An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae
An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae
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DOI：
10.1111/j.1365-2699.2010.02432.x
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发表时间：
2011-03-01
影响因子：
3.9
通讯作者：
Sanmartin, Isabel
中科院分区：
文献类型：
--
作者：
Buerki, Sven;Forest, Felix;Sanmartin, Isabel
最近发展的历史生物地理学参数方法使研究人员能够将时间和古地理信息整合到生物地理情景的重建中，从而克服了基于简约的方法的已知偏见。在这里，我们比较了参数方法，分散-灭绝-枝发生（DEC）和基于简约的方法，分散-差异分析（DIVA），后者不包括分支长度，但通过贝叶斯经验方法（Bayes-DIVA）考虑了系统发育的不确定性。我们分析了各种方法的优点和局限性，并以世界性植物家族皂荚科为例进行了研究。全球位置。方法利用贝叶斯推理方法，对具有多科属多样性的大数据集进行系统发育关系估计。谱系分化时间通过系统发育后验分布的树木样本的惩罚似然来估计，以解释生物地理重建中的年代不确定性。我们比较了Bayes-DIVA和两种不同的DEC模型之间的生物地理情景：一种没有地质约束，另一种采用分层古地理模型，其中扩散率根据四个时间片的区域连通性进行比例调整，反映了过去1.1亿年来大陆结构的变化。结果尽管潜在的生物地理模型存在差异，但Bayes-DIVA和DEC推断的生物地理情景相似。主要的差异是：(1)分散事件的时间——在Bayes-DIVA中，这有时与古地理信息相冲突；(2)由12推断的最终分散事件的频率较低，发散时间估计的不确定性既影响了祖先范围的推断，也影响了将一个区域分配给一个节点的决定性。通过考虑谱系分化时间，DEC方法的重建结果更加准确，与古地理证据一致。相比之下，Bayes-DIVA在明确重建祖先范围方面表现出最高的决定性，这可能反映了它整合系统发育不确定性的能力。在确定DEC的古地理模式时应注意，因为可能高估灭绝事件的频率，或由于该模式施加的扩散限制而推断出超出现存物种范围的祖先范围。本文提出的大跨度时空模型可用于测试植物的大尺度生物地理格局。
Aim Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersal-extinction-cladogenesis (DEC), against a parsimony-based method, dispersal-vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location World-wide.Methods Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events - which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.