Research
Spatial Phylogenetics of the North American Flora
Aim: Spatial phylogenetic tools offer an evolutionary and geographical picture of biodiversity by characterizing its multidimensional facets. Although phylogenetic trees are becoming increasingly comprehensive, they still represent only a fraction of the known terminal taxa in any biota. As an attempt to improve sample coverage, missing taxa are frequently imputed into a backbone phylogeny using their taxonomic classification. Here, we evaluated the effects of both incomplete taxon sampling and phylogenetic imputation on the quantitative, spatial patterns of phylodiversity.
Location: Japan.
Time period: Present day.
Major taxa studied: Ferns (Polypodiopsida).
Methods: Using a dataset that is nearly complete for known terminal taxa, we conducted sensitivity analyses for a set of phylodiversity and endemicity analyses. We analysed trees using two approaches: (1) an increasing proportion of tips pruned under different selection scenarios, and (2) those tips phylogenetically imputed back into the tree using taxonomic classification. Tips were pruned at random and also using two methods that mimicked: (1) biased sampling based on species range sizes and (2) biased sampling among major clades on the tree. We assessed predictive performance and bias across spatial phylogenetic metrics and endemicity categorizations.
Results: Across all pruning methods, phylogenies with considerably reduced taxon sampling (particularly with biased sampling) exhibited lower predictive performance in endemicity categorization than imputed trees. However, unbiased incomplete trees had lower mean error for observed values of all phylodiversity metrics, as compared to imputed trees and SES metrics (which were also highly volatile). Estimation biases are lowest in trees with taxa imputed at random below-crown nodes.
Main conclusions: Our work suggests exercising caution when conducting spatial phylogenetic analyses in cases of limited taxon sampling, particularly when such sampling is spatially biased. Careful taxonomic imputation at random below a crown node is an acceptable solution, especially if sampling is spatially biased against geographically range restricted taxa. Tip imputations must be done under a probabilistic framework, using multiple imputations, and regarded as temporary hypotheses instead of a replacement for empirical real data.
Elevational range limits in the Northwestern Himalaya
In this long-term study, I am evaluating a set of eco-evolutionary processes implicated in the establishment of elevational range limits in Betula utilis (Himalayan birch), as well as potential range shifts in response to phenological changes and biotic stressors. By integrating evolutionary ecology and genomic approaches, I aim to understand why and how species distributions are set along elevational gradients. This understanding will provide insights into the potential responses of trees in a montane system that has experienced a 1.2ºC increase in its mean temperature over the last century. My work is geared toward the dominant Himalayan birch, which establishes most treelines in the region and is a key resource for insects, birds, and humans. To achieve this, I integrate vegetation plots, microclimatic and phenological data, and functional traits to test whether biotic interactions primarily determine warmer limits, while abiotic factors set colder limits in two localities in the Northwestern Himalayas. Although both localities have a comparable elevational range, they offer a within-region comparison, as Manali receives summer monsoon rains while Sural is a drier locality set in the Himalayan rain shadow
