class="head no_bottom_margin" id="sec1title">IntroductionThe recent explosion of biodiversity data (spatial and genetic) along with environmental data (regarding climate, terrain, and vegetation), along with novel analytical methods and tools, has enabled an unprecedented capability to model species distributions and assemble those results into broad-scale diversity assessments (e.g., , , , ). Linking spatial ecological patterns to phylogenetic information is more powerful still (, ) given that species assemblages encompassing deeper phylogenetic nodes and more evolutionary history are arguably more diverse than other areas with the same number of species connected via shallower nodes (). Phylogenetic approaches extend diversity measurements from simplistic species counts to measures that also inform evolutionary pattern and process.One of the key measures in spatial phylogenetics is phylogenetic diversity (PD; ). PD is calculated as the sum of branch lengths from a phylogenetic tree connecting the terminal taxa from a specific location, typically to the root of the tree. PD can be interpreted either as the amount of “feature diversity” contained within a region of interest when using a phylogram, i.e., the number of apomorphies present in an area, or as the amount of “evolutionary history” when using a time-calibrated chronogram (, ). Those regions with higher PD than others may be prioritized for conservation (i.e., as containing higher genetic diversity or a greater amount of evolutionary history), although there are obviously other potential criteria, such as threat status, that should be applied in conservation assessments (). PD is typically strongly correlated with species richness, because more terminal taxa in a sample means that a larger portion of the tree is expected to be sampled. developed a compound spatial phylogenetic metric, Relative Phylogenetic Diversity (RPD), designed to examine whether unusually long or unusually short branches are present in a location. PD and RPD measures along with associated randomization tests can help elucidate the evolutionary processes that have generated biotas, which in turn support stronger assessments of conservation priorities.The evolutionary trees used in spatial phylogenetic studies are often not built by the authors performing the study, and tree building is necessarily not afforded the same level of scrutiny as analysis of spatial ecological data, despite the critical importance of trees for rigorous inference. Instead, ecologists have often relied on (1) converting a taxonomic hierarchy directly into a tree (e.g., ), (2) shortcut trees constructed for focal species via Phylomatic software (e.g., , , href="#bib85" rid="bib85" class=" bibr popnode">Wright et al., 2007, href="#bib35" rid="bib35" class=" bibr popnode">Liu et al., 2013) or the Open Tree of Life (OTL; href="#bib28" rid="bib28" class=" bibr popnode">Hinchliff et al., 2015), (3) automated assembly of published sequences such as PhyloGenerator (href="#bib50" rid="bib50" class=" bibr popnode">Pearse and Purvis, 2013), (4) literature-based trees (e.g., href="#bib5" rid="bib5" class=" bibr popnode">Beaulieu et al., 2012), or (5) framework trees vetted by the phylogenetics community (e.g., href="#bib73" rid="bib73" class=" bibr popnode">The Angiosperm Phylogeny Group IV, 2016, href="#bib65" rid="bib65" class=" bibr popnode">Soltis et al., 2011).Despite the relative ease of acquiring such trees, their quality and inherent uncertainties are rarely examined, and the impact of these factors on PD assessment has not been well studied (but see href="#bib52" rid="bib52" class=" bibr popnode">Qian et al., 2015, href="#bib72" rid="bib72" class=" bibr popnode">Swenson, 2009, href="#bib38" rid="bib38" class=" bibr popnode">Molina-Venegas and Roquet, 2013, href="#bib54" rid="bib54" class=" bibr popnode">Rangel et al., 2015, href="#bib75" rid="bib75" class=" bibr popnode">Thornhill et al., 2017). There remains a need to better document how factors involved in constructing phylogenetic trees (e.g., phylogenetic uncertainty and taxon sampling) influence these metrics. Both tree topology and branch lengths are determined by the sampling of taxa and the gene sequences employed, and these factors must be considered when computing and interpreting PD measures. For example, limited taxonomic sampling from a tree will produce longer individual branches than are truly present, whereas limited sampling of genetic data may result in unrepresentative branch lengths. Likewise, the use of phylograms versus chronograms yields branch length differences and therefore different values for PD measures. The phylogenetic depth over which trees are computed will also affect the magnitude of these metrics: older clades have longer branches in a chronogram and therefore contribute to higher estimates of PD than younger clades. Finally, failure to account for tree uncertainty might inflate the confidence in a given result. This last issue is particularly underexplored, but crucial for interpreting PD values.Here we provide a comprehensive examination of how the choice of input phylogenetic trees and inclusion of phylogenetic uncertainty affect the assessment of PD measures, utilizing Florida vascular plants as a case study. To test the importance of input trees, we developed phylogenetic trees for the specific purpose of estimating biodiversity through integration with distribution models. A key rationale for doing so was to determine if a more comprehensive, well-developed, and purpose-built phylogenetic tree would yield different estimates of PD relative to those built from easily available and existing trees obtained, for example, using Phylomatic (href="#bib79" rid="bib79" class=" bibr popnode">Webb and Donoghue, 2005), or by pruning a subtree from a pre-assembled supertree (i.e., the OTL; href="#bib28" rid="bib28" class=" bibr popnode">Hinchliff et al., 2015).We chose Florida as the focus for study because it is home to approximately 4,300 species of native or naturalized vascular plants and a broad range of terrestrial and aquatic habitats (href="#bib87" rid="bib87" class=" bibr popnode">Wunderlin et al., 2017). Furthermore, Florida is part of the North American Coastal Plain biodiversity hotspot (href="#bib46" rid="bib46" class=" bibr popnode">Noss et al., 2015). Florida's flora ranges from temperate, eastern deciduous forest taxa in the north to tropical elements in central and southern Florida (href="#bib42" rid="bib42" class=" bibr popnode">Myers and Ewel, 1990); these unique floristic elements mix at transition zones, leading to novel communities that might be expected to have unusual phylogenetic affinities. At the same time, past climatic changes caused inundation of much of the state, forming ancient shorelines, such as the Lake Wales Ridge (LWR), that still harbor an unusual, highly endemic scrub flora and fauna (href="#bib17" rid="bib17" class=" bibr popnode">Dobson et al., 1997). The southern portion of Florida has a subtropical climate and includes unique ecoregions such as the Everglades, Big Cypress and Miami Ridge, and Pine Rocklands, each with characteristic floristic elements (href="#bib36" rid="bib36" class=" bibr popnode">Long and Lakela, 1971). Florida also supports the third highest concentration of federally sensitive, threatened, and endangered species in the United States (href="#bib30" rid="bib30" class=" bibr popnode">Ihlo et al., 2014), after California and Hawaii (href="#bib17" rid="bib17" class=" bibr popnode">Dobson et al., 1997). Furthermore, one-third of the flora of Florida is now composed of exotic species (either naturalized or invasive), and habitat loss due to human development is mounting (href="#bib24" rid="bib24" class=" bibr popnode">Gordon, 1998). Still, despite the magnitude of ecological and conservation concerns in this region, little is known about the overall geographic patterns of plant diversity in Florida.The present study had both empirical and methodological goals. Our empirical goal was to test hypotheses regarding patterns of Florida biodiversity derived from previous studies of forest types, vertebrates, and butterflies. In particular, work by documented an overall decrease in diversity from north to south in Florida, although this pattern was only assessed qualitatively based on maps of richness from a variety of vertebrates and butterflies. These conversed patterns of diversity, when compared with general latitudinal diversity gradients (href="#bib83" rid="bib83" class=" bibr popnode">Wiens et al., 2009, href="#bib7" rid="bib7" class=" bibr popnode">Buckley et al., 2010), may relate more to the unique transitional zones from temperate to tropical floras in Florida and the underlying climate, soil, and terrain of the region than to temperature. Previous work has noted that transitional areas, such as the Southern Coastal Plain ecoregion and northern peninsular Florida with southern hardwood forests and temperate broad-leaved evergreen forests, harbor particularly high diversity (href="#bib25" rid="bib25" class=" bibr popnode">Greller, 1980). Observed PD patterns in plants should be strongly concordant with previous hypotheses of diversity, but some geographic areas may harbor unexpectedly high areas of PD, such as the Miami Ridge ecoregion and its tropical hammock forest flora (href="#bib42" rid="bib42" class=" bibr popnode">Myers and Ewel, 1990). In such areas, where there may be mixing of floristic elements, we predicted concentrations of significantly overdispersed (e.g., even) lineages (based on PD) and concentrations of unusually long branches (based on RPD). We further predicted concentrations of significant phylogenetic clustering in areas wherein habitat may select for specific community members (also called “habitat filtering”), as well as significant concentrations of shorter-than-expected branches in areas where lineages have potentially diversified in situ, such as the LWR. Finally, we attempted to contextualize these findings from a conservation perspective, given ongoing rapid anthropogenic changes to native landscapes in Florida.Our methodological goals were to explore the effect of the choice of phylogenetic tree on spatial phylogenetic metrics (PD and RPD) and to provide an approach to account more effectively for sources of uncertainty in phylogenetic trees. We generated PD and RPD using a variety of input phylogenetic trees and compared the results using multiple approaches to understand how to interpret differences and uncertainty in these assessments. We expected greater variation among branch lengths across the tree in chronograms than in phylograms, as branches can often be either greatly lengthened or shortened, reflecting constraints of evolutionary time. This difference was predicted to affect the distribution of observed PD, but the impact on significance tests is poorly characterized (but see href="#bib75" rid="bib75" class=" bibr popnode">Thornhill et al., 2017). We also examined how spatial phylogenetic metrics vary between trees pruned from existing supertrees and those inferred from curated analysis where stringent efforts have been made to close gaps in taxon sampling, using a strategic approach for gene sampling and branch length assessments. Finally, we used a Bayesian framework to generate a distribution of trees representing uncertainty in phylogenetic estimates to assess the impacts on PD.
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