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Lortie CJ, Brown C, Haas-Desmarais S, Lucero J, Callaway R, Braun J, Filazzola A. Plant networks are more connected by invasive brome and native shrub facilitation in Central California drylands. Sci Rep 2024; 14:8958. [PMID: 38637667 PMCID: PMC11026385 DOI: 10.1038/s41598-024-59868-w] [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: 06/05/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
Dominant vegetation in many ecosystems is an integral component of structure and habitat. In many drylands, native shrubs function as foundation species that benefit other plants and animals. However, invasive exotic plant species can comprise a significant proportion of the vegetation. In Central California drylands, the facilitative shrub Ephedra californica and the invasive Bromus rubens are widely dispersed and common. Using comprehensive survey data structured by shrub and open gaps for the region, we compared network structure with and without this native shrub canopy and with and without the invasive brome. The presence of the invasive brome profoundly shifted the network measure of centrality in the microsites structured by a shrub canopy (centrality scores increased from 4.3 under shrubs without brome to 6.3, i.e. a relative increase of 42%). This strongly suggests that plant species such as brome can undermine the positive and stabilizing effects of native foundation plant species provided by shrubs in drylands by changing the frequency that the remaining species connect to one another. The net proportion of positive and negative associations was consistent across all microsites (approximately 50% with a total of 14% non-random co-occurrences on average) suggesting that these plant-plant networks are rewired but not more negative. Maintaining resilience in biodiversity thus needs to capitalize on protecting native shrubs whilst also controlling invasive grass species particularly when associated with shrubs.
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Affiliation(s)
- C J Lortie
- Department of Biology, York University, Toronto, ON, M3J1P3, Canada
| | - Charlotte Brown
- Département de Biologie, Université de Sherbrooke, Voie 9, Sherbrooke, Québec, J1K 2R1, Canada
| | | | - Jacob Lucero
- Texas A & M, Department of Rangeland, Wildlife and Fisheries Management, 495 Horticulture Rd #305, College Station, TX, 77843-2183, USA
| | - Ragan Callaway
- Division of Biological Sciences, University of Montana, Missoula, MT, 59812, USA
| | - Jenna Braun
- Department of Biology, York University, Toronto, ON, M3J1P3, Canada
| | - Alessandro Filazzola
- Apex Resource Management Solutions, Ottawa, ON, Canada.
- Department of Biology, University of Western Ontario, London, ON, N6A 5B7, Canada.
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Schieber TA, Carpi LC, Pardalos PM, Masoller C, Díaz-Guilera A, Ravetti MG. Diffusion capacity of single and interconnected networks. Nat Commun 2023; 14:2217. [PMID: 37072418 PMCID: PMC10113202 DOI: 10.1038/s41467-023-37323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 03/10/2023] [Indexed: 04/20/2023] Open
Abstract
Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node's potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet's diffusion capacity that could contribute to the emergence of more frequent climatic events.
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Affiliation(s)
- Tiago A Schieber
- Departamento de Ciências Administrativas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Laura C Carpi
- Instituto Nacional de Ciência e Tecnologia, Sistemas Complexos, INCT-SC, CEFET-MG, Belo Horizonte, MG, Brazil
- Machine Intelligence and Data Science Laboratory (MINDS), Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Panos M Pardalos
- Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA
- Lab LATNA, National Research University, Higher School of Economics, Nizhny Novgorod, Russia
| | - Cristina Masoller
- Departament de Física, Universitat Politècnica de Catalunya, Terrassa, BCN, Spain
| | - Albert Díaz-Guilera
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, BCN, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, BCN, Spain
| | - Martín G Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
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Godzieba M, Zubrowska-Sudol M, Walczak J, Ciesielski S. Development of microbial communities in biofilm and activated sludge in a hybrid reactor. Sci Rep 2022; 12:12558. [PMID: 35869109 PMCID: PMC9307651 DOI: 10.1038/s41598-022-16570-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/12/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractMicroorganisms play a key role in biological wastewater treatment. The form in which biomass develops determines the efficiency and mechanisms of organic compound conversion, due to different conditions in various microbial structures. However, the results of studies comparing the microbial communities in biofilm and activated sludge have often conflicted. Therefore, this study compared the composition and development of the bacterial communities in biofilm and activated sludge in a hybrid reactor, employing 16S rRNA sequencing. Statistical analysis of the sequencing data included the identification of taxa characteristic to the biofilm and activated sludge, alpha and beta diversity analysis, and network analysis. These analyses indicated that the biofilm bacterial community was richer and more diverse than the activated sludge community. The mean numbers of OTU were 1614 in the biofilm and 993 in the activated sludge, and the mean values of the Chao1 (1735 vs. 1105) and Shannon (5.3 vs. 4.3) biodiversity indices were significantly higher for the biofilm. The biofilm was a better environment for development of nitrifiers (e.g., Nitrosomonas, Nitrospira) and phosphorus accumulating organisms (Candidatus Accumulibacter). Bacteria in the biofilm co-occurrence network had more connections (based on Spearman's rank correlation coefficient) with each other, indicating that they interact more than those in the activated sludge.
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Zádor Z, Zhu Z, Smith M, Gorgoni S. A Weighted and Normalized Gould–Fernandez brokerage measure. PLoS One 2022; 17:e0274475. [PMID: 36107935 PMCID: PMC9477276 DOI: 10.1371/journal.pone.0274475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 08/30/2022] [Indexed: 11/26/2022] Open
Abstract
The Gould and Fernandez local brokerage measure defines brokering roles based on the group membership of the nodes from the incoming and outgoing edges. This paper extends on this brokerage measure to account for weighted edges and introduces the Weighted–Normalized Gould–Fernandez measure (WNGF). The value added of this new measure is demonstrated empirically with both a macro level trade network and a micro level organization network. The measure is first applied to the EUREGIO inter-regional trade dataset and then to an organizational network in a research and development (R&D) group. The results gained from the WNGF measure are compared to those from two dichotomized networks: a threshold and a multiscale backbone network. The results show that the WNGF generates valid results, consistent with those of the dichotomized network. In addition, it provides the following advantages: (i) it ensures information retention; (ii) since no alterations and decisions have to be made on how to dichotomize the network, the WNGF frees the user from the burden of making assumptions; (iii) it provides a nuanced understanding of each node’s brokerage role. These advantages are of special importance when the role of less connected nodes is considered. The two empirical networks used here are for illustrative purposes. Possible applications of WNGF span beyond regional and organizational studies, and into all those contexts where retaining weights is important, for example by accounting for persisting or repeating edges compared to one-time interactions. WNGF can also be used to further analyze networks that measure how often people meet, talk, text, like, or retweet. WNGF makes a relevant methodological contribution as it offers a way to analyze brokerage in weighted, directed, and even complete graphs without information loss that can be used across disciplines and different type of networks.
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Affiliation(s)
- Zsófia Zádor
- Department of International Business and Economics, University of Greenwich, London, United Kingdom
- * E-mail:
| | - Zhen Zhu
- Kent Business School, University of Kent, Canterbury, United Kingdom
| | - Matthew Smith
- The Business School, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Sara Gorgoni
- Department of International Business and Economics, University of Greenwich, London, United Kingdom
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Botella C, Dray S, Matias C, Miele V, Thuiller W. An appraisal of graph embeddings for comparing trophic network architectures. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13738] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Christophe Botella
- Laboratoire d'Ecologie Alpine Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LECA Grenoble France
| | - Stéphane Dray
- Université de Lyon Lyon France
- Laboratoire de Biométrie et Biologie Évolutive Université Lyon 1 CNRS UMR5558 Villeurbanne France
| | - Catherine Matias
- Laboratoire de Probabilités, Statistique et Modélisation Centre National de la Recherche Scientifique Sorbonne Université Université de Paris Paris France
| | - Vincent Miele
- Université de Lyon Lyon France
- Laboratoire de Biométrie et Biologie Évolutive Université Lyon 1 CNRS UMR5558 Villeurbanne France
| | - Wilfried Thuiller
- Laboratoire d'Ecologie Alpine Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LECA Grenoble France
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McLeish M, Peláez A, Pagán I, Gavilán R, Fraile A, García-Arenal F. Structuring of plant communities across agricultural landscape mosaics: the importance of connectivity and the scale of effect. BMC Ecol Evol 2021; 21:173. [PMID: 34503449 PMCID: PMC8427894 DOI: 10.1186/s12862-021-01903-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/26/2021] [Indexed: 11/18/2022] Open
Abstract
Background Plant communities of fragmented agricultural landscapes, are subject to patch isolation and scale-dependent effects. Variation in configuration, composition, and distance from one another affect biological processes of disturbance, productivity, and the movement ecology of species. However, connectivity and spatial structuring among these diverse communities are rarely considered together in the investigation of biological processes. Spatially optimised predictor variables that are based on informed measures of connectivity among communities, offer a solution to untangling multiple processes that drive biodiversity. Results To address the gap between theory and practice, a novel spatial optimisation method that incorporates hypotheses of community connectivity, was used to estimate the scale of effect of biotic and abiotic factors that distinguish plant communities. We tested: (1) whether different hypotheses of connectivity among sites was important to measuring diversity and environmental variation among plant communities; and (2) whether spatially optimised variables of species relative abundance and the abiotic environment among communities were consistent with diversity parameters in distinguishing four habitat types; namely Crop, Edge, Oak, and Wasteland. The global estimates of spatial autocorrelation, which did not consider environmental variation among sites, indicated significant positive autocorrelation under four hypotheses of landscape connectivity. The spatially optimised approach indicated significant positive and negative autocorrelation of species relative abundance at fine and broad scales, which depended on the measure of connectivity and environmental variation among sites. Conclusions These findings showed that variation in community diversity parameters does not necessarily correspond to underlying spatial structuring of species relative abundance. The technique used to generate spatially-optimised predictors is extendible to incorporate multiple variables of interest along with a priori hypotheses of landscape connectivity. Spatially-optimised variables with appropriate definitions of connectivity might be better than diversity parameters in explaining functional differences among communities. Supplementary Information The online version contains supplementary material available at 10.1186/s12862-021-01903-9.
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Affiliation(s)
- Michael McLeish
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain.
| | - Adrián Peláez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Israel Pagán
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Rosario Gavilán
- Unidad de Botánica, Departamento de Farmacología, Farmacognosia y Botánica, Facultad de Farmacia, Universidad Complutense, Madrid, Spain
| | - Aurora Fraile
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Fernando García-Arenal
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain
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Affiliation(s)
- Vincent Miele
- Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Villeurbanne, France
| | - Catherine Matias
- Laboratoire de Probabilités, Statistique et Modélisation, Centre National de la Recherche Scientifique, Sorbonne Université et Université de Paris, Paris, France
| | - Stéphane Robin
- UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France
| | - Stéphane Dray
- Université de Lyon, F-69000 Lyon, Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Villeurbanne, France
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