Plant evolutionary ecology in mountain regions in space and time

Parisod, Christian; Lavergne, Sébastien; Sun, Hang; Kadereit, Joachim W.

doi:10.1007/s00035-022-00279-w

Cited by 2 publications

(2 citation statements)

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“…Given the broad interest in understanding the evolution of plants in mountainous areas (Antonelli et al, 2018;Parisod et al, 2022;Perrigo et al, 2020;Rahbek et al, 2019), classifying species occurrences as being above ('alpine') or below ('non-alpine') the climatic treeline offers a valuable opportunity to test the accuracy of automated tip-scoring and infer the effects of tip-scoring-errors on reconstructing the evolution of species biome occupancy.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, mountains are the epitome of topographic heterogeneity, resulting in the associated problem that the spatial resolution of actual climate types is much more finely grained than available global weather and climate data (Körner & Hiltbrunner, 2021; Scherrer & Körner, 2010). Given the broad interest in understanding the evolution of plants in mountainous areas (Antonelli et al., 2018; Parisod et al., 2022; Perrigo et al., 2020; Rahbek et al., 2019), classifying species occurrences as being above (‘alpine’) or below (‘non‐alpine’) the climatic treeline offers a valuable opportunity to test the accuracy of automated tip‐scoring and infer the effects of tip‐scoring‐errors on reconstructing the evolution of species biome occupancy.…”

Section: Introductionmentioning

confidence: 99%

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Avoiding impacts of phylogenetic tip‐state‐errors on dispersal and extirpation rates in alpine plant biogeography

Bätscher,

de Vos

2024

Journal of Biogeography

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AimMany biogeographic analyses require some form of automated state assignment to tips of phylogenetic trees, reflecting a species presence or absence in a particular area, e.g., a biome. As datasets get exponentially larger, such procedures may increasingly induce errors (here called tip‐state‐error), but the specific algorithmic cause and consequence on downstream estimation of dispersal and extinction rates remains poorly known. We aim to improve automated tip‐scoring methods in the context of the alpine biome by leveraging elevation information. We document the profound effect of tip‐state‐errors on Dispersal‐Extirpation‐Cladogenesis (DEC) models.LocationThe European Alpine Arc.TaxonThree thousand three hundred seventeen vascular plant species, emphasizing six focal genera: Campanula, Carex, Festuca, Ranunculus, Saxifraga, and Viola.MethodsWe use GBIF data to classify whether species occur above the upper climatic treeline using a newly developed algorithm ElevDistr or a gridded landscape model of thermal belts, under various filtering thresholds. We compared classification performance using the Flora Alpina as validation data. To determine if tip‐state‐error biases the dispersal and extirpation rate estimation, we fit DEC models for selected clades using tip‐states from different classification models.ResultsElevDistr is less error prone than other approaches. Filtering thresholds lower the false positive rate but increase the false negative rate. Inflated false positive rates bias the dispersal rate estimation upward, while inflated false negative rates lead to upward bias in extirpation rate estimation.Main ConclusionsEven moderate tip‐state‐error may lead to profound systematic bias in dispersal and extinction rate estimation if an unbalanced ratio between false positive and false negative rates occurs. Therefore, careful validation is imperative, though ElevDistr alleviates this problem in the context of the alpine environment. Overall, our results suggest contrasting rates of alpine biome shifts across the studied genera and have major implications for studies addressing the likelihood of niche evolution versus geographic dispersal.

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Section: Introductionmentioning

confidence: 99%