Hierarchical clustering based on dissimilarity or beta-diversity

This function generates a hierarchical tree from a dissimilarity (beta-diversity) data.frame, calculates the cophenetic correlation coefficient, and optionally retrieves clusters from the tree upon user request. The function includes a randomization process for the dissimilarity matrix to generate the tree, with two methods available for constructing the final tree. Typically, the dissimilarity data.frame is a bioregion.pairwise.metric object obtained by running similarity, or by running similarity followed by similarity_to_dissimilarity.

Usage

hclu_hierarclust(
  dissimilarity,
  index = names(dissimilarity)[3],
  method = "average",
  randomize = TRUE,
  n_runs = 100,
  keep_trials = FALSE,
  optimal_tree_method = "iterative_consensus_tree",
  n_clust = NULL,
  cut_height = NULL,
  find_h = TRUE,
  h_max = 1,
  h_min = 0,
  consensus_p = 0.5,
  verbose = TRUE
)

Arguments

dissimilarity: The output object from dissimilarity() or similarity_to_dissimilarity(), or a dist object. If a data.frame is used, the first two columns represent pairs of sites (or any pair of nodes), and the subsequent column(s) contain the dissimilarity indices.
index: The name or number of the dissimilarity column to use. By default, the third column name of dissimilarity is used.
method: The name of the hierarchical classification method, as in hclust. Should be one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC), or "centroid" (= UPGMC).
randomize: A boolean indicating whether the dissimilarity matrix should be randomized to account for the order of sites in the dissimilarity matrix.
n_runs: The number of trials for randomizing the dissimilarity matrix.
keep_trials: A boolean indicating whether all random trial results should be stored in the output object. Set to FALSE to save space if your dissimilarity object is large. Note that this cannot be set to TRUE if optimal_tree_method = "iterative_consensus_tree".
optimal_tree_method: A character string indicating how the final tree should be obtained from all trials. Possible values are "iterative_consensus_tree" (default), "best", and "consensus". We recommend "iterative_consensus_tree". See Details.
n_clust: An integer vector or a single integer indicating the number of clusters to be obtained from the hierarchical tree, or the output from bioregionalization_metrics. This parameter should not be used simultaneously with cut_height.
cut_height: A numeric vector indicating the height(s) at which the tree should be cut. This parameter should not be used simultaneously with n_clust.
find_h: A boolean indicating whether the height of the cut should be found for the requested n_clust.
h_max: A numeric value indicating the maximum possible tree height for the chosen index.
h_min: A numeric value indicating the minimum possible height in the tree for the chosen index.
consensus_p: A numeric value (applicable only if optimal_tree_method = "consensus") indicating the threshold proportion of trees that must support a region/cluster for it to be included in the final consensus tree.
verbose: A boolean (applicable only if optimal_tree_method = "iterative_consensus_tree") indicating whether to display progress messages. Set to FALSE to suppress these messages.

Value

A list of class bioregion.clusters with five slots:

name: A character string containing the name of the algorithm.
args: A list of input arguments as provided by the user.
inputs: A list describing the characteristics of the clustering process.
algorithm: A list containing all objects associated with the clustering procedure, such as the original cluster objects.
clusters: A data.frame containing the clustering results.

In the algorithm slot, users can find the following elements:

trials: A list containing all randomization trials. Each trial includes the dissimilarity matrix with randomized site order, the associated tree, and the cophenetic correlation coefficient (Spearman) for that tree.
final.tree: An hclust object representing the final hierarchical tree to be used.
final.tree.coph.cor: The cophenetic correlation coefficient between the initial dissimilarity matrix and the final.tree.

Details

The function is based on hclust. The default method for the hierarchical tree is average, i.e. UPGMA as it has been recommended as the best method to generate a tree from beta diversity dissimilarity (Kreft & Jetz, 2010).

Clusters can be obtained by two methods:

Specifying a desired number of clusters in n_clust
Specifying one or several heights of cut in cut_height

To find an optimal number of clusters, see bioregionalization_metrics()

It is important to pay attention to the fact that the order of rows in the input distance matrix influences the tree topology as explained in Dapporto (2013). To address this, the function generates multiple trees by randomizing the distance matrix.

Two methods are available to obtain the final tree:

optimal_tree_method = "iterative_consensus_tree": The Iterative Hierarchical Consensus Tree (IHCT) method reconstructs a consensus tree by iteratively splitting the dataset into two subclusters based on the pairwise dissimilarity of sites across n_runs trees based on n_runs randomizations of the distance matrix. At each iteration, it identifies the majority membership of sites into two stable groups across all trees, calculates the height based on the selected linkage method (method), and enforces monotonic constraints on node heights to produce a coherent tree structure. This approach provides a robust, hierarchical representation of site relationships, balancing cluster stability and hierarchical constraints.
optimal_tree_method = "best": This method selects one tree among with the highest cophenetic correlation coefficient, representing the best fit between the hierarchical structure and the original distance matrix.
optimal_tree_method = "consensus": This method constructs a consensus tree using phylogenetic methods with the function consensus. When using this option, you must set the consensus_p parameter, which indicates the proportion of trees that must contain a region/cluster for it to be included in the final consensus tree. Consensus trees lack an inherent height because they represent a majority structure rather than an actual hierarchical clustering. To assign heights, we use a non-negative least squares method (nnls.tree) based on the initial distance matrix, ensuring that the consensus tree preserves approximate distances among clusters.

We recommend using the "iterative_consensus_tree" as all the branches of this tree will always reflect the majority decision among many randomized versions of the distance matrix. This method is inspired by Dapporto et al. (2015), which also used the majority decision among many randomized versions of the distance matrix, but it expands it to reconstruct the entire topology of the tree iteratively.

We do not recommend using the basic consensus method because in many contexts it provides inconsistent results, with a meaningless tree topology and a very low cophenetic correlation coefficient.

For a fast exploration of the tree, we recommend using the best method which will only select the tree with the highest cophenetic correlation coefficient among all randomized versions of the distance matrix.

References

Kreft H & Jetz W (2010) A framework for delineating biogeographical regions based on species distributions. Journal of Biogeography 37, 2029-2053.

Dapporto L, Ramazzotti M, Fattorini S, Talavera G, Vila R & Dennis, RLH (2013) Recluster: an unbiased clustering procedure for beta-diversity turnover. Ecography 36, 1070–1075.

Dapporto L, Ciolli G, Dennis RLH, Fox R & Shreeve TG (2015) A new procedure for extrapolating turnover regionalization at mid-small spatial scales, tested on British butterflies. Methods in Ecology and Evolution 6 , 1287–1297.

Author

Boris Leroy (leroy.boris@gmail.com)
Pierre Denelle (pierre.denelle@gmail.com)
Maxime Lenormand (maxime.lenormand@inrae.fr)

Examples

comat <- matrix(sample(0:1000, size = 500, replace = TRUE, prob = 1/1:1001),
20, 25)
rownames(comat) <- paste0("Site",1:20)
colnames(comat) <- paste0("Species",1:25)

dissim <- dissimilarity(comat, metric = "Simpson")

# User-defined number of clusters
tree1 <- hclu_hierarclust(dissim, 
                          n_clust = 5)
#> Building the iterative hierarchical consensus tree... Note that this process can take time especially if you have a lot of sites.
#> Error in if (nrow(dist_matrix) > 2) {    subclusters <- stable_binary_split(dist_matrix, method = method,         n_runs = n_runs, binsplit = "tree")} else {    subclusters <- list(attr(dist_matrix, "Labels")[1], attr(dist_matrix,         "Labels")[2])}: argument is of length zero
tree1
#> Error: object 'tree1' not found
plot(tree1)
#> Error: object 'tree1' not found
str(tree1)
#> Error: object 'tree1' not found
tree1$clusters
#> Error: object 'tree1' not found

# User-defined height cut
# Only one height
tree2 <- hclu_hierarclust(dissim, 
                          cut_height = .05)
#> Building the iterative hierarchical consensus tree... Note that this process can take time especially if you have a lot of sites.
#> Error in if (nrow(dist_matrix) > 2) {    subclusters <- stable_binary_split(dist_matrix, method = method,         n_runs = n_runs, binsplit = "tree")} else {    subclusters <- list(attr(dist_matrix, "Labels")[1], attr(dist_matrix,         "Labels")[2])}: argument is of length zero
tree2
#> Error: object 'tree2' not found
tree2$clusters
#> Error: object 'tree2' not found

# Multiple heights
tree3 <- hclu_hierarclust(dissim, 
                          cut_height = c(.05, .15, .25))
#> Building the iterative hierarchical consensus tree... Note that this process can take time especially if you have a lot of sites.
#> Error in if (nrow(dist_matrix) > 2) {    subclusters <- stable_binary_split(dist_matrix, method = method,         n_runs = n_runs, binsplit = "tree")} else {    subclusters <- list(attr(dist_matrix, "Labels")[1], attr(dist_matrix,         "Labels")[2])}: argument is of length zero

tree3$clusters # Mind the order of height cuts: from deep to shallow cuts
#> Error: object 'tree3' not found
# Info on each partition can be found in table cluster_info
tree3$cluster_info
#> Error: object 'tree3' not found
plot(tree3)
#> Error: object 'tree3' not found