Working title: Variation in grassland community trait patterns over climate gradients.


A central goal in ecology is to identify and understand the processes that influence the distributions of species in space and time. Often, these assembly processes are not directly observable over feasible time scales and must instead by inferred through pattern (Levin 1992). One increasingly popular approach is to use the values and abundances of species traits in a community as evidence for the influence of particular assembly processes (Cavender-Bares 2004, Ackerly 2007, Kraft 2008). Trait-based approaches have several advantages over strictly taxonomic approaches in that they are quantitative, easily generalizable, and have explicit ties to ecological strategy and performance (McGill 2006, Violle 2007).

Unfortunately, inferring process from community trait patterns is not always straightforward because different processes can lead to similar patterns, multiple processes can operate simultaneously on multiple traits, and patterns can be affected by exogenous forces. For example: community assembly is sometimes depicted as a balance between environmental filtering, in which species unable to tolerate environmental conditions are filtered out resulting in a clustering of trait values, and niche differentiation, in which competition and limiting similarity result in trait values that are more evenly spaced than expected by chance (Cavender-Bares 2004, Kraft 2007). But recent work has shown that environmentally-filtered communities can result in random or overdispersed trait patterns (e.g. when there is sufficient within-community environmental heterogeneity) (D’Andrea 2016), and competition-structured communities can result in clustering patterns (Mayfield 2010). In addition, pattern-based evidence of assembly processes can be obfuscated by propagule pressure from adjacent communities (Leibold 2004), or by fluctuating environmental conditions that favor different species over time (Chesson 1981, Chesson 1994).

Although it is unlikely that a single pattern-based test will ever provide incontrovertible evidence for niche differentiation, analysis of community trait structure can still shed light on assembly processes if used properly. Different metrics should be used in complementary ways to provide more detailed, and thus more interpretable characterizations of community trait structure. In one recent study, (D’Andrea ) suggest a stepwise analysis pipeline in which potential niches along trait axes are identified using a clustering algorithm, and if clusters are identified, then the fine-scale abundance structure within each cluster is examined for evidence of distance-based competition. Next, tests of community trait structure should be conducted along environmental gradients where they can potentially be tied to mechanistic predictions derived from existing ecological theory (Webb 2010). Lastly, analyses of community trait structure should be used to develop and select hypotheses for experimental testing in the field, rather than be considered as compelling standalone evidence.

Here, we apply a suite of newly developed and classical metrics of community trait structure to a network of twelve grasslands positioned along temperature and precipitation gradients in southern Norway. Our tests include measures of clustering, fine-scale trait abundance structure, and whole-community trait abundance structure. We look for community-level patterns in four traits: leaf area, maximum potential canopy height, seed mass, and specific leaf area (SLA). Based on our knowledge of the system, we predict a gradual shift in importance of competitive interactions at the coldest sites to environmental filtering at the most stressful sites. We expect that competition for light will be the strongest competitive factor at the warmest sites, and thus there will competition-derived clustering in maximum height and leaf area. We expect there to be niche differentiation in SLA at the coldest sites, where there could be a tradeoff between risky fast-growth strategies and the ability to tolerate/avoid early season frosts. Ultimately, our work uses trait-based predictions of community assembly processes to glean information about the relative influence of assembly mechanisms on grassland community composition.



We measured four traits: leaf area (LFA), specific leaf area (SLA), maximum plant height (MXH), and seed mass (SDM). We standardize our traits by taking the logarithm of the trait value and rescaling the logarithms to range between 0 and 11. We applied our tests on each trait individually, as well as on the Euclidean space formed by these traits, which is a four-dimensional hypercube of side 1.