Influence of tree height and age on leaf drip-tip morphology in lowland tropical rain forest trees
Data files
Oct 24, 2025 version files 209.56 KB
-
indiv_tree_study.csv
5.11 KB
-
leaf_sample_data.csv
199.66 KB
-
README.md
4.78 KB
Abstract
This dataset comprises leaf morphological measurements from 23 focal tree species within the Yasuní Forest Dynamics Plot in Yasuní National Park, Ecuador. It includes data from both saplings and adult trees, sampled in 2022 and compared with herbarium specimens collected during the plot’s establishment (1995–1997). Each entry documents species identity, life-history strategy, developmental stage, and detailed leaf traits, including lamina dimensions, drip-tip presence and length, and drip-tip ratios. The dataset is structured across two components: (1) leaf-level measurements for over 1,400 samples, and (2) individual-level comparisons of leaf apex morphology over time. Data quality annotations (e.g., herbivory, apex damage) are included. This resource supports trait-based ecological analyses, ontogenetic studies, and long-term morphological change assessments in tropical forest trees. Reuse potential includes comparative morphology and climate adaptation research.
Dataset DOI: 10.5061/dryad.crjdfn3j8
Description of the data and file structure
This dataset was collected to examine variation in leaf apex morphology—specifically, drip-tip presence and dimensions—across developmental stages and tree maximum height categorizations over time in tropical tree species. Leaf samples were obtained from 23 focal species within the Yasuní Forest Dynamics Plot in Ecuador. For each species, leaf traits were measured from multiple individuals to assess drip-tip variation across life-history strategies and developmental stages. Measurements were also taken from both living trees in 2022 and herbarium specimens collected during the plot’s initial census (1995–1997), enabling individual-level comparisons of drip-tips across a ~25-year interval.
Files and variables
File: indiv_tree_study.csv
Description: Contains metadata linking individual trees sampled in both 1995–1997 and 2022. Enables longitudinal comparisons of leaf morphology at the individual tree level across a ~25-year interval. Includes species identity, life-history strategy, tree tags, and herbarium/field sample IDs.
Variables
family: Taxonomic family
genus: Genus namespecies: Species namespec_code: Species code abbreviationlife_history_strategy: Growth form (e.g., treelet, mid-sized tree, tall tree)herb_ID: Herbarium sample IDtree_tag: Unique tree identifierdate_coll_newspaper: Date of herbarium collectionfield_ID: Field sample ID (multiple leaves per tree)date_coll_field: Date of field collectionnotes: Observational notes on field sample collection
File: leaf_sample_data.csv
Description: Contains leaf-level morphological measurements for all samples analyzed. Each row represents a single leaf sample with detailed trait data and observational notes.
Variables
leaf_ID: Unique leaf sample identifier
date: Date of sample collectionspec_code: Species code abbreviationfamily,genus,species: Taxonomic classificationlife_history_strategy: Growth formtree_tag_num: Tree identifierDBH (mm): Diameter at breast height in millimetersdevel_stage: Developmental stage (adult or sapling)leaf_type: Leaf shape (simple)margin: Leaf margin type (entire or non-entire)trichome_presence: Presence of leaf hairs (yes/no)lamina_length (cm): Length from base to apexlamina_width (cm): Maximum width perpendicular to midribdrip_tip: Presence of drip-tip (yes/no)drip_tip_length (cm): Length of drip-tipdrip_tip_ratio: Ratio of drip-tip length to lamina lengthnotes: Observational notes (e.g., damage, herbivory)
Missing Data Explanation
All missing data in the dataset are indicated by the value "NA". These entries are not arbitrary; they reflect specific conditions encountered during data collection and processing. The reasons for "NA" values include:
- Leaf damage: Physical damage to the leaf apex or lamina prevented accurate measurement.
- Scan limitations: Poor scan quality or leaf positioning made trait assessment impossible.
- Trait absence: In cases where a trait (e.g., drip-tip) was not present, related measurements (e.g., drip-tip length) were recorded as
"NA"if the value could not be meaningfully assigned. - Field constraints: Some trees had fewer than the intended number of leaves available, resulting in incomplete data.
- Metadata gaps: Occasionally, tree tags, tree DBH, and collection dates were not recorded or were ambiguous.
No blank cells are present in the dataset; all missing or unavailable information is consistently marked as "NA" to ensure clarity and compatibility with downstream analysis.
Code/software
Software Used:
- R (version 4.2.1) and RStudio (version 2022.07.1) were used for data cleaning, visualization, and statistical analysis.
- Fiji (ImageJ): Open-source image analysis software used to measure leaf traits from scanned images.
- Fiji version: 2.9.0
- Key plugins: Angle Tool, Segmented Line Selection Tool
Workflow Summary:
- Leaf scans were imported into Fiji for measurement of apex angles and drip-tip lengths.
- Measurements were exported to spreadsheets.
- R was used to calculate derived metrics (e.g., drip-tip ratios), clean data, and perform statistical analyses.
- Observational notes were manually annotated during fieldwork and image review.
No custom scripts are included in this submission, but the workflow is reproducible using the software and methods described.
Access information
NA