## Date: April 24, 2023

## Dataset Title: Trait data elevational gradient Puerto Rico 2014

## Dataset creators: Maria Natalia Umaña, Nathan G. Swenson, Gabriel Arellano

## Dataset contact: maumana@umich.edu

## Funding: NSF DEB-1501341

## Key points: 
-Trees display a dynamic functional response to extreme climatic events, with leaf traits related to efficient photosynthesis driving tree growth after hurricanes with, while after the drought, tree growth increased for trees with hydraulically efficient traits (low wood density and water use efficiency). 

-In the longer term, over both drought and hurricanes, tree size was the only predictor of growth, with growth increasing with smaller trees. However, the predictive power of traits was low, suggesting that the devastating effects of these extreme events may weaken the effect of traits on growth. 

##Research overview:
Identifying the functional traits that enable recovery after extreme events is necessary for assessing forest persistence and functioning, yet this is a difficult task because the traits mediating the responses to disturbance may vary depending on the disturbance type and over time. This study investigates the effects of traits on tree growth ­–for short and longer terms– in response to two vastly different extreme climatic events, droughts, and hurricanes, in a Puerto Rican forest. 

## Methodology: Six broadly distributed and common tree species were selected for the study: Cecropia schreberiana Miq subspecies schreberiana (Urticaceae), Cordia borinquensis Urb. (Boraginaceae), Dacryodes excelsa Vahl (Burseraceae), Henriettea squamulosa (Cogn.) Judd (Melastomataceae), Micropholis garciniifolia Pierre (Sapotaceae), and Sloanea berteroana Choisy ex DC (Elaeocarpaceae) (table 1). In 2013, Iat least ten individuals of each species present at each elevation from 250 to 1000 every 50 m . I measured six leaf traits, one wood trait, crown volume, and dbh (table 2). Canopy branches and leaves for most of the trees monitored for growth were collected during June and July 2015. For the leaf traits, I selected three healthy and fully developed leaves located in the most distal part of the branches. For crown volume, I measured tree crown area and crown depth. For crown area, I measured the distance from the tree's trunk to the crown edge in the four azimuth directions using a decameter. For crown depth, I measured the height from the first branch of the tree to the top of the crown using a laser rangefinder (True Pulse 360/360B, USA). The crown area was calculated assuming an ellipsoid shape, and then this value was multiplied by the crown depth to calculate crown volume. 

## Files contained here:
Traits_EL.PR.csv: the file contains trait data for trees of six species distributed along an elevational gradient in puerto rico.

## Description of the data and file structure

LCIC: Leaf carbon isotopic content (%).
LA: leaf area (cm^2).
LNC: Leaf nitrogen content (%).
SLA: specific leaf area (cm^2 / g).
LTh: Leaf thickness (mm).
LTo: leaf toughness (N).
WSG: wood specific gravity (g/cm^3).
CV: tree crown volume (m^3).
SP.CODE: species name. Cecropia schreberiana, Cordia borinquensis,  Dacryodes excelsa, Henriettea squamulosa, Micropholis garciniifolia, Sloanea berteroana.

Related publication:
Umaña, M. N. In review. The interplay of drought and hurricanes on tree recovery: insights from dynamic and weak functional responses. Forthcoming