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Villegas P, Gili T, Caldarelli G, Gabrielli A. Evidence of scale-free clusters of vegetation in tropical rainforests. Phys Rev E 2024; 109:L042402. [PMID: 38755841 DOI: 10.1103/physreve.109.l042402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/09/2024] [Indexed: 05/18/2024]
Abstract
Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's "green lungs."
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Affiliation(s)
- Pablo Villegas
- 'Enrico Fermi' Research Center (CREF), Via Panisperna 89A, 00184 - Rome, Italy
- Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada E-18071, Spain
| | - Tommaso Gili
- Networks Unit, IMT Scuola Alti Studi Lucca, Piazza San Francesco 15, 55100- Lucca, Italy
| | - Guido Caldarelli
- DMSN, Ca' Foscari University of Venice, Via Torino 155, 30172 - Venice, Italy
- European Centre for Living Technology (ECLT), Dorsoduro 3911, 30123 - Venice, Italy
- Institute for Complex Systems (ISC), CNR, UoS Sapienza, Piazzale Aldo Moro 2, 00185 - Rome, Italy
- London Institute for Mathematical Sciences (LIMS), W1K2XF London, United Kingdom
| | - Andrea Gabrielli
- 'Enrico Fermi' Research Center (CREF), Via Panisperna 89A, 00184 - Rome, Italy
- Institute for Complex Systems (ISC), CNR, UoS Sapienza, Piazzale Aldo Moro 2, 00185 - Rome, Italy
- Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche, Università degli Studi 'Roma Tre', Via Vito Volterra 62, 00146 - Rome, Italy
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Convertino M, Reddy A, Liu Y, Munoz-Zanzi C. Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 799:149102. [PMID: 34388889 DOI: 10.1016/j.scitotenv.2021.149102] [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] [Received: 02/28/2021] [Revised: 06/29/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics - underpinning persistent criticality and neutrality or independence among areas - is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.
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Affiliation(s)
- M Convertino
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School (Tsinghua SIGS), Tsinghua University, Shenzhen, China.
| | - A Reddy
- UnitedHealth Group, Minneapolis, MN, USA
| | - Y Liu
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene and Tropical Medicine, UK
| | - C Munoz-Zanzi
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota Twin-Cities, Minneapolis, MN, USA
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Bray SR, Wang B. Forecasting unprecedented ecological fluctuations. PLoS Comput Biol 2020; 16:e1008021. [PMID: 32598364 PMCID: PMC7375592 DOI: 10.1371/journal.pcbi.1008021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/22/2020] [Accepted: 06/05/2020] [Indexed: 11/18/2022] Open
Abstract
Forecasting 'Black Swan' events in ecosystems is an important but challenging task. Many ecosystems display aperiodic fluctuations in species abundance spanning orders of magnitude in scale, which have vast environmental and economic impact. Empirical evidence and theoretical analyses suggest that these dynamics are in a regime where system nonlinearities limit accurate forecasting of unprecedented events due to poor extrapolation of historical data to unsampled states. Leveraging increasingly available long-term high-frequency ecological tracking data, we analyze multiple natural and experimental ecosystems (marine plankton, intertidal mollusks, and deciduous forest), and recover hidden linearity embedded in universal 'scaling laws' of species dynamics. We then develop a method using these scaling laws to reduce data dependence in ecological forecasting and accurately predict extreme events beyond the span of historical observations in diverse ecosystems.
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Affiliation(s)
- Samuel R. Bray
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Bo Wang
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- * E-mail:
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Optimal Microbiome Networks: Macroecology and Criticality. ENTROPY 2019; 21:e21050506. [PMID: 33267220 PMCID: PMC7514995 DOI: 10.3390/e21050506] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/04/2019] [Accepted: 05/13/2019] [Indexed: 12/11/2022]
Abstract
The human microbiome is an extremely complex ecosystem considering the number of bacterial species, their interactions, and its variability over space and time. Here, we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model, we detected potential species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detected an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and how these should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest Pareto total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not necessarily at the phase transition between healthy and unhealthy states. We stress the importance of considering portfolios of interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their interactions (i.e., species collective behavior) that shape transition from healthy to unhealthy states. The macroecological characterization of the microbiome is useful for public health and disease diagnosis and etiognosis, while species-specific analyses can detect beneficial species leading to personalized design of pre- and probiotic treatments and microbiome engineering.
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Tovo A, Suweis S, Formentin M, Favretti M, Volkov I, Banavar JR, Azaele S, Maritan A. Upscaling species richness and abundances in tropical forests. SCIENCE ADVANCES 2017; 3:e1701438. [PMID: 29057324 PMCID: PMC5647133 DOI: 10.1126/sciadv.1701438] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 09/20/2017] [Indexed: 06/07/2023]
Abstract
The quantification of tropical tree biodiversity worldwide remains an open and challenging problem. More than two-fifths of the number of worldwide trees can be found either in tropical or in subtropical forests, but only ≈0.000067% of species identities are known. We introduce an analytical framework that provides robust and accurate estimates of species richness and abundances in biodiversity-rich ecosystems, as confirmed by tests performed on both in silico-generated and real forests. Our analysis shows that the approach outperforms other methods. In particular, we find that upscaling methods based on the log-series species distribution systematically overestimate the number of species and abundances of the rare species. We finally apply our new framework on 15 empirical tropical forest plots and quantify the minimum percentage cover that should be sampled to achieve a given average confidence interval in the upscaled estimate of biodiversity. Our theoretical framework confirms that the forests studied are comprised of a large number of rare or hyper-rare species. This is a signature of critical-like behavior of species-rich ecosystems and can provide a buffer against extinction.
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Affiliation(s)
- Anna Tovo
- Dipartimento di Matematica “Tullio Levi-Civita,” Università di Padova, Via Trieste 63, 35121 Padova, Italy
| | - Samir Suweis
- Dipartimento di Fisica e Astronomia, “Galileo Galilei,” Istituto Nazionale di Fisica Nucleare, Università di Padova, Via Marzolo 8, 35131 Padova, Italy
| | - Marco Formentin
- Dipartimento di Matematica “Tullio Levi-Civita,” Università di Padova, Via Trieste 63, 35121 Padova, Italy
| | - Marco Favretti
- Dipartimento di Matematica “Tullio Levi-Civita,” Università di Padova, Via Trieste 63, 35121 Padova, Italy
| | - Igor Volkov
- Department of Physics, University of Maryland, College Park, MD 20742, USA
| | - Jayanth R. Banavar
- Department of Physics, University of Maryland, College Park, MD 20742, USA
- Department of Physics, University of Oregon, Eugene, OR 97403, USA
| | - Sandro Azaele
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK
| | - Amos Maritan
- Dipartimento di Fisica e Astronomia, “Galileo Galilei,” Istituto Nazionale di Fisica Nucleare, Università di Padova, Via Marzolo 8, 35131 Padova, Italy
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Huth G, Haegeman B, Pitard E, Munoz F. Long-Distance Rescue and Slow Extinction Dynamics Govern Multiscale Metapopulations. Am Nat 2015; 186:460-9. [DOI: 10.1086/682947] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Grilli J, Azaele S, Banavar JR, Maritan A. Spatial aggregation and the species-area relationship across scales. J Theor Biol 2012; 313:87-97. [PMID: 22902426 DOI: 10.1016/j.jtbi.2012.07.030] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Revised: 07/20/2012] [Accepted: 07/31/2012] [Indexed: 11/16/2022]
Abstract
There has been a considerable effort to understand and quantify the spatial distribution of species across different ecosystems. Relative species abundance (RSA), beta diversity and species-area relationship (SAR) are among the most used macroecological measures to characterize plants communities in forests. In this paper we introduce a simple phenomenological model based on Poisson cluster processes which allows us to exactly link RSA and beta diversity to SAR. The framework is spatially explicit and accounts for the spatial aggregation of conspecific individuals. Under the simplifying assumption of neutral theory, we derive an analytical expression for the SAR which reproduces tri-phasic behavior as sample area increases from local to continental scales, explaining how the tri-phasic behavior can be understood in terms of simple geometric arguments. We also find an expression for the endemic area relationship (EAR) and for the scaling of the RSA.
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Affiliation(s)
- Jacopo Grilli
- Department of Physics and Astronomy G. Galilei, Università di Padova, CNISM and INFN, Padova, Italy.
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Suweis S, Bertuzzo E, Mari L, Rodriguez-Iturbe I, Maritan A, Rinaldo A. On species persistence-time distributions. J Theor Biol 2012; 303:15-24. [PMID: 22763130 DOI: 10.1016/j.jtbi.2012.02.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2011] [Revised: 02/17/2012] [Accepted: 02/21/2012] [Indexed: 10/28/2022]
Abstract
We present new theoretical and empirical results on the probability distributions of species persistence times in natural ecosystems. Persistence times, defined as the timespans occurring between species' colonization and local extinction in a given geographic region, are empirically estimated from local observations of species' presence/absence. A connected sampling problem is presented, generalized and solved analytically. Species persistence is shown to provide a direct connection with key spatial macroecological patterns like species-area and endemics-area relationships. Our empirical analysis pertains to two different ecosystems and taxa: a herbaceous plant community and a estuarine fish database. Despite the substantial differences in ecological interactions and spatial scales, we confirm earlier evidence on the general properties of the scaling of persistence times, including the predicted effects of the structure of the spatial interaction network. The framework tested here allows to investigate directly nature and extent of spatial effects in the context of ecosystem dynamics. The notable coherence between spatial and temporal macroecological patterns, theoretically derived and empirically verified, is suggested to underlie general features of the dynamic evolution of ecosystems.
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Affiliation(s)
- S Suweis
- Laboratory of Ecohydrology (ECHO/IIE/ENAC), Ecole Polytechnique Fédérale Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering (ENAC), 1015 Lausanne, (CH), Switzerland.
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Abstract
Some epidemics have been empirically observed to exhibit outbreaks of all possible sizes, i.e., to be scale-free or scale-invariant. Different explanations for this finding have been put forward; among them there is a model for "accidental pathogens" which leads to power-law distributed outbreaks without apparent need of parameter fine tuning. This model has been claimed to be related to self-organized criticality, and its critical properties have been conjectured to be related to directed percolation. Instead, we show that this is a (quasi) neutral model, analogous to those used in Population Genetics and Ecology, with the same critical behavior as the voter-model, i.e. the theory of accidental pathogens is a (quasi)-neutral theory. This analogy allows us to explain all the system phenomenology, including generic scale invariance and the associated scaling exponents, in a parsimonious and simple way.
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Affiliation(s)
- Oscar A. Pinto
- Departamento de Física, Instituto de Física Aplicada, Universidad Nacional de San Luis - CONICET, San Luis, Argentina
- Departamento de Electromagnetismo y Física de la Materia, Facultad de Ciencias, Instituto de Física Teórica y Computacional Carlos I, Universidad de Granada, Granada, Spain
| | - Miguel A. Muñoz
- Departamento de Electromagnetismo y Física de la Materia, Facultad de Ciencias, Instituto de Física Teórica y Computacional Carlos I, Universidad de Granada, Granada, Spain
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