4.7 Article

A hybrid behavioural rule of adaptation and drift explains the emergent architecture of antagonistic networks

Journal

Publisher

ROYAL SOC
DOI: 10.1098/rspb.2015.0320

Keywords

adaptive behaviour; functional response; modularity; nestedness; node degree; optimal foraging

Funding

  1. South African Research Chair Initiative (SARChI)
  2. National Research Foundation of South Africa [81825, 76912]
  3. Australian Research Council [DP150103017]
  4. National Natural Science Foundation of China [31360104]
  5. DST-NRF Centre of Excellence for Invasion Biology
  6. African Institute for Mathematical Sciences (AIMS)
  7. German Academic Exchange Service (DAAD)

Ask authors/readers for more resources

Ecological processes that can realistically account for network architectures are central to our understanding of how species assemble and function in ecosystems. Consumer species are constantly selecting and adjusting which resource species are to be exploited in an antagonistic network. Here we incorporate a hybrid behavioural rule of adaptive interaction switching and random drift into a bipartite network model. Predictions are insensitive to the model parameters and the initial network structures, and agree extremely well with the observed levels of modularity, nestedness and node-degree distributions for 61 real networks. Evolutionary and community assemblage histories only indirectly affect network structure by defining the size and complexity of ecological networks, whereas adaptive interaction switching and random drift carve out the details of network architecture at the faster ecological time scale. The hybrid behavioural rule of both adaptation and drift could well be the key processes for structure emergence in real ecological networks.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available