A trait-based framework for discerning drivers of species co-occurrence across heterogeneous landscapes

5 April 2018

Kohli, Brooks; Terry, Rebecca; Rowe, Rebecca

Null model analysis of species co-occurrence patterns has long been used to gain insight into community assembly but is often limited to identifying non-random patterns without providing clarity about underlying ecological mechanisms. This challenge is especially apparent when sampling units are spread across a heterogeneous landscape or along an environmental gradient because multiple mechanisms can produce similar co-occurrence patterns. We developed a trait-based approach for discriminating between environmental filtering and biotic interactions as the probable driver of co-occurrence patterns across environmentally heterogeneous sites. We demonstrate our framework by analyzing the co-occurrence of small mammals over elevation in three independent mountain ranges in the Great Basin of the western United States. Our sampling design accounts for landscape scale environmental variability and within-site habitat heterogeneity. We identified 52 non-random species pairs, of which 36 were aggregated and 16 were segregated. For each pair, we determined which mechanism was the likely ecological explanation using a hypothesis-testing framework based on functional trait similarity. Expectations of biotic interactions were based on similarity of diet and body size whereas habitat affinity and geographic range were used for environmental filtering. Only four pairs were consistent with expectations under biotic interactions, including pairs for which competitive exclusion has previously been documented. In addition to analyzing individual pairs, we used binomial tests of observed versus expected totals of intra- and inter-guild pairs to determine assemblage-wide deviations from random community structure. Signatures of environmental filtering were consistent across mountain ranges and scales. Despite differences in species composition and significant pairs among data sets, our approach revealed consistent mechanistic conclusions, emphasizing the value of trait-based methods to co-occurrence and community assembly.