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Briefly, Morrone detailed biogeographic regions, sub regions, and provinces of Latin America and the Caribbean, pointing out characteric taxa and their predominant vegetation. Even though those areas are defined by contemporary species, they are a useful methodological tool to avoid the use of randomly defined areas.


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We chose the following provinces based on the georeferenced distribution of the species included Fig. To infer the processes which modeled the current distribution of the species of the L. This method is based on Dispersal-Extinction-Cladogenesis DEC models which require information about a single ultrametric-dated phylogeny and distributional information of extant species.

The values were: 0. A third analysis Matrix III was set to apply dispersal cost plus geographic barriers cost, considering probability values from the second analysis plus values assigned to geographic barriers in this case, the only clear geographical barrier was the Andes mountain range. When a geographic barrier was present, a probability value of close to 0 was assigned 0. The analyses showed two topologies: one recovered under MP and the other under BI. The topologies found are congruent in the main groups SPR distance 0. The Liolaemus alticolor-bibronii group was recovered in both MP and BI analyses, with different composition and with high support Figs.

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In the BI tree, a clade formed by the L. On the other hand, the MP analysis recovered the L. Within the Liolaemus alticolor-bibronii group, two main groups were recovered: the L. The latter group contains two clades: 1 a clade formed by species distributed in central-southern Argentina referred to as L. As we mentioned above, Liolaemus paulinae, previously considered member of the L.

The same topology was recovered for L. The two topologies resulted in two time calibrated trees with similar times of divergence. Our results suggest that, according to the BI topology, the Liolaemus gravenhorsti group sister group of the L. This clade is formed by species distributed in Argentina and Chile. We estimated the divergence time of the Liolaemus alticolor-bibronii group to Middle Miocene around Considering the clades of the Liolaemus alticolor-bibronii group, we estimated that the L.

This clade splits into two clades: the L. The latter originated two clades, one distributed in central-southern Argentina, which diverged 6. The Liolaemus walkeri clade dates back to the Late Miocene, around Liolaemus walkeri group includes a clade distributed exclusively in Peru with an origin estimated at While the topology recovered under MP L.

We compared six different scenarios in DEC, for each topology. The best Likelihood scores were obtained with the unconstrained adjacency matrix Table 4. Based on this, we show the results recovered with the unconstrained adjacency matrix for both topologies BI and MP.

We obtained the same biogeographic scenarios for all three dispersion cost matrices used Figs. Alternative scenarios obtained with the constrained adjacency matrix can be found in File S3. Divergence times, ancestral range and major probabilities of the main groups recovered are shown in Table 5. The ancestral area of the Liolaemus alticolor-bibronii and the L. Our results suggest that the ancestral area of the Liolaemus alticolor-bibronii group is formed by the areas BL Puna and Patagonia Central; p: 0.

With lower probabilities the analyses shows the following ancestral areas for the L.

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The Liolaemus gracilis group has its origins in L Patagonia Central; p: 0. A dispersion event inside the L. One occupied the range BC Puna and Yungas, p: 0. In the topology recovered under MP, the L. We recovered the Liolaemus gravenhorsti group as sister taxon of the Liolaemus alticolor-bibronii group.

Relationships within the Liolaemus alticolor-bibronii group were previously studied by Quinteros and Aguilar et al. In his research, Quinteros includes 33 terminal taxa which belong to the L.

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On the other hand, the molecular-based phylogeny by Aguilar et al. Aguilar et al. The relationships found in the present study are similar to those detected by Morando et al. The topology recovered by Quinteros showed low support and low resolution in the clades inside the Liolaemus alticolor-bibronii group. Nevertheless, we agree with Quinteros, who detected the L. Previous studies found both groups outside of the L. Schulte et al.

Our BI tree is congruent with the latter topologies. Reviewing the literature, the most similar topology is described by Aguilar et al. The Liolaemus walkeri group recovered here shows the same composition as the Peruvian clade of Aguilar et al. As we briefly mentioned above, the main differences between our parsimony and BI trees is the location of the L. Using parsimony, this group is recovered inside the L. The topology recovered under BI is more congruent regarding species distribution, since the species of the L.

The species of the L. The Liolaemus gracilis group has not been previously proposed as such. Again, the BI tree shows more biogeographical congruence than the parsimony tree. Nevertheless, the two main clades L. In addition, the BI tree recovers L. The greater congruence between the BI and previous studies, compared to the parsimony tree, can be explained by similarities of the dataset, given that they share the same gene sequences Morando et al.

The Liolaemus lemniscatus group was recovered inside the Liolaemus alticolor-bibronii group nested in the L. In previous molecular-based phylogenies, members of the L. In our analyses under BI excluding morphology data , the L. Unfortunately, we cannot include species members of the L. This will be are search avenue to further investigate in future studies. We included 15 populations of uncertain taxonomic status. These populations are currently assigned to known species members of the L.

According to our study BI and Parsimony trees , most of them can be considered candidate species. Liolaemus sp4 and L. In both trees, BI and Parsimony, L. This result, in addition to the varying character states, converts these terminal taxa into candidate species. Liolaemus bibronii was characterized as a species complex by Morando et al. Over the last few years, three species previously confused with L. In this study, we include L. Some of these terminal taxa were included in the phylogeographic study of Morando et al.

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Historical Biogeography: Fundamentals and Applications

Our results are consistent with those of Morando et al. Liolaemus robertmertensi was described by Hellmich , but since its description no further taxonomic studies related to it. In this study, we included three populations attributed to L.

Historical Biogeography

Liolaemus sp12 has previously been included in molecular-based phylogenies Schulte et al. In both analyses Bayesian and Parsimony performed here, these terminal taxa are not closely related to L.


  1. Historical Biogeography: An Introduction?
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  4. Liolaemus sp14 corresponds to a population distributed near L. This terminal taxon was discovered only recently and is currently under description. S4 and S5. Nevertheless, similar main groups are recovered. Under MP the Liolaemus alticolor-bibronii group is paraphyletic and includes members of the L.

    BI topologies are similar to those detected under MP. The species members of the L. Also, main clades of the L. Many authors mention homoplastic morphology Alvarez et al. Jarvis et al. Here, the inclusion of morphological data increases clade resolution, and support the monophyly of some groups.

    The study of continuous characters is limited to MP analysis, as it can only be performed by TNT software. Consequently, we tested which role continuous characters play in the recovered topology by excluding them. Without continuous characters the Liolaemus alticolor-bibronii group appears polyphiletyc.

    The topology recovers the main groups under MP and BI, but their composition include species members of the L. In the present study, continuous characters employed in morphological analyses assisted to recover the monophyly of the L. When comparing the results obtained under BI and MP without continuous characters , we find that continuous characters as well as the optimality criterion influence the different topologies. Implementation of DEC models in Lagrange allows biogeographic analyses to infer biogeographic events under different probabilities, taking into account the distances among areas and the presence of geographic barriers.

    In this study, we applied two different adjacency matrices and three area-dispersal matrices, recovering different general patterns of diversification and species distribution in the Liolaemus alticolor-bibronii group, which allow inference of its evolutionary history. They concluded that results were most influenced by adjacency matrices, while the area—dispersal matrix and dispersal probabilities had negligible effects. In this study, we applied three different matrices with dispersal probabilities. Using the unconstrained adjacency matrix, the ancestral reconstructions were very congruent among each other; we found only one difference between the three matrices applied.

    In this study, we tested both, an unconstrained as well as a constrained adjacency matrix. The number of assigned ambiguous ancestral areas possible in each node was higher in the constrained than in unconstrained analyses Table 4. Furthermore, the results obtained using the unconstrained matrix display higher Likelihood scores than the constrained matrix Table 4. The use of two different phylogenetic topologies to estimate the time of divergences and reconstruction of ancestral distribution returned similar results see Table 5.

    Despite the differences recovered in the composition of main groups, their divergence times were congruent. The exceptions were the Liolaemus robertmertensi and L. The ancestral area reconstruction also shows some differences between the topologies, but overall most results display great congruence. The main difference is in the ancestral area of the L. The fossil-calibrated dating analysis indicates that the initial divergence within Eulaemus occurred approximately Though biogeography may be simply defined--the study of the geographic distributions of organisms--the subject itself is extraordinarily complex, involving a range of scientific disciplines and a bewildering diversity of approaches.

    For convenience, biogeographers have recognized two research traditions: ecological biogeography and historical biogeography. This book makes sense of the profound revolution that historical biogeography has undergone in the last two decades, and of the resulting confusion over its foundations, basic concepts, methods, and relationships to other disciplines of comparative biology.

    Biogeography - Geography - Oxford Bibliographies

    Using case studies, the authors explain and illustrate the fundamentals and the most frequently used methods of this discipline. They show the reader how to tell when a historical biogeographic approach is called for, how to decide what kind of data to collect, how to choose the best method for the problem at hand, how to perform the necessary calculations, how to choose and apply a computer program, and how to interpret results.

    I Methods in Historical Biogeography.