The structural importance of less abundant species in Prince William Sound food web

in Israel Journal of Ecology and Evolution
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Rarity of species is often considered to set priorities for biodiversity conservation. Less abundant species are expected to be at higher risk of extinction and make significant contribution to food web functioning. However, the relationship between species abundance and position in food webs is still unclear. Here we tested possible correlations between species abundance and structural position in Prince William Sound food web. Species abundance was inferred from biomass data and structural position was characterized by 13 centrality indices.

We found that less abundant species have higher trophic positions and display more generalist feeding strategies. However, positive correlations link most of the centrality indices to population size. Thus, being locally rare translates into more peripheral food web positions and implies marginal roles in the spread of indirect effects. Species characterized by largest population size are responsible for the transfer of largest amounts of biomass and regulate the transmission of indirect effects. Less abundant species are of marginal structural importance and are exposed to impacts mediated by larger populations. In Prince William Sound ecosystem, rarity is associated with critical food web positions and does not simply reflect a marginal contribution to biodiversity.

We suggest that knowing the food web position of rare species might help to formulate more effective, system-level solutions for their conservation, rather than simply focusing on the direct treatment of symptoms.

  • AllesinaSBodiniA. 2004. Who dominates whom in the ecosystem? Energy flow bottlenecks and cascading extinctions. J Theor Biol. 230:351358.

    • Search Google Scholar
    • Export Citation
  • ArponenA. 2012. Prioritizing species for conservation planning. Biodivers Conserv. 21:875893.

  • BishopMAWatsonJTKuletzKMorganT. 2014. Pacific herring (Clupea pallasii) consumption by marine birds during winter in Prince William Sound, Alaska. Fish Oceanogr.

    • Search Google Scholar
    • Export Citation
  • BondavalliCUlanowiczRE. 1999. Unexpected effects of predators upon their prey: the case of the American alligator. Ecosystems. 2:4963.

    • Search Google Scholar
    • Export Citation
  • BorerETHalpernBSSeabloomEW. 2006. Asymmetry in community regulation: effects of predators and productivity. Ecology. 87:28132820.

    • Search Google Scholar
    • Export Citation
  • CarsonWPRootRB. 2000. Herbivory and plant species coexistence: community regulation by an outbreaking phytophagous insect. Ecol Monogr. 70:7399.

    • Search Google Scholar
    • Export Citation
  • ChristianouMEbenmanB. 2005. Keystone species and vulnerable species in ecological communities: strong or weak interactors? J Theor Biol. 235:95103.

    • Search Google Scholar
    • Export Citation
  • CsardiGNepuszT. 2006. The igraph software package for complex network research. InterJ Complex Syst. 1695:19.

  • CuryPBakunACrawfordRJJarreAQuiñonesRAShannonLJVerheyeHM. 2000. Small pelagics in upwelling systems: patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J Mar Sci. 57:603618.

    • Search Google Scholar
    • Export Citation
  • DaleVHBeyelerSC. 2001. Challenges in the development and use of ecological indicators. Ecol Indic. 1:310.

  • DobsonALodgeDAlderJCummingGSKeymerJMcGladeJMooneyHRusakJASalaOWoltersV. 2006. Habitat loss, trophic collapse, and the decline of ecosystem services. Ecology. 87:19151924.

    • Search Google Scholar
    • Export Citation
  • DunneJAWilliamsRJMartinezND. 2002. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol Lett. 5:558567.

    • Search Google Scholar
    • Export Citation
  • EbenmanBJonssonT. 2005. Using community viability analysis to identify fragile systems and keystone species. Trends Ecol Evol. 20:568575.

    • Search Google Scholar
    • Export Citation
  • EstesJADugginsDO. 1995. Sea otters and kelp forests in Alaska: generality and variation in a community ecological paradigm. Ecol Monogr. 65:75100.

    • Search Google Scholar
    • Export Citation
  • EstesJESmithNSPalmisanoJF. 1978. Sea otter predation and community organization in the western Aleutian Islands, Alaska. Ecology. 59:822833.

    • Search Google Scholar
    • Export Citation
  • HooperDUAdairECCardinaleBJByrnesJEHungateBAMatulichKLGonzalezADuffyJEGamfeldtLO'ConnorMI. 2012. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature. 486:105108.

    • Search Google Scholar
    • Export Citation
  • JonesCGLawtonJHShachakM. 1994. Organisms as ecosystem engineers. Oikos. 69:373386.

  • JordánF. 2009. Keystone species and food webs. Philos Trans R Soc B. 364:17331741.

  • JordánFBenedekZPodaniJ. 2007. Quantifying positional importance in food webs: a comparison of centrality indices. Ecol Model. 205:270275.

    • Search Google Scholar
    • Export Citation
  • JordánFLiuWCDavisAJ. 2006. Topological keystone species: measures of positional importance in food webs. Oikos. 112:535546.

  • JordánFLiuWCWyattT. 2005. Topological constraints on the dynamics of wasp-waist ecosystems. J Mar Syst. 57:250263.

  • JordánFScottiMMikeÁOrtizM. 2014. Strong asymmetrical inter-specific relationships in food web simulations. Mar Ecol Prog Ser. 512:8998.

    • Search Google Scholar
    • Export Citation
  • Leader-WilliamsNDublinHT. 2000. Charismatic megafauna as “flagship species”. In: EntwistleADunstoneN editors. Priorities for the conservation of mammalian diversity: has the panda had its day? Cambridge: Cambridge University Press; p. 5381.

    • Search Google Scholar
    • Export Citation
  • LiviCMJordánFLeccaPOkeyTA. 2011. Identifying key species in ecosystems with stochastic sensitivity analysis. Ecol Model. 222:25422551.

    • Search Google Scholar
    • Export Citation
  • LyonsKGSchwartzMW. 2001. Rare species loss alters ecosystem function-invasion resistance. Ecol Lett. 4:358365.

  • MargulesCRDaviesKFMeyersJAMilkovitsGA. 1995. The responses of some selected arthropods and the frog Crinia signifera to habitat fragmentation. In: BradstockRAAuldTAKeithDAKingsfordRTLunneyDSivertsenDP editors. Conserving biodiversity: threats and solutions. Sydney: Surrey Beatty; p. 94103.

    • Search Google Scholar
    • Export Citation
  • McSheaWJUnderwoodHBRappoleJHeditors. 1997. The science of overabundance: deer ecology and population management. Washington (DC): Smithsonian Institution Press.

    • Search Google Scholar
    • Export Citation
  • MüllerCBAdriaanseICTBelshawRGodfrayHCJ. 1999. The structure of an aphid-parasitoid community. J Anim Ecol. 68:346370.

  • NguyenTPScottiMMorineMJPriamiC. 2011. Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins. BMC Syst Biol. 5:195.

    • Search Google Scholar
    • Export Citation
  • OkeyTAPaulyDeditors. 1999. Trophic mass-balance model of Alaska's Prince William Sound ecosystem for the post-spill period 1994-1996. Vol. 7. Vancouver (Canada): Fisheries Centre, University of British Columbia.

    • Search Google Scholar
    • Export Citation
  • OkeyTAWrightBA. 2004. Toward ecosystem-based extraction policies for Prince William Sound, Alaska: integrating conflicting objectives and rebuilding pinnipeds. Bull Mar Sci. 74:727747.

    • Search Google Scholar
    • Export Citation
  • PowerMETilmanDEstesJAMengeBABondWJMillsLSDailyGCastillaJCLubchencoJPaineRT. 1996. Challenges in the quest for keystones. BioScience. 46:609620.

    • Search Google Scholar
    • Export Citation
  • RobergeJMAngelstamP. 2004. Usefulness of the umbrella species concept as a conservation tool. Conserv Biol. 18:7685.

  • SalaOEChapinFSArmestoJJBerlowEBloomfieldJDirzoRHuber-SanwaldEHuennekeLFJacksonRBKinzigA. 2000. Global biodiversity scenarios for the year 2100. Science. 287:17701774.

    • Search Google Scholar
    • Export Citation
  • ScottiMAllesinaSBondavalliCBodiniAAbarca-ArenasLG. 2006. Effective trophic positions in ecological acyclic networks. Ecol Model. 198:495505.

    • Search Google Scholar
    • Export Citation
  • ScottiMBondavalliCBodiniAAllesinaS. 2009. Using trophic hierarchy to understand food web structure. Oikos. 118:16951702.

  • ScottiMGjataNLiviCMJordánF. 2012. Dynamical effects of weak trophic interactions in a stochastic food web simulation. Community Ecol. 13:230237.

    • Search Google Scholar
    • Export Citation
  • ScottiMJordánF. 2010. Relationships between centrality indices and trophic levels in food webs. Community Ecol. 11:5967.

  • ScottiMPodaniJJordánF. 2007. Weighting, scale dependence and indirect effects in ecological networks: a comparative study. Ecol Complex. 4:148159.

    • Search Google Scholar
    • Export Citation
  • ShannonPMarkielAOzierOBaligaNSWangJTRamageDAminNSchwikowskiBIdekerT. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13:24982504.

    • Search Google Scholar
    • Export Citation
  • SimberloffD. 1988. The contribution of population and community biology to conservation science. Annu Rev Ecol Syst. 19:473511.

  • ThomasGLThorneRE. 2001. Night-time predation by Steller sea lions. Nature. 411:10131013.

  • ThomasGLThorneRE. 2003. Acoustical-optical assessment of Pacific herring and their predator assemblage in Prince William Sound, Alaska. Aquat Living Resour. 16:247253.

    • Search Google Scholar
    • Export Citation
  • ThorneREThomasGL. 2008. Herring and the “Exxon Valdez” oil spill: an investigation into historical data conflicts. ICES J Mar Sci. 65:4450.

    • Search Google Scholar
    • Export Citation
  • UlanowiczREPucciaCJ. 1990. Mixed trophic impacts in ecosystems. Coenoses. 5:716.

  • ValentiniRJordánF. 2010. CoSBiLab Graph: the network analysis module of CoSBiLab. Environ Model Softw. 25:886888.

  • VasasVJordánF. 2006. Topological keystone species in ecological interaction networks: considering link quality and non-trophic effects. Ecol Model. 196:365378.

    • Search Google Scholar
    • Export Citation
  • WassermanSFaustK. 1994. Social network analysis: methods and applications. Vol. 8. Cambridge (UK): Cambridge University Press.

  • WatsonJFreudenbergerDPaullD. 2001. An assessment of the focal‐species approach for conserving birds in variegated landscapes in southeastern Australia. Conserv Biol. 15:13641373.

    • Search Google Scholar
    • Export Citation
  • WormBBarbierEBBeaumontNDuffyJEFolkeCHalpernBSJacksonJBCLotzeHKMicheliFPalumbiSR. 2006. Impacts of biodiversity loss on ocean ecosystem services. Science. 314:787790.

    • Search Google Scholar
    • Export Citation

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