# Web Interface

In general, a 'trait' is a phenotype (a characteristic that the plant exhibits). The traits that we are primarily interested in collecting data for are listed in Table \ref{tab:traits}. Before adding trait data, it is necessary to have the citation, treatments, and site information already entered. If the correct citation is not identified at the top of the page Figure 8. To add a new Trait, go to the new trait page:

`Trait`

→ `new`

.**Key Traits Stored in BETYdb**

Variable | Units | Median (90%CI) or Range | Definition |

Vcmax | (\mu) mol CO(_2) m(^{2}) s(^{-1}) | (44 (12, 125)) | maximum rubisco carboxylation capacity |

SLA | m(^2) kg(^{-1}) | (15(4,27)) | Specific Leaf Area area of leaf per unit mass of leaf |

LMA | kg m(^{-2}) | (0.09 (0.03, 0.33)) | Leaf Mass Area (LMA = SLM = 1/SLA) mass of leaf per unit area of leaf |

leafN | % | (2.2(0.8, 17)) | leaf percent nitrogen |

c2n leaf | leaf C:N ratio | (39(21,79)) | use only if leafN not provided |

leaf turnover rate | 1/year | (0.28(0.03,1.0) ) | |

Jmax | (\mu) mol photons m(^{-2}) s(^{-1}) | (121(30, 262)) | maximum rate of electron transport |

stomatal slope | | (9(1, 20)) | |

GS | | | stomatal conductance (= gs(_{\textrm{max}}) |

q* | | 0.2--5 | ratio of fine root to leaf biomass |

*grasses | ratio of root:leaf = below:above ground biomass | | |

aboveground biomass | g m(^{-2}) or g plant(^{-1}) | | |

root biomass | g m(^{-2}) or g plant(^{-1}) | | |

*trees | ratio of fine root:leaf biomass | | |

leaf biomass | g m(^{-2}) or g plant(^{-1}) | | |

fine root biomass (<2mm) | g m(^{-2}) or g plant(^{-1}) | | |

root turnover rate | 1/year | 0.1--10 | rate of fine root loss (temperature dependent) year(^{-1}) |

leaf width | mm | 22(5,102) | |

growth respiration factor | % | 0--1 | proportion of daily carbon gain lost to growth respiration |

R(_{\textrm{dark}}) | | (\mu) mol CO(_2) m(^{-2}) s(^{-1}) | dark respiration |

quantum efficiency | % | 0--1 | efficiency of light conversion to carbon fixation, see Farqhuar model |

dark respiration factor | % | 0--1 | converts Vm to leaf respiration |

seedling mortality | % | 0--1 | proportion of seedlings that die |

r fraction | % | 0--1 | fraction of storage to seed reproduction |

root respiration rate* | CO(_2) kg(^{-1}) fine roots s(^{-1}) | 1--100 | rate of fine root respiration at reference soil temperature |

f labile | % | 0--1 | fraction of litter that goes into the labile carbon pool |

water conductance | | | |

Presently, we are also using the Trait table to record ecosystem level measurements other than Yield. Such ecosystem level measurements can include leaf area index or net primary productivity, but are only collected when required for a particular project. Most of the fields in the Traits table are also used in the Yields table. Here is a list of the fields with a brief description, followed by more thorough explanations:

**Species**: Search for species in the database using the search box; if speciesis not found, then the new species should be added to the database.**Cultivar**: primarily used for crops; If the cultivar being used is not found indrop-down box**DateLOC**: Date Level of confidence. See for values.**TimeLOC**: Time Level of confidence. See for values.**Mean**: For yield, mean is in units of tons per hectare per year (t/ha)**Stat name**: is the name of the statistical method used (usually one of SE, SD, MSE,CI, LSD, HSD, MSD). See for more details.**Statistic**: is the value of the statistic associated with Stat name.**N**: Always record N if provided. N is the number of experimentalreplicates, often referred to as the sample size; N represents thenumber of independent units within each treatment: in a fieldsetting, this is often the number of plots in each treatment, but ina greenhouse, growth chamber, or pot-study this may be the number ofchambers, pots, or individual plants. Sometimes this value is notclearly stated.

The date level of confidence (DateLOC, Table \ref{tab:dateloc}) provides an indication of how accurately the date associated with the trait or yield observation is known. It provides the values that should be entered in this field. If the event occurred at a level of precision not defined by an integer in this table, then use fractions. For example, we commonly use 5.5 to indicate a one week level of precision. If the exact year is not known, but the time of year is, then use 91 to 97, with the second digit to indicate the information known within the year.

**Table Date level of confidence (DateLOC) field**Numbering convention for the DateLOC (Date level of confidence) and TimeLOC (Time level of confidence) field, used in managements, traits, and yields table.

Dateloc | Definition |

9 | no data |

8 | year |

7 | season |

6 | month |

5 | day |

95 | unknown year, known day |

96 | unknown year, known month |

...etc | |

The time level of confidence (TimeLOC) provides an indication of how accurately the time associated with the trait or yield observation is known. It provides the values that should be entered in this field.

Timeloc | Definition |

9 | no data |

4 | time of day i.e. morning, afternoon |

3 | hour |

2 | minute |

1 | second |

Our goal is to record statistics that can be used to estimate standard deviation or standard error (https://www.authorea.com/users/5574/articles/6811/). Many different methods can be used to summarize data, and this is reflected in the diversity of statistics that are reported. An overview of these methods is given in a description below.

Where available, direct estimates of variance are preferred, including Standard Error (SE), sample Standard Deviation (SD), or Mean Squared Error (MSE). SE is usually presented in the format of mean (±SE). MSE is usually presented in a table. When extracting SE or SD from a figure, measure from the mean to the upper or lower bound. This is different than confidence intervals and range statistics (described below), for which the entire range is collected.

If MSE, SD, or SE are not provided, it is possible that LSD, MSD, HSD, or CI will be provided. These are range statistics and the most frequently found range statistics include a Confidence Interval (95%CI), Fisher’s Least Significant Difference (LSD), Tukey’s Honestly Significant Difference (HSD), and Minimum Significant Difference (MSD). Fundamentally, these methods calculate a range that indicates whether two means are different or not, and this range uses different approaches to penalize multiple comparisons. The important point is that these are ranges and that we record the entire range.

Another type of statistic is a “test statistic”; most frequently there will be an F-value that can be useful, but this should not be recorded if MSE is available. Only if there is no other information available should you record the P-value.

The protocol for entering yield data is identical to entering data for a trait, with a few exceptions:

- 1.There are no covariates associated with yield data
- 2.Yield data is always the dry harvestable biomass; if necessary, moisture content can be added as a trait

Yield is equivalent to aboveground biomass on a per-area basis, and has units of Mg ha^-1 y^-1

Covariates are required for many of the traits. Covariates generally indicate the environmental conditions under which a measurement was made. Without covariate information, the trait data will have limited value.

A complete list of required covariates can be found in Table \ref{tab:covariates}. For all respiration rates and photosynthetic parameters, temperature is recorded as a covariate. Soil moisture, humidity, and other such variables that were measured at the time of the measurement may be required in order to standardize across studies.

When root data is recorded, the root size class needs to be entered as a covariate. The term ’fine root’ often refers to the (<)2mm size class, and in this case, the covariate

`root_maximum_diameter`

would be set to 2. If the size class is a range, then the `root_minimum_diameter`

can also be used.**Table \ref{tab:covariates}: Traits with required covariates**\label{tab:covariates} A list of traits and the covariates that must be recorded along with the trait value in order to be converted to a constant scale from across studies.

*notes:*stomatal conductance (

`gs`

) is only useful when reported in conjunction with other photosynthetic data, such as `Amax`

. Specifically, if we have `Amax`

and `gs`

, then estimation of `Vcmax`

only covaries with `dark_respiration_factor`

and atmospheric CO2 concentration.
We also now have information to help constrain `stomatal_slope`

. If we have `Amax`

but not `gs`

, then our estimate of `Vcmax`

will covary with: `dark_respiration_factor`

, `CO2`

, `stomatal_slope`

, `cuticular_conductance`

, and vapor-pressure deficit `VPD`

(which is more difficult to estimate than CO2, but still possible given lat, lon, and date). Most important, there will be a strong covariance between `Vcmax`

and `stomatal_slope`

.Variable | Required Covariates | Optional Covariates |

vcmax | temperature (leafT or airT) | irradiance |

any leaf measurement | | canopy_height or canopy_layer |

root_respiration_rate | temperature (rootT or soilT) | soil moisture |

root_diameter_max | | root size class (usually 2mm) |

any respiration | temperature | |

root biomass | | min. size cutoff, max. size cutoff |

root, soil | depth (cm) | used for max and min depths of soil, if only one value, assume min depth = 0; negative values indicate above ground |

gs (stomatal conductance) | (A_{max}) | see notes in caption |

stomatal_slope (m) | humidity, temperature | specific humidity, assume leaf T = air T |

SLA | | canopy_level |

Last modified 4yr ago