# FunAndes – A functional trait database of Andean plants

Selene Báez, Luis Cayuela, Manuel J Macía, Esteban Álvarez-Dávila, Amira Apaza-Quevedo, Itziar Arnelas, Natalia Baca-Cortes, Guillermo Bañares de Dios, Marijn Bauters, Celina Ben Saadi, et al.

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To cite this version:

Selene Báez, Luis Cayuela, Manuel J Macía, Esteban Álvarez-Dávila, Amira Apaza-Quevedo, et al. FunAndes – A functional trait database of Andean plants. Scientific Data, 2022, 9, pp.511. 10.1038/s41597-022-01626-6. hal-03811943

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# FunAndes – A functional trait database of Andean plants

Selene Báez et al. #

We introduce the FunAndes database, a compilation of functional trait data for the Andean flora spanning six countries. FunAndes contains data on 24 traits across 2,694 taxa, for a total of 105,466 entries. The database features plant-morphological attributes including growth form, and leaf, stem, and wood traits measured at the species or individual level, together with geographic metadata (i.e., coordinates and elevation). FunAndes follows the field names, trait descriptions and units of measurement of the TRY database. It is currently available in open access in the FIGSHARE data repository, and will be part of TRY's next release. Open access trait data from Andean plants will contribute to ecological research in the region, the most species rich terrestrial biodiversity hotspot.

## Background & Summary

Functional traits are measurable properties of a plant describing its structure, function or life history strategy that determine species responses to biotic and abiotic environmental conditions across scales of biological complexity, from communities to ecosystems¹⁻⁴. Exploring variation in plant functional traits provides key insights into plant species distribution, community assembly mechanisms, evolutionary strategies, and ecosystem level potential responses to global environmental change⁵⁻¹³. Global databases of plant functional traits currently feature an unprecedented amount of trait information that supports scientific work on plant functional ecology, including BIEN¹⁴, GIFT¹⁵, and TRY¹⁶,¹⁷. Yet, the geographical coverage of trait measurements still remains limited for highly diverse tropical areas, especially in mountainous regions¹⁵,¹⁶.

The tropical Andes is a major hotspot of global biodiversity and endemism. With about 2% of the terrestrial area of the planet, it holds 10% of the species of vascular plants¹⁸⁻²⁰. However, trait information for Andean plants is underrepresented in global plant trait databases. These information gap limits our understanding of variation in plant trait composition and diversity at regional, continental, and global scales. Synthesizing and harmonizing trait measurements from remote and understudied areas is critical for global and regional data archiving initiatives²¹, and for advancing empirical biodiversity research. Here, we present the FunAndes database, a compilation of plant functional traits in the tropical Andes (Fig. 1). The records in FunAndes stem from 18 unpublished datasets contributed by different research groups conducting fieldwork in the region. FunAndes follows the structure and terminology of the TRY database, and is available in the FIGSHARE data repository²². In total, FunAndes contains 105,466 records of 24 traits, covering 2,694 Andean (morpho-) species in 670 genera and 175 families. Assembling FunAndes encompassed the following steps: 1) developing a TRY-based format for data contributors, 2) revising comparability among protocols used for trait data collection, 3) checking trait measurement units for each contributed dataset, 4) detecting and deleting suspicious or erroneous trait measurements, 5) compiling the contributed data into a unique source with common taxonomic names, units, and terminology. To our knowledge, FunAndes is the first open access trait database of the Andean flora, filling a substantial gap in global functional trait data. We hope that providing a standardized and curated database on Andean plant traits will encourage plant trait ecological research in Andean ecosystems, as well as comparative studies across tropical regions.

<footnote># A full list of authors and their affiliations appears at the end of the paper.</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1** Geographic distribution of plant traits in FunAndes and TRY version 5¹⁷ in 1-degree cells (~1 km). Montane sites above 500 m of elevation and buffer areas of 50 km below such elevation show density distribution of the most representative plant traits in FunAndes and TRY along the latitudinal gradient.</figcaption></figure>

<table><caption>**Table 1.** Species and trait observations per country in FunAndes.</caption><thead><tr><td></td><td>Country</td><td>SpeciesNames (n)</td><td>Trait observations (n)</td><td>Trait observations (%)</td></tr></thead><tbody><tr><td>1</td><td>Argentina</td><td>97</td><td>1457</td><td>1.38</td></tr><tr><td>2</td><td>Bolivia</td><td>692</td><td>19,463</td><td>18.45</td></tr><tr><td>3</td><td>Colombia</td><td>294</td><td>7,150</td><td>6.78</td></tr><tr><td>4</td><td>Ecuador</td><td>1170</td><td>50,401</td><td>47.79</td></tr><tr><td>5</td><td>Peru</td><td>1287</td><td>26,372</td><td>25.01</td></tr><tr><td>6</td><td>Venezuela</td><td>27</td><td>623</td><td>0.59</td></tr><tr><td></td><td>Total</td><td>NA</td><td>105,466</td><td>100</td></tr></tbody></table>

## Methods

**Primary sources.** We first developed a basic data template containing trait names, trait descriptions and units of measurement, together with information (e.g., site coordinates and collection dates, number of samples collected). This template was distributed to potential data contributors, scientists collecting vascular plant functional trait data mainly in tropical forests of the Andean region. Filled templates were returned to the writing team, and FunAndes was assembled from 18 distinct datasets containing field data of Andean plant traits (Tables 1 and 2).

**Trait definitions and protocols.** Trait definitions and trait units of measurement in FunAndes follow those of the TRY database, for a total of 24 plant traits, two categorical and 22 numerical (Table 3). All trait data contributed to FunAndes were obtained from individuals growing in natural vegetation, following standard and comparable methods²³,²⁴. Furthermore, traits were measured mostly in adult individuals, never in seedlings or saplings. Leaf traits were quantified from exposed mature leaves in the plant canopy. A summary of trait geographical representation in FunAndes is presented in Fig. 1. A comparison between trait data in FunAndes and TRY version 5¹⁷ is presented in Table 4.
<table><caption>**Table 2.** Summary of the 18 datasets inFunAndes.</caption><thead><tr><td>Dataset ID</td><td>PI Last Name</td><td>PI First Name</td><td>Country</td><td>Number of entries</td></tr></thead><tbody><tr><td>ABERG</td><td>Farfán-Ríos</td><td>William</td><td>Peru</td><td>2522</td></tr><tr><td>Amira Project</td><td>Apaza</td><td>Amira</td><td>Bolivia</td><td>2040</td></tr><tr><td>BAMBOOTRAITS</td><td>Fadrique</td><td>Belen</td><td>Peru</td><td>1860</td></tr><tr><td>BOTROPANDES ECU</td><td>Bañares de Dios</td><td>Guillermo</td><td>Ecuador</td><td>9083</td></tr><tr><td>BOTROPANDES</td><td>Bañares de Dios</td><td>Guillermo</td><td>Peru</td><td>9225</td></tr><tr><td>COFOREC</td><td>Bauters</td><td>Marijn</td><td>Ecuador</td><td>996</td></tr><tr><td>DISPLAMAZ</td><td>Macía</td><td>Manuel J.</td><td>Peru</td><td>12907</td></tr><tr><td>E., ALVAREZ TRAIT DATABASE</td><td>Alvarez-Davila</td><td>Esteban</td><td>Colombia</td><td>623</td></tr><tr><td>FPY</td><td>Blundo</td><td>Cecilia</td><td>Argentina</td><td>1457</td></tr><tr><td>Homeier Projects</td><td>Homeier</td><td>Jürgen</td><td>Bolivia</td><td>1015</td></tr><tr><td>Homeier Projects ECU</td><td>Homeier</td><td>Jürgen</td><td>Ecuador</td><td>30557</td></tr><tr><td>Iguaque</td><td>Salgado-Negret</td><td>Beatriz</td><td>Colombia</td><td>2272</td></tr><tr><td>Jadan Project</td><td>Jadán</td><td>Oswaldo</td><td>Ecuador</td><td>9623</td></tr><tr><td>LCP UDENAR IAVH</td><td>Solarte</td><td>Maria Elena</td><td>Colombia</td><td>611</td></tr><tr><td>Madidi Project</td><td>Tello</td><td>J Sebastian</td><td>Bolivia</td><td>16408</td></tr><tr><td>Rastrojos</td><td>Norden</td><td>Natalia</td><td>Colombia</td><td>3008</td></tr><tr><td>Sumapaz-Cruz Verde</td><td>Garnica-Díaz</td><td>Claudia</td><td>Colombia</td><td>636</td></tr><tr><td>VEN-SEU</td><td>Vilanova</td><td>Emilio</td><td>Venezuela</td><td>623</td></tr><tr><td>Total</td><td></td><td></td><td></td><td>105,466</td></tr></tbody></table>

<table><caption>**Table 3.** Plant functional traits represented in FunAndes. Trait definitions and units of measurement follow those of TRY¹⁶ (https://www.try-db.org/de/TabDetails.php).</caption><thead><tr><td>Trait Name</td><td>Unit</td></tr></thead><tbody><tr><td>Bark thickness</td><td>mm</td></tr><tr><td>Leaf area (in case of compound leaves: leaf, petiole excluded)</td><td>mm<sup>2</sup></td></tr><tr><td>Leaf area (in case of compound leaves: leaf, petiole included)</td><td>mm<sup>2</sup></td></tr><tr><td>Leaf aluminium (Al) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA): petiole included</td><td>mm<sup>2</sup> mg<sup>-1</sup></td></tr><tr><td>Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA) petiole, rhachis and midrib excluded</td><td>mm<sup>2</sup> mg<sup>-1</sup></td></tr><tr><td>Leaf calcium (Ca) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf carbon (C) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf carbon (C) isotope signature (delta 13 C)</td><td>mg kg<sup>-1</sup></td></tr><tr><td>Leaf compoundness</td><td>unitless</td></tr><tr><td>Leaf dry mass per leaf fresh mass (leaf dry matter content, LDMC)</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf magnesium (Mg) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf nitrogen (N) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf nitrogen (N) isotope signature (delta 15 N)</td><td>mg kg<sup>-1</sup></td></tr><tr><td>Leaf phosphorus (P) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf potassium (K) content per leaf dry mass</td><td>mg g<sup>-1</sup></td></tr><tr><td>Leaf texture (sclerophylly, physical strength, toughness)</td><td>kN m<sup>-1</sup></td></tr><tr><td>Leaf thickness</td><td>mm</td></tr><tr><td>Plant growth form</td><td>unitless</td></tr><tr><td>Stem conduit cross-sectional area (vessels and tracheids)</td><td>μm</td></tr><tr><td>Stem conduit density (vessels and tracheids)</td><td>mm<sup>-2</sup></td></tr><tr><td>Stem dry mass per stem fresh volume (stem specific density, SSD, wood density): branch</td><td>g/cm<sup>3</sup></td></tr><tr><td>Stem dry mass per stem fresh volume (stem specific density, SSD, wood density): sapwood</td><td>g/cm<sup>3</sup></td></tr><tr><td>Wood (sapwood) specific conductivity (stem specific conductivity)</td><td>kg m<sup>-1</sup> Mpa<sup>-1</sup> s<sup>-1</sup></td></tr></tbody></table>

**Database structure.** The database contains 24 fields to provide contextual information about data collection, including association of trait data to permanent vegetation plots, site coordinates and collection dates; and information about the trait value provided (e.g., if the value provided is a single observation or an average of trait measurements) (Table 5).

**Harmonization.** We followed various steps to ensure the quality of the data before adding a contributed dataset to FunAndes. Our workflow consisted of a series of operations, including generating dataset IDs for
<table><caption>Table 4. Plant functional traits in FunAndes in comparison to TRY version 5¹⁷ for the Andean region.</caption><thead><tr><th rowspan="2">Trait</th><th colspan="3">FunAndes</th><th colspan="2">TRY</th></tr><tr><th>Number of Project IDs</th><th>Entries</th><th>Entries identified to genus or species level</th><th>Species</th><th>Entries</th></tr></thead><tbody><tr><td>Bark thickness</td><td>2</td><td>1242</td><td>1232</td><td>340</td><td>0</td></tr><tr><td>Leaf aluminium (Al) content per leaf dry mass</td><td>2</td><td>1712</td><td>1689</td><td>402</td><td>318</td></tr><tr><td>Leaf area (in case of compound leaves: leaf, petiole excluded)</td><td>2</td><td>686</td><td>670</td><td>162</td><td>0</td></tr><tr><td>Leaf area (in case of compound leaves: leaf, petiole included)</td><td>10</td><td>6512</td><td>6399</td><td>1534</td><td>0</td></tr><tr><td>Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA) petiole, rhachis and midrib excluded</td><td>2</td><td>681</td><td>665</td><td>161</td><td>247</td></tr><tr><td>Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA): petiole included</td><td>14</td><td>16458</td><td>15577</td><td>2423</td><td>0</td></tr><tr><td>Leaf calcium (Ca) content per leaf dry mass</td><td>3</td><td>2289</td><td>2213</td><td>558</td><td>318</td></tr><tr><td>Leaf carbon (C) content per leaf dry mass</td><td>5</td><td>2785</td><td>2700</td><td>654</td><td>882</td></tr><tr><td>Leaf carbon (C) isotope signature (delta 13 C)</td><td>2</td><td>259</td><td>257</td><td>72</td><td>68</td></tr><tr><td>Leaf compoundness</td><td>18</td><td>18570</td><td>17642</td><td>2600</td><td>423</td></tr><tr><td>Leaf dry mass per leaf fresh mass (leaf dry matter content, LDMC)</td><td>6</td><td>2058</td><td>2049</td><td>403</td><td>68</td></tr><tr><td>Leaf magnesium (Mg) content per leaf dry mass</td><td>2</td><td>2096</td><td>2023</td><td>519</td><td>318</td></tr><tr><td>Leaf nitrogen (N) content per leaf dry mass</td><td>6</td><td>2853</td><td>2768</td><td>669</td><td>1698</td></tr><tr><td>Leaf nitrogen (N) isotope signature (delta 15 N)</td><td>2</td><td>259</td><td>257</td><td>72</td><td>0</td></tr><tr><td>Leaf phosphorus (P) content per leaf dry mass</td><td>4</td><td>2378</td><td>2302</td><td>577</td><td>1566</td></tr><tr><td>Leaf potassium (K) content per leaf dry mass</td><td>2</td><td>2170</td><td>2096</td><td>523</td><td>318</td></tr><tr><td>Leaf texture (sclerophyll, physical strength, toughness)</td><td>2</td><td>1423</td><td>1407</td><td>345</td><td>0</td></tr><tr><td>Leaf thickness</td><td>7</td><td>8200</td><td>7414</td><td>1779</td><td>257</td></tr><tr><td>Plant growth form</td><td>17</td><td>18780</td><td>17818</td><td>2657</td><td>2064</td></tr><tr><td>Stem conduit cross-sectional area (vessels and tracheids)</td><td>1</td><td>933</td><td>912</td><td>367</td><td>3</td></tr><tr><td>Stem conduit density (vessels and tracheids)</td><td>1</td><td>930</td><td>909</td><td>367</td><td>0</td></tr><tr><td>Stem dry mass per stem fresh volume (stem specific density, SSD, wood density) branch</td><td>8</td><td>8418</td><td>7625</td><td>1795</td><td>0</td></tr><tr><td>Stem dry mass per stem fresh volume (stem specific density, SSD, wood density) sapwood</td><td>5</td><td>2845</td><td>2814</td><td>683</td><td>0</td></tr><tr><td>Wood (sapwood) specific conductivity (stem specific conductivity)</td><td>1</td><td>929</td><td>908</td><td>367</td><td>3</td></tr></tbody><tfoot><tr><td>Total</td><td></td><td>105,466</td><td>100,346</td><td>20,029</td><td>8551</td></tr></tfoot></table>

each contributed dataset, harmonizing data into common measurement units, translating terms (trait values) for categorical variables, verifying and correcting collection coordinates, and identifying erroneous trait data measurements. Each data contributor was contacted to double check methods used for trait collection, correct or eliminate suspicious trait values. Finally, duplicates were removed to create the final version of the database. All steps taken toward data standardization were done in R¹⁶ using built-in functions and the package 'dplyr'²⁵.

**Taxonomy.** Species names standardization was conducted with the R package 'LCVP' of The Leipzig Catalogue of Vascular Plants¹⁸. Original species names were compared to LCVP names by searching for matches. Non-matches (mainly caused by incorrect spelling) were revised by an expert in Andean flora (J.H.), and corrected following LCVP. The final FunAndes database reports both the original and the updated taxon name alongside each trait record. For each morphospecies, higher taxonomic affiliations obtained from the LCVP were included.

## Data Records

**Access.** FunAndes database is stored and available for direct download from the FIGSHARE data repository²² and will become available from the TRY Plant Trait Database in the next release (https://www.try-db.org).

**Data coverage.** FunAndes includes 105,466 trait records for 24 traits of 2,694 Andean morpho-species in 670 genera and 175 taxonomic families. Therefore, FunAndes presents trait information for roughly nine percent of the ~30,000 species of vascular plants estimated to occur in the Tropical Andes²⁰,²⁶. Three traits of FunAndes (plant growth form, leaf compoundness, specific leaf area) make up half of the records in the database (Table 4). Leaf trait data make up 67.7% of the database, followed by whole plant (i.e., plant growth form and leaf compoundness) (17.8 and 17.6%, respectively) and stem traits (14.5%). Each species has an average of 7.4 (SD = 5.1) distinct traits. All observations have geographic coordinates.

Considering the Andean countries, Ecuador has 47.8% of all the trait observations in FunAndes, followed by Peru (25.0%) and Bolivia (19.5%) (Fig. 1, Table 1). Data in FunAndes comes from 788 collection sites (i.e., unique combinations of latitude and longitude) and is associated to 570 forest plots. Furthermore, trait
<table><caption>Table 5. Definitions of fields in the FunAndes database.</caption><thead><tr><td>Number</td><td>Field</td><td>Definition</td></tr></thead><tbody><tr><td>1</td><td>Project_ID</td><td>Project name of the contributed dataset</td></tr><tr><td>2</td><td>Plot_ID</td><td>Plot identification code</td></tr><tr><td>3</td><td>Plant_ID</td><td>Plant identification code or voucher</td></tr><tr><td>4</td><td>Sample_ID</td><td>Sample number</td></tr><tr><td>5</td><td>SpeciesOriginal</td><td>Species of the plant in the original dataset</td></tr><tr><td>6</td><td>OrderLCVP</td><td>Taxonomic order provided by the Leipzig Cataloge of Vascular Plants</td></tr><tr><td>7</td><td>FamilyLCVP</td><td>Taxonomic family provided by the Leipzig Cataloge of Vascular Plants</td></tr><tr><td>8</td><td>GenusLCVP</td><td>Taxonomic genus provided by the Leipzig Cataloge of Vascular Plants</td></tr><tr><td>9</td><td>SpeciesLCVP</td><td>Taxonomic species provided by the Leipzig Cataloge of Vascular Plants</td></tr><tr><td>10</td><td>Long</td><td>Longitude in decimal degrees</td></tr><tr><td>11</td><td>Lat</td><td>Latitude in decimal degrees</td></tr><tr><td>12</td><td>Elevation</td><td>Elevation in m</td></tr><tr><td>13</td><td>Country</td><td>Country</td></tr><tr><td>14</td><td>Collection_year</td><td>Year of collection</td></tr><tr><td>15</td><td>ValueKindName</td><td>Value kind (single measurement, mean, median, etc.)</td></tr><tr><td>16</td><td>SpeciesName</td><td>Revised species name</td></tr><tr><td>17</td><td>OrigValueStr</td><td>Trait value</td></tr><tr><td>18</td><td>OriginalName</td><td>Trait name following TRY</td></tr><tr><td>19</td><td>OrigUnitStr</td><td>Trait units</td></tr><tr><td>20</td><td>Last Name</td><td>Last Name of the PI contributing the dataset</td></tr><tr><td>21</td><td>First Name</td><td>First Name of the PI contributing the dataset</td></tr><tr><td>22</td><td>Email</td><td>Email of the PI of the contributed dataset</td></tr><tr><td>23</td><td>Dataset</td><td>Identifier of the dataset in TRY (FunAndes)</td></tr><tr><td>24</td><td>Observation_ID</td><td>Unique identifier of each observation in FunAndes</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 2 Distribution of plant trait data in FunAndes along gradients of (a) elevation, (b) Mean annual temperature and Mean total annual precipitation. Climatic variables were extracted from the Chelsa climate database²⁷.</figcaption></figure>

observations are grouped mainly around 500, 1,000, 2,000 and 3,000 m of elevation (Fig. 2a). The data is widely distributed along a gradient of mean annual temperature, but clustered toward lower values of total mean annual precipitation (Fig. 2b).

The five most represented plant functional traits in FunAndes - plant growth form, leaf compoundness, specific leaf area (SLA), wood density, leaf thickness - are homogeneously distributed in the tree phylogeny (Fig. 3).

TRY version 5²³ hosts 8,548 entries for Andean plants, corresponding to 1,123 species, and 15 of the 24 functional traits held in FunAndes (Table 4). FunAndes, therefore, will increase available trait data by a factor of 12, and at least double the current representation of traits per species in TRY. In consequence, FunAndes is a substantial contribution to plant functional trait data availability for the Andean region.
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Phylogenetic distribution of trait data in FunAndes showing the total number of observations per taxa for the five most represented functional traits: Plant growth form, leaf compoundness, specific leaf area (SLA), wood density, and leaf thickness. The phylogenetic tree shows information for 150 families and 2,690 species. The tree is based on a recent plant phylogeny²⁸, nomenclature of The Plant List (http://www.theplantlist.org), and was created with the package 'V.phylomaker'²⁹.</figcaption></figure>

## Technical Validation

For each contributed dataset we visually inspected all data and metadata producing histograms of each trait value to identify outliers or mistaken measures. In most cases, extreme values were discussed with data contributors to make decisions toward correcting or eliminating erroneous observations. With the final version of the database, histograms were produced once again to check for outliers or mistaken values.

## Usage Notes

The data can be downloaded from the FIGSHARE data repository under the terms of Creative Commons Zero (CC0) waiver. We also provide FunAndes database in the TRY Plant Trait Database (https://www.try-db.org). Users of FunAndes data are invited to cite this publication: Báez *et al*. xx. FunAndes – A functional trait database of Andean plants. Scientific Data. 00:00-00, and the accompanying FIGSHARE dataset²².

## Code availability

The contributed datasets were provided in Excel spreadsheets (Microsoft Office 2013), therefore no code is available for this step. Scripts to conduct taxonomic standardization using the LCVP, to plot environmental distribution, and trait representation in the plant phylogeny are available at FIGSHARE²². The scripts were developed in R.

Received: 24 March 2022; Accepted: 8 August 2022;
Published online: 20 August 2022
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## Acknowledgements

This work was possible thanks to funding from the *Living Earth Collaborative (LEC) at Washington University in St. Louis*, for the working group 'A synthesis of patterns and mechanisms of diversity and forest change in the Andes: A global biodiversity hotspot' (organized by JST, JAM, and SB), and by the *German Centre for Integrative Biodiversity Research (iDiv)*, for the working group 'sAndes: Tree diversity, composition and carbon storage in Andean tropical montane forests' (organized by LC and MJM). The authors are grateful to Jens Kattge for making available the TRY data presented in this paper. AA-Q thanks the Herbario Nacional de Bolivia, the German Academic Exchange Service (DAAD) and DFG for funding (HE3041/20-1); to Ricardo Sonco and Marcelo Reguerin, Arely Palabral, Heike Heklau, and the Chulumani community. SB thanks the financial support of the Alexander von Humboldt Foundation through a Georg Foster Fellowship; SB, MB and HV acknowledge VLIR-UOS grants COFOREC (EC2018SIN223A103) and COFOREC II (EC2020SIN279A103). GBD was funded through a PhD grant by the Spanish Ministry of Education (MINEDU; FPU14/05303) and is indebted to Alex Nina, Jorge Armijos, Gonzalo Bañares, José Sánchez, Ángel Delso, Anselmo Vergaray, “Rosho” Tamayo, Reynerio Ishuiza, and national parks rangers for assistance in the field. CB thanks CONICET, Argentina, for a Doctoral Scholarship. LC and MJM were partly funded through projects CGL2013-45634-P, CGL2015-72431-EXP, CGL2016-75414-P, PID2019-105064GB-I00, and S2018/EMT-4 338. JGA and CBS acknowledge Luis Torres, Mara Paneghel, Iñigo Gómez, Maaike Pyck, Manuel Marca, Daniel Irygoin, Piher Maceda and Pontificia Universidad Católica de Perú (PUCP). JH thanks DFG (projects HO3296/2, HO3296/4, HO3296/6) and the Erasmus Go International Plus Program for funding and Katherine Angulo-Schipper, Michel Edelmann, Julius Joosten, Julia Falk, Roman Link, Johanna Lindemann, Stephanie Lorenz, Phillip Obst, Jaime Peña,
Bernhard Schuldt, Julia Siegel and Sebastian Wagner for field and lab assistance. OJ thanks the Vicerrectorado de Investigación de la Universidad de Cuenca, Ecuador. NN thanks the support of Instituto Alexander von Humboldt, Pontificia Universidad Javeriana, Programa de Bosques Andinos and Jardín Botánico de Bogotá. BS-N, CG-D, SS, RL-C acknowledge the support of Instituto de Investigación de Recursos Biológicos Alexander von Humboldt for financing the project, to the research group Biodiversidad de Alta Montaña (BAM) and William Ariza, Christian Beltran, Camilo Dumar, Janis Morales, Andrés Otalora, and Abelardo Rodriguez-Bolaños. BC-N, CM, SC-M acknowledge the financial and technical support of the Instituto Alexander von Humboldt to obtain the plant trait data. JST and JAM acknowledge the support of the National Science Foundation (DEB 0101775, DEB 0743457, DEB 1836353); the National Geographic Society (NGS 7754-04 and NGS 8047-06); the International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis; the Taylor and Davidson families; Dirección General de Biodiversidad, the Bolivian Park Service (SERNAP). EV thanks the Corkery Family Fund and the Center for Sustainable Forestry at Pack Forest, both from University of Washington, USA for funding support during his PhD program, and the financial support from the 'Bosques Andinos' Initiative (http://www.bosquesandinos.org/) that was important for the collection of trait data at San Eusebio. Data collection in Peru was supported by NSF LTREB DEB 1754647, the Gordon and Betty Moore Foundation's Andes to Amazon initiative, and RAINFOR. We thank the Open Access Publication Funds of University of Göttingen.

**Author contributions**

L.C., J.H., M.J.M. and S.B. conceived the idea. S.B., L.C., M.J.M., J.A.M. and J.S.T. obtained funding and coordinated the L.E.C. and iDiv workshops. L.C., S.B., J.H. and K.P. compiled the data sets and performed data quality checks. L.C., S.B., J.H. and K.P. conceived and developed the figures. S.B., J.H. and L.C. wrote the manuscript. The rest of authors (ordered alphabetically) contributed data, revised and agreed on the final version of the manuscript.

**Competing interests**

The authors declare no competing interests.

**Additional information**

Correspondence and requests for materials should be addressed to S.B., L.C. or J.H.

Reprints and permissions information is available at www.nature.com/reprints.

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© The Author(s) 2022

Selene Báez<fnref n="1" />✉, Luis Cayuela²✉, Manuel J. Macía ID 3,4, Esteban Álvarez-Dávila⁵, Amira Apaza-Quevedo⁶, Itziar Arnelas ID 7, Natalia Baca-Cortes⁸, Guillermo Bañares de Dios ID 2, Marijn Bauters ID 9, Celina Ben Saadi ID 3, Cecilia Blundo ID 10, Marian Cabrera⁸, Felipe Castaño¹¹, Leslie Cayola¹²,¹³, Julia G. de Aledo ID 3, Carlos Iván Espinosa⁷, Belén Fadrique ID 14, William Farfán-Rios ID 13,¹⁵, Alfredo Fuentes¹²,¹³, Claudia Garnica-Díaz¹⁶, Mailyn González¹⁷, Diego González¹⁸, Isabell Hensen¹⁹, Ana Belén Hurtado ID 17, Oswaldo Jadán²⁰, Denis Lippok¹⁹, M. Isabel Loza¹²,¹³,²¹, Carla Maldonado¹², Lucio Malizia²², Laura Matas-Granados³, Jonathan A. Myers²³, Natalia Norden ID 17, Imma Oliveras Menor²⁴,²⁵, Kerstin Pierick ID 26, Hirma Ramírez-Angulo²⁷, Beatriz Salgado-Negret ID 28, Matthias Schleuning²⁹, Miles Silman³⁰, María Elena Solarte-Cruz⁸, J. Sebastián Tello¹³, Hans Verbeeck⁹, Emilio Vilanova ID 31, Greta Weithmann²⁵ & Jürgen Homeier ID 26,32,33 ✉

<footnote n="1">¹Departamento de Biología, Escuela Politécnica Nacional del Ecuador, Ladrón de Guevara E11-253 y Andalucía, Quito, Ecuador. ²Biology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Calle Tulipán s/n, Móstoles, Madrid, Spain. ³Departamento de Biología, Área de Botánica, Universidad Autónoma de Madrid, Madrid, Calle Darwin 2, ES-28049, Madrid, Spain. ⁴Centro de Investigación en Biodiversidad y Cambio Global (CIBCUAM), Universidad Autónoma de Madrid, Calle Darwin 2, ES-28049, Madrid, Spain. ⁵Escuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente, Universidad Nacional Abierta a Distancia de Colombia, Sede José Celestino Mutis, Cl. 14 Sur 14-23, Bogotá, Colombia. ⁶Instituto Experimental de Biología Luis Adam Briancon, Universidad Mayor</footnote>
Real y Pontificia San Francisco Xavier de Chuquisaca, Dalence 235, Sucre, Bolivia. ⁷Departamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, Ecuador. San Cayetano Alto s/n. Paris y Marcelino Chamagnat, 1101608, Loja, Ecuador. ⁸Departamento de Biología. Grupo de Biología de Páramos y Ecosistemas Andinos, Universidad de Nariño, Calle 18 # 50-02 Ciudadela Universitaria Torobajo, Pasto, Colombia. ⁹Department of Environment, CAVELab - Computational and Applied Vegetation Ecology, Ghent University, Coupure links 653, B-9000, Gent, Belgium. ¹⁰Instituto de Ecología Regional, Universidad Nacional de Tucumán, CONICET, Residencia Universitaria Horco Molle, Edificio Las Cúpulas, 4107, Tucumán, Argentina. ¹¹Herbario UIS, Escuela de Biología, Universidad Industrial de Santander, Carrera. 27, calle 9a, Bucaramanga, Colombia. ¹²Herbario Nacional de Bolivia, Instituto de Ecología, Universidad Mayor de San Andrés, Calle 27 s/n, La Paz, Bolivia. ¹³Center for Conservation and Sustainable Development, Missouri Botanical Garden, 4344 Shaw Blvd., St. Louis, MO, 63110, USA. ¹⁴School of Geography, University of Leeds, Leeds, LS2 9JT, UK. ¹⁵Living Earth Collaborative, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USA. ¹⁶Department of Biology, University of Florida, 876 Newell Drive, ZIP 32611, Gainesville, Florida, USA. ¹⁷Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Calle 28 A # 15-09, Bogotá, Colombia. ¹⁸Conservación Internacional, Colombia, Carrea 13 # 71-41, Bogotá, Colombia. ¹⁹Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, D-06108, Halle, Germany. ²⁰Escuela de Ingeniería Agronómica, Universidad de Cuenca, Av. 12 de Abril y Av. Loja s/n, Cuenca, Ecuador. ²¹Global Tree Conservation Program and the Center for Tree Science, The Morton Arboretum, Lisle, IL, 60532-1293, USA. ²²Facultad de Ciencias Agrarias, Universidad Nacional de Jujuy, Alberdi 47, San Salvador de Jujuy, CP 4600, Jujuy, Argentina. ²³Department of Biology, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USA. ²⁴AMAP (Botanique et Modélisation de l'Architecture des Plantes et des Végétations), CIRAD, CNRS. INRA, IRD, Université de Montpellier, TA-A51/PS, Boulevard de la Lironde, 34398 cedex 5, Montpellier, France. ²⁵Environmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, UK. ²⁶Plant Ecology and Ecosystems Research, University of Goettingen, Untere Karspüle 2, 37073, Goettingen, Germany. ²⁷Instituto de Investigaciones para el Desarrollo Forestal (Indefor), Vía los Chorros de Milla, Mérida, Venezuela. ²⁸Departamento de Biología, Universidad Nacional de Colombia, Cra 45 #26-85, Bogotá, Colombia. ²⁹Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325, Frankfurt, Germany. ³⁰Department of Biology, Wake Forest University, Winston-Salem, NC, 27109, USA. ³¹Wildlife Conservation Society (WCS), 2300 Southern Boulevard Bronx, New York, 10460, USA. ³²Faculty of Resource Management, HAWK University of Applied Sciences and Arts, Büsgenweg 1 A, 37077, Goettingen, Germany. ³³Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, Germany. e-mail: selene.baez@epn.edu.ec; luis.cayuela@urjc.es; jhomeie@gwdg.de