TUTORIAL 2 -Customized training database

library(ResNatSeed)

If the study area is beyond the Piedmont Region (North-Western Italy), it is advisable to provide a customized training database (see “Introduction to ResNatSeed” vignette for definitions) through the trainingDB function before using the RestInd function.

In order to be processed with the trainingDB function, the vegetation and topographical variables database (see “Introduction to ResNatSeed” vignette for definitions) must be structured as follows (see Table 1 for an example):

Rows of the dataframe are the surveys
The first column reports the survey codes
The second, third and fourth columns report the topographical variables, respectively:
- Elevation: expressed in meters above sea level (m a.s.l.)
- Slope: expressed in degrees (°)
- Aspect: expressed in degrees from North (°N) as it will be converted to “southness” to avoid circular variable issues in the models run with RestInd (Chang et al. 2004)
From the fifth column onwards, the plant species names. Plant species names do not necessarily have to be coded, they can be left in full. Afterwards, abbreviation codes will be automatically created in CEP names format: an abbreviation of species names according to the Cornell Ecology Programs (CEP), which uses eight-letter abbreviations for species. The CEP names code will be used to formulate the seed mixture or donor grassland composition.

Table 1. Example of the structure of the vegetation and topographical variables database to provide in the function trainingDB. This database includes 703 vegetation surveys and 532 plant species

#> # A tibble: 532 × 703
#>    Cod_ril  eleva…¹ slope aspect Nardu…² Festu…³ Carex…⁴ Festu…⁵ Trifo…⁶ Poa a…⁷
#>    <chr>      <dbl> <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 survey_…   1949. 23.9    244.    0       8.87    4.84   1.61    0       0    
#>  2 survey_…   2589. 31.9    145.    5.17    0      12.9    0       0       4.31 
#>  3 survey_…   2634. 44.1    240.    0       0       2.82   0       0       1.41 
#>  4 survey_…   2593. 42.3    188.    0       0      18.3    0       0       0.962
#>  5 survey_…   2195. 30.3    284.    0       2.59    0      0       0       2.59 
#>  6 survey_…   2105.  8.99   185.    0      23.0     0      0       0      15.9  
#>  7 survey_…   2082. 31.0    235.    1.83    6.10    4.27   1.22    0.610   0    
#>  8 survey_…   2078. 20.0    275.    0       8.33    3.70   0       0       4.63 
#>  9 survey_…   1880.  5.24   170.    1.53   29.8     0      0       0      20.6  
#> 10 survey_…   2053. 40.6    176.    0       1.53    0      0.763   0       0    
#> # … with 522 more rows, 693 more variables: `Festuca paniculata` <dbl>,
#> #   `Agrostis capillaris` <dbl>, `Brachypodium rupestre` <dbl>,
#> #   `Festuca violacea aggr.` <dbl>, `Anthoxanthum odoratum aggr.` <dbl>,
#> #   `Phleum rhaeticum` <dbl>, `Plantago aggr. alpina/serpentina` <dbl>,
#> #   `Helianthemum nummularium` <dbl>, `Achillea millefolium aggr.` <dbl>,
#> #   `Thymus serpyllum aggr.` <dbl>, `Avenella flexuosa` <dbl>,
#> #   `Ranunculus montanus aggr.` <dbl>, `Sesleria caerulea` <dbl>, …

At this point, the customized training database can be generated by specifying the name of the vegetation and topographical variables database in the data argument of the trainingDB function. Through this function it is possible to select the species eligible for the statistical modeling through two arguments:

spe.freq: this argument allows to set a threshold of minimum frequency of each species in the surveys. It is advisable to set values greater than or equal to 30 to allow appropriate statistical modeling.
min.spe.abundance: this argument allows to set a threshold of minimum abundance (greater than) of each species in each survey. When NULL the parameter is set to 0.

training.custom<-trainingDB(data=veg.composition.example,
           spe.freq = 30,# only species occurring at least in 30 surveys will be retained
           min.spe.abundance = 1# only species with at least 1% of relative abundance will be retained
           )
#> [1] "A list containing two dataframes has been created:"
#> [1] "1 - cep.names: database with the list of species suitable for modeling and their species codes (CEP names) to be used for the seed mixture or donor grassland composition"
#> [1] "2 - trainingDB.ResNatSeed: database to use in the 'RestInd' function"

Through trainingDB function, two dataframes are created and stored in a list, named in this example as ‘training.custom’. The two dataframes are:

cep.names: dataframe with the list of species suitable for modeling and their species codes (CEP names) to be used for the seed mixture or donor grassland composition. In this example 65 species (out of 532) are eligible for modeling

training.custom$cep.names
#>                             species  cep.names
#> 1        Achillea millefolium aggr.   Achiaggr
#> 2                   Agrostis alpina   Agroalpi
#> 3               Agrostis capillaris   Agrocapi
#> 4         Alchemilla vulgaris aggr.   Alchaggr
#> 5                Alopecurus alpinus   Alopalpi
#> 6                 Antennaria dioica   Antedioi
#> 7       Anthoxanthum odoratum aggr.   Anthaggr
#> 8                    Arnica montana   Arnimont
#> 9                 Avenella flexuosa   Avenflex
#> 10            Brachypodium rupestre   Bracrupe
#> 11                      Briza media   Brizmedi
#> 12            Campanula scheuchzeri   Campsche
#> 13               Carduus defloratus   Carddefl
#> 14               Carex sempervirens   Caresemp
#> 15                  Carlina acaulis   Carlacau
#> 16               Centaurea uniflora   Centunif
#> 17                Cerastium arvense   Ceraarve
#> 18                Crocus albiflorus   Crocalbi
#> 19                  Cruciata glabra   Crucglab
#> 20               Dactylis glomerata   Dactglom
#> 21                Dianthus pavonius   Dianpavo
#> 22              Festuca ovina aggr.   Festaggr
#> 23           Festuca violacea aggr. Festaggr.1
#> 24               Festuca paniculata   Festpani
#> 25              Festuca quadriflora   Festquad
#> 26                    Festuca rubra   Festrubr
#> 27             Galium lucidum aggr.   Galiaggr
#> 28           Gentiana acaulis aggr.   Gentaggr
#> 29                    Geum montanum   Geummont
#> 30         Helianthemum nummularium   Helinumm
#> 31          Hieracium glanduliferum   Hierglan
#> 32              Hieracium pilosella   Hierpilo
#> 33                  Juncus trifidus   Junctrif
#> 34             Leontodon helveticus   Leonhelv
#> 35               Leontodon hispidus   Leonhisp
#> 36               Lotus corniculatus   Lotucorn
#> 37                     Luzula lutea   Luzulute
#> 38                   Nardus stricta   Nardstri
#> 39               Onobrychis montana   Onobmont
#> 40                 Phleum rhaeticum   Phlerhae
#> 41          Phyteuma betonicifolium   Phytbeto
#> 42 Plantago aggr. alpina/serpentina   Planserp
#> 43                       Poa alpina    Poaalpi
#> 44                    Poa pratensis    Poaprat
#> 45                    Poa variegata    Poavari
#> 46               Polygonum bistorta   Polybist
#> 47              Polygonum viviparum   Polyvivi
#> 48              Potentilla crantzii   Potecran
#> 49           Potentilla grandiflora   Potegran
#> 50                 Ranunculus acris   Ranuacri
#> 51        Ranunculus montanus aggr.   Ranuaggr
#> 52              Ranunculus kuepferi   Ranukuep
#> 53                   Salix herbacea   Saliherb
#> 54                Sesleria caerulea   Seslcaer
#> 55       Taraxacum officinale aggr.   Taraaggr
#> 56           Thymus serpyllum aggr.   Thymaggr
#> 57                Trifolium alpinum   Trifalpi
#> 58               Trifolium pratense   Trifprat
#> 59                 Trifolium repens   Trifrepe
#> 60                 Trifolium thalii   Trifthal
#> 61              Trisetum flavescens   Trisflav
#> 62         Vaccinium gaultherioides   Vaccgaul
#> 63              Vaccinium myrtillus   Vaccmyrt
#> 64                Veronica allionii   Veroalli
#> 65                  Viola calcarata   Violcalc

trainingDB.ResNatSeed: training database to use in the RestInd function

head(training.custom$trainingDB.ResNatSeed)
#>       Codril elevation    slope  southness                    species cep.names
#> 1 survey_310  2093.494 39.45094 100.181328 Achillea millefolium aggr.  Achiaggr
#> 2  survey_40  2446.180 33.13994   0.308533 Achillea millefolium aggr.  Achiaggr
#> 3 survey_516  2042.679 29.37408 178.086090 Achillea millefolium aggr.  Achiaggr
#> 4 survey_198  1630.240 22.73879 129.326096 Achillea millefolium aggr.  Achiaggr
#> 5 survey_133  1945.885 12.64068 101.486450 Achillea millefolium aggr.  Achiaggr
#> 6  survey_58  2435.455 26.73384 162.208801 Achillea millefolium aggr.  Achiaggr
#>   abundance
#> 1  0.000000
#> 2  0.000000
#> 3  0.000000
#> 4  2.586207
#> 5  8.982036
#> 6  0.000000

From the list of species in training.custom$cep.names, it is necessary to create a database containing the seed mixture or donor grassland composition. The seed mixture or donor grassland composition must be a dataframe with two columns:

First column: species code abbreviated in CEP names format
Second column: abundance of each species. Abundance must be a number bounded between 0 and 100, which can be either a species relative abundance or a species cover (sensu Pittarello et al. (2016); Verdinelli et al. (2022)].

In this example, the donor grassland composition is characterized by four species:

Dactylis glomerata (CEP name: Dactglom), abundance: 25%
Festuca ovina aggr. (CEP name: Festaggr), abundance: 35%
Thymus serpyllum aggr. (CEP name: Thymaggr), abundance: 15%
Lotus corniculatus (CEP name: Lotucorn), abundance: 8%

Total abundances amount to 83%. The total abundance of the seed mixture or donor grassland composition should not necessarily amount 100%.

We can generate the dataframe of the donor grassland composition:

donor.composition<-data.frame(
  species=c("Dactglom","Festaggr","Thymaggr","Lotucorn"),
  abundance=c(25,35,15,8)
)

donor.composition
#>    species abundance
#> 1 Dactglom        25
#> 2 Festaggr        35
#> 3 Thymaggr        15
#> 4 Lotucorn         8

It is now possible to use the RestInd function to calculate the Suitability Index (SI) and Reliability Index (RI) by means of the donor grassland composition (dataframe ‘donor.composition’) and of the elevation, slope, and aspect of restoration site:

elevation: 1600 m a.s.l.
slope: 10°
aspect: 110° N

Remember that in the trainingDB argument it must be specified the one created above, i.e. training.custom$trainingDB.ResNatSeed

RestInd(trainingDB = training.custom$trainingDB.ResNatSeed, 
        composition=donor.composition,  
        elevation=1600, 
        slope=10, 
        aspect=110
        )
#> $DESCRIPTIVES
#>          cep.names                species n.obs min.ele max.ele min.slope
#> Dactglom  Dactglom     Dactylis glomerata    31     760    2254       1.8
#> Festaggr  Festaggr    Festuca ovina aggr.    39    1447    2756       1.5
#> Lotucorn  Lotucorn     Lotus corniculatus    45    1010    2756       3.8
#> Thymaggr  Thymaggr Thymus serpyllum aggr.    39     985    2756       5.9
#>          max.slope min.south max.south
#> Dactglom        45        18       173
#> Festaggr        47       6.9       173
#> Lotucorn        45       1.9       179
#> Thymaggr        45       6.9       179
#> 
#> $SPECIES_ABUNDANCES
#>   cep.names                species  PMA  POA ratio R2.adj RMSE SmDgA    EA
#> 1  Dactglom     Dactylis glomerata 16.6 27.0  0.61   0.36  5.3    25 15.25
#> 2  Festaggr    Festuca ovina aggr. 14.0 37.3  0.38   0.44  7.8    35 13.30
#> 3  Lotucorn     Lotus corniculatus  2.8 22.9  0.12   0.61  3.2     8  0.96
#> 4  Thymaggr Thymus serpyllum aggr.  5.7  8.5  0.67  -0.06  3.6    15 10.05
#> 
#> $INDEXES
#>     SI RI
#> 1 0.48  1

References

Chang, Chi-Ru, Pei-Fen Lee, Mei-Ling Bai, and Tzer-Tong Lin. 2004. “Predicting the Geographical Distribution of Plant Communities in Complex Terrain - a Case Study in Fushian Experimental Forest, Northeastern Taiwan.” Ecography 27 (5): 577588. https://doi.org/10.1111/j.0906-7590.2004.03852.x.

Pittarello, Marco, Massimiliano Probo, Michele Lonati, and Giampiero Lombardi. 2016. “Restoration of Sub-Alpine Shrub-Encroached Grasslands Through Pastoral Practices: Effects on Vegetation Structure and Botanical Composition.” Edited by Vigdis Vandvik. Applied Vegetation Science 19 (3): 381–90. https://doi.org/10.1111/avsc.12222.

Verdinelli, Marcello, Marco Pittarello, Maria Carmela Caria, Giovanna Piga, Pier Paolo Roggero, Gian Marco Marrosu, Stefano Arrizza, et al. 2022. “Congruent Responses of Vascular Plant and Ant Communities to Pastoral Land-Use Abandonment in Mountain Areas Throughout Different Biogeographic Regions.” Ecological Processes 11 (1): 35. https://doi.org/10.1186/s13717-022-00379-9.

Marco Pittarello and Davide Barberis

References