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Published on: November 5, 2015
Dissimilarity in flea and host assemblages and their interaction networks along a spatial distance gradient:
Boris R Krasnov1, Goni Barki2, Irina S Khokhlova3
1Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 84990, Midreshet Ben-Gurion, Israel. krasnov@bgu.ac.il.
Abstract:
We investigated the distance-decay pattern (an increase in dissimilarity with increasing geographic distance) in regional assemblages of fleas and their small mammalian hosts, as well as their interaction networks, in four biogeographic realms. Dissimilarity of assemblages (βtotal) was partitioned into species richness differences (βrich) and species replacement (βrepl) components. Dissimilarity of networks was assessed using two metrics: (a) whole network dissimilarity (βWN) partitioned into species replacement (βST) and interaction rewiring (βOS) components and (b) D statistics, measuring dissimilarity in the pure structure of the networks, without using information on species identities and calculated for hosts-shared-by-fleas networks (Dh) and fleas-shared-by-hosts networks (Df). We asked whether the distance-decay pattern (a) occurs among interactor assemblages or their interaction networks; (b) depends on the network dissimilarity metric used; and (c) differs between realms. The βtotal and βrepl of flea and host assemblages increased with distance in all realms except for host assemblages in the Afrotropics. βrich for flea and host assemblages increased with distance in the Nearctic only. In networks, βWN and βST demonstrated a distance-decay pattern, whereas βOS was mainly spatially invariant except in the Neotropics. Correlations of Dh or Df and geographic distance were mostly non-significant. We conclude that investigations of dissimilarity in interaction networks should include both types of dissimilarity metrics (those that consider partner identities and those that consider the pure structure of networks). This will allow elucidating the predictability of some facets of network dissimilarity and the unpredictability of other facets.
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