Department of Biology

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Kristine Engemann

The drivers of plant diversity: a "big data" approach

Research output: Book/anthology/dissertation/reportPh.D. thesisResearch

In this thesis we use a “big data” approach to describe and explain large-scale patterns of plant diversity. The botanical data used for the six papers come from three different databases covering the New World, North America, and Europe respectively. The data on plant distributions were combined with environmental data on climate, soil, topography, and disturbance to identify the drivers of macroecological plant diversity patterns. Unless otherwise stated, the botanical data used in the papers come from the Botanical Information and Ecology Network.

Paper I describes how we compiled a new plant growth form dataset consisting of 72,533 vascular plant species in 432 families covering the New World. Eight plant growth forms were defined based on woodiness, structure, and root traits, and species names were standardized to the latest accepted scientific name. The data is used in Paper II and IV

In Paper II we assess existing theory linking spatial dominance patterns of plant functional groups to natural and anthropogenic environmental drivers. We found that the functional groups had distinct geographical patterns across the New World strongly linked to climate. Anthropogenic disturbance was also important, and these results highlight that even large-scale vegetation patterns can be influenced by climate change and anthropogenic pressures.

In Paper III we investigate how sampling bias in botanical dataset can influence patterns of species richness and parameter estimation from statistical analyses. We test the effectiveness of seven commonly used sample-sizecorrecting
methods to analyse species richness patterns for Ecuador. We found that species richness was strongly connected to sampling even after correcting for sampling effort. One method, rarefaction, performed considerably better than the other methods. The study emphasise that using big, collected datasets is not without limitations, and we recommend using rarefaction for species richness estimation from such datasets.

Paper IV investigates a well-known macroecological pattern, the latitudinal diversity gradient, for nine vascular plant functional groups. We combine macroecological and community phylogenetic methods to gain insight into
the connection between species richness and phylogenetic community structure. High species richness was connected to both phylogenetic clustering and over-dispersion and varied along climatic and topographic gradients depending on the functional group. We conclude that to explain the diversity patterns for all functional groups, the interaction between functional traits and current ecological limits and historical factors need to be considered.

Paper V assesses the impact of climate change and disturbance on alpha and beta diversity over time for woody forest communities in North America, using a 20 year forest plot dataset from the United States Department of Agriculture Forest Inventory and Analysis program. To assess functional diversity, we combined the plot data with data on four functional traits. Over time, alpha species diversity decreased, but did not result in consistent loss of beta species diversity or taxonomic homogenization. Functional alpha diversity decreased or increased depending on the trait, and functional beta diversity increased over time consistent with forests communities becoming moredistinct. Climate change, forest fires, and forest loss were identified as the most important drivers of forest alpha and beta diversity change over time.

In Paper VI we utilize a new concept in community ecology, dark diversity, to quantify the effect of present and historical environmental factors on species diversity in the European Alps using monitoring data from the Alps
Vegetation Database. We identified grassland and rocky habitats to have the highest dark diversity intensified by high human influence. We conclude that dark diversity is a valuable conservation tool that can guide conservation
planning towards areas with high restoration potential.
Translated title of the contributionDrivkræfterne bag plantediversitet: en "big data" tilgang
Original languageEnglish
PublisherAarhus University, Science and Technology
Number of pages198
Publication statusPublished - 30 Oct 2015

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