Species functions¶
Formatting species data¶
Species data tend to come in the format Genus species, but most functions in silvR require separate vectors for Genus and species.
silvR has a function to split character strings in the format Genus species or Genus.species into two vectors, and enforces correct capitalisation:
> splitGenusSpecies(mstems$species_name)
genus species
1 Parinari curatellifolia
2 Terminalia sericea
3 Afzelia quanzensis
4 Erythrophleum africanum
5 Terminalia sericea
6 Brachystegia spiciformis
...
These can be attached to the original data.frame using the cbind function from base R to be used as inputs to other functions:
> mstems <- cbind(mstems, splitGenusSpecies(mstems$species_name))
> head(mstems$genus) # Show the first few lines of the new field
[1] "Parinari" "Terminalia"
[3] "Afzelia" "Erythrophleum"
[5] "Terminalia" "Brachystegia"
Correcting species data¶
On occasion we’ll want to group species by order or family, requiring time consuming work. Getting consistent spellings in forest inventory data is a further challenge, yet necessary in order to produce accurate assessments of species distributions and biodiversity. There already exist R packages that are capable of automating such spelling corrections (e.g. BIOMASS and taxize), in silvR we have aimed to simplify some of this processing based on functions from the BIOMASS package.
The function getTaxonomicNames has multiple purposes. First, it can identify order and family given a genus:
> taxonomy <- getTaxonomicNames(mstems$genus)
> head(taxonomy)
order family genus species
1 Malpighiales Chrysobalanaceae Parinari <NA>
2 Myrtales Combretaceae Terminalia <NA>
3 Fabales Fabaceae Afzelia <NA>
4 Fabales Fabaceae Erythrophleum <NA>
5 Myrtales Combretaceae Terminalia <NA>
6 Fabales Fabaceae Brachystegia <NA>
If we’re only interested in one taxonomic rank, we can specify it using the ranks input:
> mstems$family <- getTaxonomicNames(mstems$genus, ranks = c('family')) # Add to original data.frame
> head(mstems$family)
[1] "Chrysobalanaceae" "Combretaceae" "Fabaceae" "Fabaceae" "Combretaceae"
[6] "Fabaceae"
getTaxonomicNames can also search through species databases and perform automated correcion of species names using the clean_input option:
> species_corrected <- getTaxonomicNames(mstems$genus, species = mstems$species, clean_input = TRUE)
> head(species_corrected)
order family genus species
1 Malpighiales Chrysobalanaceae Parinari curatellifolia
2 Myrtales Combretaceae Terminalia sericea
3 Fabales Fabaceae Afzelia quanzensis
4 Fabales Fabaceae Erythrophleum africanum
5 Myrtales Combretaceae Terminalia sericea
6 Fabales Fabaceae Brachystegia spiciformis
To also show which species names have been changed, use the return_changes option which will return a nameModified field:
> species_corrected <- getTaxonomicNames(mstems$genus, species = mstems$species, clean_input = TRUE, return_changes = TRUE)
> head(species_corrected)
order family genus species nameModified
1 Malpighiales Chrysobalanaceae Parinari curatellifolia FALSE
2 Myrtales Combretaceae Terminalia sericea FALSE
3 Fabales Fabaceae Afzelia quanzensis FALSE
4 Fabales Fabaceae Erythrophleum africanum FALSE
5 Myrtales Combretaceae Terminalia sericea FALSE
6 Fabales Fabaceae Brachystegia spiciformis FALSE
Note
We advise that you use these functions with caution and don’t rely on their outputs 100%; they can occasionally make mistakes leaving you with records of species that don’t actually exist in your inventory plots.
Loading wood density¶
Wood density is a useful plant trait, and is required by some allometric equations to predict tree biomass. Wood density is species specific, and can be loaded from existing databases. silvR uses the BIOMASS library to produce estimates of wood density.
At it’s simplest, this function requires a genus as input:
> mstems$wood_density <- loadWoodDensity(mstems$genus)
We can get better estimates with both genus and species:
> mstems$wood_density <- loadWoodDensity(mstems$genus, species = mstems$genus)
In some cases a species will not be present in the wood density database. For these cases, we can also input family:
> mstems$wood_density <- loadWoodDensity(mstems$genus, species = mstems$genus, family = mstems$family)
For cases where family is also not enough to retrieve a wood density, we can input the plot code for each stem which will apply the average wood density for each plot to unknown species:
> mstems$wood_density <- loadWoodDensity(mstems$genus, species = mstems$genus, family = mstems$family, plot = mstems$plotcode)
Note
By default silvR assumes a location of tropical Africa. If you’re working on data from outside Africa, take a look at at the documentation using ?loadWoodDensity and the region option.
Esimating aboveground biomass¶
For some more complex allometric equations, species names and tree height are used to extract wood density estimates. An example of these are the ‘Chave’ allometric equations applicable to tropical trees, described in:
- Chave, Jérôme, et al. “Tree allometry and improved estimation of carbon stocks and balance in tropical forests.” Oecologia 145.1 (2005): 87-99.
- Chave, Jérôme, et al. “Improved allometric models to estimate the aboveground biomass of tropical trees.” Global change biology 20.10 (2014): 3177-3190.
To use these equations in silvR, we’ll return to the calculateBiomass function, using additional inputs describing species:
> mstems$AGB_Chave05 <- calculateBiomass(mstems$DBH, height = mstems$height, model = 'Chave05', family = mstems$family, genus = mstems$genus, species = mstems$species)
> mstems$AGB_Chave14 <- calculateBiomass(mstems$DBH, height = mstems$height, model = 'Chave14', family = mstems$family, genus = mstems$genus, species = mstems$species)
See ?calculateBiomass for more information.
Identifying dominant species¶
A simple way of summarising species data is to identify the dominant species at each plot.
By default, the function identifies 5 species by stocking density at each plot:
> dominant_species <- getDominantSpecies(mstems$species_name, mstems$plot_code)
> head(dominant_species)
plot_code dominant_sp_1 dominant_sp_2 dominant_sp_3
1 plot_01 Isoberlinia angolensis Brachystegia spiciformis Julbernardia globiflora
2 plot_02 Brachystegia spiciformis Isoberlinia angolensis Erythrophleum africanum
3 plot_03 Faurea saligna Isoberlinia angolensis Brachystegia spiciformis
4 plot_04 Isoberlinia angolensis Brachystegia spiciformis Julbernardia globiflora
5 plot_05 Isoberlinia angolensis Brachystegia spiciformis Burkea africana
6 plot_06 Julbernardia globiflora Brachystegia spiciformis Erythrophleum africanum
dominant_sp_4 dominant_sp_5 dominant_sp_mt_1 dominant_sp_mt_2 dominant_sp_mt_3
1 Parinari curatellifolia Diplorhynchus condylocarpon 21 13 13
2 Faurea saligna Anisophyllea boehmii 13 9 8
3 Julbernardia globiflora Diplorhynchus condylocarpon 18 18 17
4 Anisophyllea boehmii Burkea africana 24 22 20
5 Marquesia macroura Brachystegia boehmii 16 15 11
6 Burkea africana Dombeya rotundifolia 14 10 10
dominant_sp_mt_4 dominant_sp_mt_5 dominant_sp_pc_1 dominant_sp_pc_2 dominant_sp_pc_3 dominant_sp_pc_4
1 13 11 13.20755 8.176101 8.176101 8.176101
2 8 7 10.83333 7.5 6.666667 6.666667
3 17 13 7.725322 7.725322 7.296137 7.296137
4 17 15 9.125475 8.365019 7.604563 6.463878
5 11 9 9.69697 9.090909 6.666667 6.666667
6 9 9 9.090909 6.493506 6.493506 5.844156
dominant_sp_pc_5
1 6.918239
2 5.833333
3 5.579399
4 5.703422
5 5.454545
6 5.844156
Note
This function uses complete species names (i.e. Genus species) in place of individual genus and species vectors.
The data.frame output by this function is complex. Columns dominant_sp_# show the dominant species for each plot (from 1 to n), dominant_sp_mt_# show the value of the comparison metric (in this case stocking density), and dominant_sp_pc_# the percentage of the comparison metric by each species.
We can combine this function with structural information, such as basal area or biomass. For example:
> dominant_species <- getDominantSpecies(mstems$species_name, mstems$plot_code, metric = calculateBasalArea(mstems$DBH))
or aboveground biomass
> dominant_species <- getDominantSpecies(mstems$species_name, mstems$plot_code, metric = calculateBiomass(mstems$DBH, model = 'Ryan11'))
Calculating biodiversity¶
Biodiversity describes the varaibility of species in a forest inventory plot. Measures of biodiversity vary from very simple (species richness) to more complex measures based on the distribution of individuals amongst species.. These measures of diveristy are collectively called ‘diversity indices’.
silvR is pre-programmed with a range of diversity indices (see documentation at ?calculateBiodiversity).
To calculate species richness:
> calculateBiodiversity(mstems$species_name, mstems$plot_code, index = 'richness')
plot_01 plot_02 plot_03 plot_04 plot_05 plot_06 plot_07 plot_08 plot_09 plot_10 plot_11 plot_12 plot_13
25 26 27 27 25 25 25 25 22 20 25 26 15
plot_14 plot_15 plot_16 plot_17 plot_18 plot_19 plot_20 plot_21 plot_22 plot_23 plot_24 plot_25
22 26 25 25 25 26 26 23 24 20 23 24
For a more complex measure of diveristy, use the ‘simpson’ or ‘shannon’ indices:
> calculateBiodiversity(mstems$species_name, mstems$plot_code, index = 'shannon')
plot_01 plot_02 plot_03 plot_04 plot_05 plot_06 plot_07 plot_08 plot_09 plot_10 plot_11
2.930107 3.025561 3.117128 3.089926 3.009947 3.088370 3.060124 2.978967 2.887835 2.767140 3.012447
plot_12 plot_13 plot_14 plot_15 plot_16 plot_17 plot_18 plot_19 plot_20 plot_21 plot_22
2.984938 2.440725 2.917779 2.954540 3.023698 3.015096 2.959135 3.062585 3.005983 2.944163 2.998152
plot_23 plot_24 plot_25
2.815136 2.944381 2.940585
If you want to calculate diodiversity based on another metric, such as basal area, use the abundance option:
calculateBiodiversity(mstems$species_name, mstems$plot_code, index = 'shannon', abundance = calculateBasalArea(mstems$DBH))
plot_01 plot_02 plot_03 plot_04 plot_05 plot_06 plot_07 plot_08 plot_09 plot_10 plot_11
2.645848 2.846774 3.063116 2.854390 2.710108 2.982382 2.983830 2.747758 2.654970 2.366331 2.973090
plot_12 plot_13 plot_14 plot_15 plot_16 plot_17 plot_18 plot_19 plot_20 plot_21 plot_22
2.865765 1.988800 2.796020 2.862064 2.613919 2.917068 2.728365 2.991442 2.868902 2.707835 2.750818
plot_23 plot_24 plot_25
2.435320 2.654030 2.455297
Preparing data for vegan (advanced)¶
If you’re interested in assessment of biodiversity and species distributions, we can recommend looking at the vegan package in R, which provides a wide variety of very useful functions. Many functions in vegan require data to be arranged in a different format to silvR.
To convert to a vegan applicable format, run:
mstems_vegan <- prepareForVegan(mstems$species_name, mstems$plot_code)
See ?prepareForVegan for further options.