This tutorial explains how to compute the family of indices presented
in Chao
*et al.* (2019) using `mFD`

.

The data set used to illustrate this tutorial is the **fruits
dataset** based on 25 types of fruits (*i.e.* species)
distributed in 10 fruits baskets (*i.e.* assemblages). Each fruit
is characterized by six traits values summarized in the following
table:

Trait name | Trait measurement | Trait type | Number of classes | Classes code | Unit |
---|---|---|---|---|---|

Size | Maximal diameter | Ordinal | 5 | 0-1 ; 1-3 ; 3-5 ; 5-10 ; 10-20 | cm |

Plant | Growth form | Categorical | 4 | tree; shrub; vine; forb | NA |

Climate | Climatic niche | Ordinal | 3 | temperate ; subtropical ; tropical | NA |

Seed | Seed type | Ordinal | 3 | none ; pip ; pit | NA |

Sugar | Sugar | Continuous | NA | NA | g/kg |

Use | Use as food | Fuzzy | 3 | raw ; pastry ; jam | % |

We load the three objects used to compute functional framework (for more explanations, see mFD General Workflow):

- a
**data frame**summarizing traits values for each species called`fruits_traits`

in this tutorial:

```
data("fruits_traits", package = "mFD")
knitr::kable(head(fruits_traits),
caption = "Species x traits data frame based on the **fruits** dataset")
```

Size | Plant | Climate | Seed | Sugar | Use.raw | Use.pastry | Use.jam | |
---|---|---|---|---|---|---|---|---|

apple | 5-10cm | tree | temperate | pip | 103.9 | 50 | 50 | 0 |

apricot | 3-5cm | tree | temperate | pit | 92.4 | 40 | 10 | 50 |

banana | 10-20cm | tree | tropical | none | 122.3 | 70 | 20 | 10 |

currant | 0-1cm | shrub | temperate | pip | 73.7 | 10 | 10 | 80 |

blackberry | 1-3cm | shrub | temperate | pip | 48.8 | 30 | 10 | 60 |

blueberry | 0-1cm | forb | temperate | pip | 100.0 | 10 | 40 | 50 |

- a
**matrix**summarizing species assemblages called`baskets_fruits_weights`

in this tutorial. Weights in this matrix can be occurrence data, abundance, biomass, coverage, etc. The studied example works with biomass (*i.e.*grams of a fruit in a basket) and this matrix looks as follows:

```
data("baskets_fruits_weights", package = "mFD")
knitr::kable(as.data.frame(baskets_fruits_weights[1:6, 1:6]),
caption = "Species x assemblages matrix based on the **fruits** dataset")
```

apple | apricot | banana | currant | blackberry | blueberry | |
---|---|---|---|---|---|---|

basket_1 | 400 | 0 | 100 | 0 | 0 | 0 |

basket_2 | 200 | 0 | 400 | 0 | 0 | 0 |

basket_3 | 200 | 0 | 500 | 0 | 0 | 0 |

basket_4 | 300 | 0 | 0 | 0 | 0 | 0 |

basket_5 | 200 | 0 | 0 | 0 | 0 | 0 |

basket_6 | 100 | 0 | 200 | 0 | 0 | 0 |

- a
**data frame**summarizing traits types called`fruits_traits_cat`

in this tutorial (for details, see mFD General Workflow):

```
data("fruits_traits_cat", package = "mFD")
knitr::kable(head(fruits_traits_cat),
caption = "Traits types based on **fruits & baskets** dataset")
```

trait_name | trait_type | fuzzy_name |
---|---|---|

Size | O | NA |

Plant | N | NA |

Climate | O | NA |

Seed | O | NA |

Sugar | Q | NA |

Use.raw | F | Use |

Then, we can compute functional distance using the
`mFD::funct.dist()`

function as follows:

**USAGE**

```
## [1] "Running w.type=equal on groups=c(Size)"
## [1] "Running w.type=equal on groups=c(Plant)"
## [1] "Running w.type=equal on groups=c(Climate)"
## [1] "Running w.type=equal on groups=c(Seed)"
## [1] "Running w.type=equal on groups=c(Sugar)"
## [1] "Running w.type=equal on groups=c(Use,Use,Use)"
```

The family of indices presented in Chao
*et al.* (2019) allows computing FD based on pairwise
distance between species and their weights in assemblages. This
generalization of Hill numbers framework is based on two parameters:

`q`

: the importance of species weight compared to species distance. Values allowed in`mFD`

are 0, 1, 2 (the most often used, see below).`tau`

: the threshold of functional distinctiveness between any two species (*i.e.*all species with distance above this threshold are considered as functionally equally distinct). Values allowed in`mFD`

are “min(imum)”, “mean(imum)” and “max(imum)”.

Indices are expressed as effective number of functionally equally
distinct species (or *virtual functional groups*) and could thus
be directly compared to taxonomic Hill numbers (including species
richness).

**NOTE** For more details about the properties of Hill
numbers FD read Chao
*et al.* (2019) and especially its Figures 1 & 2.

All these indices can be computed with the function
`mFD::alpha.fd.hill()`

.

Here we start by comparing the **‘classical’ Rao’s quadratic
entropy expressed in Hill numbers** following Ricotta
& Szeidl (2009) which is the special case with
`q = 2`

and `tau = "max"`

.

**USAGE**

```
baskets_FD2max <- mFD::alpha.fd.hill(
asb_sp_w = baskets_fruits_weights,
sp_dist = fruits_gower,
tau = "max",
q = 2)
```

Then, we can compute **Hill numbers FD of order 2**
computed with `tau = "mean"`

and `q = 2`

as
recommended in Chao
*et al.* (2019)

**USAGE**

```
baskets_FD2mean <- mFD::alpha.fd.hill(
asb_sp_w = baskets_fruits_weights,
sp_dist = fruits_gower,
tau = "mean",
q = 2)
```

We can now compare these two metrics:

```
round(cbind(FD2max = baskets_FD2max$"asb_FD_Hill"[ , 1],
FD2mean = baskets_FD2mean$"asb_FD_Hill"[ , 1]), 2)
```

```
## FD2max FD2mean
## basket_1 1.50 2.62
## basket_2 1.83 3.97
## basket_3 1.86 4.10
## basket_4 1.27 1.72
## basket_5 1.30 1.85
## basket_6 1.74 3.73
## basket_7 1.82 3.94
## basket_8 1.40 2.16
## basket_9 1.53 2.75
## basket_10 1.53 3.01
```

We can see that FD computed with `tau = "max"`

is less
variable (ranging from 1.50 to only 1.86) than FD computed with
`tau = "min"`

(ranging from 1.72 to 4.10) illustrating its
**higher sensitivity to functional differences between
species**.

**NB** **Note that even with**
`q = 0`

**, weights of species are still accounted for
by FD.** Hence, if the goal is to compute a richness-like index
(*i.e.* accounting only for distance between species present),
function `mFD::alpha.fd.hill()`

should be applied to
**species occurrence data** (coded as 0/1, previously
computed using sp.tr.summary) so that all species have the same weight).
Species occurrence data can be retrieve through the
`mFD::asb.sp.summary()`

function:

**USAGE**

```
# Retrieve species occurrences data:
baskets_summary <- mFD::asb.sp.summary(baskets_fruits_weights)
baskets_fruits_occ <- baskets_summary$"asb_sp_occ"
head(baskets_fruits_occ)
```

```
## apple apricot banana currant blackberry blueberry cherry grape
## basket_1 1 0 1 0 0 0 1 0
## basket_2 1 0 1 0 0 0 1 0
## basket_3 1 0 1 0 0 0 1 0
## basket_4 1 0 0 0 0 0 0 0
## basket_5 1 0 0 0 0 0 0 0
## basket_6 1 0 1 0 0 0 0 0
## grapefruit kiwifruit lemon lime litchi mango melon orange
## basket_1 0 0 1 0 0 0 1 0
## basket_2 0 0 1 0 0 0 1 0
## basket_3 0 0 1 0 0 0 1 0
## basket_4 0 1 1 0 0 0 0 1
## basket_5 0 1 1 0 0 0 0 1
## basket_6 0 0 0 1 1 1 0 1
## passion_fruit peach pear pineapple plum raspberry strawberry tangerine
## basket_1 1 0 1 0 0 0 1 0
## basket_2 1 0 1 0 0 0 1 0
## basket_3 1 0 1 0 0 0 1 0
## basket_4 0 1 1 0 1 0 0 1
## basket_5 0 1 1 0 1 0 0 1
## basket_6 0 0 0 1 0 0 0 0
## water_melon
## basket_1 0
## basket_2 0
## basket_3 0
## basket_4 0
## basket_5 0
## basket_6 1
```

```
# Compute alpha FD Hill with q = 0:
baskets_FD0mean <- mFD::alpha.fd.hill(
asb_sp_w = baskets_fruits_occ,
sp_dist = fruits_gower,
tau = "mean",
q = 0)
round(baskets_FD0mean$"asb_FD_Hill", 2)
```

```
## FD_q0
## basket_1 4.73
## basket_2 4.73
## basket_3 4.73
## basket_4 1.93
## basket_5 1.93
## basket_6 4.57
## basket_7 4.57
## basket_8 3.67
## basket_9 3.67
## basket_10 3.52
```

We can see that baskets with same composition of fruits species have
same FD values (*e.g* *basket_1*, *basket_2* and
*basket_3*)

Framework of Chao
*et al.* (2019) also allows computing beta-diversity, with 2
framework similar to Jaccard and Sorensen ones for taxonomic diversity.
The `mFD:beta.fd.hill()`

function computes these indices.

**NB** Note that **total weight of assemblages is
affecting computation of functional beta-diversity**. Hence
**if it is does not reflect an ecological pattern**
(*e.g*. rather difference in sampling effort), it is recommended
to **apply** `mFD::beta.fd.hill()`

**to
relative weight of species in assemblages**.

```
## basket_1 basket_2 basket_3 basket_4 basket_5 basket_6 basket_7 basket_8
## 2000 2000 2000 2000 2000 2000 2000 2000
## basket_9 basket_10
## 2000 2000
```

```
# Here baskets all contain 2000g of fruits, we illustrate how to compute...
# relative weights using the output of asb.sp.summary:
baskets_fruits_relw <- baskets_fruits_weights / baskets_summary$"asb_tot_w"
apply(baskets_fruits_relw, 1, sum)
```

```
## basket_1 basket_2 basket_3 basket_4 basket_5 basket_6 basket_7 basket_8
## 1 1 1 1 1 1 1 1
## basket_9 basket_10
## 1 1
```

Now we can compute functional beta-diversity of order
`q = 2`

(with `tau = "mean"`

for higher
sensitivity) with Jaccard-type index:

**USAGE**

```
# Compute index:
baskets_betaq2 <- mFD::beta.fd.hill(
asb_sp_w = baskets_fruits_relw,
sp_dist = fruits_gower,
q = 2,
tau = "mean",
beta_type = "Jaccard")
# Then use the mFD::dist.to.df function to ease visualizing result
mFD::dist.to.df(list_dist = list("FDq2" = baskets_betaq2$"beta_fd_q"$"q2"))
```

```
## x1 x2 FDq2
## 1 basket_1 basket_2 0.058982325
## 2 basket_1 basket_3 0.078716397
## 3 basket_1 basket_4 0.029573623
## 4 basket_1 basket_5 0.027059789
## 5 basket_1 basket_6 0.484115290
## 6 basket_1 basket_7 0.292594562
## 7 basket_1 basket_8 0.290545545
## 8 basket_1 basket_9 0.185475113
## 9 basket_1 basket_10 0.011136995
## 10 basket_2 basket_3 0.004420448
## 11 basket_2 basket_4 0.161833512
## 12 basket_2 basket_5 0.162571972
## 13 basket_2 basket_6 0.260541701
## 14 basket_2 basket_7 0.097053161
## 15 basket_2 basket_8 0.294504888
## 16 basket_2 basket_9 0.225897615
## 17 basket_2 basket_10 0.058298760
## 18 basket_3 basket_4 0.172877455
## 19 basket_3 basket_5 0.178123024
## 20 basket_3 basket_6 0.207102590
## 21 basket_3 basket_7 0.081951839
## 22 basket_3 basket_8 0.308336365
## 23 basket_3 basket_9 0.241482168
## 24 basket_3 basket_10 0.082649928
## 25 basket_4 basket_5 0.001049851
## 26 basket_4 basket_6 0.511165067
## 27 basket_4 basket_7 0.421141181
## 28 basket_4 basket_8 0.482330219
## 29 basket_4 basket_9 0.342926459
## 30 basket_4 basket_10 0.050817451
## 31 basket_5 basket_6 0.532084800
## 32 basket_5 basket_7 0.438841544
## 33 basket_5 basket_8 0.496554512
## 34 basket_5 basket_9 0.336894275
## 35 basket_5 basket_10 0.044052657
## 36 basket_6 basket_7 0.068382884
## 37 basket_6 basket_8 0.759422492
## 38 basket_6 basket_9 0.680332414
## 39 basket_6 basket_10 0.453325136
## 40 basket_7 basket_8 0.478528108
## 41 basket_7 basket_9 0.431531941
## 42 basket_7 basket_10 0.265928889
## 43 basket_8 basket_9 0.020812705
## 44 basket_8 basket_10 0.345652088
## 45 basket_9 basket_10 0.219780509
```

We can see that *basket 1* is similar (beta < 0.1) to
*baskets 2,3,4,5,10* and that it is the most dissimilar to
*basket 8 (beta > 0.5)*. *Baskets 4 and 5* are highly
dissimilar (beta > 0.8) to *basket 8*.

Chao

*et al.*(2019) An attribute diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures.*Ecological Monographs*,**89**, e01343.Ricotta & Szeidl (2009) Diversity partitioning of Rao’s quadratic entropy.

*Theoretical Population Biology*,**76**, 299-302.