Different methods, different results: temporal trends in the study of nested subset patterns in parasite communities.
The search for nested subset patterns has become a powerful tool for understanding the processes shaping parasite communities. Here, we re-examine the results of past studies on nestedness in parasite communities, to assess how sensitive they are to the analytical method used. Using the metric N and the null model RANDOM1, the first method available to study nested patterns, early studies concluded that nestedness was infrequent in parasite communities. In contrast later studies, using instead the metric T and the nestedness temperature calculator (NTC), found that nested subset patterns were very common in parasite communities. Recently, a new algorithm, the binary matrix nestedness temperature calculator (BINMATNEST), has been proposed to quantify nestedness. Using data on 31 helminth communities of fish hosts, we show that applying the NTC yields consistently more significant nested patterns than when N and RANDOM1 are used on the same data. The use of BINMATNEST produced results that depend on the choice of the null model. To provide a benchmark, a straightforward comparison between the observed frequencies of co-occurrences of species with those expected from their prevalence under random assembly was also made for each community. This test indicates that random structure occurs in practically all communities, even those where one of the nestedness analyses found a significant pattern. We demonstrate that the probability of finding a nested pattern in a parasite community depends entirely on the metric and null model chosen for analysis.
Timi
J T
JT
Departamento de Biología, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas. jtimi@mdp.edu.ar
Poulin
R
R
D000465:Algorithms; D000818:Animals; D003710:Demography; D005399:Fishes / Q000469:parasitology; D006790:Host-Parasite Interactions; D008954:Models, Biological; D010271:Parasites / Q000502:physiology*; D010272:Parasitic Diseases / Q000469:parasitology*; D010274:Parasitology / Q000379:methods* / Q000592:standards / Q000639:trends