**4. Discussion**

Establishing a robust understanding of phylogenetic relationships in Dendrobatidae is crucial for addressing questions about dendrobatid evolution and directing conservation e fforts towards areas of diversity in maximum need. While a handful of studies have generated phylogenies of the family in recent years, none has used genomic data in their analyses. We used maximum likelihood and coalescent methods in conjunction with a large matrix of genomic markers to construct the first phylogenomic tree of Dendrobatidae. The usage of ML and coalescent methods in conjunction with genome-scale UCE data is intended to bring the status of dendrobatid phylogenetics more in line with current studies in herpetological phylogenetics, which frequently make use of these techniques [53–57]. Concerns with parsimony, which was used to construct the most recent large-scale phylogenetic analysis of dendrobatids [41], as a statistically-consistent phylogenetic method [79–81], and the presence of potential incomplete lineage sorting among large numbers of genes [32,58], also compelled us to use these techniques. Additionally, we estimated divergence times using a Bayesian method, which is currently the most widely-used and accepted type for divergence time estimation [82]. We found that most relationships among dendrobatid genera are largely congruous with the results of past studies, with some exceptions (see below). Our estimated divergence times are very similar to those estimated by Santos et al. (2009), which is to be expected since we used secondary calibrations taken from their study (Figure S3) [44]. However, our divergence time estimation involves di fferent methods (BEAST 2 [71] rather than MULTIDIVTIME [83]) and considerably more genetic data (92,742 characters vs 2380 characters in Santos et al. [44]). Additionally, we recognize that since we used a secondary calibration taken from Santos et al.'s study due to the lack of poison frog fossils, our divergence time estimates may be biased towards younger node dates [84].

Much of our phylogeny is consistent with past phylogenies from mitochondrial and nuclear datasets, but with some key di fferences. Our analyses place Hyloxalinae sister to Colostethinae rather than Dendrobatinae [22,23], contrary to more recent studies on the family [21,41,44,45]. We also find support for placing the genus *Dendrobates* sister to *Oophaga* [21–23,35,44], in contrast to previous placements of *Dendrobates* as sister to *Adelphobates* [29,41,45], or even the rest of Dendrobatinae [43]. Lastly, we find strong support for placing *Minyobates* sister to *Adelphobates*. This problematic taxon has previously been placed anywhere from sister to *Excidobates* [29], to the rest of Dendrobatinae (excluding *Phyllobates*) [21,41]. Our conclusion for the placement of *Minyobates* corroborates placements recovered by more recent analyses that utilized maximum likelihood and Bayesian methods [35,44,45] rather than parsimony [21,41].

Maximizing e fforts to conserve poison frogs (and other species) requires identifying both vulnerable lineages and geographical areas. A crucial step in this process is clarifying the evolutionary relationships of the taxa of interest, followed by the collection of basic population, distributional, and life history data for each taxon. Like many tropical amphibians, poison frogs face threats including habitat destruction [15,16] and smuggling for the pet trade [17,18,43]. Despite being one of the better-studied groups of frogs, a surprising number of poison frog species evaluated by the IUCN were classified as "data deficient" (37.5%, 107 of 285 species), hampering basic aspects of their conservation. Many of these data-deficient taxa belong to understudied genera with mostly cryptic coloration. In particular, the four genera *Hyloxalus, Colostethus, Allobates,* and *Anomaloglossus* contain a majority (70.1%, 75 of 107) of the "data deficient" taxa (Figure 2). In addition, though comprised of only a few species, little is known of the genera *Paruwrobates* and *Ectopoglossus*, where 3 (of 3) and 7 (of 8) of the contained species are classified as "data deficient", respectively (Table S3). Further, in *Ectopoglossus*, the only species not classified as "data deficient" is classified as "endangered," increasing the urgency for collecting basic life-history data in this group.

**Figure 2.** An evolutionary perspective of the Red List status of Aromobatidae and Dendrobatidae. Each species is characterized by a ribbon that is connected to its current Red List status (bottom). The numerical values below each genus depict the number of species with the associated Red List status. Bars on the outer ring depict the current population status of the corresponding genus, either: decreasing, stable or unknown (black, dark grey, or light grey respectively). A tree representing the evolutionary relationships of the genera surrounds the main diagram. Relationships for taxa not included in our study (Dendrobatidae: *Paruwrobates, Ectopoglossus, Leucostethus;* Aromobatidae: *Aromobates, Anomaloglossus, Mannophryne, Rheobates*) are reproduced from Grant et al. 2017 [41].

Roughly a quarter (22.1%, 63 of 285) of poison frog species were classified as "critically endangered" or "endangered" (18 and 45 species, respectively). Many of these at-risk taxa are concentrated in a few genera, most notably the clade that contains the two Aromobatid genera endemic to the northern Andes, *Aromobates* and *Mannophryne* (containing six "critically endangered" and 14 "endangered" species). The genera *Allobates*, *Hyloxalus*, and *Ameerega* contain a majority of the remaining at-risk species, though the proportions of at-risk species are similar to those of other genera. Additional unique evolutionary lineages of concern, though represented by only a few species, are the genera *Phyllobates*, *Excidobates* and *Minyobates*, where most surveyed taxa are at-risk (Table S3).

Furthermore, some geographic zones possess much higher at-risk diversity than others ("critically endangered" and "endangered" in Figure 3). The northern Andean countries possess both the highest species diversity and the highest diversity of at-risk species, especially Colombia, Peru, and Venezuela. (Figure 3). However, only Venezuela's proportions of 'at-risk' species are much greater than the country average, with 25.5% "endangered" and 16.4% "critically endangered" (average of 11.3% and 5.1%,

respectively; Table S4). In contrast, countries with mostly lower elevation species (e.g., Brazil or Bolivia) seem to be well below the average of at-risk species (Table S4).

**Figure 3.** A geographic perspective of the Red List status of Aromobatidae and Dendrobatidae. The species composition of each country is characterized by ribbons connected to the current Red List status for each species (bottom). The numerical values below each country name depict the number of species with the associated Red List status. Bars on the outer ring depict the corresponding color of that country on the main map (top right). Maps to the left display Red List groups, where the intensity of color depicts larger number of species. If a species exists in more than one country, it was represented in each country of occurrence in the plot.

The circular infographics presented here are intended to represent a tractable way to visualize relationships between IUCN Red List categorizations, phylogenetic relationships, and geographic distributions of large numbers of related taxa (here two sister families). It is important to acknowledge that IUCN assessments are updated, on average, every decade. Thus, assessments and population trends represent a coarse temporal grain. Further, the spatial categorization by countries is overly simplistic and does not accurately reflect most species' actual ranges, as environment transcends political boundaries. However, given that environmental policy often occurs at the country level, this remains a practical spatial scope for summarizing assessment data. Lastly, our visualizations are not intended to replace more detailed quantitative assessments e.g., [85,86], but provide a novel perspective of the widely available IUCN data.

Here, we present the first broad-scale phylogenomic reconstruction of Dendrobatidae, furthering the continual study of poison frog systematics. In the future, we hope to improve taxon sampling, as here we were unable to acquire genetic samples for the newly erected genera *Leucostethus, Paruwrobates* and *Ectopoglossus* [41], and so their placement in the dendrobatid phylogeny is still predicated on Grant et al.'s (2017) analysis [41]. We were also unable to corroborate the paraphyly of *Colostethus* on account of missing genetic data for *C. ruthveni*. A future phylogenomic reconstruction of this group would benefit from inclusion of these taxa to ensure representation of all groups within Dendrobatidae and its sister family Aromobatidae. Lastly, we hope we have inspired researchers, field biologists, and conservation biologists to help address the highlighted conservation issues in these wonderful amphibians.

**Supplementary Materials:** Raw reads have been deposited in the NCBI Sequence Read Archive under project number PRJNA547821. The following are available online at http://www.mdpi.com/1424-2818/11/8/126/s1, Figure S1: Species-level phylogeny of all 61 samples included in the study, constructed using IQ-TREE (maximum likelihood), Figure S2: Comparison of (a) maximum likelihood tree made using IQ-TREE with the restricted dataset, (b) species tree made using ASTRAL-III with the comprehensive dataset, and (c) species made using ASTRAL-III with the restricted dataset, Figure S3: Species-level chronogram calibrated with BEAST 2 showing node numbers and uncertainty in divergence time estimation, Table S1: List of dendrobatoid samples included in our phylogenomic analyses and associated locality data, Table S2: Summary of divergence time estimation with BEAST 2, Table S3: IUCN Red List categories of dendrobatoids by genus, Table S4: IUCN Red List categories of dendrobatoids by country.

**Author Contributions:** Conceptualization: W.X.G. and J.L.B. Methodology: W.X.G. and J.L.B. Validation: W.X.G., M.R.M., and J.L.B. Formal Analysis: W.X.G. and M.R.M. Investigation: J.L.B. Resources: J.L.B., K.S. Data Curation: W.X.G. and M.R.M. Writing—Original Draft Preparation: W.X.G., M.R.M., and J.L.B. Writing – Review and Editing: W.X.G., M.R.M., J.L.B., and K.S. Author Contribution List-Maker: M.R.M. Food Stylist: W.X.G. Visualization: W.X.G. and J.L.B. Supervision: J.L.B. Project Administration: J.L.B. Funding Acquisition: J.L.B.

**Funding:** This research was funded by start-up funding to J.L.B. from Southern Illinois University Carbondale.

**Acknowledgments:** We are grateful for the continued support of Southern Illinois University, East Carolina University, Servicio Nacional Forestal y de Fauna Silvestre (Peru), and Centro de Ornitología y Biodiversidad (CORBIDI). We thank Ivan Prates and Miguel T. Rodrigues for generously providing several tissue samples. We are thankful to Peter Larson, Ryan Campbell and Anne Yoder for providing inspiration for Figures 2 and 3. M.R.M. and W.X.G. are grateful for support from the Students United in Preserving, Exploring, and Researching Biodiversity (SUPERB) fellowship, funded by the US National Science Foundation (NSF).

**Conflicts of Interest:** The authors declare no conflict of interest.
