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):
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 |
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 |
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 3.01We 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 3.52We 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$"asb_tot_w"
apply(baskets_fruits_relw, 1, sum)## basket_1 basket_2 basket_3 basket_4 basket_5 basket_6 basket_7 basket_8
## 1 1 1 1 1 1 1 1
## basket_9 basket_10
## 1 1Now we can compute functional beta-diversity of order
q = 2 (with tau = "mean" for higher
sensitivity) with Jaccard-type index:
USAGE
# Compute index:
baskets_betaq2 <- mFD::beta.fd.hill(
asb_sp_w = baskets_fruits_relw,
sp_dist = fruits_gower,
q = 2,
tau = "mean",
beta_type = "Jaccard")
# Then use the mFD::dist.to.df function to ease visualizing result
mFD::dist.to.df(list_dist = list("FDq2" = baskets_betaq2$"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 0.219780509We 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.