The dimensionality of ecological networks
How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, includi...
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| Published in | Ecology letters Vol. 16; no. 5; pp. 577 - 583 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.05.2013
Blackwell |
| Subjects | |
| Online Access | Get full text |
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| Abstract | How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high‐quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large‐scale community structure. |
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| AbstractList | How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. [PUBLICATION ABSTRACT] How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high‐quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large‐scale community structure. How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small (<10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure.How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. How many dimensions (trait-axes) are required to predict whether two species interact? This unansweredquestion originated with the idea of ecological niches, and yet bears relevance today for understanding whatdetermines network structure. Here, we analyse a set of 200 ecological networks, including food webs,antagonistic and mutualistic networks, and find that the number of dimensions needed to completelyexplain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-qualitywebs including several species traits, we identify which traits contribute the most to explaining networkstructure. We show that accounting for a few traits dramatically improves our understanding of the structureof ecological networks. Matching traits for resources and consumers, for example, fruit size and billgape, are the most successful combinations. These results link ecologically important species attributes tolarge-scale community structure. |
| Author | Chacoff, Natacha P. Pizo, Marco Aurelio Eklöf, Anna Tylianakis, Jason M. Galetti, Mauro Lomáscolo, Silvia Beatriz Martín González, Ana M. Guimarães, Paulo R. Vázquez, Diego P. Bosch, Jordi Rader, Romina Castro-Urgal, Rocío de Sassi, Claudio Allesina, Stefano Jacob, Ute Rodrigo, Anselm Kopp, Jason Dalsgaard, Bo |
| Author_xml | – sequence: 1 givenname: Anna surname: Eklöf fullname: Eklöf, Anna email: Correspondence: , anna.eklof@liu.se organization: Department of Ecology & Evolution, University of Chicago, IL, Chicago, USA – sequence: 2 givenname: Ute surname: Jacob fullname: Jacob, Ute organization: Institute for Hydrobiology and Fisheries Science, Hamburg, Germany – sequence: 3 givenname: Jason surname: Kopp fullname: Kopp, Jason organization: Department of Ecology & Evolution, University of Chicago, IL, Chicago, USA – sequence: 4 givenname: Jordi surname: Bosch fullname: Bosch, Jordi organization: CREAF - Ecology Unit, Universitat Autónoma de Barcelona, Barcelona, Spain – sequence: 5 givenname: Rocío surname: Castro-Urgal fullname: Castro-Urgal, Rocío organization: Institut Mediterrani d'Estudis Avanc¸ats (CSIC-UIB), Mallorca, Balearic Islands, Spain – sequence: 6 givenname: Natacha P. surname: Chacoff fullname: Chacoff, Natacha P. organization: Instituto Argentino de Investigaciones de las Zonas Áridas, CONICET, Mendoza, Argentina – sequence: 7 givenname: Bo surname: Dalsgaard fullname: Dalsgaard, Bo organization: Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Copenhagen, Denmark – sequence: 8 givenname: Claudio surname: de Sassi fullname: de Sassi, Claudio organization: School of Biological Sciences, University of Canterbury, Canterbury, New Zealand – sequence: 9 givenname: Mauro surname: Galetti fullname: Galetti, Mauro organization: Departamento de Ecologia, Universidade Estadual Paulista, Rio Claro, Brazil – sequence: 10 givenname: Paulo R. surname: Guimarães fullname: Guimarães, Paulo R. organization: Departamento de Ecologia, I.B, Universidade de São Paulo, Sao Paulo, Brazil – sequence: 11 givenname: Silvia Beatriz surname: Lomáscolo fullname: Lomáscolo, Silvia Beatriz organization: Instituto Argentino de Investigaciones de las Zonas Áridas, CONICET, Mendoza, Argentina – sequence: 12 givenname: Ana M. surname: Martín González fullname: Martín González, Ana M. organization: Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Copenhagen, Denmark – sequence: 13 givenname: Marco Aurelio surname: Pizo fullname: Pizo, Marco Aurelio organization: Departamento de Zoologia, Universidade Estadual Paulista, São Paulo, Brazil – sequence: 14 givenname: Romina surname: Rader fullname: Rader, Romina organization: Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, Sweden – sequence: 15 givenname: Anselm surname: Rodrigo fullname: Rodrigo, Anselm organization: CREAF - Ecology Unit, Universitat Autónoma de Barcelona, Barcelona, Spain – sequence: 16 givenname: Jason M. surname: Tylianakis fullname: Tylianakis, Jason M. organization: School of Biological Sciences, University of Canterbury, Canterbury, New Zealand – sequence: 17 givenname: Diego P. surname: Vázquez fullname: Vázquez, Diego P. organization: Instituto Argentino de Investigaciones de las Zonas Áridas, CONICET, Mendoza, Argentina – sequence: 18 givenname: Stefano surname: Allesina fullname: Allesina, Stefano organization: Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27283696$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/23438174$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89672$$DView record from Swedish Publication Index https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-161703$$DView record from Swedish Publication Index |
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| ContentType | Journal Article |
| Copyright | 2013 Blackwell Publishing Ltd/CNRS 2014 INIST-CNRS 2013 Blackwell Publishing Ltd/CNRS. Copyright © 2013 Blackwell Publishing Ltd/CNRS |
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| Issue | 5 |
| Keywords | scaling Food web niche space Trophic structure species traits Ecological niche intervality food web structure Ecological networks |
| Language | English |
| License | CC BY 4.0 2013 Blackwell Publishing Ltd/CNRS. |
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| References | Kratochvíl, J. (1994). A special planar satisfiability problem and a consequence of its np-completeness. Discrete Appl. Math., 52, 233-252. Silvertown, J. (2004). Plant coexistence and the niche. Trends Ecol. Evol., 19, 605-611. Cagnolo, L., Salvo, A. & Valladares, G. (2011). Network topology: patterns and mechanisms in plant-herbivore and host-parasitoid food webs. J. Anim. Ecol., 80, 342-351. Optiz, S. (1996). Trophic Interactions in Caribbean Coral Reefs. Tech. Rep. 43, ICLARM, Manila. Roberts, F.S. (1969). On the Boxicity and Cubicity of a Graph. Academic Press, New York. Ueckert, D.N. & Hansen, R.M. (1971). Dietary overlap of grasshoppers on sandhill rangeland in northeastern colorado. Oecologia, 8, 276-295. Stouffer, D.B., Camacho, J. & Amaral, L.A.N. (2006). A robust measure of food web intervality. P. Nat. Acad. Sci. U.S.A., 103, 19015-19020. Allesina, S., Alonso, D. & Pascual, M. (2008). A general model for food web structure. Science, 320, 658-661. Jacob, U. (2005). Trophic Dynamics of Antarctic Shelf Ecosystems - Food Webs and Energy Flow Budgets, Thesis. University of Bremen, Germany. Rossberg, A.G., Brännström, Å. & Dieckmann, U. (2010). Food-web structure in low-and high-dimensional trophic niche spaces. J. Roy. Soc. Int., 7, 1735-1743. Forrest, J., Miller-Rushing, A.J., Forrest, J. & Miller-Rushing, A.J. (2010). Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. T. Roy. Soc. B, 365, 3101-3112. Riede, J.O., Rall, B.C., Banasek-Richter, C., Navarrete, S.A., Wieters, E.A., Emmerson, M.C., Jacob, U. & Brose, U. (2010). Scaling of food-web properties with diversity and complexity across ecosystems. Adv. Ecol. Res., 42, 139-170. Galetti, M. & Pizo, M.A. (1996). Fruit eating by birds in a forest fragment in southeastern brazil. Ararajuba, 4, 71-79. Williams, R.J. & Purves, D.W. (2011). The probabilistic niche model reveals substantial variation in the niche structure of empirical food webs. Ecology, 92, 1849-1857. Bosch, J., Martín González, A.M., Rodrigo, A. & Navarro, D. (2009). Plant-pollinator networks: adding the pollinators perspective. Ecol. Lett., 12, 409-419. Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M. & West, G.B. (2004). Toward a metabolic theory of ecology. Ecology, 85, 1771-1789. Dalsgaard, B., Martín González, A.M., Olesen, J.M., Ollerton, J., Timmermann, A., Andersen, L.H. & Tossas, A.G. (2009). Plant-hummingbird interactions in the west indies: floral specialisation gradients associated with environment and hummingbird size. Oecologia, 159, 757-766. Bersier, L.F. & Kehrli, P. (2008). The signature of phylogenetic constraints on food-web structure. Ecol. Compl., 5, 132-139. Cattin, M.F., Bersier, L.F., Banasˇek-Richter, C., Baltensperger, R. & Gabriel, J.P. (2004). Phylogenetic constraints and adaptation explain food-web structure. Nature, 427, 835-839. Jacob, U., Thierry, A., Brose, U., Arntz, W.E., Berg, S., Brey, T., Fetzer, I., Jonsson, T., Mintenbeck, K., Mollmann, C. et al. (2011). The role of body size in complex food webs: a cold case. Adv. Ecol. Res, 45, 181-223. Williams, R.J. & Martinez, N.D. (2000). Simple rules yield complex food webs. Nature, 404, 180-183. Chase, J.M. & Leibold, M.A. (2003). Ecological Niches: Linking Classical and Contemporary Approaches. University of Chicago Press, Chicago, IL. Cohen, J.E., Schittler, D.N., Raffaelli, D.G. & Reuman, D.C. (2009). Food webs are more than the sum of their tritrophic parts. P. Nat. Acad. Sci. U.S.A., 106, 22335-22340. Stouffer, D.B., Rezende, E.L. & Amaral, L.A.N. (2011). The role of body mass in diet contiguity and food-web structure. J. Anim. Ecol., 80, 632-639. McCullen, C.K. (1993). Flower-visiting insects of the galapagos islands. Pan-Pac. Entomol., 69, 95-106. Thompson, J.N. (2005). The Geographic Mosaic of Coevolution. University of Chicago Press, Chicago, IL. 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Organizing and understanding a winter's seagrass foodweb network through effective trophic levels. Ecol. Model., 117, 99-124. Burnham, K.P. & Anderson, D.R. (2002). Model Selection and Multimodel Inference: A Practical Information-theoretic Approach. Springer Verlag, New York. Petanidou, T. (1991). Pollination Ecology in a Phryganic Ecosystem, Thesis. Aristotelian University, Greece. Brännström, Å., Carlsson, L. & Rossberg, A.G. (2011). Rigorous conditions for food-web intervality in high-dimensional trophic niche spaces. J. Math. Biol., 63, 575-592. Gilman, R.T., Nuismer, S.L. & Jhwueng, D.C. (2012). Coevolution in multidimensional trait space favours escape from parasites and pathogens. Nature, 483, 328-330. Eklöf, A., Helmus, M.R., Moore, M. & Allesina, S. (2012). Relevance of evolutionary history for food web structure. P. Roy. Soc. Lond. B: Bio., 279, 1588-1596. Chacoff, N.P., Vázquez, D.P., Lomáscolo, S.B., Stevani, E.L., Dorado, J. & Padrón, B. (2012). Evaluating sampling completeness in a desert plant-pollinator network. J. Anim. Ecol., 81, 190-200. Inouye, D.W. (1980). The terminology of floral larceny. Ecology, 61, 1251-1253. Mouillot, D., Krasnov, B.R. & Poulin, R. (2008). High intervality explained by phylogenetic constraints in host-parasite webs. Ecology, 89, 2043-2051. Zook, A.E., Eklöf, A., Jacob, U. & Allesina, S. (2011). Food webs: ordering species according to body size yields high degree of intervality. J. Theor. Biol., 271, 106-113. Arroyo, M.T.K., Primack, R. & Armesto, J. (1982). Community studies in pollination ecology in the high temperate andes of central chile. i. pollination mechanisms and altitudinal variation. Am. J. Bot., 69, 82-97. Jordano, P., Bascompte, J. & Olesen, J.M. (2003). Invariant properties in coevolutionary networks of plant-animal interactions. Ecol. Lett., 6, 69-81. 1993; 69 2007; 445 2011; 279 2004; 85 2012; 81 2012; 483 2011; 80 1980; 61 2010; 365 2008 1996 2008; 5 2005 2008; 105 2003 2002 1991 2008; 320 2004; 427 2011; 271 2009; 159 1978 2009; 12 1982; 69 1971; 8 2010; 42 2009; 309 2004; 19 2003; 6 2011; 92 2000; 404 2011; 63 2008; 89 2011; 45 1999; 117 2012; 279 1996; 4 1994; 52 1969 2010; 7 2009; 103 1968 2006; 103 2009; 106 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_40_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_41_1 e_1_2_7_14_1 e_1_2_7_42_1 e_1_2_7_13_1 e_1_2_7_43_1 e_1_2_7_12_1 e_1_2_7_44_1 e_1_2_7_11_1 e_1_2_7_45_1 e_1_2_7_46_1 Optiz S. (e_1_2_7_31_1) 1996 e_1_2_7_47_1 e_1_2_7_26_1 e_1_2_7_48_1 e_1_2_7_27_1 e_1_2_7_28_1 Stouffer D.B. (e_1_2_7_39_1) 2006; 103 Burnham K.P. (e_1_2_7_10_1) 2002 Roberts F.S. (e_1_2_7_35_1) 1969 Galetti M. (e_1_2_7_22_1) 1996; 4 Jacob U. (e_1_2_7_25_1) 2005 Petanidou T. (e_1_2_7_32_1) 1991 e_1_2_7_30_1 McCullen C.K. (e_1_2_7_29_1) 1993; 69 e_1_2_7_24_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_34_1 e_1_2_7_21_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_37_1 e_1_2_7_38_1 |
| References_xml | – reference: Dalsgaard, B., Martín González, A.M., Olesen, J.M., Ollerton, J., Timmermann, A., Andersen, L.H. & Tossas, A.G. (2009). Plant-hummingbird interactions in the west indies: floral specialisation gradients associated with environment and hummingbird size. Oecologia, 159, 757-766. – reference: Bersier, L.F. & Kehrli, P. (2008). The signature of phylogenetic constraints on food-web structure. Ecol. Compl., 5, 132-139. – reference: Williams, R.J. & Purves, D.W. (2011). The probabilistic niche model reveals substantial variation in the niche structure of empirical food webs. Ecology, 92, 1849-1857. – reference: McCullen, C.K. (1993). Flower-visiting insects of the galapagos islands. Pan-Pac. Entomol., 69, 95-106. – reference: Silvertown, J. (2004). Plant coexistence and the niche. Trends Ecol. Evol., 19, 605-611. – reference: Vázquez, D.P., Blüthgen, N., Cagnolo, L. & Chacoff, N.P. (2009). Uniting pattern and process in plant-animal mutualistic networks: a review. Ann. Bot. London, 103, 1445-1457. – reference: Arroyo, M.T.K., Primack, R. & Armesto, J. (1982). Community studies in pollination ecology in the high temperate andes of central chile. i. pollination mechanisms and altitudinal variation. Am. J. Bot., 69, 82-97. – reference: Kratochvíl, J. (1994). A special planar satisfiability problem and a consequence of its np-completeness. Discrete Appl. Math., 52, 233-252. – reference: Brännström, Å., Carlsson, L. & Rossberg, A.G. (2011). Rigorous conditions for food-web intervality in high-dimensional trophic niche spaces. J. Math. Biol., 63, 575-592. – reference: Riede, J.O., Rall, B.C., Banasek-Richter, C., Navarrete, S.A., Wieters, E.A., Emmerson, M.C., Jacob, U. & Brose, U. (2010). Scaling of food-web properties with diversity and complexity across ecosystems. Adv. Ecol. Res., 42, 139-170. – reference: Sunil, C.L. & Ashik, M.K. (2009). 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| Snippet | How many dimensions (trait‐axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches,... How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches,... How many dimensions (trait-axes) are required to predict whether two species interact? This unansweredquestion originated with the idea of ecological niches,... |
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| SubjectTerms | Animal and plant ecology Animal, plant and microbial ecology Animals Biological and medical sciences Community ecology Community structure Ecological networks Ecology Ecosystem Food Chain Food chains food web structure Food webs Fundamental and applied biological sciences. Psychology General aspects intervality Models, Biological Models, Theoretical Multifactorial Inheritance niche space scaling species traits Symbiosis |
| Title | The dimensionality of ecological networks |
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