Talus microbes and plant assessment for the upper Green Lake Valley from 2007 to 2008.

It is increasingly recognized that species distributions are driven by
both abiotic factors and biotic interactions. Despite much recent work
incorporating competition, predation, and mutualism into species
distribution models (SDMs), the focus has been confined to aboveground
macroscopic interactions. Biotic interactions between plants and soil
microbial communities are understudied as potentially important
drivers of plant distributions. Some soil bacteria promote plant
growth by cycling nutrients, while others are pathogenic; thus they
have a high potential for influencing plant occurrence. We
investigated the influence of soil bacterial clades on the
distributions of bryophytes and 12 vascular plant species in a high
elevation talus-field ecosystem in the Rocky Mountain Front Range,
Colorado, USA. We used an information- theoretic criterion (AICc)
modeling approach to compare SDMs with the following different sets of
predictors: abiotic variables, abiotic variables and other plant
abundances, abiotic variables and soil bacteria clade relative
abundances, and a full model with abiotic factors, plant abundances,
and bacteria relative abundances. We predicted that bacteria would
influence plant distributions both positively and negatively, and that
these interactions would improve prediction of plant species
distributions. We found that inclusion of either plant or bacteria
biotic predictors generally improved the fit, deviance explained, and
predictive power of the SDMs, and for the majority of the species,
adding information on both other plants and bacteria yielded the best
model. Interactions between the modeled species and biotic predictors
were both positive and negative, suggesting the presence of
competition, parasitism, and facilitation. While our results indicate
that plant–plant co-occurrences are a stronger driver of plant
distributions than plant–bacteria co-occurrences, they also show that
bacteria can explain parts of plant distributions that remain
unexplained by abiotic and plant predictors. Our results provide
further support for including biotic factors in SDMs, and suggest that
belowground factors be considered as well.