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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 different 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 \cite{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) \cite{DAndrea2016}, and competition-structured communities can result in clustering patterns \cite{Mayfield_2010}. In addition, pattern-based evidence of assembly processes can be can be obfuscated by propagule pressure from adjacent communities a framework for multi-scale community ecology \cite{Leibold_2004}, or by fluctuating environmental conditions that favor different species over time \cite{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. For example, \cite{DAndrea2017}  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 et al. 2010). \cite{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 are selected to be complementary, and include metrics 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 (and competitive exclusion) at the least stressful 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 behavior 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.