe in a target direction regardless of the community dynamics. Overall, our framework can provide a time-efficient solution to select effective control inputs for reliable manipulation in varying microbial networks, opening up new possibilities across a range of biological fields, including wastewater treatment plants.
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Affiliation(s)
- Zhong Yu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Yue Huang
- Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Zhihao Gan
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Yabing Meng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Fangang Meng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China
- Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
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18
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Daugaard U, Munch SB, Inauen D, Pennekamp F, Petchey OL. Forecasting in the face of ecological complexity: Number and strength of species interactions determine forecast skill in ecological communities. Ecol Lett 2022; 25:1974-1985. [PMID: 35831269 PMCID: PMC9540476 DOI: 10.1111/ele.14070] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/12/2022] [Accepted: 06/17/2022] [Indexed: 11/28/2022]
Abstract
The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5-month-long laboratory experiment. We tested how forecasting of species abundances depends on the number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more interactions a species had, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.
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Affiliation(s)
- Uriah Daugaard
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Stephan B Munch
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California, USA
| | - David Inauen
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Owen L Petchey
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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19
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Huang Y, Kausrud K, Hassim A, Ochai SO, van Schalkwyk OL, Dekker EH, Buyantuev A, Cloete CC, Kilian JW, Mfune JKE, Kamath PL, van Heerden H, Turner WC. Environmental drivers of biseasonal anthrax outbreak dynamics in two multihost savanna systems. ECOL MONOGR 2022. [DOI: 10.1002/ecm.1526] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yen‐Hua Huang
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology University of Wisconsin‐Madison Madison WI USA
| | - Kyrre Kausrud
- Norwegian Veterinary Institute, PO. box 64 Ås Norway
| | - Ayesha Hassim
- Department of Veterinary Tropical Diseases University of Pretoria Onderstepoort South Africa
| | - Sunday O. Ochai
- Department of Veterinary Tropical Diseases University of Pretoria Onderstepoort South Africa
| | - O. Louis van Schalkwyk
- Department of Veterinary Tropical Diseases University of Pretoria Onderstepoort South Africa
- Office of the State Veterinarian, Department of Agriculture, Land Reform and Rural Development Government of South Africa Skukuza South Africa
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Edgar H. Dekker
- Office of the State Veterinarian, Department of Agriculture, Land Reform and Rural Development Government of South Africa Skukuza South Africa
| | - Alexander Buyantuev
- Department of Geography and Planning, University at Albany State University of New York Albany NY USA
| | - Claudine C. Cloete
- Etosha Ecological Institute, Etosha National Park, Ministry of Environment, Forestry and Tourism Namibia
| | - J. Werner Kilian
- Etosha Ecological Institute, Etosha National Park, Ministry of Environment, Forestry and Tourism Namibia
| | - John K. E. Mfune
- Department of Environmental Science University of Namibia Windhoek Namibia
| | | | - Henriette van Heerden
- Department of Veterinary Tropical Diseases University of Pretoria Onderstepoort South Africa
- Faculty of Veterinary Science, Department of Veterinary Tropical Diseases University of Pretoria Onderstepoort South Africa
| | - Wendy C. Turner
- U.S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology University of Wisconsin‐Madison Madison WI USA
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20
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Chang CW, Miki T, Ye H, Souissi S, Adrian R, Anneville O, Agasild H, Ban S, Be'eri-Shlevin Y, Chiang YR, Feuchtmayr H, Gal G, Ichise S, Kagami M, Kumagai M, Liu X, Matsuzaki SIS, Manca MM, Nõges P, Piscia R, Rogora M, Shiah FK, Thackeray SJ, Widdicombe CE, Wu JT, Zohary T, Hsieh CH. Causal networks of phytoplankton diversity and biomass are modulated by environmental context. Nat Commun 2022; 13:1140. [PMID: 35241667 PMCID: PMC8894464 DOI: 10.1038/s41467-022-28761-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/11/2022] [Indexed: 11/21/2022] Open
Abstract
Untangling causal links and feedbacks among biodiversity, ecosystem functioning, and environmental factors is challenging due to their complex and context-dependent interactions (e.g., a nutrient-dependent relationship between diversity and biomass). Consequently, studies that only consider separable, unidirectional effects can produce divergent conclusions and equivocal ecological implications. To address this complexity, we use empirical dynamic modeling to assemble causal networks for 19 natural aquatic ecosystems (N24◦~N58◦) and quantified strengths of feedbacks among phytoplankton diversity, phytoplankton biomass, and environmental factors. Through a cross-system comparison, we identify macroecological patterns; in more diverse, oligotrophic ecosystems, biodiversity effects are more important than environmental effects (nutrients and temperature) as drivers of biomass. Furthermore, feedback strengths vary with productivity. In warm, productive systems, strong nitrate-mediated feedbacks usually prevail, whereas there are strong, phosphate-mediated feedbacks in cold, less productive systems. Our findings, based on recovered feedbacks, highlight the importance of a network view in future ecosystem management. Disentangling causal interactions among biodiversity, ecosystem functioning and environmental factors is key to understanding how ecosystems respond to changing environment. This study presents a global scale analysis quantifying causal interactions and feedbacks among phytoplankton diversity, biomass and nutrients along environmental gradients of aquatic ecosystems.
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Affiliation(s)
- Chun-Wei Chang
- National Center for Theoretical Sciences, Taipei, 10617, Taiwan.,Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan
| | - Takeshi Miki
- Faculty of Advanced Science and Technology, Ryukoku University, Otsu, Shiga, 520-2194, Japan.,Institute of Oceanography, National Taiwan University, Taipei, 10617, Taiwan.,Center for Biodiversity Science, Ryukoku University, Otsu, Shiga, 520-2194, Japan
| | - Hao Ye
- Health Science Center Libraries, University of Florida, Gainesville, FL, 32611, USA
| | - Sami Souissi
- Univ. Lille, CNRS, Univ, Littoral Côte D'Opale, IRD, UMR 8187, LOG- Laboratoire D'Océanologie et de Géosciences, Station Marine de Wimereux, F- 59000, Lille, France
| | - Rita Adrian
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, IGB, 12587, Berlin, Germany.,Freie Universität Berlin, Department of Biology, Chemistry and Pharmacy, 14195, Berlin, Germany
| | - Orlane Anneville
- National Research Institute for Agriculture, Food and Environment (INRAE), CARRTEL, Université Savoie Mont Blanc, 74200, Thonon les Bains, France
| | - Helen Agasild
- Centre for Limnology, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5D, 51014, Tartu, Estonia
| | - Syuhei Ban
- Department of Ecosystem Studies, School of Environmental Science, The University of Shiga Prefecture, Hikone, 522-8533, Shiga, Japan
| | - Yaron Be'eri-Shlevin
- Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, Israel
| | - Yin-Ru Chiang
- Biodiversity Research Center, Academia Sinica, Taipei, 11529, Taiwan
| | - Heidrun Feuchtmayr
- UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, Lancashire, LA1 4AP, UK
| | - Gideon Gal
- Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, Israel
| | - Satoshi Ichise
- Lake Biwa Environmental Research Institute, Otsu, 520-0022, Japan
| | - Maiko Kagami
- Faculty of Environment and Information Sciences, Yokohama National University, Yokohama, 240-8502, Kanagawa, Japan.,Department of Environmental Science, Faculty of Science, Toho University, Funabashi, Chiba, 274-8510, Japan
| | - Michio Kumagai
- Lake Biwa Environmental Research Institute, Otsu, 520-0022, Japan.,Research Center for Lake Biwa & Environmental Innovation, Ritsumeikan University, Kusatsu, 525-0058, Shiga, Japan
| | - Xin Liu
- Department of Ecosystem Studies, School of Environmental Science, The University of Shiga Prefecture, Hikone, 522-8533, Shiga, Japan
| | - Shin-Ichiro S Matsuzaki
- Biodiversity Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
| | - Marina M Manca
- CNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, Italy
| | - Peeter Nõges
- Centre for Limnology, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5D, 51014, Tartu, Estonia
| | - Roberta Piscia
- CNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, Italy
| | - Michela Rogora
- CNR Water Research Institute (IRSA), L.go Tonolli 50, 28922, Verbania, Pallanza, Italy
| | - Fuh-Kwo Shiah
- Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan.,Institute of Oceanography, National Taiwan University, Taipei, 10617, Taiwan
| | - Stephen J Thackeray
- UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster, Lancashire, LA1 4AP, UK
| | | | - Jiunn-Tzong Wu
- Biodiversity Research Center, Academia Sinica, Taipei, 11529, Taiwan
| | - Tamar Zohary
- Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research, P.O. Box 447, 14950, Migdal, Israel
| | - Chih-Hao Hsieh
- National Center for Theoretical Sciences, Taipei, 10617, Taiwan. .,Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan. .,Institute of Oceanography, National Taiwan University, Taipei, 10617, Taiwan. .,Institute of Ecology and Evolutionary Biology, Department of Life Science, National Taiwan University, Taipei, 10617, Taiwan.
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21
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Abstract
How patterns in community diversity emerge is a long-standing question in ecology. Studies suggested that community diversity and interspecific interactions are interdependent. However, evidence from high-diversity ecological communities is lacking because of practical challenges in characterizing speciose communities and their interactions. Here, I analysed time-varying interaction networks that were reconstructed using 1197 species, DNA-based ecological time series taken from experimental rice plots and empirical dynamic modelling, and introduced 'interaction capacity', namely, the sum of interaction strength that a single species gives and receives, as a potential driver of community diversity. As community diversity increases, the number of interactions increases exponentially but the mean interaction capacity of a community becomes saturated, weakening interspecific interactions. These patterns are modelled with simple mathematical equations, based on which I propose the 'interaction capacity hypothesis': that interaction capacity and network connectance can be two fundamental properties that influence community diversity. Furthermore, I show that total DNA abundance and temperature influence interaction capacity and connectance nonlinearly, explaining a large proportion of diversity patterns observed in various systems. The interaction capacity hypothesis enables mechanistic explanations of community diversity. Therefore, analysing ecological community data from the viewpoint of interaction capacity would provide new insight into community diversity.
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Affiliation(s)
- Masayuki Ushio
- Hakubi Center, Kyoto University, Kyoto 606-8501, Japan,Center for Ecological Research, Kyoto University, Otsu 520-2113, Japan
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