DOI: 10.5061/dryad.0k6djhb03
title: Pitfalls of ignoring trait resolution when drawing conclusions about ecological processes

description: Aim: Understanding how ecological communities are assembled
 remains a grand challenge in ecology with direct implications for charting
 the future of biodiversity. Trait-based methods have emerged as the
 leading approach for quantifying functional community structure
 (convergence, divergence) but their potential for inferring assembly
 processes rests on accurately measuring functional dissimilarity among
 community members. Here, we argue that trait resolution (from
 finest-resolution continuous measurements to coarsest-resolution binary
 categories) remains a critically overlooked methodological variable, even
 though categorical classification is known to mask functional variability
 and inflate functional redundancy among species or individuals.
 Innovation: We present the first detailed predictions of trait
 resolution biases and demonstrate, with simulations, how the distortion of
 signal strength by increasingly coarse-resolution traits can fundamentally
 alter functional structure patterns and the interpretation of causative
 ecological processes (e.g., abiotic filters, biotic interactions). We show
 that coarser trait data impart different impacts on the signals of
 divergence and convergence, implying that the role of biotic interactions
 may be underestimated when using coarser traits. Furthermore, in some
 systems, coarser traits may overestimate the strength of trait
 convergence, leading to erroneous support for abiotic processes as the
 primary drivers of community assembly or change. Main
 conclusions: Inferences of assembly processes must account for
 trait resolution to ensure robust conclusions, especially for broad-scale
 studies of comparative community assembly and biodiversity change. Despite
 recent improvements in the collection and availability of trait data,
 great disparities continue to exist among taxa in the number and
 availability of continuous traits, which are more difficult to acquire for
 large numbers of species than coarse categorial assignments. Based on our
 simulations, we urge the consideration of trait resolution in the design
 and interpretation of community assembly studies and suggest a suite of
 practical solutions to address the pitfalls of trait resolution biases.
description: All data in this project are simulated following previously
 developed approaches to generate species pools and trait values to serve
 as the starting data for simulation iterations. The primary method,
 reported in the main text, follow those of Botta-Dukát & Czúcz,
 2016 (Testing the ability of functional diversity indices to detect trait
 convergence and divergence using individual-based simulation. Methods in
 Ecology and Evolution, 7, 114–126).  An alternate method based largely on
 McPherson et al. 2017 (A simulation tool to scrutinise the behavior of
 functional diversity metrics. Methods in Ecology and Evolution, 9,
 200–206) and Kraft & Ackerly, 2010 (Functional trait and
 phylogenetic tests of community assembly across spatial scales in an
 Amazonian forest. Ecological Monographs, 80, 401–422) was also used, with
 results reported primarily in the Supporting Information of the
 paper.
description: Files here include the initial species pool data (abundance
 matrices and continuous trait values) used for the simulation results
 reported in the main text (9 files; names starting with
 "MainSims_") of the article to enable exact replication of our
 results.  In addition, all simulation data and outputs relevant to
 Supplementary Methods and Results described in Appendix S1 and S2 (4
 files; names starting with "SI_Methods_") of the article are
 also provided, including the initial regional pools and trait values,
 summary output of SES values, Wilcoxon tests, and p-values across all
 simulation iterations. Associated R code for main results and
 supplementary results can be found at https://github.com/Jarzyna-Lab and
 on Zenodo: https://doi.org/10.5281/zenodo.4497961.