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Table 1 Checklist of recommended guidelines for documenting allometric equations in scientific or technical publications

From: Guidelines for documenting and reporting tree allometric equations

Guideline category

Information to include

Definitions and concepts

Mandatory

Tree components measured (e.g. bole, crown, roots)

Type of height measurements (total, commercial)

Type of diameter measurements (point of measurement)

Units of measures

Definitions of variables used

Target population and environmental conditions

Mandatory

Geographic coordinates (latitude and longitude) and projection system

Elevation (in m above sea level)

Climate variables: mean annual temperature (°C), mean annual precipitation (mm/year), length of dry season (in month with rainfall <100 mm)

Estimated age or successional status

Highly recommended

Biographic or ecological classification system used (e.g. Holdridge Life Zone System)

Additional climate variables (: e.g. maximum climatological water deficit, seasonality in precipitation and in temperature.)

Dominant species

Stand structure (e.g. basal area, stand density–number of individuals per unit of area)

Phenology (e.g. deciduous, evergreen)

Landscape characteristics (slope, aspect)

Soil information (texture, depth)

Sampling and laboratory analysis

Mandatory

Sampling criteria (e.g. diameter classes, species composition or guild, plot-based sampling)

Sample size

Range of values for diameter, height, wood specific gravity, tree components, etc.

Scientific and vernacular (if used) names

Methods used in the field or in the laboratory (e.g. method used to measure wood specific gravity)

Highly recommended

Number of replicates

Instruments used in the field or in the laboratory (e.g. laser model for tree height measurement)

Precision of instruments used

Calculation procedures

Model fitting, prediction and uncertainty

Mandatory

Functional form of the model(s) (e.g. power, non-linear, log-log)

Model mathematical formula, including form of the error term (multiplicative versus additive)

Data transformations (if any, e.g. log transformation)

Statistical parameters (R², RSE, mean bias, at a minimum)

Parameter values and confidence intervals of the parameters

Comparative statistics (e.g. F-value, AIC, BIC, Furnival index)

Software (and version)

Highly recommended

Analysis scripts

Correlation matrix between parameters

Correlation between compartments in the case of SUR regressions

Meta data

Highly recommended

Purpose of the data

Dates of project

Data owner (contact information)

Storage and rights of use