Open AccessFood webs over time: evaluating structural differences and variability of degree distributions in food webs
Author(s) -
López Daniela N.,
Camus Patricio A.,
Valdivia Nelson,
Estay Sergio A.
Publication year - 2018
Publication title -
ecosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.255
H-Index - 57
ISSN - 2150-8925
DOI - 10.1002/ecs2.2539
Subject(s) - degree (music) , degree distribution , niche , ecology , ecological network , distribution (mathematics) , environmental science , mathematics , community structure , statistics , complex network , biology , ecosystem , physics , combinatorics , mathematical analysis , acoustics
Abstract Ecological networks are usually analyzed as time‐aggregated units, where time‐specific interactions are aggregated into one single network. However, the extent to which the structure of these time‐aggregated networks represents natural community dynamics is poorly understood. Here, we compared the topology, represented as the in‐ and out‐degree, of seasonal and time‐aggregated networks. The statistical distributions of both metrics were compared seasonal networks and the resultant time‐aggregated networks. Our results showed that the exponential models best described the general degree distribution. The functional forms of the in‐ and out‐degree distribution differ significantly. We found that the discrete generalized beta and the exponential models best described the in‐degree and out‐degree distributions, respectively, of both seasonal and time‐aggregated networks. Except for the connectance, all topological descriptors of seasonal networks showed values lower than those of time‐aggregated networks. The a and μ parameters of observed seasonal and time‐aggregated networks were significantly lower than those of a simulated niche model. The structure of seasonal and aggregated networks differed significantly in the values of a and μ parameters, converging toward a common structure only after 18 months of seasonal samplings. Our results highlight the risk of underestimating significant ecological variability when time‐aggregated ecological networks are analyzed.