DOI: 10.5061/dryad.r2280gbg0
title: Plant dispersal characteristics shape the relationship of diversity with area and isolation

description: 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.
description: 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. <strong>Environmental
 data</strong> 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). <strong>Species occurrence
 data</strong> 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/. <strong>Trait data</strong> 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).
description: 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.