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Tang B, Kamakura RP, Barnett DT, Clark JS. Learning from monitoring networks: Few-large vs. many-small plots and multi-scale analysis. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1114569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
In order to learn about broad scale ecological patterns, data from large-scale surveys must allow us to either estimate the correlations between the environment and an outcome and/or accurately predict ecological patterns. An important part of data collection is the sampling effort used to collect observations, which we decompose into two quantities: the number of observations or plots (n) and the per-observation/plot effort (E; e.g., area per plot). If we want to understand the relationships between predictors and a response variable, then lower model parameter uncertainty is desirable. If the goal is to predict a response variable, then lower prediction error is preferable. We aim to learn if and when aggregating data can help attain these goals. We find that a small sample size coupled with large observation effort coupled (few large) can yield better predictions when compared to a large number of observations with low observation effort (many small). We also show that the combination of the two values (n and E), rather than one alone, has an impact on parameter uncertainty. In an application to Forest Inventory and Analysis (FIA) data, we model the tree density of selected species at various amounts of aggregation using linear regression in order to compare the findings from simulated data to real data. The application supports the theoretical findings that increasing observational effort through aggregation can lead to improved predictions, conditional on the thoughtful aggregation of the observational plots. In particular, aggregations over extremely large and variable covariate space may lead to poor prediction and high parameter uncertainty. Analyses of large-range data can improve with aggregation, with implications for both model evaluation and sampling design: testing model prediction accuracy without an underlying knowledge of the datasets and the scale at which predictor variables operate can obscure meaningful results.
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A network simplification approach to ease topological studies about the food-web architecture. Sci Rep 2022; 12:13948. [PMID: 35977970 PMCID: PMC9385703 DOI: 10.1038/s41598-022-17508-1] [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: 03/07/2022] [Accepted: 07/26/2022] [Indexed: 11/19/2022] Open
Abstract
Food webs studies are intrinsically complex and time-consuming. Network data about trophic interaction across different large locations and ecosystems are scarce in comparison with general ecological data, especially if we consider terrestrial habitats. Here we present a complex network strategy to ease the gathering of the information by simplifying the collection of data with a taxonomic key. We test how well the topology of three different food webs retain their structure at the resolution of the nodes across distinct levels of simplification, and we estimate how community detection could be impacted by this strategy. The first level of simplification retains most of the general topological indices; betweenness and trophic levels seem to be consistent and robust even at the higher levels of simplification. This result suggests that generalisation and standardisation, as a good practice in food webs science, could benefit the community, both increasing the amount of open data available and the comparison among them, thus providing support especially for scientists that are new in this field and for exploratory analysis.
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Gupta A, Furrer R, Petchey OL. Simultaneously estimating food web connectance and structure with uncertainty. Ecol Evol 2022; 12:e8643. [PMID: 35342563 PMCID: PMC8928887 DOI: 10.1002/ece3.8643] [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: 05/19/2021] [Revised: 11/29/2021] [Accepted: 12/22/2021] [Indexed: 11/23/2022] Open
Abstract
Food web models explain and predict the trophic interactions in a food web, and they can infer missing interactions among the organisms. The allometric diet breadth model (ADBM) is a food web model based on the foraging theory. In the ADBM, the foraging parameters are allometrically scaled to body sizes of predators and prey. In Petchey et al. (Proceedings of the National Academy of Sciences, 2008; 105: 4191), the parameterization of the ADBM had two limitations: (a) the model parameters were point estimates and (b) food web connectance was not estimated. The novelty of our current approach is: (a) We consider multiple predictions from the ADBM by parameterizing it with approximate Bayesian computation, to estimate parameter distributions and not point estimates. (b) Connectance emerges from the parameterization, by measuring model fit using the true skill statistic, which takes into account prediction of both the presences and absences of links. We fit the ADBM using approximate Bayesian computation to 12 observed food webs from a wide variety of ecosystems. Estimated connectance was consistently greater than previously found. In some of the food webs, considerable variation in estimated parameter distributions occurred and resulted in considerable variation (i.e., uncertainty) in predicted food web structure. These results lend weight to the possibility that the observed food web data is missing some trophic links that do actually occur. It also seems likely that the ADBM likely predicts some links that do not exist. The latter could be addressed by accounting in the ADBM for additional traits other than body size. Further work could also address the significance of uncertainty in parameter estimates for predicted food web responses to environmental change.
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Affiliation(s)
- Anubhav Gupta
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
| | - Reinhard Furrer
- Department of Mathematics and Department of Computational ScienceUniversity of ZurichZurichSwitzerland
| | - Owen L. Petchey
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
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Puche E, Jordán F, Rodrigo MA, Rojo C. Non‐trophic key players in aquatic ecosystems: a mesocosm experiment. OIKOS 2020. [DOI: 10.1111/oik.07476] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Eric Puche
- Cavanilles Inst. of Biodiversity and Evolutionary Biology, Univ. of Valencia Spain
| | - Ferenc Jordán
- Balaton Limnological Inst., Centre for Ecological Research, Tihany, Hungary, and Evolutionary Systems Research Group, Centre for Ecological Research Tihany Hungary
| | - María A. Rodrigo
- Cavanilles Inst. of Biodiversity and Evolutionary Biology, Univ. of Valencia Spain
| | - Carmen Rojo
- Cavanilles Inst. of Biodiversity and Evolutionary Biology, Univ. of Valencia Spain
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Affiliation(s)
- Louie H. Yang
- Department of Entomology and Nematology University of California Davis California
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Aggregating a Plankton Food Web: Mathematical versus Biological Approaches. MATHEMATICS 2018. [DOI: 10.3390/math6120336] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Species are embedded in a web of intricate trophic interactions. Understanding the functional role of species in food webs is of fundamental interests. This is related to food web position, so positional similarity may provide information about functional overlap. Defining and quantifying similar trophic functioning can be addressed in different ways. We consider two approaches. One is of mathematical nature involving network analysis where unique species can be defined as those whose topological position is very different to others in the same food web. A species is unique if it has very different connection pattern compared to others. The second approach is of biological nature, based on trait-based aggregations. Unique species are not easy to aggregate with others because their traits are not in common with the ones of most others. Our goal here is to illustrate how mathematics can provide an alternative perspective on species aggregation, and how this is related to its biological counterpart. We illustrate these approaches using a toy food web and a real food web and demonstrate the sensitive relationships between those approaches. The trait-based aggregation focusing on the trait values of size (sv) can be best predicted by the mathematical aggregation algorithms.
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López DN, Camus PA, Valdivia N, Estay SA. Food webs over time: evaluating structural differences and variability of degree distributions in food webs. Ecosphere 2018. [DOI: 10.1002/ecs2.2539] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Daniela N. López
- Instituto de Ciencias Ambientales y Evolutivas; Facultad de Ciencias; Universidad Austral de Chile; Campus Isla Teja s/n Valdivia Chile
| | - Patricio A. Camus
- Departamento de Ecología; Facultad de Ciencias; Universidad Católica de la Santísima Concepción; Alonso de Ribera 2850 Concepcion Chile
- Centro de Investigación en Biodiversidad y Ambientes Sustentables (CIBAS); Universidad Católica de la Santísima Concepción; Alonso de Ribera 2850 Concepcion Chile
| | - Nelson Valdivia
- Instituto de Ciencias Marinas y Limnológicas; Facultad de Ciencias; Universidad Austral de Chile; Campus Isla Teja s/n Valdivia Chile
- Centro FONDAP de Investigación en Dinámica de Ecosistemas Marinos de Altas Latitudes (IDEAL); Universidad Austral de Chile; Campus Isla Teja s/n Valdivia Chile
| | - Sergio A. Estay
- Instituto de Ciencias Ambientales y Evolutivas; Facultad de Ciencias; Universidad Austral de Chile; Campus Isla Teja s/n Valdivia Chile
- Center of Applied Ecology and Sustainability (CAPES); Pontificia Universidad Católica de Chile; Av. L. B. O'Higgins 340 Santiago Chile
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Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity. PLoS One 2018; 13:e0193822. [PMID: 29579077 PMCID: PMC5868776 DOI: 10.1371/journal.pone.0193822] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 02/20/2018] [Indexed: 11/19/2022] Open
Abstract
Most of earth’s biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships between consumer diet breadth, interaction diversity, and species diversity within multi-trophic communities, which is critical for the conservation of biodiversity in this period of accelerated global change.
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Staniczenko PPA, Lewis OT, Tylianakis JM, Albrecht M, Coudrain V, Klein AM, Reed-Tsochas F. Predicting the effect of habitat modification on networks of interacting species. Nat Commun 2017; 8:792. [PMID: 28986532 PMCID: PMC5630616 DOI: 10.1038/s41467-017-00913-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Accepted: 08/07/2017] [Indexed: 11/21/2022] Open
Abstract
A pressing challenge for ecologists is predicting how human-driven environmental changes will affect the complex pattern of interactions among species in a community. Weighted networks are an important tool for studying changes in interspecific interactions because they record interaction frequencies in addition to presence or absence at a field site. Here we show that changes in weighted network structure following habitat modification are, in principle, predictable. Our approach combines field data with mathematical models: the models separate changes in relative species abundance from changes in interaction preferences (which describe how interaction frequencies deviate from random encounters). The models with the best predictive ability compared to data requirement are those that capture systematic changes in interaction preferences between different habitat types. Our results suggest a viable approach for predicting the consequences of rapid environmental change for the structure of complex ecological networks, even in the absence of detailed, system-specific empirical data. In a changing world, the ability to predict the impact of environmental change on ecological communities is essential. Here, the authors show that by separating species abundances from interaction preferences, they can predict the effects of habitat modification on the structure of weighted species interaction networks, even with limited data.
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Affiliation(s)
- Phillip P A Staniczenko
- National Socio-Environmental Synthesis Center (SESYNC), Annapolis, MD, 21401, USA. .,Department of Biology, University of Maryland College Park, Maryland, MD, 20742, USA. .,CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford, OX1 1HP, UK.
| | - Owen T Lewis
- Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK
| | - Jason M Tylianakis
- Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, 8140, New Zealand.,Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, SL5 7PY, UK
| | - Matthias Albrecht
- Institute for Sustainability Sciences, Agroscope, Zurich, 8046, Switzerland
| | - Valérie Coudrain
- Mediterranean Institute of Marine and Terrestrial Biodiversity and Ecology, Aix-Marseille University, University of Avignon, CNRS, IRD, IMBE, Marseille, 13284, France
| | - Alexandra-Maria Klein
- Chair of Nature Conservation and Landscape Ecology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, D-79106, Germany
| | - Felix Reed-Tsochas
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford, OX1 1HP, UK.,Oxford Martin School, University of Oxford, Oxford, OX1 3BD, UK
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Patonai K, Jordán F. Aggregation of incomplete food web data may help to suggest sampling strategies. Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2017.02.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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12
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Vinagre C, Costa MJ, Dunne JA. Effect of spatial scale on the network properties of estuarine food webs. ECOLOGICAL COMPLEXITY 2017. [DOI: 10.1016/j.ecocom.2017.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Navia AF, Cruz-Escalona VH, Giraldo A, Barausse A. The structure of a marine tropical food web, and its implications for ecosystem-based fisheries management. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2016.02.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Falcão JC, Dáttilo W, Rico-Gray V. Sampling effort differences can lead to biased conclusions on the architecture of ant–plant interaction networks. ECOLOGICAL COMPLEXITY 2016. [DOI: 10.1016/j.ecocom.2016.01.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Gauzens B, Thébault E, Lacroix G, Legendre S. Trophic groups and modules: two levels of group detection in food webs. J R Soc Interface 2015; 12:20141176. [PMID: 25878127 PMCID: PMC4424665 DOI: 10.1098/rsif.2014.1176] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 03/23/2015] [Indexed: 11/12/2022] Open
Abstract
Within food webs, species can be partitioned into groups according to various criteria. Two notions have received particular attention: trophic groups (TGs), which have been used for decades in the ecological literature, and more recently, modules. The relationship between these two group concepts remains unknown in empirical food webs. While recent developments in network theory have led to efficient methods for detecting modules in food webs, the determination of TGs (groups of species that are functionally similar) is largely based on subjective expert knowledge. We develop a novel algorithm for TG detection. We apply this method to empirical food webs and show that aggregation into TGs allows for the simplification of food webs while preserving their information content. Furthermore, we reveal a two-level hierarchical structure where modules partition food webs into large bottom-top trophic pathways, whereas TGs further partition these pathways into groups of species with similar trophic connections. This provides new perspectives for the study of dynamical and functional consequences of food-web structure, bridging topological and dynamical analysis. TGs have a clear ecological meaning and are found to provide a trade-off between network complexity and information loss.
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Affiliation(s)
- Benoit Gauzens
- UMR 7618-iEES Paris (CNRS, UPMC, UPEC, Paris Diderot, IRD, INRA), Université Pierre et Marie Curie, Bâtiment A, 7 quai St Bernard, 75252 Paris cedex 05, France UMR 6553 Ecobio, Université de Rennes 1, Avenue du Général Leclerc, Campus de Beaulieu, 35042 RENNES Cedex, France
| | - Elisa Thébault
- UMR 7618-iEES Paris (CNRS, UPMC, UPEC, Paris Diderot, IRD, INRA), Université Pierre et Marie Curie, Bâtiment A, 7 quai St Bernard, 75252 Paris cedex 05, France
| | - Gérard Lacroix
- UMR 7618-iEES Paris (CNRS, UPMC, UPEC, Paris Diderot, IRD, INRA), Université Pierre et Marie Curie, Bâtiment A, 7 quai St Bernard, 75252 Paris cedex 05, France UMS 3194 (CNRS, ENS), CEREEP - Ecotron Ile De France, Ecole Normale Supérieure, 78 rue du Château, 77140 St-Pierre-lès-Nemours, France
| | - Stéphane Legendre
- UMR 8197 IBENS (CNRS, ENS), École Normale Supérieure, 46, rue d'Ulm, 75230 Paris cedex 05, France
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Devoto M, Bailey S, Memmott J. Ecological meta-networks integrate spatial and temporal dynamics of plant-bumble bee interactions. OIKOS 2013. [DOI: 10.1111/j.1600-0706.2013.01251.x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Miranda M, Parrini F, Dalerum F. A categorization of recent network approaches to analyse trophic interactions. Methods Ecol Evol 2013. [DOI: 10.1111/2041-210x.12092] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- María Miranda
- Centre for African Ecology; School of Animal, Plant and Environmental Sciences; University of the Witwatersrand; Private Bag 3, Wits 2050; Johannesburg; South Africa
| | - Francesca Parrini
- Centre for African Ecology; School of Animal, Plant and Environmental Sciences; University of the Witwatersrand; Private Bag 3, Wits 2050; Johannesburg; South Africa
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Torres-Alruiz MD, Rodríguez DJ. A topo-dynamical perspective to evaluate indirect interactions in trophic webs: New indexes. Ecol Modell 2013. [DOI: 10.1016/j.ecolmodel.2012.11.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Ceneviva-Bastos M, Casatti L, Uieda V. Can seasonal differences influence food web structure on preserved habitats? Responses from two Brazilian streams. COMMUNITY ECOL 2012. [DOI: 10.1556/comec.13.2012.2.15] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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21
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Effect of Stipa tenacissima L. on the structure of plant co-occurrence networks in a semi-arid community. Ecol Res 2011. [DOI: 10.1007/s11284-011-0818-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Chen HW, Shao KT, Liu CWJ, Lin WH, Liu WC. The reduction of food web robustness by parasitism: fact and artefact. Int J Parasitol 2011; 41:627-34. [PMID: 21296081 DOI: 10.1016/j.ijpara.2010.12.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2010] [Revised: 12/17/2010] [Accepted: 12/20/2010] [Indexed: 11/17/2022]
Abstract
A robust food web is one which suffers few secondary extinctions after primary species losses. While recent research has shown that a food web with parasitism is less robust than one without, it still remains unclear whether the reduction in robustness is due to changes in network complexity or unique characteristics associated with parasitism. Here, using several published food webs, simulation experiments with different food web models and extinction scenarios were conducted to elucidate how such reduction can be achieved. Our results show that, regardless of changes in network complexity and preferential parasitism, the reduction in food web robustness is mainly due to the life cycle constraint of parasites. Our findings further demonstrate that parasites are prone to secondary extinctions and that their extinctions occur earlier than those involving free-living species. These findings suggest that the vulnerable nature of parasites to species loss makes them highly sensitive indicators of food web integrity.
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Affiliation(s)
- Hsuan-Wien Chen
- Department of Life Science, National Taiwan Normal University, Taipei, Taiwan
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