Global patterns of mainland and insular pollination networks

Aim: Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the structure of pollination networks from islands (of oceanic and continental origin) and mainlands. For oceanic islands, we further evaluated the effe...

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Published inGlobal ecology and biogeography Vol. 25; no. 7; pp. 880 - 890
Main Authors Traveset, Anna, Tur, Cristina, Trøjelsgaard, Kristian, Heleno, Ruben, Castro-Urgal, Rocío, Olesen, Jens M.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.07.2016
John Wiley & Sons Ltd
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Abstract Aim: Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the structure of pollination networks from islands (of oceanic and continental origin) and mainlands. For oceanic islands, we further evaluated the effects of key island traits on network structural parameters. Location: Fifty-two quantitative plant-pollinator networks from continental islands (n = 23), oceanic islands (n = 18) and mainlands (n = 11) located world-wide. Methods: The effect of geographical origin upon network structure was explored by means of generalized mixed models, accounting for biogeographical region, sampling intensity, latitude and network size. For oceanic island networks, the influence of area, age, elevation and isolation was also evaluated. Results: The structure of pollination networks was fairly consistent between mainland and continental islands and only a few differences were noted. Oceanic island networks, however, were smaller and topologically simplified, showing a lower interaction diversity, and higher plant niche overlap than mainland and continental island networks. Isolation and elevational range of oceanic islands influenced the total number of species and interactions. Networks from higherelevation oceanic islands were less nested and those located towards the equator exhibited higher interaction richness. Island area showed no significant effect on any of the network metrics studied here. Main conclusions: Pollination networks appear structurally similar regardless of their geographical origin. However, networks from continental islands are more similar to their mainland counterparts than to those from oceanic islands, probably due to the geological nature of continental islands, which are fragments of the mainland to which they were once connected. Oceanic island networks are the least species- and link-rich, and exhibit the lowest interaction diversity and the highest plant niche overlap, possibly due to lower pollinator richness. The most isolated and low-elevation islands show the simplest networks, and are thus probably the most vulnerable to pollination disruptions.
AbstractList Aim Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the structure of pollination networks from islands (of oceanic and continental origin) and mainlands. For oceanic islands, we further evaluated the effects of key island traits on network structural parameters. Location Fifty-two quantitative plant-pollinator networks from continental islands (n=23), oceanic islands (n=18) and mainlands (n=11) located world-wide. Methods The effect of geographical origin upon network structure was explored by means of generalized mixed models, accounting for biogeographical region, sampling intensity, latitude and network size. For oceanic island networks, the influence of area, age, elevation and isolation was also evaluated. Results The structure of pollination networks was fairly consistent between mainland and continental islands and only a few differences were noted. Oceanic island networks, however, were smaller and topologically simplified, showing a lower interaction diversity, and higher plant niche overlap than mainland and continental island networks. Isolation and elevational range of oceanic islands influenced the total number of species and interactions. Networks from higher-elevation oceanic islands were less nested and those located towards the equator exhibited higher interaction richness. Island area showed no significant effect on any of the network metrics studied here. Main conclusions Pollination networks appear structurally similar regardless of their geographical origin. However, networks from continental islands are more similar to their mainland counterparts than to those from oceanic islands, probably due to the geological nature of continental islands, which are fragments of the mainland to which they were once connected. Oceanic island networks are the least species- and link-rich, and exhibit the lowest interaction diversity and the highest plant niche overlap, possibly due to lower pollinator richness. The most isolated and low-elevation islands show the simplest networks, and are thus probably the most vulnerable to pollination disruptions.
Aim Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the structure of pollination networks from islands (of oceanic and continental origin) and mainlands. For oceanic islands, we further evaluated the effects of key island traits on network structural parameters. Location Fifty‐two quantitative plant–pollinator networks from continental islands (n = 23), oceanic islands (n = 18) and mainlands (n = 11) located world‐wide. Methods The effect of geographical origin upon network structure was explored by means of generalized mixed models, accounting for biogeographical region, sampling intensity, latitude and network size. For oceanic island networks, the influence of area, age, elevation and isolation was also evaluated. Results The structure of pollination networks was fairly consistent between mainland and continental islands and only a few differences were noted. Oceanic island networks, however, were smaller and topologically simplified, showing a lower interaction diversity, and higher plant niche overlap than mainland and continental island networks. Isolation and elevational range of oceanic islands influenced the total number of species and interactions. Networks from higher‐elevation oceanic islands were less nested and those located towards the equator exhibited higher interaction richness. Island area showed no significant effect on any of the network metrics studied here. Main conclusions Pollination networks appear structurally similar regardless of their geographical origin. However, networks from continental islands are more similar to their mainland counterparts than to those from oceanic islands, probably due to the geological nature of continental islands, which are fragments of the mainland to which they were once connected. Oceanic island networks are the least species‐ and link‐rich, and exhibit the lowest interaction diversity and the highest plant niche overlap, possibly due to lower pollinator richness. The most isolated and low‐elevation islands show the simplest networks, and are thus probably the most vulnerable to pollination disruptions.
Aim: Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the structure of pollination networks from islands (of oceanic and continental origin) and mainlands. For oceanic islands, we further evaluated the effects of key island traits on network structural parameters. Location: Fifty-two quantitative plant-pollinator networks from continental islands (n = 23), oceanic islands (n = 18) and mainlands (n = 11) located world-wide. Methods: The effect of geographical origin upon network structure was explored by means of generalized mixed models, accounting for biogeographical region, sampling intensity, latitude and network size. For oceanic island networks, the influence of area, age, elevation and isolation was also evaluated. Results: The structure of pollination networks was fairly consistent between mainland and continental islands and only a few differences were noted. Oceanic island networks, however, were smaller and topologically simplified, showing a lower interaction diversity, and higher plant niche overlap than mainland and continental island networks. Isolation and elevational range of oceanic islands influenced the total number of species and interactions. Networks from higherelevation oceanic islands were less nested and those located towards the equator exhibited higher interaction richness. Island area showed no significant effect on any of the network metrics studied here. Main conclusions: Pollination networks appear structurally similar regardless of their geographical origin. However, networks from continental islands are more similar to their mainland counterparts than to those from oceanic islands, probably due to the geological nature of continental islands, which are fragments of the mainland to which they were once connected. Oceanic island networks are the least species- and link-rich, and exhibit the lowest interaction diversity and the highest plant niche overlap, possibly due to lower pollinator richness. The most isolated and low-elevation islands show the simplest networks, and are thus probably the most vulnerable to pollination disruptions.
Author Trøjelsgaard, Kristian
Olesen, Jens M.
Heleno, Ruben
Traveset, Anna
Tur, Cristina
Castro-Urgal, Rocío
Author_xml – sequence: 1
  givenname: Anna
  surname: Traveset
  fullname: Traveset, Anna
  email: Correspondence: Anna Traveset, Laboratorio Internacional de Cambio Global (LINC-Global), Institut Mediterrani d'Estudis Avançats (CSIC-UIB), Terrestrial Ecology Group, C/ Miquel Marqués 21, 07190-Esporles, Mallorca, Balearic Islands, Spain., atraveset@imedea.csic-uib.es
  organization: Laboratorio Internacional de Cambio Global (LINC-Global), Institut Mediterrani d'Estudis Avançats (CSIC-UIB), Terrestrial Ecology Group, C/ Miquel Marqués 21, 07190-Esporles, Balearic Islands, Mallorca, Spain
– sequence: 2
  givenname: Cristina
  surname: Tur
  fullname: Tur, Cristina
  organization: Laboratorio Internacional de Cambio Global (LINC-Global), Institut Mediterrani d'Estudis Avançats (CSIC-UIB), Terrestrial Ecology Group, C/ Miquel Marqués 21, 07190-Esporles, Balearic Islands, Mallorca, Spain
– sequence: 3
  givenname: Kristian
  surname: Trøjelsgaard
  fullname: Trøjelsgaard, Kristian
  organization: Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
– sequence: 4
  givenname: Ruben
  surname: Heleno
  fullname: Heleno, Ruben
  organization: Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456, Coimbra, Portugal
– sequence: 5
  givenname: Rocío
  surname: Castro-Urgal
  fullname: Castro-Urgal, Rocío
  organization: Laboratorio Internacional de Cambio Global (LINC-Global), Institut Mediterrani d'Estudis Avançats (CSIC-UIB), Terrestrial Ecology Group, C/ Miquel Marqués 21, 07190-Esporles, Balearic Islands, Mallorca, Spain
– sequence: 6
  givenname: Jens M.
  surname: Olesen
  fullname: Olesen, Jens M.
  organization: Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000, Aarhus C, Denmark
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Table S1 Quantitative pollination networks used in this study, with information on sampling location and the type of data collected. Table S2 Island traits used as predictors of metrics describing the pollination network of oceanic islands. Table S3 Metrics describing of 52 pollination networks used in this study. Table S4 Summary of the models predicting the effect of different factors on metrics describing pollination network structure. Table S5 Effects of oceanic island traits on the structure of pollination networks. Table S6 Summary of the models predicting the effect of different factors on metrics describing pollination network structure, treating Jamaica as a continental island.Appendix 1 Definitions of the metrics used in this study to describe network structure. Appendix 2 List of databases consulted to gather information on the percentage of alien plant species in each network. Appendix 3 Caveats to be considered when comparing studies on mutualistic networks at the macroecological scale.
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Plein, M., Längsfeld, L., Neuschulz, E.L., Schultheiß, C., Ingmann, L., Töpfer, T., Böhning-Gaese, K. & Schleuning, M. (2013) Constant properties of plant-frugivore networks despite fluctuations in fruit and bird communities in space and time. Ecology, 94, 1296-1306.
Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A. & Smith, G.M. (2009) Mixed effects models and extensions in ecology with R. Springer, New York.
Guimerà, R. & Amaral, L.A.N. (2005) Functional cartography of complex metabolic networks. Nature, 433, 895-900.
Padrón, B., Traveset, A., Biedenweg, T., Díaz, D., Olesen, J.M. & Nogales, M. (2009) Impact of invasive species in the pollination networks of two different archipelagos. PLoS ONE, 4, e6275.
Bernardello, G., Anderson, G.J., Stuessy, T.F. & Crawford, D.J. (2001) A survey of floral traits, breeding systems, floral visitors, and pollination systems of the angiosperms of the Juan Fernández Islands (Chile). Botanical Review, 67, 255-308.
Dormann, C.F., Fründ, J., Blüthgen, N. & Gruber, B. (2009) Indices, graphs and null models: analyzing bipartite ecological networks. The Open Ecology Journal, 2, 7-24.
Linsley, E.G., Rick, C.M. & Stephens, S.G. (1966) Observations on the floral relationships of the Galápagos carpenter bee. Pan-Pacific Entomologist, 42, 1-18.
Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer Verlag, New York.
Castro-Urgal, R. & Traveset, A. (2014) Differences in flower visitation networks between an oceanic and a continental island. Botanical Journal of the Linnean Society, 174, 477-488.
Schleuning, M., Fründ, J., Klein, A.M. et al. (2012) Specialisation of networks decreases towards tropical latitudes. Current Biology, 22, 1925-1931.
Heleno, R.H., Olesen, J.M., Nogales, M., Vargas, P. & Traveset, A. (2013) Seed dispersal networks in the Galápagos and the consequences of alien plant invasions. Proceedings of the Royal Society B: Biological Sciences, 280, 20122112.
Kaiser-Bunbury, C.N., Memmott, J. & Müller, C.B. (2009) Community structure of pollination webs of Mauritian heathland habitats. Perspectives in Plant Ecology, Evolution and Systematics, 11, 241-254.
Olesen, J.M., Bascompte, J., Dupont, Y.L. & Jordano, P. (2007) The modularity of pollination networks. Proceedings of the National Academy of Sciences USA, 104, 19891-19896.
Dupont, Y.L., Hansen, D.M. & Olesen, J.M. (2003) Structure of a plant-flower-visitor network in the high-altitude sub-alpine desert of Tenerife, Canary Islands. Ecography, 26, 301-310.
Blüthgen, N., Menzel, F., Hovestadt, T., Fiala, B. & Blüthgen, N. (2007) Specialization, constraints, and conflicting interests in mutualistic networks. Current Biology, 17, 341-346.
Trøjelsgaard, K., Báez, M., Espadaler, X., Nogales, M., Oromí, P., La Roche, F. & Olesen, J.M. (2013) Island biogeography of mutualistic interaction networks. Journal of Biogeography, 40, 2020-2031.
Gibson, R.H., Knott, B., Eberlein, T. & Memmott, J. (2011) Sampling method influences the structure of plant-pollinator networks. Oikos, 120, 822-831.
Schleuning, M., Katrin Böhning-Gaese, K., Dehling, D.M. & Burns, K.C. (2014) At a loss for birds: insularity increases asymmetry in seed-dispersal networks. Global Ecology and Biogeography, 23, 385-394.
Olesen, J.M. & Jordano, P. (2002) Geographic patterns in plant-pollinator mutualistic networks. Ecology, 83, 2416-2424.
Olesen, J.M., Eskildsen, L.I. & Venkatasamy, S. (2002) Invasion of pollination networks on oceanic islands: importance of invader complexes and endemic super generalists. Diversity and Distributions, 8, 181-192.
Banasek-Richter, C., Cattin, M.F. & Bersier, L.F. (2004) Sampling effects and the robustness of quantitative and qualitative food-web descriptors. Journal of Theoretical Biology, 226, 23-32.
Thornton, I. (2007) Island colonization: the origin and development of island communities. Cambridge University Press, Cambridge.
Dalsgaard, B., Trøjelsgaard, K., Martín González, A.M., Nogués-Bravo, D., Ollerton, J., Petanidou, T., Sandel, B., Schleuning, M., Wang, Z., Rahbek, C., Sutherland, W.J., Svenning, J.C. & Olesen, J.M. (2013) Historical climate change influences modularity and nestedness of pollination networks. Ecography, 36, 1331-1340.
Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J., Stevens, M.H.H. & White, J.S.S. (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127-135.
Kueffer, C., Daehler, C.C., Torres-Santana, C.W. et al. (2010) A global comparison of plant invasions on oceanic islands. Perspectives in Plant Ecology, Evolution and Systematics, 12, 145-161.
Hagen, M., Kissling, W.D., Rasmussen, C. et al. (2012) Biodiversity, species interactions and ecological networks in a fragmented world. Advances in Ecological Research, 46, 89-210.
Aizen, M.A., Sabatino, M. & Tylianakis, J.M. (2012) Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science, 335, 1486-1489.
Carstensen, D.W., Dalsgaard, B., Svenning, J. et al. (2013) The functional biogeography of species: biogeographical species roles of birds in Wallacea and the West Indies. Ecography, 36, 1-9.
Vázquez, D., Morris, W.F. & Jordano, P. (2005) Interaction frequency as a surrogate for the total effect of animal mutualists on plants. Ecology Letters, 8, 1088-1094.
Fortuna, M.A., Stouffer, D.B., Olesen, J.M., Jordano, P., Mouillot, D., Krasnov, B.R., Poulin, R. & Bascompte, J. (2010) Nestedness versus modularity in ecological networks: two sides of the same coin? Journal of Animal Ecology, 79, 811-817.
Whittaker, R.J. & Fernández-Palacios, J.M. (2007) Island biogeography. Oxford University Press, Oxford.
Sugiura, S. (2010) Species interactions-area relationships: biological invasions and network structure in relation to island area. Proceedings of the Royal Society B: Biological Sciences, 277, 1807-1815.
Traveset, A. & Richardson, D.M. (2006) Biological invasions as disruptors of plant-animal reproductive mutualisms. Trends in Ecology and Evolution, 21, 208-216.
Traveset, A., Heleno, R., Chamorro, S., Vargas, P., McMullen, C.K., Castro-Urgal, R., Nogales, M., Herrera, H.W. & Olesen, J.M. (2013) Invaders of pollination networks in the Galápagos Islands? Emergence of novel communities. Proceedings of the Royal Society B: Biological Sciences, 280, 20123040.
Triantis, K.A., Mylonas, M. & Whittaker, R.J. (2008) Evolutionary species-area curves as revealed by single-island endemics: insights for the inter-provincial species-area relationship. Ecography, 31, 401-407.
Trøjelsgaard, K. & Olesen, J.M. (2013) Macroecology of pollination networks. Global Ecology and Biogeography, 22, 149-162.
Almeida-Neto, M. & Ulrich, W. (2011) A straightforward computational approach for quantifying nestedness using quantitative matrices. Environmental Modelling and Software, 26, 173-178.
Blüthgen, N. (2010) Why network analysis is often disconnected from community ecology? A critique and an ecologist's guide. Basic and Applied Ecology, 11, 1-11.
Rivera-Hutinel, A., Bustamante, R.O., Marín, V.H. & Medel, R. (2012) Effects of sampling completeness on the structure of plant-pollinator networks. Ecology, 93, 1593-1603.
Sebastián-González, E., Dalsgaard, B., Sandel, B. & Guimarães, P.R., Jr (2015) Macroecological trends in nestedness and modularity of seed-dispersal networks: human impact matters. Global Ecology and Biogeography, 24, 293-303.
MacArthur, R.H. & Wilson, E.O. (1967) The theory of island biogeography. Princeton University Press, Princeton, NJ.
Bascompte, J. & Jordano, P. (2007) Plant-animal mutualistic networks: the architecture of biodiversity. Annual Review of Ecology, Evolution, and Systematics, 38, 567-593.
Woodell, S.R.J. (1979) The role of unspecialized pollinators in the reproductive success of Aldabran plants. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 286, 99-108.
Benadi, G., Hovestadt, T., Poethke, H. & Blüthgen, N. (2014) Specialization and phonological synchrony of plant-pollinator interactions along an altitudinal gradient. Journal of Animal Ecology, 83, 639-650.
Takhtajan, A. (1986) Floristic regions of the world. University of California Press, Berkeley, CA.
Bascompte, J., Jordano, P. & Olesen, J.M. (2006) Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science, 312, 431-433.
2010; 12
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References_xml – reference: Benadi, G., Hovestadt, T., Poethke, H. & Blüthgen, N. (2014) Specialization and phonological synchrony of plant-pollinator interactions along an altitudinal gradient. Journal of Animal Ecology, 83, 639-650.
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– reference: Bernardello, G., Anderson, G.J., Stuessy, T.F. & Crawford, D.J. (2001) A survey of floral traits, breeding systems, floral visitors, and pollination systems of the angiosperms of the Juan Fernández Islands (Chile). Botanical Review, 67, 255-308.
– reference: Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J., Stevens, M.H.H. & White, J.S.S. (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127-135.
– reference: Dormann, C.F., Fründ, J., Blüthgen, N. & Gruber, B. (2009) Indices, graphs and null models: analyzing bipartite ecological networks. The Open Ecology Journal, 2, 7-24.
– reference: Aizen, M.A., Sabatino, M. & Tylianakis, J.M. (2012) Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science, 335, 1486-1489.
– reference: Olesen, J.M., Eskildsen, L.I. & Venkatasamy, S. (2002) Invasion of pollination networks on oceanic islands: importance of invader complexes and endemic super generalists. Diversity and Distributions, 8, 181-192.
– reference: Blüthgen, N., Menzel, F., Hovestadt, T., Fiala, B. & Blüthgen, N. (2007) Specialization, constraints, and conflicting interests in mutualistic networks. Current Biology, 17, 341-346.
– reference: Bascompte, J., Jordano, P. & Olesen, J.M. (2006) Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science, 312, 431-433.
– reference: Dalsgaard, B., Trøjelsgaard, K., Martín González, A.M., Nogués-Bravo, D., Ollerton, J., Petanidou, T., Sandel, B., Schleuning, M., Wang, Z., Rahbek, C., Sutherland, W.J., Svenning, J.C. & Olesen, J.M. (2013) Historical climate change influences modularity and nestedness of pollination networks. Ecography, 36, 1331-1340.
– reference: Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer Verlag, New York.
– reference: Kueffer, C., Daehler, C.C., Torres-Santana, C.W. et al. (2010) A global comparison of plant invasions on oceanic islands. Perspectives in Plant Ecology, Evolution and Systematics, 12, 145-161.
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– reference: Trøjelsgaard, K. & Olesen, J.M. (2013) Macroecology of pollination networks. Global Ecology and Biogeography, 22, 149-162.
– reference: Sebastián-González, E., Dalsgaard, B., Sandel, B. & Guimarães, P.R., Jr (2015) Macroecological trends in nestedness and modularity of seed-dispersal networks: human impact matters. Global Ecology and Biogeography, 24, 293-303.
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– reference: Banasek-Richter, C., Cattin, M.F. & Bersier, L.F. (2004) Sampling effects and the robustness of quantitative and qualitative food-web descriptors. Journal of Theoretical Biology, 226, 23-32.
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– reference: Olesen, J.M. & Jordano, P. (2002) Geographic patterns in plant-pollinator mutualistic networks. Ecology, 83, 2416-2424.
– reference: Traveset, A. & Richardson, D.M. (2006) Biological invasions as disruptors of plant-animal reproductive mutualisms. Trends in Ecology and Evolution, 21, 208-216.
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  year: 2013
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  year: 2007
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  year: 2009
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  publication-title: Ecology
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  year: 2013
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  publication-title: Proceedings of the Royal Society B: Biological Sciences
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  year: 2013
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  year: 2009
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Snippet Aim: Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the...
Aim Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the...
Aim Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the...
AIM: Interaction networks are being increasingly used to evaluate macroecological patterns. We explored a global dataset to identify differences in the...
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SubjectTerms Continental islands
data collection
ecological networks
Islands
latitude
macroecology
Magma
mutualistic networks
oceanic islands
Plant reproduction
plant-insect interactions
pollination
pollinators
provenance
sampling effort
Title Global patterns of mainland and insular pollination networks
URI https://api.istex.fr/ark:/67375/WNG-G38PN6JS-B/fulltext.pdf
https://www.jstor.org/stable/43871675
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fgeb.12362
https://www.proquest.com/docview/1797003413
https://www.proquest.com/docview/1808653236
https://www.proquest.com/docview/1817828073
Volume 25
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