Data from: Water availability and evolutionary similarity shape the global distribution of ferns with chlorophyllous spores

Mellado, Daniela 1 ; Mellado-Mansilla, Daniela1; Weigelt, Patrick2; Kessler, Michael3; Craven, Dylan4; Zotz, Gerhard5; Kreft, Holger1

Published Sep 18, 2025 on Dryad. https://doi.org/10.5061/dryad.k6djh9wm2

Data files

Sep 18, 2025 version files 102.11 KB

mpds_values_greensporedferns.rds

13.74 KB
README.md

7.63 KB
richness_greensporedferns.rds

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Abstract

About 14% of all fern species have chlorophyllous spores, which lack dormancy, have thin walls, and have a shorter viability (only a few days in some species). These spores should have limited dispersal distances and be more susceptible to harsher climatic conditions, raising questions about the evolutionary and ecological significance of this trait. Here, we assemble the global distribution of chlorophyllous-spored ferns and assess the underlying environmental and evolutionary factors. We first evaluated the environmental predictors of the proportional representation of 1387 chlorophyllous-spored species (CSS) across 577 geographical regions using generalized linear mixed models. We then estimated the phylogenetic signal of spore type and assessed the relative importance of environmental factors in the phylogenetic structure of fern assemblages. Species richness of CSS peaked in the tropics, while their proportional representation was highest in temperate and island floras. The proportion of CSS was positively associated with water availability and less seasonal climates. Spore type was strongly conserved phylogenetically, and CSS assemblages were phylogenetically clustered towards higher latitudes. Our study provides strong evidence that chlorophyllous spores do not limit the geographical distribution of fern species and that their latitudinal distribution patterns can be explained by a combination of environmental and evolutionary factors.

Dataset DOI: 10.5061/dryad.k6djh9wm2

Description of the data and file structure

Data used to model the global distribution and phylogenetic relationships of ferns with chlorophyllous spores. The sources of information to extract the species lists per region are described in the methods. You can also find a detailed list of the spore type per species at https://doi.org/10.5061/dryad.bnzs7h4cr

The R scripts used in this study are publicly accessible in the following repository: https://github.com/DMelladoMansilla/Biogeography-of-green-spored-ferns

Files and variables

File: mpds_values_greensporedferns.rds

Description: Contains the values of the mean phylogenetic distance (MPD) per geographic entity. You can use the column "entity_ID" to join this dataset with "richness_greensporedferns.rds" and obtain the environmental variables used in the generalized linear models. Columns descriptions:

entity_ID: the code number of the geographic units (polygons) used in this study according to the R package "GIFT" (Denelle et al. 2023; https://doi.org/10.1111/2041-210X.14213).

mpd.obs.z: The value of the "Mean Pairwise Distance Observed, standardized effect size (z)" of the species assemblages per each entity_ID. This phylogenetic metric was calculated using the R package picante using the function ses.mpd.

group: We grouped ferns of each entity_ID in three categories: Epiphytes, all epiphytic ferns with chlorophyllous spores; Terrestrials, all terrestrial ferns with chlorophyllous spores; and overall, all ferns (terrestrials + epiphytic) with chlorophyllous spores.

File: richness_greensporedferns.rds

Description: Contains the species richness (and proportions) used to perform the generalized linear mixed models and the comparison of richness between islands and mainland regions. It also contains the information on environmental variables extracted for each geographic entity (see methods for sources). The geographic units (entity_ID) correspond to those used by GIFT, and you can match the identities with other information using the R package GIFT (Denelle et al. 2023; https://doi.org/10.1111/2041-210X.14213). Columns descriptions:

entity_ID: the code number of the geographic units (polygons) used in this study according to the R package "GIFT" (Denelle et al. 2023; https://doi.org/10.1111/2041-210X.14213).

total_rich: total fern species richness per entity_ID, including species with chlorophyllous spores, species with non-chlorophyllous spores, and species lacking information on spore type.

green_spore: number of fern species with chlorophyllous spores per entity_ID.

epi_green: number of epiphytic fern species with chlorophyllous spores per entity_ID.

epi_rich: total richness of epiphytic fern species per entity_ID, including species with chlorophyllous spores, species with non-chlorophyllous spores, and species lacking information on spore type.

terr_green: number of terrestrial fern species with chlorophyllous spores per entity_ID.

terr_rich: total richness of terrestrial fern species per entity_ID, including species with chlorophyllous spores, species with non-chlorophyllous spores, and species lacking information on spore type.

green_prop: proportion of fern species with chlorophyllous spores relative to total richness per entity_ID (green_spore / total_rich).

epi_green_prop: proportion of epiphytic fern species with chlorophyllous spores relative to total epiphytic richness per entity_ID (epi_green / epi_rich).

terr_green_prop: proportion of terrestrial fern species with chlorophyllous spores relative to total terrestrial richness per entity_ID (terr_green / terr_rich).

mean_annual_temp: mean annual temperature per entity_ID, extracted from CHELSA 1.2 bioclimatic variables. Units: °C.

mean_Annual_Precipitation: mean annual precipitation per entity_ID, extracted from CHELSA 1.2 bioclimatic variables. Units: mm.

mean_Ppt_Driest_Month: mean precipitation of the driest month per entity_ID. extracted from CHELSA 1.2 bioclimatic variables. Units: mm.

mean_Ppt_Seasonality: precipitation seasonality per entity_ID, measured as coefficient of variation. extracted from CHELSA 1.2 bioclimatic variables. Unitless.

mean_Ppt_Warm_Qtr: mean precipitation during the warmest quarter per entity_ID. extracted from CHELSA 1.2 bioclimatic variables. Units: mm.

mean_Temp_Ann_Range: annual temperature range per entity_ID, defined as the difference between average temperatures of the warmest and coldest months. extracted from CHELSA 1.2 bioclimatic variables. Units: °C.

mean_Homogeneity: habitat homogeneity per entity_ID, expressed as the similarity of Enhanced Vegetation Index between adjacent pixels. Tuanmu and Jetz 2015. Unitless.

mean_MODCF_meanannual: mean annual cloud frequency per entityID (2000–2014). Wilson and Jetz 2016. Units: frequency (%).

mean_aet_yr: mean actual evapotranspiration per entity_ID over the annual period. Trabucco and Zomer 2010. Units: mm.

mean_elev: mean elevation of each entity_ID. Extracted from WorldClim 2.1. Units: meters.

mean_pet_he_yr: mean annual potential evapotranspiration per entity_ID. Zomer et al. 2008. Units: mm.

entity_class: biogeographical classification for each entity_ID (e.g., islands, continent, part of Island, Group of Islands). Extracted from R package GIFT. Category.

longitude: longitude coordinate of entity_ID centroid. Units: decimal degrees.

latitude: latitude coordinate of entity_ID centroid. Units: decimal degrees.

area: total area of each entity_ID. Units: km².

elevational_range: difference between maximum and minimum elevation per entity_ID. Extracted from WorldClim 2.1. Units: meters.

REALM: major biogeographical region assigned to each entity_ID. AA: Australasian, AT: Afrotropical, IM:Indomalyan, NAR: Neartic, NT: Neotropical, AN: Antartic, OC: Oceanic, PA: Paleartic. Category.

References:

Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P. and Kessler, M. 2017. Climatologies at high resolution for the earth’s land surface areas. – Sci. Data 4: 170122.

Trabucco, A., and Zomer, R.J. 2010. Global Soil Water Balance Geospatial Database. CGIAR Consortium for Spatial Information. Published online, available from the CGIAR-CSI GeoPortal at https://csidotinfo.wordpress.com.

Tuanmu, M.-N. and Jetz, W. 2015. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. – Global Ecol. Biogeogr. 24:1329–1339.

Wilson, A. M. and Jetz, W. 2016. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. – PLoS Biol. 14: e1002415.

Zomer, R. J., Trabucco, A., Bossio, D. A. and Verchot, L. V. 2008. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. – Agric. Ecosyst. Environ. 126: 67–80.