Aim: Factors affecting bromeliad distribution depend on the life forms of the studied species, some could grow as terrestrial, saxicolous, or epiphytic depending on the type of habitat. We analyzed the distribution patterns of the life forms of a bromeliad species on different biogeographic domains and associated them with environmental variables and vegetation types. Location: Chaquenian, Amazonian, and Seasonally Dry Tropical Forest domains; South America. Taxon: The tank bromeliad Aechmea distichantha (Bromeliaceae: Bromelioideae). Methods: We compiled records of the biogeographic distribution and the vegetation types where Aechmea distichantha occurs based on bibliographic data, digital datasets, herbaria, and personal observations. We associated the distributional records of this species with altitude, five selected bioclimatic variables, four soil variables, and with the vegetation types where it occurs. Results: Aechmea distichantha has been recorded as epiphytic, terrestrial, and saxicolous in all biogeographic domains, but showed contrasting patterns in life form proportions among them. In the Amazonian domain, characterized by evergreen tropical and subtropical forests with high precipitation, it mainly grows as epiphytic. In the Chaquenian domain, dominated by xerophytic forests with low rainfall, high soil pH, and base saturation, it mainly grows as terrestrial, whereas in the Seasonally Dry Tropical Forest domain the three life forms were recorded in similar proportions. In azonal plant communities of all domains, it mainly grows as saxicolous. Main conclusions: This tank bromeliad species can thrive in sites with contrasting habitat and environmental conditions. Its ability to survive in different environments could be associated with its high frost tolerance, the presence of the CAM photosynthetic pathway, a well-developed phytotelma, and high phenotypic plasticity. The life form prevailing in each domain is influenced by water availability (i.e. the quantity of water available during each year, the precipitation in the driest month, and the plant water supply relative to demand).

Occurrences data survey

Occurrence points were obtained by extensively searching the Google Scholar and Scopus databases for literature reporting information on its appearance, as well as reports about the interaction of this species with animal or fungi species. Specimens deposited in FACEN, FCQ, PY (Paraguay), and UNR (Argentina) herbaria, and other specimens available in digital databases (GBIF, 2019; Tropicos, 2019; Flora do Brasil, 2019), as well as journal datasets (Ramos et al., 2019), were also explored. JStor Global Plants (JSTOR, 2019) and ‘Flora del Conosur’ (Zuloaga & Belgrano, 2019) websites were consulted to check types or synonyms. Given that most occurrences and literature citations mentioned only the species name ‘Aechmea distichantha’, for the present analysis we did not make any distinction between infraspecific taxa.

As there could be many sources of potential errors when using large online datasets (Maldonado et al., 2015; Zizka et al., 2019, Zizka et al., 2020), the dataset was compiled and filtered by comparing recorded distributions with areas noted in the literature, as well as with the field experience of the authors. Obvious distribution outliers were checked and deleted when necessary and cultivated specimens were excluded from the analysis. For specimens lacking georeferenced data, coordinates were estimated only for records with accurate locality level spatial data (e. g. municipality or town name, station, farm, finca, estancia or mountain location, roads or rivers intersections, park, reserve or forested area, etc.). We performed manual georeferencing by meticulous interpretation of site descriptions. When available, we checked the original field notes, specimen labels, etc. to improve georeferencing precision and reduce spatial error. To assign the coordinates of each occurrence record we analyzed the site location, classified the type of locality, and then georeferenced it by using the point or point-radius methods in Google Maps and/or Google Earth respectively, following Chapman & Wieczorek (2020) Georeferencing Best Practices.

Records with unambiguous life form information were classified according to the presence of this species on the canopy (epiphytic), on the soil (terrestrial), and on rocky outcrops (saxicolous; Zotz, 2016). A final dataset of 1232 occurrences of A. distichantha was compiled, containing information either on life form, geographic coordinates, or biogeographic regions (provided or inferred).

 

Environmental data survey

For those records with vegetation description, vegetation types were identified for each biogeographic domain based on community structure description or from its floristic composition (DRYFLOR, 2016; Oliveira-Filho & Fontes, 2000; Prado, 2000). For the Amazonian domain (sensu Cabrera & Willink, 1980) we classified the records into wet forests, savannas, and azonal communities. For the Seasonally Dry Tropical Forest domain (sensu Prado, 2000; Särkinen, Iganci, Linares-Palomino, Simon, & Prado, 2011), we classified the vegetation as mesophytic forests or azonal communities. Finally, for the Chaquenian domain (sensu Prado, 1993a, b), we recognized the following vegetation types: tall xerophytic forests, low xerophytic forests, savannas, and azonal communities. We did not include in the vegetation dataset those records that corresponded to transitions between different domains (N=83; i.e. 63 transitions Chaquenian-SDTF domains, and 20 transitions SDTF-Amazonian domains). For the present contribution, we consider that other bioregionalization schemes (e.g. Morrone, 2014) are not suitable because they do not take into account the unique identity of the SDTF in South America (sensu DRYFLOR, 2016), to which the studied species shows an important association.

We selected altitude and five bioclimatic variables based on the effects that they could have on the growth and survival of a facultative epiphytic bromeliad, and therefore on its distribution (Males & Griffiths, 2017; Males, 2018). Mean Annual Precipitation (MAP, mm) was considered as a proxy for the absolute quantity of water available during each year (Males, 2018). Precipitation in the driest month (P_dry, mm) was a proxy for the absolute degree of water limitation during the dry season (Males, 2018). Precipitation Seasonality (P_seas, %) was used as a proxy for the severity of the dry season relative to the remainder of the year (Males, 2018). Aridity Index (AI, mm mm^-1) is measured as MAP/MAE, where MAE is Mean Annual Evapotranspiration, and hence is affected by precipitation, potential evaporation, and temperature. It was used as a proxy for the degree of dryness, where higher AI values denote lower dryness (Zomer et al., 2007). Actual Evapotranspiration/Potential Evapotranspiration (AET/PET, mm mm⁻¹) was used as a proxy for plant water supply relative to demand (Males, 2018).

As terrestrial bromeliad distribution could also be affected by soil features (Benzing, 2000; Barberis et al., 2014), we selected four soil variables (i.e. pH, Percentage of Clay (Clay, %), Cation Exchange Capacity (CEC, cmolc kg^-1), and Base Saturation (BSAT, %)) that are known to vary widely among biomes (Rubio et al., 2019).

Altitude, MAP, P_dry, and P_seas, were taken from Worldclim version 2.0 (Fick & Hijmans, 2017, available in http://www.diva-gis.org/), at 30 seconds spatial resolution (~1 km²). Aridity Index, Actual Evapotranspiration (AET), and Potential Evapotranspiration (PET) layers were obtained at the same resolution from the CGIAR-CSI portal (Zomer et al., 2007). The selected soil variables were taken from The Soil and Terrain database for Latin America and the Caribbean (SOTERLAC), version 2.0, at a scale 1:5 million (available in http://www.isric.org/). DIVA GIS v7.5 (Hijmans et al., 2012) was used to extract the environmental information associated with each record.

 

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