- 1 Much ecological theory is based on the characterization of ecological habits of species as ‘generalist’ or ‘specialist’, but standard measures for placing species along a generalist-specialist gradient do not exist.
- 2We introduce a method for quantifying habitat specialization (i.e. relative niche widths) using species co-occurrence data. Generalists should co-occur with many species, whereas specialists should co-occur with relatively few species, given equal plot occurrences. We quantify this concept using a generalist-specialist metric (θ) derived from a beta diversity statistic.
- 3We evaluate the ability of our generalist-specialist metric to correctly rank species according to simulated (known) niche widths. Our technique is generally robust to a wide variety of niche distribution structures and sampling designs, but surveys strongly biased toward certain habitats can undermine the ability of θ to accurately describe niche widths for underrepresented species.
- 4We apply our technique to three spatially nested surveys of the large woody flora (> 1 cm d.b.h.) of the south-eastern USA. For each dataset we rank the generalist-specialist tendencies of all species of non-trivial occurrences, including 113 species across the Southeast, 71 species of southern Appalachian forests, and 44 species of the 6800-ha Joyce Kilmer-Slickrock Wilderness Area (NC and TN, USA).
- 5Rankings of species’θ-values were generally consistent among datasets of different spatial extent. Generalist species (e.g. Ilex opaca, Ulmus rubra, Morus rubra, Prunus serotina, Acer rubrum) were often those with large geographical ranges, particularly for θ-values from the largest dataset, and overall were more likely to be bird-dispersed, deciduous, and shade tolerant. South-eastern specialist species (e.g. Taxodium spp., Abies fraseri, Quercus laevis, Pinus pungens, Pinus palustris) were those associated with stressful or unusual conditions, such as a long duration of flooding, high fire frequency, or extreme cold or dry climates.
- 6Our study demonstrates that increasingly available, large-survey datasets can contribute niche-related species information in the absence of detailed environmental or habitat measurements. Applications include new assessments of relationships between species traits, ecological and environmental tolerances, and species packing in different assemblages.