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Liu X, Zhang J, Wu Y, Yu Y, Sun J, Mao D, Zhang G. Intensified effect of nitrogen forms on dominant phytoplankton species succession by climate change. WATER RESEARCH 2024; 264:122214. [PMID: 39116610 DOI: 10.1016/j.watres.2024.122214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 07/04/2024] [Accepted: 08/03/2024] [Indexed: 08/10/2024]
Abstract
Nutrient proportion, light intensity, and temperature affect the succession of dominant phytoplankton species. Despite these insights, this transformation mechanism in highly turbid lakes remains a research gap, especially in response to climate change. To fill this gap, we investigated the mechanism by which multi-environmental factors influence the succession of dominant phytoplankton species in Lake Chagan. This investigation deployed the structural equation model (SEM) and the hydrodynamic-water quality-water ecology mechanism model. Results demonstrated that the dominant phytoplankton species in Lake Chagan transformed from diatom to cyanobacteria during 2012 and 2022. Notably, Microcystis was detected in 2022. SEM revealed the primary environment variables for this succession, including water temperature (Tw), nutrients (total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH4N)), and total suspended solids (TSS). Moreover, this event was not the consequence of zooplankton grazing. An integrated hydrodynamic-water quality-bloom mechanism model was built to explore the mechanism driving phytoplankton succession and its response to climate change. Nutrients determined the phytoplankton biomass and dominant species succession based on various proportions. High NH4N:NO3N ratios favored cyanobacteria and inhibited diatom under high TSS. Additionally, the biomass proportions of diatom (30.77 % vs. 22.28 %) and green (30.56 % vs. 23.30 %) decreased dramatically. In contrast, cyanobacteria abundance remarkably increased (35.78 % to 51.71 %) with the increasing NH4-N:NO3-N ratios. In addition, the proportion of non-nitrogen-fixing cyanobacteria was higher than that of the nitrogen-fixing cyanobacteria counterparts when TN:TP≥20 and NH4N:NO3N ≥ 10. Light-limitation phenotypes also experienced an increase with the rising NH4N:NO3N ratios. Notably, the cyanobacterial biomass reached 3-6 times that in the baseline scenario when the air temperature escalated by 3.0 °C until 2061 under the SSP585 scenario. We highlighted the effect of nitrogen forms on the succession of dominant phytoplankton species. Climate warming will increase nitrogen proportion, providing an insightful reference for controlling cyanobacterial blooms.
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Affiliation(s)
- Xuemei Liu
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Jingjie Zhang
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Yanfeng Wu
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Yexiang Yu
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Jingxuan Sun
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Dehua Mao
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Guangxin Zhang
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
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2
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Damos PT. On formal limitations of causal ecological networks. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230170. [PMID: 39034692 PMCID: PMC11293863 DOI: 10.1098/rstb.2023.0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/02/2023] [Accepted: 02/22/2024] [Indexed: 07/23/2024] Open
Abstract
Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.
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Affiliation(s)
- Petros T. Damos
- Minstry of Education, Religious and Sports, Directorate of Secondary Education Veroia, Ergohori59132, Greece
- Department of Agriculture, School of Agricultural Studies, University of Western Macedonia, Florina, 53100, Greece
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, Kozani50100, Greece
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3
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Peng Y, Wu C, Ma G, Chen H, Wu QL, He D, Jeppesen E, Ren L. Insight into diversity change, variability and co-occurrence patterns of phytoplankton assemblage in headwater streams: a study of the Xijiang River basin, South China. Front Microbiol 2024; 15:1417651. [PMID: 39224213 PMCID: PMC11367421 DOI: 10.3389/fmicb.2024.1417651] [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: 04/15/2024] [Accepted: 07/31/2024] [Indexed: 09/04/2024] Open
Abstract
Phytoplankton has been used as a paradigm for studies of coexistence of species since the publication of the "paradox of the plankton." Although there are a wealth of studies about phytoplankton assemblages of lakes, reservoirs and rivers, our knowledge about phytoplankton biodiversity and its underlying mechanisms in mountain headwater stream ecosystems is limited, especially across regional scales with broad environmental gradients. In this study, we collected 144 phytoplankton samples from the Xijiang headwater streams of the Pearl River across low altitude (< 1,000 m) located in Guangxi province, intermediate altitude (1,000 m < altitude <2,000 m) in Guizhou province and high altitude (> 2,000 m) in Yunnan province of China. Our study revealed high phytoplankton diversity in these streams. Freshwater phytoplankton, including cyanobacteria, Bacillariophyta, Chlorophyta, Rhodophyta, Chrysophyta, Euglenophyta, Glaucophyta, Phaeophyta and Cryptophyta, were all detected. However, phytoplankton alpha diversity exhibited a monotonic decreasing relationship with increasing altitude. High altitudes amplified the "isolated island" effect of headwater streams on phytoplankton assemblages, which were characterized by lower homogeneous selection and higher dispersal limitation. Variability and network vulnerability of phytoplankton assemblages increased with increasing altitudes. Our findings demonstrated diversity, variability and co-occurrence patterns of phytoplankton assemblages linked to environmental factors co-varying with altitude across regional scales.
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Affiliation(s)
- Yuyang Peng
- Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou, China
| | - Chuangfeng Wu
- Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou, China
| | - Guibin Ma
- Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou, China
| | - Haiming Chen
- Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou, China
| | - Qinglong L. Wu
- Center for Evolution and Conservation Biology, Southern Marine Sciences and Engineering Guangdong Laboratory, Guangzhou, China
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
| | - Dan He
- Center for Evolution and Conservation Biology, Southern Marine Sciences and Engineering Guangdong Laboratory, Guangzhou, China
| | - Erik Jeppesen
- Sino-Danish Centre for Education and Research, University of Chinese Academy of Sciences, Beijing, China
- Department of Ecoscience, Aarhus University, Aarhus, Denmark
- Limnology Laboratory, Department of Biological Sciences and Centre for Ecosystem Research and Implementation, Middle East Technical University, Ankara, Türkiye
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming, China
| | - Lijuan Ren
- Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou, China
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4
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Sun B, Deng J, Scheel N, Zhu DC, Ren J, Zhang R, Li T. Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis. ENTROPY (BASEL, SWITZERLAND) 2024; 26:539. [PMID: 39056902 PMCID: PMC11276553 DOI: 10.3390/e26070539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications.
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Affiliation(s)
- Boxin Sun
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; (B.S.); (J.D.); (J.R.)
| | - Jinxian Deng
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; (B.S.); (J.D.); (J.R.)
| | - Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA;
| | - David C. Zhu
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY 10461, USA;
| | - Jian Ren
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; (B.S.); (J.D.); (J.R.)
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, TX 75231, USA;
- Department of Neurology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tongtong Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; (B.S.); (J.D.); (J.R.)
- Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI 48109, USA
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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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Deng J, Sun B, Scheel N, Renli AB, Zhu DC, Zhu D, Ren J, Li T, Zhang R. Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis. PNAS NEXUS 2024; 3:pgad422. [PMID: 38169910 PMCID: PMC10758925 DOI: 10.1093/pnasnexus/pgad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
Convergent cross-mapping (CCM) has attracted increased attention recently due to its capability to detect causality in nonseparable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure DI flow? Here, we first causalize CCM so that it aligns with the presumption in causality analysis-the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings, either random or deterministic, or both.
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Affiliation(s)
- Jinxian Deng
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Boxin Sun
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
| | - Alina B Renli
- Department of Neuroscience, Michigan State University, East Lansing, MI 48824, USA
| | - David C Zhu
- Department of Radiology, Michigan State University, East Lansing, MI 48824, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, University of Texas, Arlington, TX 76010, USA
| | - Jian Ren
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Tongtong Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, TX 75231, USA
- Departments of Neurology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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7
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Tu C, Yang Y, Wang J, Su H, Guo J, Cao D, Lian J, Wang D. In situ effects of microplastics on the decomposition of aquatic macrophyte litter in eutrophic shallow lake sediments, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 337:122543. [PMID: 37716693 DOI: 10.1016/j.envpol.2023.122543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 08/15/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
The toxicity of microplastics (MPs) to aquatic organisms has been extensively studied recently. However, few studies have investigated the effects of MPs in sediments on aquatic ecosystem functioning. In the present study, we conducted an in situ experiment to explore the concentration-dependent effects (0.025%, 0.25%, 2.5%) and size-dependent effects (150-300 μm and 500-1000 μm) of polypropylene microplastics (PP MPs) on Vallisneria natans litter decomposition dynamics, in particular, the process associated with macroinvertebrates, microorganisms, as well as microalgae and/or cyanobacteria. The results showed that exposure to high concentrations and large sizes of PP MPs can accelerate leaf litter biomass loss and nutrition release. Moreover, microbial respiration, microalgal and/or cyanobacteria chlorophyll-a were also significantly affected by PP MPs. However, PP MPs have no effect on the abundance of associated macroinvertebrate during the experiment, despite the collection of five macroinvertebrate taxa from two functional feeding groups (i.e., collectors and scrapers). Therefore, our experiment demonstrated that PP MPs may enhance leaf litter decomposition through effected microbial metabolic activity, microalgal and/or cyanobacteria biomass in the sedimentary lake. Overall, our findings highlight that PP MPs have the potential to interfere with the basic ecological functions such as plant litter decomposition in aquatic environments.
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Affiliation(s)
- Chang Tu
- School of Life Sciences, Central China Normal University, Hubei Province, Wuhan, 430079, China
| | - Yongqing Yang
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, China
| | - Jinbo Wang
- School of Life Sciences, Central China Normal University, Hubei Province, Wuhan, 430079, China
| | - Hailong Su
- School of Life Sciences, Central China Normal University, Hubei Province, Wuhan, 430079, China
| | - Jieying Guo
- School of Life Sciences, Central China Normal University, Hubei Province, Wuhan, 430079, China
| | - Dandan Cao
- School of Life Sciences, Central China Normal University, Hubei Province, Wuhan, 430079, China
| | - Jiapan Lian
- Ministry of Education (MOE) Key Laboratory of Environmental Remediation and Ecosystem Health, College of Environmental and Resources Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dong Wang
- School of Life Sciences, Central China Normal University, Hubei Province, Wuhan, 430079, China; Bio-resources Key Laboratory of Shaanxi Province, Shaanxi University of Technology, Hanzhong, 723001, Shaanxi Province, PR China.
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8
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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: 13] [Impact Index Per Article: 13.0] [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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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: 0] [Impact Index Per Article: 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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10
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O'Brien DA, Gal G, Thackeray SJ, Matsuzaki SS, Clements CF. Planktonic functional diversity changes in synchrony with lake ecosystem state. GLOBAL CHANGE BIOLOGY 2023; 29:686-701. [PMID: 36370051 PMCID: PMC10100413 DOI: 10.1111/gcb.16485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/23/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Managing ecosystems to effectively preserve function and services requires reliable tools that can infer changes in the stability and dynamics of a system. Conceptually, functional diversity (FD) appears as a sensitive and viable monitoring metric stemming from suggestions that FD is a universally important measure of biodiversity and has a mechanistic influence on ecological processes. It is however unclear whether changes in FD consistently occur prior to state responses or vice versa, with no current work on the temporal relationship between FD and state to support a transition towards trait-based indicators. There is consequently a knowledge gap regarding when functioning changes relative to biodiversity change and where FD change falls in that sequence. We therefore examine the lagged relationship between planktonic FD and abundance-based metrics of system state (e.g. biomass) across five highly monitored lake communities using both correlation and cutting edge non-linear empirical dynamic modelling approaches. Overall, phytoplankton and zooplankton FD display synchrony with lake state but each lake is idiosyncratic in the strength of relationship. It is therefore unlikely that changes in plankton FD are identifiable before changes in more easily collected abundance metrics. These results highlight the power of empirical dynamic modelling in disentangling time lagged relationships in complex multivariate ecosystems, but suggest that FD cannot be generically viable as an early indicator. Individual lakes therefore require consideration of their specific context and any interpretation of FD across systems requires caution. However, FD still retains value as an alternative state measure or a trait representation of biodiversity when considered at the system level.
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Affiliation(s)
| | - Gideon Gal
- Kinneret Limnological LaboratoryIsrael Oceanographic and Limnological ResearchMigdalIsrael
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11
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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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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: 3.5] [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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