Plant dispersal characteristics shape the relationship of diversity with area and isolation
Walentowitz, Anna et al. (2022), Plant dispersal characteristics shape the relationship of diversity with area and isolation, Dryad, Dataset, https://doi.org/10.5061/dryad.r2280gbg0
Aim This study disentangles how plant dispersal syndromes influence the relationship of species richness with area and isolation while also accounting for the human impact on island biodiversity. It builds on the potential of islands at the mesoscale and of similar origin to contribute to the ongoing discussion in island biogeography on what determines species richness and filtering.
Location Denmark, 54 saltwater and brackish water islands in the North and Baltic Sea
Taxon Vascular plants, including pteridophytes (ferns, clubmosses and horsetails)
Methods Generalized linear models (GLMs) and linear regressions are used to analyse how dispersal syndromes influence the relationships of species numbers with island area and isolation, as well as island inhabitation and human density, respectively.
Results Species numbers, as well as mean seed mass and the proportion of zoochore and anemochore species, are positively related to island area while the share of water-dispersed species decreases with increasing area. The slope of the regression line representing the species-area relationship (SAR) was 0.34 and lies within the common range for this relationship. Isolation is weakly related to mean seed mass but has no explaining power for species numbers and the presence of specific dispersal syndrome on the target islands. Species richness and seed mass was positively related to human presence.
Main conclusions Human impact for centuries has not overwritten the strong relationship of species richness with area on the Danish Islands but is affecting the shape of this relationship. Island area constitutes a strong filter for different dispersal syndromes and leads to the assumption that heavier and animal-dispersed seeds are positively related to island area due to the presence of more bird and mammal species. Human-induced loss of isolation caused by ongoing traffic and the connection of landmasses by bridges and ferries may be a reason for the overall low explanatory power of island isolation. Higher species richness on inhabited islands may further be linked to higher habitat diversity in human modified landscapes.
The dataset consists of three parts: (I) Environmental data listing the 54 studied Danish Islands including island characteristics, (II) plant species occurrence data (presence/absence data) on these islands, and (III) trait data of vascular plants that form part of the study.
For all target islands, information on isolation to continental land masses, island area, and the number of island inhabitants were gathered. The exact geographic position and precise boundaries of the 54 target islands were determined in GIS. This allowed us to calculate isolation as the shortest distance to the nearest mainland (species pool; considering the largest islands Saelland, Vendsyssel-Thyto, and Fyn to be part of continental Denmark), and surface area of the individual islands. To account for human alterations we identified inhabited and uninhabited islands and calculated human density (number of island inhabitants per ha). The number of island inhabitants was compiled from Danmarks Statistik (2021) and for smaller islands, we used Google Earth images (© Google Earth 2021) to verify that no houses were present on the island (human density = 0).
Danmarks Statistik (2021). www.statbank.dk/BEF4 (last accessed on 25.10.2021).
Species occurrence data
Species occurrence data was extracted from a comprehensive data set compiled by Erik Wessberg and co-workers since 1979. It became available in 2011 on the homepage of the Danish Botanical Society as a series of commented species lists, one for each of the islands or cluster of islands surveyed in total (Wessberg et al. 2011).
Wessberg, E. et al. (2011). Homepage of the Danish Botanical Society, accessed 10 June 2012, https://botaniskforening.dk/botanik/ofloraer/.
Trait information on seed mass (mg) and dispersal syndromes (zoochory, hydrochory, anemochory, and autochory) were gathered for the 1201 species found on 54 Danish islands from a set of databases: mainly Royal Botanic Gardens Kew (2016), LEDA database (Kleyer et al., 2008) and additionally Ecological Flora of The British Isles (Fitter & Peat, 1994), BiolFlor (Klotz, et al., 2002), BROT trait database for plant species of the Mediterranean Basin (Paula et al., 2009), and D³, The Dispersal and Diaspore Database (Hintze, et al. 2013). Gaps in the data (roughly 100 species) were filled, when possible, by interpolation based on the traits of other species of the same genus, and ferns and clubmosses were assigned the smallest seed mass value in the dataset.
Fitter, A. H. & Peat, H. J. 1994. The Ecological Flora Database. Journal of Ecology 82, 415-425.
Hintze, C., Heydel F, Hoppe C, Cunze S, König A & Tackenberg O. (2013). D³: The Dispersal and Diaspore Database - Baseline data and statistics on seed dispersal. – Perspectives in Plant Ecology and Evolutionary Syst., 15, 180-192.
Kleyer, M., Bekker, R., Knevel, I., Bakker, J., Thompson, K., Sonnenschein, M., … Peco, B. (2008). The LEDA Traitbase: A database of life-history traits of Northwest European flora. Journal of Ecology, 96, 1266-1274
Klotz, S., Kühne, I., & Walter, D. S. (2002). BIOLFLOR - Eine Datenbank zu biologisch-ökologischen Merkmalen der Gefäßpflanzen in Deutschland. – Schriftenreihe für Vegetationskunde 38. Bonn: Bundesamt für Naturschutz.
Paula, S., Arianoutsou, M., Kazanis, D., Tavsanoglu, Ç., Lloret, F., Buhk, C., Ojeda, F., Luna, B., Moreno, J. M., Rodrigo, A., Espelta, J. M., Palacio, S., Fernández-Santos, B., Fernandes, P. M., & Pausas, J.G. (2009). Fire-related traits for plant species of the Mediterranean Basin. Ecology, 90, 1420
Royal Botanic Gardens Kew. (2016). Seed Information Database (SID). Version 7.1. Available from: http://data.kew.org/sid/ (June 2016).
The data file (.xlsx file) can be opened in Excel or Libre Office. It might be easiest to access the data file in R as it can then be used in combination with the provided R code.