Inferring microhabitat preferences of Lilium catesbaei (Liliaceae)

Microhabitat studies use varied statistical methods, some treating site occupancy as a dependent and others as an independent variable. Using the rare Lilium catesbaei as an example, we show why approaches to testing hypotheses of differences between occupied and unoccupied sites can lead to erroneous conclusions about habitat preferences. Predictive approaches like logistic regression can better lead to understanding of habitat requirements.

Using 32 lily locations and 30 random locations >2 m from a lily (complete data: 31 lily and 28 random spots), we measured physical conditions--photosynthetically active radiation (PAR), canopy cover, litter depth, distance to and height of nearest shrub, and soil moisture--and number and identity of neighboring plants. Twelve lilies were used to estimate a photosynthetic assimilation curve. Analyses used logistic regression, discriminant function analysis (DFA), (multivariate) analysis of variance, and resampled Wilcoxon tests.

Logistic regression and DFA found identical predictors of presence (PAR, canopy cover, distance to shrub, litter), but hypothesis tests pointed to a different set (PAR, litter, canopy cover, height of nearest shrub). Lilies are mainly in high-PAR spots, often close to light saturation. By contrast, PAR in random spots was often near the lily light compensation point. Lilies were near Serenoa repens less than at random; otherwise, neighbor identity had no significant effect.

Predictive methods are more useful in this context than the hypothesis tests. Light availability plays a big role in lily presence, which may help to explain increases in flowering and emergence after fire and roller-chopping.