1
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Song C, Spaak JW. Trophic tug-of-war: Coexistence mechanisms within and across trophic levels. Ecol Lett 2024; 27:e14409. [PMID: 38590122 DOI: 10.1111/ele.14409] [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: 03/23/2023] [Revised: 02/26/2024] [Accepted: 03/06/2024] [Indexed: 04/10/2024]
Abstract
Ecological communities encompass rich diversity across multiple trophic levels. While modern coexistence theory has been widely applied to understand community assembly, its traditional formalism only allows assembly within a single trophic level. Here, using an expanded definition of niche and fitness differences applicable to multitrophic communities, we study how diversity within and across trophic levels affects species coexistence. If each trophic level is analysed separately, both lower- and higher trophic levels are governed by the same coexistence mechanisms. In contrast, if the multitrophic community is analysed as a whole, different trophic levels are governed by different coexistence mechanisms: coexistence at lower trophic levels is predominantly limited by fitness differences, whereas coexistence at higher trophic levels is predominantly limited by niche differences. This dichotomy in coexistence mechanisms is supported by theoretical derivations, simulations of phenomenological and trait-based models, and a case study of a primeval forest ecosystem. Our work provides a general and testable prediction of coexistence mechanism operating in multitrophic communities.
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Affiliation(s)
- Chuliang Song
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA
| | - Jurg W Spaak
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA
- Institute for Environmental Sciences, RPTU Kaiserslautern-Landau, Landau, Germany
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2
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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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3
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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: 5] [Impact Index Per Article: 5.0] [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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4
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Song C, Fortin MJ, Gonzalez A. Metapopulation persistence can be inferred from incomplete surveys. Proc Biol Sci 2022; 289:20222029. [PMID: 36515114 PMCID: PMC9748775 DOI: 10.1098/rspb.2022.2029] [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: 10/09/2022] [Accepted: 11/17/2022] [Indexed: 12/15/2022] Open
Abstract
Habitat destruction and fragmentation are principal causes of species loss. While a local population might go extinct, a metapopulation-populations inhabiting habitat patches connected by dispersal-can persist regionally by recolonizing empty patches. To assess metapopulation persistence, two widely adopted indicators in conservation management are metapopulation capacity and patch importance. However, we face a fundamental limitation in that assessing metapopulation persistence requires that we survey or sample all the patches in a landscape: often these surveys are logistically challenging to conduct and repeat, which raises the question whether we can learn enough about the metapopulation persistence from an incomplete survey. Here, we provide a robust statistical approach to infer metapopulation capacity and patch importance by sampling a portion of all patches. We provided analytic arguments on why the metapopulation capacity and patch importance can be well predicted from sub-samples of habitat patches. Full-factorial simulations with more complex models corroborate our analytic predictions. We applied our model to an empirical metapopulation of mangrove hummingbirds (Amazilia boucardi). On the basis of our statistical framework, we provide some sampling suggestion for monitoring metapopulation persistence. Our approach allows for rapid and effective inference of metapopulation persistence from incomplete patch surveys.
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Affiliation(s)
- Chuliang Song
- Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Canada H3A 1B1
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada M5S 3B2
| | - Marie-Josée Fortin
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada M5S 3B2
| | - Andrew Gonzalez
- Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Canada H3A 1B1
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5
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Kaniadakis G. Novel predator-prey model admitting exact analytical solution. Phys Rev E 2022; 106:044401. [PMID: 36397588 DOI: 10.1103/physreve.106.044401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/02/2022] [Indexed: 06/16/2023]
Abstract
The Lotka-Volterra predator-prey model still represents the paradigm for the description of the competition in population dynamics. Despite its extreme simplicity, it does not admit an analytical solution, and for this reason, numerical integration methods are usually adopted to apply it to various fields of science. The aim of the present work is to investigate the existence of new predator-prey models sharing the broad features of the standard Lotka-Volterra model and, at the same time, offer the advantage of possessing exact analytical solutions. To this purpose, a general Hamiltonian formalism, which is suitable for treating a large class of predator-prey models in population dynamics within the same framework, has been developed as a first step. The only existing model having the property of admitting a simple exact analytical solution, is identified within the above class of models. The solution of this special predator-prey model is obtained explicitly, in terms of known elementary functions, and its main properties are studied. Finally, the generalization of this model, based on the concept of power-law competition, as well as its extension to the case of N-component competition systems, are considered.
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Affiliation(s)
- G Kaniadakis
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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6
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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: 1.5] [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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7
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Chang CW, Miki T, Ushio M, Ke PJ, Lu HP, Shiah FK, Hsieh CH. Reconstructing large interaction networks from empirical time series data. Ecol Lett 2021; 24:2763-2774. [PMID: 34601794 DOI: 10.1111/ele.13897] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 09/03/2021] [Indexed: 01/03/2023]
Abstract
Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named "multiview distance regularised S-map," generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems.
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Affiliation(s)
- Chun-Wei Chang
- National Center for Theoretical Sciences, Taipei, Taiwan.,Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
| | - Takeshi Miki
- Institute of Oceanography, National Taiwan University, Taipei, Taiwan.,Faculty of Advanced Science and Technology, Ryukoku University, Otsu, Japan.,Center for Biodiversity Science, Ryukoku University, Otsu, Japan
| | - Masayuki Ushio
- Hakubi Center, Kyoto University, Kyoto, Japan.,Center for Ecological Research, Kyoto University, Otsu, Japan
| | - Po-Ju Ke
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA.,Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan
| | - Hsiao-Pei Lu
- Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan, Taiwan
| | - Fuh-Kwo Shiah
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.,Institute of Oceanography, National Taiwan University, Taipei, Taiwan
| | - Chih-Hao Hsieh
- National Center for Theoretical Sciences, Taipei, Taiwan.,Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.,Institute of Oceanography, National Taiwan University, Taipei, Taiwan.,Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan
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