Computational protocol: Incorporating phylogenetic information for the definition of floristic districts in hyperdiverse Amazon forests: Implications for conservation

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Protocol publication

[…] We created a phylogenetic tree for 1,687 operational taxonomic units (OTUs) using as backbone the tree R20120829 (Li et al., ) from Phylomatic (Webb & Donoghue, ), which is based on the Angiosperm Phylogeny Group's system (APGIII, ). In order to assign branch lengths, we used the BLADJ algorithm in Phylocom (Webb, Ackerly, & Kembel, ) based on inferred nodes ages (Wikström, Savolainen, & Chase, ). Despite the fact that our regional phylogenetic tree is not fully resolved, recent studies have demonstrated that there is no significant difference between supertrees based on inferred node ages and trees using DNA in order to detect patterns at community or regional scale (Swenson, ). [...] To estimate species diversity at each location/plot, we used Fisher's alpha index which calculates the number of species in a sample relative to the number of individuals therein based on the following formula: S=αln(1+nα) Where S is the number of species, FA is the Fisher's value per assemblage, and N is the number of individuals per plot. We used the Fisher's alpha index (α) based on two basic assumptions: The first one implies that tree species abundances usually follow a log series distribution and secondly the regional species pool is spatially homogeneous. Based on previous evidence, we can argue the first assumption is fulfilled (ter Steege et al., ), while the second assumption is still matter of debate but could be a good approximation for the Ecuadorian Amazon forests (Pitman et al.,). In addition, Fisher's alpha is a scale‐independent estimator that has a good discriminatory power to detect richness under the assumption that the number of species tends to infinity (Schulte et al. ).In order to evaluate the standardized effect size of PD in each local community, we calculated the ses.mpd value for each plot using the independent swap algorithm as the null model (Gotelli, ) implemented in the “picante” package in R (Kembel et al., ). This metric measures the standardized effect of mean pairwise phylogenetic distance between communities. Positive values over a 1.96 confidence interval determine communities were mainly structured by more closely related species (phylogenetic clustering) than expected by chance, and negative values less than −1.96 confidence interval were communities assembled by more distantly related species than expected by chance (overdispersion) (Webb ) [...] Several software packages for the spatial analysis of biodiversity have been developed in the past 10 years (e.g., Biodiverse, GDM) (Ferrier, Manion, Elith, & Richardson, ; Laffan et al., ), radically changing and improving our understanding of the spatial distribution of both taxonomic and phylogenetic diversity. The great majority of these analyses use a moving window approach that predefine a window around a group (e.g., site collection, plots) in a dataset to then calculate appropriate statistics for each group based on the neighborhoods that fall within such window (Laffan et al., ). However, as a caveat one must consider that when there is not complete spatial coverage within a region there is no way to predict values of taxonomic and phylogenetic turnover across space. Therefore, we used a different approach to predict the spatial variation of both taxonomic and phylogenetic beta diversity and abundance‐based metrics for taxonomic and phylogenetic diversity. In order to perform this analysis, we divided the Ecuadorian Amazon into 0.5 degree grid cells (55 × 55 km) which is a spatial scale that allows us to have a balance between accuracy and detail when performing the spatial analysis (Kreft & Jetz, ; Keil et al. ). It has been demonstrated that grain size affects beta diversity estimations and that increasing grain size should produce lower beta diversity in high species richness areas (Lennon et al., ; Keil et al. ). This is mainly determined by the fact that there is an intrinsic relationship between the SAR and species turnover. In other words by increasing the grain size, there is less room for variation in species composition because more of the regional species pool is being accounted for (Lennon et al., ). On the other hand by reducing the grain size, we would increase the number of grid cells containing plots in contrasting habitats (terra firme vs. white sands) therefore overestimating the predicted values of both beta and phylogenetic beta diversity (Keil et al. ). In addition, because finer grain size could lead us to increase the sampling bias introduced by the nonuniform distribution of plots, intermediate grid cell size may avoid underestimation of phylogenetic and taxonomic beta diversity values. Moreover, while there is some level of uncertainty in the interpolation of phylogenetic metrics of unsampled or under sampled areas, we argue this may not affect the patterns we found. In fact the grain size we defined to perform our spatial analysis has been demonstrated to be appropriate to not under or overestimate predicted values of dissimilarity. In addition, because broader or finer grain size could lead us to increase the sampling bias introduced by the nonuniform distribution of plots, intermediate grid cell size may avoid underestimation of phylogenetic and taxonomic beta diversity values (Kreft & Jetz, , Keil et al. ).In order to avoid these bias and because our data are not presence–absence records of each grid cell we calculated the mean values of both PBD and TBD for each plot with respect any other in the plot network. Then we used these average values to perform interpolation across the region. A Loess spatial regression model was used to predict both taxonomic and phylogenetic turnover. To obtain the most accurate fit, we used default parameters for our Loess regression: a 0.75 span was used to find the best smoothing average, and a degree 2 polynomial was set to reduce variance. We used this method due to its inherent flexibility compared with other interpolation techniques. Because our data are irregularly distributed, Loess interpolation allows us to fit at the local scale individual values of taxonomic and phylogenetic diversity across space using the average of each of these values at location x with grid cells in the neighborhood of x. In order to perform this, the Loess method sets the size of the neighborhood with respect to location x with the parameter α. All the analyses were performed with the packages picante (Kembel et al., ), vegan (Oksanen et al.,) and using custom functions on the R platform. […]

Pipeline specifications

Software tools Phylomatic, Phylocom, Picante, Biodiverse
Application Phylogenetics