DOI: 10.5061/dryad.v6wwpzgw8
title: A unifying framework for analyzing temporal changes in functional and taxonomic diversity along disturbance gradients

description: Frameworks exclusively considering functional diversity are gaining
 popularity, as they complement and extend the information provided by
 taxonomic diversity metrics, particularly in response to disturbance.
 Taxonomic diversity should be included in functional diversity frameworks
 to uncover the functional mechanisms causing species loss following
 disturbance events. We present and test a predictive framework that
 considers temporal functional and taxonomic diversity responses along
 disturbance gradients. Our proposed framework allows us to test different
 multidimensional metrics of taxonomic diversity that can be directly
 compared to calculated multidimensional functional diversity metrics. It
 builds on existing functional diversity-disturbance frameworks both by
 using a gradient approach and by jointly considering taxonomic and
 functional diversity. We used previously unpublished stream insect
 community data collected prior to, and for the two years following, an
 extreme flood event that occurred in 2013. Using 14 northern Colorado
 mountain streams, we tested our framework and determined that taxonomic
 diversity metrics calculated using multidimensional methods resulted in
 concordance between taxonomic and functional diversity responses. By
 considering functional and taxonomic diversity together and using a
 gradient approach, we were able to identify some of the mechanisms driving
 species losses following this extreme disturbance event.
description: We sampled 14 streams across three drainages in summer 2011 prior
 to the flood for another study (Harrington et al. 2016) and resampled them
 again post-flood in the summers of 2014 and 2015 to assess species
 persistence to this extreme event (Poff et al. 2018). Each site received a
 normalized channel disturbance score, hereafter referred to as channel
 disturbance. Values across the 14 sites ranged from 0.2 to 1.0 (See Poff
 et al. 2018 for more complete methods). In the current
 work, we use previously unreported community data to test our predictions.
 The stream insect community was sampled using a 0.01 m2 Hess sampler, a
 standard method for quantitatively sampling stream insect density (Hauer
 and Lamberti 2006). In each year, we collected five substrate samples in
 run/riffle habitat by agitating the substrate to 10 cm depth. Invertebrate
 samples were stored in 100% ethanol prior to being cleaned of debris to
 allow invertebrate identification and enumeration in the lab using a
 dissecting microscope to the lowest practical taxonomic level. All taxa
 were identified to genus except for the early instars of chloroperlid
 stoneflies, simuliid blackflies, elmid beetles, and dytiscid beetles,
 which were all identified to family and assigned traits at the
 family-level.
description: We calculated taxonomic richness, Pielou’s evenness, taxonomic
 dispersion, distance between non-metric multidimensional scaling points
 (NMDS distance), and taxonomic distance for each site before and after the
 disturbance using the ‘vegan’ and ‘FD’ packages in R (Laliberté and
 Legendre 2010, Laliberté et al. 2014, Oksanen et al. 2017). We selected
 Pielou’s evenness to compare to functional dispersion because both are
 sensitive to changes in richness (Mason et al. 2012) and contrasted it
 with taxonomic dispersion to determine which taxonomic dispersion metric
 best correlated with functional dispersion. Taxonomic dispersion was
 calculated by using the ‘FD’ package to calculate dispersion based on a
 taxonomic dissimilarity matrix that was created using a Linnean
 classification (Clarke and Warwick 1998, 2001). Taxonomic distance was
 calculated on the same taxonomic dissimilarity matrix based on the methods
 from Boersma et al. (2016). Finally, non-metric dimensional scaling (NMDS)
 distance was calculated based on the Euclidean distance between points for
 each site at each sampling time point in NMDS space, constructed using
 Bray-Curtis dissimilarities (Oksanen et al. 2017). Abundance changes were
 calculated as the change in overall density of individuals at each site
 between sampling years. We calculated functional
 distance between communities from different years at the same site using
 presence/absence data (Boersma et al. 2016). Using the FD package
 (Laliberté and Legendre 2010, Laliberté et al. 2014) in R v. 3.4.4 (R
 Core Team 2018), we calculated abundance-weighted functional richness and
 functional dispersion for community data in different years at each site
 using a Gower distance matrix (Gower 1971). Functional richness was the
 number of unique functional groups, because all of our traits were
 categorical. We used the same functional space to calculate functional
 diversity metrics across sites because, given the spatial extent of our
 study area, we assume all of our sites have a similar regional species
 pool (Harrington et al. 2016).