1
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Lue CH, Abram PK, Hrcek J, Buffington ML, Staniczenko PPA. Metabarcoding and applied ecology with hyperdiverse organisms: Recommendations for biological control research. Mol Ecol 2023; 32:6461-6473. [PMID: 36040418 DOI: 10.1111/mec.16677] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/12/2022] [Accepted: 08/22/2022] [Indexed: 11/29/2022]
Abstract
Metabarcoding is revolutionizing fundamental research in ecology by enabling large-scale detection of species and producing data that are rich with community context. However, the benefits of metabarcoding have yet to be fully realized in fields of applied ecology, especially those such as classical biological control (CBC) research that involve hyperdiverse taxa. Here, we discuss some of the opportunities that metabarcoding provides CBC and solutions to the main methodological challenges that have limited the integration of metabarcoding in existing CBC workflows. We focus on insect parasitoids, which are popular and effective biological control agents (BCAs) of invasive species and agricultural pests. Accurately identifying native, invasive and BCA species is paramount, since misidentification can undermine control efforts and lead to large negative socio-economic impacts. Unfortunately, most existing publicly accessible genetic databases cannot be used to reliably identify parasitoid species, thereby limiting the accuracy of metabarcoding in CBC research. To address this issue, we argue for the establishment of authoritative genetic databases that link metabarcoding data to taxonomically identified specimens. We further suggest using multiple genetic markers to reduce primer bias and increase taxonomic resolution. We also provide suggestions for biological control-specific metabarcoding workflows intended to track the long-term effectiveness of introduced BCAs. Finally, we use the example of an invasive pest, Drosophila suzukii, in a reflective "what if" thought experiment to explore the potential power of community metabarcoding in CBC.
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Affiliation(s)
- Chia-Hua Lue
- Department of Biology, Brooklyn College, City University of New York, New York City, New York, USA
| | - Paul K Abram
- Agriculture and Agri-Food Canada, Agassiz Research and Development Centre, Agassiz, British Columbia, Canada
| | - Jan Hrcek
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
| | - Matthew L Buffington
- Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Phillip P A Staniczenko
- Department of Biology, Brooklyn College, City University of New York, New York City, New York, USA
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2
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French CM, Bertola LD, Carnaval AC, Economo EP, Kass JM, Lohman DJ, Marske KA, Meier R, Overcast I, Rominger AJ, Staniczenko PPA, Hickerson MJ. Global determinants of insect mitochondrial genetic diversity. Nat Commun 2023; 14:5276. [PMID: 37644003 PMCID: PMC10465557 DOI: 10.1038/s41467-023-40936-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 08/15/2023] [Indexed: 08/31/2023] Open
Abstract
Understanding global patterns of genetic diversity is essential for describing, monitoring, and preserving life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrates, which comprise only a small fraction of Earth's biodiversity. Here, we construct a global map of predicted insect mitochondrial genetic diversity from cytochrome c oxidase subunit 1 sequences, derived from open data. We calculate the mitochondrial genetic diversity mean and genetic diversity evenness of insect assemblages across the globe, identify their environmental correlates, and make predictions of mitochondrial genetic diversity levels in unsampled areas based on environmental data. Using a large single-locus genetic dataset of over 2 million globally distributed and georeferenced mtDNA sequences, we find that mitochondrial genetic diversity evenness follows a quadratic latitudinal gradient peaking in the subtropics. Both mitochondrial genetic diversity mean and evenness positively correlate with seasonally hot temperatures, as well as climate stability since the last glacial maximum. Our models explain 27.9% and 24.0% of the observed variation in mitochondrial genetic diversity mean and evenness in insects, respectively, making an important step towards understanding global biodiversity patterns in the most diverse animal taxon.
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Affiliation(s)
- Connor M French
- Biology Department, City College of New York, New York, NY, USA.
- Biology Ph.D. Program, Graduate Center, City University of New York, New York, NY, USA.
| | - Laura D Bertola
- Biology Department, City College of New York, New York, NY, USA
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, N 2200, Denmark
| | - Ana C Carnaval
- Biology Department, City College of New York, New York, NY, USA
- Biology Ph.D. Program, Graduate Center, City University of New York, New York, NY, USA
| | - Evan P Economo
- Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan
| | - Jamie M Kass
- Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan
- Macroecology Laboratory, Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - David J Lohman
- Biology Department, City College of New York, New York, NY, USA
- Biology Ph.D. Program, Graduate Center, City University of New York, New York, NY, USA
- Entomology Section, National Museum of Natural History, Manila, Philippines
| | | | - Rudolf Meier
- Institut für Biologie, Humboldt-Universität zu Berlin, Berlin, Germany
- Center for Integrative Biodiversity Discovery, Leibniz Institute for Evolution and Biodiversity Science, Museum für Naturkunde Berlin, Berlin, Germany
| | - Isaac Overcast
- Biology Ph.D. Program, Graduate Center, City University of New York, New York, NY, USA
- Institut de Biologie de l'Ecole Normale Superieure, Paris, France
- Department of Vertebrate Zoology, American Museum of Natural History, New York, NY, USA
| | - Andrew J Rominger
- School of Biology and Ecology, University of Maine, Orono, ME, USA
- Maine Center for Genetics in the Environment, University of Maine, Orono, ME, USA
| | | | - Michael J Hickerson
- Biology Department, City College of New York, New York, NY, USA
- Biology Ph.D. Program, Graduate Center, City University of New York, New York, NY, USA
- Division of Invertebrate Zoology, American Museum of Natural History, New York, NY, USA
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3
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Zambrano J, Arellano G, Swenson NG, Staniczenko PPA, Thompson J, Fagan WF. Analyses of three-dimensional species associations reveal departures from neutrality in a tropical forest. Ecology 2022; 103:e3681. [PMID: 35315513 DOI: 10.1002/ecy.3681] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 12/01/2021] [Accepted: 01/14/2022] [Indexed: 11/09/2022]
Abstract
The study of community spatial structure is central to understanding diversity patterns over space and species co-occurrence at local scales. While most analytical approaches consider horizontal and vertical dimensions separately, in this study we introduce a three-dimensional spatial analysis that simultaneously includes horizontal and vertical species associations. Using tree census data (2000 to 2016) and allometries from the Luquillo forest plot in Puerto Rico, we show that spatial organization becomes less random over time as the forest recovered from land-use legacy effects and hurricane disturbance. Tree species vertical segregation is predominant in the forest with almost all species that co-occur in the horizontal plane avoiding each other in the vertical dimension. Horizontal segregation is less common than vertical, while three-dimensional aggregation (a proxy for direct tree competition) is the least frequent type of spatial association. Furthermore, dominant species are involved in more non-random spatial associations, implying that species co-occurrence is facilitated by species segregation in space. This novel three-dimensional analysis allowed us to identify and quantify tree species spatial distributions, how interspecific competition was reduced through forest structure, and how it changed over time after disturbance, in ways not detectable from two-dimensional analyses alone.
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Affiliation(s)
- Jenny Zambrano
- School of Biological Sciences, Washington State University, Pullman, U.S.A
| | - Gabriel Arellano
- Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, U.S.A.,Oikobit LLC, www.oikobit.com
| | - Nathan G Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, U.S.A
| | | | - Jill Thompson
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Scotland
| | - William F Fagan
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, U.S.A.,National Socio-Environmental Synthesis Center, Annapolis, Maryland, U.S.A
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4
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Lue CH, Buffington ML, Scheffer S, Lewis M, Elliott TA, Lindsey ARI, Driskell A, Jandova A, Kimura MT, Carton Y, Kula RR, Schlenke TA, Mateos M, Govind S, Varaldi J, Guerrieri E, Giorgini M, Wang X, Hoelmer K, Daane KM, Abram PK, Pardikes NA, Brown JJ, Thierry M, Poirié M, Goldstein P, Miller SE, Tracey WD, Davis JS, Jiggins FM, Wertheim B, Lewis OT, Leips J, Staniczenko PPA, Hrcek J. DROP: Molecular voucher database for identification of Drosophila parasitoids. Mol Ecol Resour 2021; 21:2437-2454. [PMID: 34051038 DOI: 10.1111/1755-0998.13435] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 05/11/2021] [Accepted: 05/20/2021] [Indexed: 01/03/2023]
Abstract
Molecular identification is increasingly used to speed up biodiversity surveys and laboratory experiments. However, many groups of organisms cannot be reliably identified using standard databases such as GenBank or BOLD due to lack of sequenced voucher specimens identified by experts. Sometimes a large number of sequences are available, but with too many errors to allow identification. Here, we address this problem for parasitoids of Drosophila by introducing a curated open-access molecular reference database, DROP (Drosophila parasitoids). Identifying Drosophila parasitoids is challenging and poses a major impediment to realize the full potential of this model system in studies ranging from molecular mechanisms to food webs, and in biological control of Drosophila suzukii. In DROP, genetic data are linked to voucher specimens and, where possible, the voucher specimens are identified by taxonomists and vetted through direct comparison with primary type material. To initiate DROP, we curated 154 laboratory strains, 856 vouchers, 554 DNA sequences, 16 genomes, 14 transcriptomes, and six proteomes drawn from a total of 183 operational taxonomic units (OTUs): 114 described Drosophila parasitoid species and 69 provisional species. We found species richness of Drosophila parasitoids to be heavily underestimated and provide an updated taxonomic catalogue for the community. DROP offers accurate molecular identification and improves cross-referencing between individual studies that we hope will catalyse research on this diverse and fascinating model system. Our effort should also serve as an example for researchers facing similar molecular identification problems in other groups of organisms.
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Affiliation(s)
- Chia-Hua Lue
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Department of Biology, Brooklyn College, City University of New York (CUNY), Brooklyn, NY, USA
| | - Matthew L Buffington
- Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Sonja Scheffer
- Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Matthew Lewis
- Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Tyler A Elliott
- Centre for Biodiversity Genomics, University of Guelph, Guelph, ON, Canada
| | | | - Amy Driskell
- Laboratories of Analytical Biology, Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Anna Jandova
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
| | | | - Yves Carton
- "Évolution, Génomes, Comportement, Écologie", CNRS et Université Paris-Saclay, Paris, France
| | - Robert R Kula
- Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Todd A Schlenke
- Department of Entomology, University of Arizona, Tucson, AZ, USA
| | - Mariana Mateos
- Wildlife and Fisheries Sciences Department, Texas A&M University, College Station, TX, USA
| | - Shubha Govind
- The Graduate Center of the City University of New York, New York, NY, USA
| | - Julien Varaldi
- CNRS, Laboratoire de Biométrie et Biologie Evolutive, UMR 5558, Université de Lyon, Université Lyon 1, Villeurbanne, France
| | - Emilio Guerrieri
- CNR-Institute for Sustainable Plant Protection (CNR-IPSP), National Research Council of Italy, Portici, Italy
| | - Massimo Giorgini
- CNR-Institute for Sustainable Plant Protection (CNR-IPSP), National Research Council of Italy, Portici, Italy
| | - Xingeng Wang
- United States Department of Agriculture, Agricultural Research Services, Beneficial Insects Introduction Research Unit, Newark, DE, USA
| | - Kim Hoelmer
- United States Department of Agriculture, Agricultural Research Services, Beneficial Insects Introduction Research Unit, Newark, DE, USA
| | - Kent M Daane
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA
| | - Paul K Abram
- Agriculture and Agri-Food Canada, Agassiz Research and Development Centre, Agassiz, BC, Canada
| | - Nicholas A Pardikes
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
| | - Joel J Brown
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Branisovska 31, Czech Republic
| | - Melanie Thierry
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Branisovska 31, Czech Republic
| | - Marylène Poirié
- INRAE, CNRS. and Evolution and Specificity of Multitrophic Interactions (ESIM) Sophia Agrobiotech Institute, Université "Côte d'Azur", Sophia Antipolis, France
| | - Paul Goldstein
- Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - Scott E Miller
- Smithsonian Institution, National Museum of Natural History, Washington, DC, USA
| | - W Daniel Tracey
- Department of Biology, Indiana University Bloomington, Bloomington, IN, USA
- Gill Center for Biomolecular Science, Indiana University Bloomington, Bloomington, IN, USA
| | - Jeremy S Davis
- Department of Biology, Indiana University Bloomington, Bloomington, IN, USA
- Biology Department, University of Kentucky, Lexington, KY, USA
| | | | - Bregje Wertheim
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, the Netherlands
| | - Owen T Lewis
- Department of Zoology, University of Oxford, Oxford, UK
| | - Jeff Leips
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, USA
| | - Phillip P A Staniczenko
- Department of Biology, Brooklyn College, City University of New York (CUNY), Brooklyn, NY, USA
| | - Jan Hrcek
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic
- Faculty of Science, University of South Bohemia, Branisovska 31, Czech Republic
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5
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Thompson PR, Fagan WF, Staniczenko PPA. Predictor species: Improving assessments of rare species occurrence by modeling environmental co-responses. Ecol Evol 2020; 10:3293-3304. [PMID: 32273987 PMCID: PMC7140998 DOI: 10.1002/ece3.6096] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 11/09/2022] Open
Abstract
Designing an effective conservation strategy requires understanding where rare species are located. Because rare species can be difficult to find, ecologists often identify other species called conservation surrogates that can help inform the distribution of rare species. Species distribution models typically rely on environmental data when predicting the occurrence of species, neglecting the effect of species' co-occurrences and biotic interactions. Here, we present a new approach that uses Bayesian networks to improve predictions by modeling environmental co-responses among species. For species from a European peat bog community, our approach consistently performs better than single-species models and better than conventional multi-species approaches that include the presence of nontarget species as additional independent variables in regression models. Our approach performs particularly well with rare species and when calibration data are limited. Furthermore, we identify a group of "predictor species" that are relatively common, insensitive to the presence of other species, and can be used to improve occurrence predictions of rare species. Predictor species are distinct from other categories of conservation surrogates such as umbrella or indicator species, which motivates focused data collection of predictor species to enhance conservation practices.
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Affiliation(s)
- Peter R. Thompson
- Department of BiologyUniversity of MarylandCollege ParkMDUSA
- Department of Biological SciencesUniversity of AlbertaEdmontonABCanada
| | - William F. Fagan
- Department of BiologyUniversity of MarylandCollege ParkMDUSA
- National Socio‐Environmental Synthesis Center (SESYNC)AnnapolisMDUSA
| | - Phillip P. A. Staniczenko
- Department of BiologyUniversity of MarylandCollege ParkMDUSA
- National Socio‐Environmental Synthesis Center (SESYNC)AnnapolisMDUSA
- Present address:
Department of BiologyBrooklyn CollegeCity University of New YorkNew YorkNYUSA
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6
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Staniczenko PPA, Suttle KB, Pearson RG. Negative biotic interactions drive predictions of distributions for species from a grassland community. Biol Lett 2018; 14:rsbl.2018.0426. [PMID: 30429245 PMCID: PMC6283927 DOI: 10.1098/rsbl.2018.0426] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 10/23/2018] [Indexed: 01/18/2023] Open
Abstract
Understanding the factors that determine species' geographical distributions is important for addressing a wide range of biological questions, including where species will be able to maintain populations following environmental change. New methods for modelling species distributions include the effects of biotic interactions alongside more commonly used abiotic variables such as temperature and precipitation; however, it is not clear which types of interspecific relationship contribute to shaping species distributions and should therefore be prioritized in models. Even if some interactions are known to be influential at local spatial scales, there is no guarantee they will have similar impacts at macroecological scales. Here we apply a novel method based on information theory to determine which types of interspecific relationship drive species distributions. Our results show that negative biotic interactions such as competition have the greatest effect on model predictions for species from a California grassland community. This knowledge will help focus data collection and improve model predictions for identifying at-risk species. Furthermore, our methodological approach is applicable to any kind of species distribution model that can be specified with and without interspecific relationships.
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Affiliation(s)
- Phillip P A Staniczenko
- National Socio-Environmental Synthesis Center (SESYNC), Annapolis, MD, USA .,Department of Biology, University of Maryland College Park, College Park, MD, USA
| | - K Blake Suttle
- Department of Ecology and Evolutionary Biology, UC Santa Cruz, Santa Cruz, CA, USA
| | - Richard G Pearson
- Centre for Biodiversity and Environment Research, University College London, London, UK
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7
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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8
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Abstract
[This corrects the article DOI: 10.1371/journal.pone.0157876.].
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9
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Abstract
Stability is a desirable property of complex ecosystems. If a community of interacting species is at a stable equilibrium point then it is able to withstand small perturbations to component species' abundances without suffering adverse effects. In ecology, the Jacobian matrix evaluated at an equilibrium point is known as the community matrix, which describes the population dynamics of interacting species. A system's asymptotic short- and long-term behaviour can be determined from eigenvalues derived from the community matrix. Here we use results from the theory of pseudospectra to describe intermediate, transient dynamics. We first recover the established result that the transition from stable to unstable dynamics includes a region of 'transient instability', where the effect of a small perturbation to species' abundances-to the population vector-is amplified before ultimately decaying. Then we show that the shift from stability to transient instability can be affected by uncertainty in, or small changes to, entries in the community matrix, and determine lower and upper bounds to the maximum amplitude of perturbations to the population vector. Of five different types of community matrix, we find that amplification is least severe when predator-prey interactions dominate. This analysis is relevant to other systems whose dynamics can be expressed in terms of the Jacobian matrix.
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Affiliation(s)
- Francesco Caravelli
- Invenia Labs, 27 Parkside Place, Cambridge, CB1 1HQ, United Kingdom
- London Institute of Mathematical Sciences, 35a South Street, London, W1K 2XF, United Kingdom
- Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Phillip P. A. Staniczenko
- Department of Biology, University of Maryland, College Park, Maryland, MD 20742, United States of America
- National Socio-Environmental Synthesis Center (SESYNC), Annapolis, MD 21401, United States of America
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10
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Affiliation(s)
- Phillip P. A. Staniczenko
- Centre for Biodiversity and Environment Research; University College London; Gower Street London WC1E 6BT UK
- Department of Ecology and Evolution; University of Chicago; 1101 E 57th Street Chicago IL 60637 USA
| | - Matthew J. Smith
- Centre for Biodiversity and Environment Research; University College London; Gower Street London WC1E 6BT UK
| | - Stefano Allesina
- Centre for Biodiversity and Environment Research; University College London; Gower Street London WC1E 6BT UK
- Computation Institute; University of Chicago; 5735 South Ellis Avenue Chicago IL 60637 USA
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11
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de Sassi C, Staniczenko PPA, Tylianakis JM. Warming and nitrogen affect size structuring and density dependence in a host-parasitoid food web. Philos Trans R Soc Lond B Biol Sci 2013; 367:3033-41. [PMID: 23007092 DOI: 10.1098/rstb.2012.0233] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Body size is a major factor constraining the trophic structure and functioning of ecological communities. Food webs are known to respond to changes in basal resource abundance, and climate change can initiate compounding bottom-up effects on food-web structure through altered resource availability and quality. However, the effects of climate and co-occurring global changes, such as nitrogen deposition, on the density and size relationships between resources and consumers are unknown, particularly in host-parasitoid food webs, where size structuring is less apparent. We use a Bayesian modelling approach to explore the role of consumer and resource density and body size on host-parasitoid food webs assembled from a field experiment with factorial warming and nitrogen treatments. We show that the treatments increased resource (host) availability and quality (size), leading to measureable changes in parasitoid feeding behaviour. Parasitoids interacted less evenly within their host range and increasingly focused on abundant and high-quality (i.e. larger) hosts. In summary, we present evidence that climate-mediated bottom-up effects can significantly alter food-web structure through both density- and trait-mediated effects.
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Affiliation(s)
- Claudio de Sassi
- School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.
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12
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Abstract
Food web structure plays an important role when determining robustness to cascading secondary extinctions. However, existing food web models do not take into account likely changes in trophic interactions ('rewiring') following species loss. We investigated structural dynamics in 12 empirically documented food webs by simulating primary species loss using three realistic removal criteria, and measured robustness in terms of subsequent secondary extinctions. In our model, novel trophic interactions can be established between predators and food items not previously consumed following the loss of competing predator species. By considering the increase in robustness conferred through rewiring, we identify a new category of species--overlap species--which promote robustness as shown by comparing simulations incorporating structural dynamics to those with static topologies. The fraction of overlap species in a food web is highly correlated with this increase in robustness; whereas species richness and connectance are uncorrelated with increased robustness. Our findings underline the importance of compensatory mechanisms that may buffer ecosystems against environmental change, and highlight the likely role of particular species that are expected to facilitate this buffering.
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13
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Staniczenko PPA, Lee CF, Jones NS. Rapidly detecting disorder in rhythmic biological signals: a spectral entropy measure to identify cardiac arrhythmias. Phys Rev E Stat Nonlin Soft Matter Phys 2009; 79:011915. [PMID: 19257077 DOI: 10.1103/physreve.79.011915] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2008] [Revised: 11/14/2008] [Indexed: 05/27/2023]
Abstract
We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the power spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s, it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s, this becomes 89.5%, and with 60 s, it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.
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Affiliation(s)
- Phillip P A Staniczenko
- Physics Department, Clarendon Laboratory, CABDyN Complexity Centre, Oxford University, Oxford OX1 1HP, United Kingdom
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