A framework for evaluating the influence of climate, dispersal limitation, and biotic interactions using fossil pollen associations across the late Quaternary

Abstract

Environmental conditions, dispersal lags, and interactions among species are major factors structuring communities through time and across space. Ecologists have emphasized the importance of biotic interactions in determining local patterns of species association. In contrast, abiotic limits, dispersal limitation, and historical factors have commonly been invoked to explain community structure patterns at larger spatiotemporal scales, such as the appearance of late Pleistocene no‐analog communities or latitudinal gradients of species richness in both modern and fossil assemblages. Quantifying the relative influence of these processes on species co‐occurrence patterns is not straightforward. We provide a framework for assessing causes of species associations by combining a null‐model analysis of co‐occurrence with additional analyses of climatic differences and spatial pattern for pairs of pollen taxa that are significantly associated across geographic space. We tested this framework with data on associations among 106 fossil pollen taxa and paleoclimate simulations from eastern North America across the late Quaternary. The number and proportion of significantly associated taxon pairs increased over time, but only 449 of 56 194 taxon pairs were significantly different from random. Within this significant subset of pollen taxa, biotic interactions were rarely the exclusive cause of associations. Instead, climatic or spatial differences among sites were most frequently associated with significant patterns of taxon association. Most taxon pairs that exhibited co‐occurrence patterns indicative of biotic interactions at one time did not exhibit significant associations at other times. Evidence for environmental filtering and dispersal limitation was weakest for aggregated pairs between 16 and 11 kyr BP, suggesting enhanced importance of positive species interactions during this interval. The framework can thus be used to identify species associations that may reflect biotic interactions because these associations are not tied to environmental or spatial differences. Furthermore, temporally repeated analyses of spatial associations can reveal whether such associations persist through time.

Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence

“…The first 'Classic framework' has been recently proposed and applied to examine taxon associations through time for late Quaternary fossil pollen assemblages at relatively large spatial extent and small spatial grain (Blois et al 2014). The first 'Classic framework' has been recently proposed and applied to examine taxon associations through time for late Quaternary fossil pollen assemblages at relatively large spatial extent and small spatial grain (Blois et al 2014).…”

“…Diamond (1975) proposed that species coexistence is regulated by 'community assembly rules' based primarily on species interactions, and inferred these rules from the pattern of species co-occurrence in replicated island assemblages. Since then, the search for community assembly rules has dominated community ecology (Gotelli and Graves 1996, Ovaskainen et al 2010, Boulangeat et al 2012, HilleRisLambers et al 2012, Blois et al 2014), and null model analysis has become a standard tool to search for patterns that may reflect processes of community assembly (Gotelli and Ulrich 2012). Since then, the search for community assembly rules has dominated community ecology (Gotelli and Graves 1996, Ovaskainen et al 2010, Boulangeat et al 2012, HilleRisLambers et al 2012, Blois et al 2014), and null model analysis has become a standard tool to search for patterns that may reflect processes of community assembly (Gotelli and Ulrich 2012).…”

“…1a, b left box), the pattern of spatial overlap in pairwise occurrences (Fig. 1d left box), (Blois et al 2014 Is the environment at different types of sites significant? 1d left box), (Blois et al 2014 Is the environment at different types of sites significant?…”

Biology in the Anthropocene: Challenges and insights from young fossil records

“…Such modern-day corroborations are stimulating novel analyses of living populations in light of geologic history, finding, for example, that present-day endemism is most strongly correlated with the velocity of postglacial climate change rather than with a threshold temperature or particular direction of change (86) and that populations along the margin of a species' range still exhibit low genetic diversity, a legacy of Quaternary expansion (87). Young fossil records alone show (i) just how pervasive changes in species' distributions are likely to become, constituting both local losses and additions, (ii) that novel groupings are most likely to emerge near the edges rather than centers of biogeographic provinces or ranges (88,89), (iii) that species most able to cross former boundaries and those showing strongest population declines are not random draws from their parent community (79,90), and (iv) that species associations suggesting biotic interactions are rare and inconstant (91).…”

Contrasting Patterns of Species Richness and Functional Diversity in Bird Communities of East African Cloud Forest Fragments

“…However, these authors used indirect evidence within the community assembly framework that assumes that overdispersion of phylogenetic and functional traits is a consequence of competitive interactions (Darwin’s competition-relatedness hypothesis, reviewed in Allan et al 2013, Götzenberger et al 2012) [85–86]. However, Cahill et al (2008) [87] found little evidence for this assertion and it is now well known that overdispersion (spatial segregation) might stem from different processes, particularly from filter effects within heterogeneous landscapes [88] and even from dispersal limited neutral community assembly [89]. Here we used a direct way to assess whether any set of competitive strength relationships between species is able to predict observed abundance distributions.…”