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Data from: Megafauna biogeography explains plant functional trait variation in the tropics

Citation

Dantas, Vinícius; Pausas, Juli (2020), Data from: Megafauna biogeography explains plant functional trait variation in the tropics, Dryad, Dataset, https://doi.org/10.5061/dryad.3xsj3txc0

Abstract

Aim

Biomes can diverge substantially in plant functional traits and disturbance regimens among regions. Given that Neotropical and Afrotropical regions have contrasting histories of the megafauna (because of the Holocene megafaunal extinction in the Neotropics), we hypothesize that they should harbour plants with different traits in relationship to herbivory and fire, especially in savannas. We predicted that herbivory resistance traits should be more prominent in Afrotropical savanna plants and fire resistance in Neotropical savanna plants.

Location

Tropics.

Time period

Not applicable.

Major taxa studied

Angiosperms (woody).

Methods

We compiled data for five key plant functional traits (wood density, specific leaf area, maximum tree height, spinescence and proportion of geoxyles) for forest and savanna woody species from the two distant regions (Afrotropics and Neotropics). We related these data to climate, soil and fire variables and tested predictions for megafauna selection.

Results

Spines and high wood density were more common among Afrotropical than Neotropical savanna species and species from the two forests. Moreover, the Neotropical savanna region contained more geoxyles than the Afrotropical savanna region. Finally, Afrotropical species were taller than Neotropical species. These differences were consistent with our predictions for trait selection by the megafauna, and these patterns did not change when considering climate, soil and fire regimens in the models.

Main conclusions

Our results highlight the great potential of these traits for summarizing disturbance strategy axes in tropical woody species and suggest that global variation in plant traits is unlikely to be understood fully without consideration of historical factors, especially the direct and indirect impacts of megafauna.

Methods

We compiled data on the presence of spines, SLA, WD, HMax and presence of spines for Afrotropical and Neotropical savanna and forest woody species (trees and shrubs), from literature and herbarium sources (a list of the data sources is found in Appendix 1). We first compiled SLA and WD data from the primary literature and calculated species means at the biogeographic scale. Then, for Afrotropical species, we searched for HMax and spine data in JSTOR Global Plant (http://plants.jstor.org/) and in the African Plant Database of the Conservatoire et Jardin botaniques de la Ville de Genève and South African National Biodiversity Institute, using the list of species for which we obtained SLA and WD data as reference. For Neotropical species we obtained HMax and presence of spine information from the NeoTropTree dataset (Oliveira-filho, 2017) and Flora do Brasil (2020) for all the available species recorded in Brazilian savanna (Cerrado) and forest (Amazon and Atlantic forest) biomes. For spinescence, we only considered species with detailed descriptions of stem and branch features. We classified species as savanna, forest, or generalist (occurring in both savanna and forest) species, based on the study site descriptions reported in the literature sources from which the data were acquired, and on Mendonça et al. (2008). We only considered species that were consistently classified as forest or savanna, excluding species reported to occur in both ecosystem types, to better pinpoint the patterns and simplify the results. We also classified species according to the biogeographic region in which they occur as Afrotropical or Neotropical species (based on the data reference sources). Introduced species were also excluded using occurrence information from the flora websites and datasets used to compile height and spine data.

We obtained data for number and proportion of African geoxylic plants from Maurin et al., (2014). This data is based on the flora of the Zambesian region, a savanna dominated region that includes 12 African countries. Maurin et al., (2014) present two datasets, a sampled dataset, with 53 geoxyles out of the 1400 woody species, and a provisional list of 266 African geoxylic suffrutices taxa occurring south of the Equator. We did not use the latter because an accurate quantification of the southern African flora was not provided. However, a preliminary estimate based on Germishuizen et al. (2003) indicates a total of  8169 woody taxa for southern Africa (including trees, shrubs, dwarf shrubs, subshrubs and suffrutex, but excluding scrumbers, as the proportion of woody stems was not reported). Based in these figures, we found that the first and second datasets represent a similar proportion of geoxyles for African woody species (4 and 3 %, respectively), and would provide very similar results in the statistical analyses. Thus, we only report the results for the sampled species of Maurin et al., (2014). For the Neotropical savanna region, we searched for information on stem and underground organs for subshrub species in the checklist of Mendonça et al. (1998). The list comprises 6429 savanna plant species from the Cerrado region (the largest Neotropical savanna-dominated region) and represents an older version of a more recent checklist with almost twice the number of plant species (but more difficult to work with because only the printed version is available; Mendonça et al., 2008). We then searched for information in plant species descriptions compiled by the Rio de Janeiro Botanical garden and publicly available in Portuguese at the Flora do Brasil (2020) website. We only considered information for species containing detailed descriptions of aerial and underground structures. We found information of this sort for 220 subshrubs out of the 816 subshrub species in the checklist, of which 101 were geoxyles (according to the definition used by Maurin et al., 2014). Based in the observed proportion (46%), we estimated the number of geoxyles among the 816 subshrubs to be 376 species, from a total of 3599 woody species. Thus, the comparison is between savanna regions, not actual savanna or forest vegetation (unlike the comparison for other traits), and only includes subshrub geoxyles, to match the criteria used by Maurin et al., (2014). 

 

Environmental Data

We obtained decimal geographic coordinates for the species for which we obtained WD, SLA and HMax in GBIF.org (28 February 2020; see reference list for doi) and the R package “rgbif”. In order to exclude very close occurrences and, thus, match the resolution of the available satellite-derived environmental data (see below), we rounded the decimal coordinates to include only three decimal digits and then remove repeated species occurrences. We also excluded coordinates falling outside Africa, South and Central Americas, and outside the following biomes (according to Dinerstein et al. 2017): Tropical and Subtropical Moist and Dry Broadleaf Forests; Tropical and Subtropical Grasslands, Savannas and Shrublands; Montane Grasslands and Shrublands; Tropical and Subtropical Coniferous Forests; and, Deserts and Xeric Shrublands. This was directed at minimizing errors, standardizing the latitude ranges and biomes considered for each biogeographic regions, and to exclude flooded ecosystems in which plant relationships with climate and soil are likely different. Thus, from the initial approximately 2,8 million occurrences, we only used 87,739 occurrences, and the number of coordinates per species varied from 1 to 1432.

Based on these coordinates, we obtained climate, soil and fire data for each occurrence location from global datasets. We obtained climate data from WorldClim 2 (Fick & Hijmans, 2017), soil data from SoilGrid (250 m of spatial resolution; Hengl et al. 2017), and fire data from the MODIS product MCD14ML collection 6 v.3 (Giglio et al., 2018). We used mean annual precipitation and temperature, as well as rainfall seasonality for the years 1970-2000, as climate variables; cation exchange capacity, organic carbon content, weight percentages of clay (<0.0002 mm), silt (0.0002–0.05 mm), and sand particles (0.05–2 mm), as well as the volumetric percentage of coarse fragments (>2 mm), as soil variables; and fire count per area (as a proxy for fire frequency) and radiative power (a proxy for fire intensity) as fire variables. Soil variables were the averages between two depth, 0.05 and 2 m. Fire data was obtained from a circular area of 5 km centered on the occurrence coordinates between the years 2000 and 2019 (both included). For each species and biogeographic region, we calculated the overall means as an indicator of species habitat preferences as defined by their average position in environmental niche space.

 

References

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Fick, S.E. & Hijmans, R.J. (2017) Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302–4315.

Flora do Brasil (2020) in construction. Jardim Botânico do Rio de Janeiro.

GBIF.org (28 February 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.pxxewu

Germishuizen, G., Meyer, N.L. & (eds) (2003) Plants of southern Africa: an annotated checklist, Strelitzia 14. National Botanical Institute, Pretoria.

Giglio, L., Schroeder, W., Hall, J. V. & Justice, C.O. (2018) MODIS Collection 6 Active Fire Product User’s Guide Revision B. NASA.

Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B., Guevara, M.A., Vargas, R., MacMillan, R.A., Batjes, N.H., Leenaars, J.G.B., Ribeiro, E., Wheeler, I., Mantel, S. & Kempen, B. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PloS one, 12, e0169748.

Maurin, O., Davies, T.J., Burrows, J.E., Daru, B.H., Yessoufou, K., Muasya, A.M., van der Bank, M. & Bond, W.J. (2014) Savanna fire and the origins of the “underground forests” of Africa. New Phytologist, 204, 201–214.

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Usage Notes

The dataset that is made available here cosists of two files in .csv format. The first is the complete trait dataset for specific leaf area (sla; mm2.mg-1), wood density (woo; g.cm-3), HMax (m) and Spines (yes/no). The list of reference sources for trait data is presentes in the end of this note. Other abreviations in this file are: ref.sla: reference sources for sla data; ref.woo: reference sources for wood density data; ref.hmax: reference sources for hmax data; mat:  mean annual temperature; map: mean annual precipitation; rs: rainfall seasonality; nfires5: number of fires per 5 km area (our proxy for fire frequency); avgfrp: average fire radiative power (our proxy for fire intensity); cec: soil cation exchange capacity; orc: soil organic carbon content; cly: weight percentage of clay particles (<0.0002 mm) in the soil; slt: weight percentage of silt particles (0.0002–0.05 mm) in the soil; snd: weight percentage of the sand particles (0.05–2 mm) in the soil; crf: volumetric percentage of coarse fragments (>2 mm) in the soil. The second file attached is a dataset of Geoxyle species (geox; y(yes)/n(no)) for a subset of the Brazilian Cerrado species.

 

Complete Reference Sources for the Funcitonal Trait Data

 

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Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (Finance Code 001), Award: 88887.311538/2018-00