Abstract:
1. Biotic interactions exert considerable influence on the distribution of individual species and
should, thus, strongly impact communities. Implementing biotic interactions in spatial models of
community assembly is therefore essential for accurately modelling assemblage properties. However,
this remains a challenge due to the difficulty of detecting the role of species interactions and because
accurate paired community and environment data sets are required to disentangle biotic influences
from abiotic effects.
2. Here, we incorporate data from three dominant species into community-level models as a proxy
for the frequency and intensity of their interactions with other species and predict emergent assemblage
properties for the co-occurring subdominant species. By analysing plant community and fieldquantified
environmental data from specially designed and spatially replicated monitoring grids, we
provide a robust in vivo test of community models.
3. Considering this well-defined and easily quantified surrogate for biotic interactions consistently
improved realism in all aspects of community models (community composition, species richness and
functional structure), irrespective of modelling methodology.
4. Dominant species reduced community richness locally and favoured species with similar leaf dry
matter content. This effect was most pronounced under conditions of high plant biomass and cover,
where stronger competitive impacts are expected. Analysis of leaf dry matter content suggests that
this effect may occur through efficient resource sequestration.
5. Synthesis. We demonstrate the strong role of dominant species in shaping multiple plant community
attributes, and thus the need to explicitly include interspecific interactions to achieve robust predictions
of assemblage properties. Incorporating information on biotic interactions strengthens our
capacity not only to predict the richness and composition of communities, but also how their structure
and function will be modified in the face of global change.
