| Attribute Name -- Attribute Label |
Unit |
Attribute definition |
Site_name
--
Site name
|
no unit |
Site name |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Name of the sampled site |
|
Description
:
|
Name of the sampled site |
|
|
Tree_species
--
Tree species
|
no unit |
Tree species |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Identification by comparison with type specimens, identified museum specimens or photos thereof, or by external experts. |
|
Description
:
|
Identification by comparison with type specimens, identified museum specimens or photos thereof, or by external experts. |
|
|
bark_thickness
--
Tree bark thickness
|
Centimeter |
Measured with a bark gauge at 1m above ground |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Bark thickness |
|
Description
:
|
Measured with a bark gauge at 1m above ground |
|
|
WSG
--
Wood specific gravity
|
Unitless |
Wood specific gravity was calculated as the sample dry mass divided by sample fresh volume |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
WSG |
|
Description
:
|
Wood cores (5 mm diameter, 5 cm length) of the outer sapwood were taken at 1 m above ground with an increment corer, volume of the fresh samples was determined and samples were subsequently dried at 102°C for 72 hours to determine wood dry mass. Wood specific gravity (WSG) was calculated as the sample dry mass divided by sample fresh volume. |
|
|
vessel_density
--
Mean sapwood vessel density
|
density |
Digitized cross-sectional cuts were used to estimate mean vessel density |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
- |
|
Description
:
|
- |
|
|
vessel_diameter
--
Mean sapwood vessel diameter
|
Micrometer |
Digitized cross-sectional cuts were used to estimate mean vessel diameter |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
- |
|
Description
:
|
- |
|
|
sapwood_specific_conductivity
--
Sapwood-specific conductivity
|
conductivity |
Theoretical sapwood-specific conductivity calculated according to the Hagen-Poiseuille equation |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
- |
|
Description
:
|
- |
|
|
leaf_area
--
leaf area
|
Square centimeter |
total area of leaves calculated in square centimeters |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Leaf size (canopy) |
|
Description
:
|
Estimating mean leaf size from a representative number of scanned canopy leaves. |
|
|
specific_leaf_area
--
Specific leaf area
|
Square centimeters per gram |
the specific leaf area (SLA) in square centimeters per gram |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Calculated |
|
Description
:
|
Calculated |
|
|
leaf_N
--
Foliar N concentration
|
Milligram per gram |
concentrations of N in foliar tissue |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
CN analyzer |
|
Description
:
|
Leaf dry mass was analyzed for its C and N content with a CN analyzer (Vario EL III, Hanau, Germany) |
|
Instrumentation:
|
Instrument:
Description: CNS analysis with high temperature combustion of organic and inorganic solid samples and detection of the gaseous products with WLD
|
Instrument: CNS-Analyser HERAEUS
|
Vendor: HERAEUS
|
|
|
|
leaf_P
--
Foliar P concentration
|
Milligram per gram |
concentrations of P in foliar tissue |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Determine foliar nutrient concentration with Inductively Coupled Plasma Analyzer |
|
Description
:
|
Foliar nutrient concentration was determined with an Inductively Coupled Plasma Analyzer (Thermo Fisher Scientific) after digesting the ground leaf material with concentrated HNO3 |
|
Instrumentation:
|
Instrument:
Description:
|
Instrument: Inductively Coupled Plasma Analyzer
|
Vendor: Thermo Fisher Scientific
|
|
|
|
leaf_toughness
--
Leaf toughness
|
force |
Force to punch a leaf using a digital penetrometer (2.0 mm diameter) on fresh leaves (excluding the midrib and other major veins) |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
Leaf toughness |
|
Description
:
|
Force to punch a leaf using a digital penetrometer (2.0 mm diameter) on fresh leaves (excluding the midrib and other major veins) |
|
|
Species_mean_temp
--
Species mean of mean annual temperature
|
Degrees Celsius |
We estimated the thermal optima of tree species present based on the locations of observation and collection records relative to large-scale climate patterns following protocols modified from Fadrique et al. (2018). We downloaded all available georeferenced records of the target species from the Andean countries of Venezuela, Colombia, Ecuador, Peru, Bolivia and Argentina from the Botanical Information and Ecological Network (BIEN) database. We added distribution data from the BioWeb database for those species that were absent or that had <20 records in BIEN. To minimize possible bias, we eliminated records with obvious georeferencing errors or that fell outside the Andean region and we only used one record per coordinate for each species to avoid including duplicates. Next, we extracted the mean annual temperature (MAT) values at the record coordinates from the CHELSA extrapolated climate map with a spatial resolution of 30 arcsec (approximately 1 km2 at the equator). We then calculated each species’ thermal optimum as the average of the extracted MAT values. Reference: Fadrique et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature. 2018;514:207-12. doi: 10.1038/s41586-018-0715-9. |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
- |
|
Description
:
|
- |
|
|
Species_mean_precip
--
Species mean of total annual precipitation
|
Millimeter |
We estimated the precipitation optima of the tree species based on the locations of observation and collection records relative to large-scale climate patterns following protocols modified from Fadrique et al. (2018). Specifically, we downloaded all available georeferenced records of the target species from the Andean countries of Venezuela, Colombia, Ecuador, Peru, Bolivia and Argentina from the Botanical Information and Ecological Network (BIEN) database. We added distribution data from the BioWeb database for those species that were absent or that had <20 records in BIEN. To minimize possible bias, we eliminated records with obvious georeferencing errors or that fell outside the Andean region and we only used one record per coordinate for each species to avoid including duplicates. Next, we extracted the mean total annual precipitation (MAP) values at the record coordinates from the CHELSA extrapolated climate map with a spatial resolution of 30 arcsec (approximately 1 km2 at the equator). We then calculated each species’ precipitation optimum as the average of the extracted MAP values. Reference:Fadrique et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature. 2018;514:207-12. doi: 10.1038/s41586-018-0715-9. |
|
Method
(Sampling/processing description of the values of
this attribute by one ore more steps.)
|
|
--- Step
1
---
|
|
Title
:
|
- |
|
Description
:
|
- |
|
|
