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

Abstract

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.

Spatial overlap in lake phytoplankton: Relations with environmental factors and consequences for diversity

“…Estimates of overlap in the spatial distribution (SO) of phytoplankton FP spectral groups were calculated according to a niche overlap index developed and described in detail by Mouillot et al (2005). In a nutshell, the overlap index is based on kernel density functions that can fit any distribution in standardized data without prior assumptions (non-parametric; no assumption of normality).…”

“…After population density functions are fit to standardized FP biomass data, overlap is defined as the area under the smaller of the joint density functions. We used a companion script in R (Mouillot et al 2005) to generate these estimates of SO where the value varies from 0 (no overlap) to 1 (complete overlap). The script is designed so that overlap is calculated in this way for all groups at once.…”

The disruption of dominance hierarchies by a non-native species: an individual-based analysis

“…YOY rainbow trout used habitat where depth was 16.20 cm (range 7-30 cm) and current velocity was 8.11 cm s -1 (range 0-45 cm s -1 ) [non-parametric overlap index (Mouillot et al 2005) between species was 0.64 and 0.63 for depth and current velocity, respectively; Blanchet et al, unpublished data]. Later in the summer, at the end of August, Atlantic salmon were found in habitats where depth was 22.60 cm in average (range 5-35 cm) and current velocity was 23.40 cm s -1 (range 4-72 cm s -1 ), and YOY rainbow trout used habitat where depth was 18.97 cm (range 10-35 cm) and current velocity was 21.99 cm s -1 (range 2-60 cm s -1 ) [non-parametric overlap index (Mouillot et al 2005) between species was 0.72 and 0.82 for depth and current velocity, respectively; Blanchet et al, unpublished data]. Atlantic salmon fry emerged from their nests earlier than rainbow trout fry and consequently they maintained a size advantage until the end of their first summer of life (i.e.…”

The emergence and promise of functional biogeography

“…As a whole, functional traits can be implemented into more or less complex integrative functions (e.g., CWM is a simple integrative function) to scale up from organs to higher organizational levels including ecosystems and biomes (40,43,44). Traitbased approaches have also been extensively used to describe the diversity of forms and functions within a study unit-often termed functional diversity sensu lato-using different distance metrics (e.g., variance based) (45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55) and how it scales spatially (56)(57)(58)(59)(60)(61).…”