A niche-based modeling approach to phytoplankton community assembly rules.

Six niche-based models proposed by Tokeshi, based on different assumptions of resource allocation by species, were fitted on phytoplankton relative abundance distributions, and potential environmental and biotic factors supporting the applicability of the fitted models were discussed. Overall 16 assemblages corresponding to different sampling times, various environmental conditions, and resource regimes within a year were fitted to the models. Phytoplankton biovolume was used as a measure of abundance, and a randomization test was applied to compare the model fit to the field data. The majority of the phytoplankton assemblages (11 of 16) were successfully described by the Random Fraction model, which is based on the theoretical assumption that resource is apportioned by the species in a random way. Only a few assemblages (three of 16), characterized by extremes in resource availability or disturbance, were not fitted by any of the models. The Random Fraction model in particular was rejected due to a steep slope during the first ranks, while the rest of the distribution remained relatively even, providing further evidence of resilience in phytoplankton communities. Although larger cells seem to have the potential to develop higher biomass, it seems that other factors, including the surface-to-volume ratio, counterbalance this advantage, resulting in a random-like behaviour in resource acquisition by phytoplankton, irrespective of cell size or species identity.

Niche overlap estimates based on quantitative functional traits: a new family of non-parametric indices.

The concept of niche overlap appears in studies of the mechanisms of the maintenance of species diversity, in searches for assembly rules, and in estimation of within-community species redundancy. For plant traits measured on a continuous scale, existing indices are inadequate because they split the scale into a number of categories thus losing information. An index is easy to construct if we assume a normal distribution for each trait within a species, but this assumption is rarely true. We extend and apply an index, NO(K), which is based on kernel density functions, and can therefore work with distributions of any shape without prior assumptions. For cases where the ecologist wishes to downweight traits that are inter-correlated, we offer a variant that does this: NO(Kw). From either of these indices, an index of the mean niche overlap in a community can be calculated: NO(K,community) and NO(Kw,community). For all these indices, the variance can be calculated and formulae for this are given. To give examples of the new indices in use, we apply them to a coastal fish dataset and a sand-dune plant dataset. The former exhibits considerable non-normality, emphasising the need for kernel-based indices. Accordingly, there was a considerable difference in index values, with those for an index based on a normal distribution being significantly higher than those from an index which, being based on kernel fitting, is not biased by an assumption for the distribution. The NO(K) values were ecologically consistent for the fish species concerned, varying from 0.02 to 0.53. The sand-dune plant data also showed a wide range of overlap values. Interestingly, the least overlap was between two graminoids, which would have been placed in the same functional group in the coarse classification often used in functional-type/ecosystem-function work.