Introduction

BOLDNODE (No-API Offline Data Explorer) is a sister package to BOLDconnectR that provides local access to BOLD data packages through DuckDB, enabling exploration and analysis of public BOLD data without API limitations. BOLDNODE therefore, offers an offline solution for working with large scale BOLD datasets locally. Please refer to the package documentation https://github.com/sameerpadhye/BOLDNODE for more details about the package.

This vignette demonstrates a complete workflow integrating the search, collect, get_ and bcdm_to functions in BOLDNODE for efficient and scalable exploration of BOLD data packages.

As a case study, we use Canadian Cerambycidae (longhorn beetles) to illustrate how these functions can be combined to search, retrieve, and transform the BCDM data.

Objectives

  1. Search all public Canadian Cerambycidae records from the BOLD data package.
  2. Collect (download) the search.
  3. Summarize the retrieved data using a concise overview.
  4. Determine the lowest supported taxonomic identification for each Cerambycidae BIN through BIN-level taxonomic concordance.
  5. Construct a BIN × province occurrence matrix, calculate the species richness estimates,pairwise beta diversity using the vegan package and visualize the species X province association via Non Metric Multidimensional Scaling (nMDS) using vegan and ggplot2.
  6. Obtain representative records for each BIN based on sequence length and identification method.
  7. Extract Clytus sequences as a DNAStringSet object, calculate nucleotide base proportions using the Biostrings package, align the sequences using Muscle and visualize the alignment using ape and phangorn.
  8. Convert Monochamus occurrence records into an sf object and visualize their geographic distribution using ggplot2 package.

Practical Workflow

Installing and importing BOLDNODE and other packages

The latest (in development) version of BOLDNODE can be installed via pak. Packages like Biostrings require BiocManager since they are hosted by Bioconductor.

Data packages

BOLD data packages can be downloaded in the parquet format from the BOLD website (https://boldsystems.org/data/data-packages/). Historical Data of quarterly BOLD snapshots (each assigned a stable DOI to support reproducible research) can also be accessed by scrolling down the same webpage.

Data package import

The path where the file is downloaded is saved as a variable that can be used as the input for the search function.

# Path to the downloaded BOLD Parquet release file
# parquet_file <- "G:/usr/path/file.parquet"

1. Search from the data package

bold_parquet_search queries the parquet data package using user-defined search terms such as COI-5P for marker type, Cerambycidae for taxonomic group, and Canada for geographic region.

# Search the BOLD dataset
cerambycidae_search <- bold_parquet_search(
  input.parquet = parquet_file,
  taxonomy = "Cerambycidae",
  geography = "Canada",
  marker = "COI-5P"
)

The total number of records is shown in the console.

#> The search has 3330 records in the dataset

2. Collect the dataset

The search result is then collected into memory (i.e., downloaded in the R session) as a data frame using bold_search_collect (Please note that only first 100 results and a few columns are shown here).

# Collect the search results into memory
cerambycidae_data <- bold_search_collect(
  cerambycidae_search,
  chunk.size = 50000,
  export = FALSE
)
# Inspect the collected data
DT::datatable(
  head(cerambycidae_data, 100) %>% select(processid, sampleid, bin_uri, family, genus, species),
  options = list(pageLength = 10, scrollX = TRUE)
)

3. Generate a concise summary

get_concise_summary provides a summary of the search results, including total records, unique species, BINs, countries, institutes, markers, and marker length range.

# Generate concise summary
cerambycidae_summary <- get_concise_summary(cerambycidae_search)
DT::datatable(cerambycidae_summary)

4. Compute BIN consensus taxonomy

get_bin_consensus computes the consensus taxonomic identifications for each BIN in the Cerambycidae dataset. The function works upwards through taxonomic ranks (i.e., from subspecies to kingdom) to find the lowest concordant identification that satisfies the provided threshold (Please note that only first 50 results are shown here).

# Get BIN consensus with strict consensus threshold (1.0)
bin_consensus <- get_bin_consensus(
  cerambycidae_search,
  threshold = 1.0,
  min.ids = 1
)

# View consensus at different taxonomic ranks
table(bin_consensus$concordant_rank)
#> 
#>     family      genus    species  subfamily subspecies 
#>          1         29        271          2          8

# Examine the results
DT::datatable(head(bin_consensus, 50),
  options = list(
    pageLength = 10,
    scrollX = TRUE
  )
)

5. Obtain a BIN-by-province occurrence matrix for all cerambycids

bcdm_to_occmatrix creates a site-by-taxon occurrence matrix from the full Cerambycidae dataset, using province.state as the geographic grouping variable and species as the taxonomic rank (Please note only 20 species are shown here).

# Generate occurrence matrix at species level, grouped by province/state
occ_matrix <- bcdm_to_occmatrix(
  cerambycidae_search,
  kingdom = "Animalia",
  taxon.rank = "species",
  site.cat = "province.state"
)

# Visualize the output
DT::datatable(
  head(occ_matrix, 20),
  options = list(
    pageLength = 10,
    scrollX = TRUE
  )
)

The resulting occurrence matrix can be directly used as input data for many functions in widely used packages like vegan.

5a. Species richness estimate (using external package vegan)

library(vegan)
species_richness_est <- poolaccum(occ_matrix)
species_richness_est
#>   N      S     Chao Jackknife 1 Jackknife 2 Bootstrap
#>   3 114.70 509.2237    180.2000    210.6517  144.3219
#>   4 139.83 423.3368    223.1925    271.1842  176.4379
#>   5 159.58 421.0545    256.2280    315.6145  201.5049
#>   6 182.71 427.9597    290.7933    357.6440  229.5597
#>   7 203.13 435.3686    322.3843    396.9474  254.6864
#>   8 217.81 441.1566    343.1625    421.1004  272.0712
#>   9 236.93 443.3329    369.9878    451.1742  294.7678
#>  10 255.09 450.3986    394.0590    476.9186  315.8369
#>  11 273.11 469.0037    419.3100    506.0896  337.1291
#>  12 287.00 485.0655    438.2500    528.0227  353.3101

Species based richness estimates

5b. Beta diversity between the provinces (using external package vegan)

# Calculate pairwise beta diversity
beta_diversity <- vegdist(occ_matrix, method = "bray") |> round(2)

# Visualize
DT::datatable(
  data.frame(as.matrix(beta_diversity)),
  options = list(
    pageLength = 10,
    scrollX = TRUE
  )
)

Pairwise Bray Curtis dissimilarity between the provinces

5c. Using Non Metric Multidimensional Scaling for visualizing the pairwise distances between provinces (using external packages vegan & ggplot2)

library(ggrepel)
# Using the occurrence matrix generated above for nMDS
nmds <- metaMDS(
  occ_matrix,
  distance = "bray",
  k = 2,
  trymax = 100,
  trace = FALSE
)

# Site scores
sites <- as.data.frame(scores(nmds, display = "sites"))
sites$Site <- rownames(sites)

# Species scores
species <- as.data.frame(scores(nmds, display = "species"))
species$Species <- rownames(species)

# Keep only the 40 species furthest from the origin
species$dist <- sqrt(species$NMDS1^2 + species$NMDS2^2)
species20 <- species[order(species$dist, decreasing = TRUE), ][1:40, ]

# Plot
ggplot() +
  geom_point(
    data = sites,
    aes(NMDS1, NMDS2),
    size = 4
  ) +
  geom_text(
    data = sites,
    aes(NMDS1, NMDS2, label = Site),
    size = 4,
    alpha = 0.6,
    vjust = -0.4
  ) +
  geom_text_repel(
    data = species20,
    aes(NMDS1, NMDS2, label = Species),
    colour = "red",
    fontface = "bold",
    size = 3,
    max.overlaps = Inf
  ) +
  coord_equal() +
  theme_bw() +
  labs(
    title = "nMDS (Bray-Curtis)",
    x = "NMDS1",
    y = "NMDS2"
  )

A nMDS plot showing the associations between cerambycid species and provinces (Only 40 species shown here for representation)

6. Select representative records for each BIN

get_bin_reps sub-samples the search results by BIN and selects representative records according to the provided criteria, yielding a representative sample for downstream analyses. As we saw above, some BINs include multiple species, therefore we will select representatives for all BIN-taxon combinations. Here, we select up to three records per BIN, prioritizing 658-bp sequences from specimens identified via morphological examination. With enforce.scientific = TRUE, interim or provisional names are ignored as distinct taxa (e.g. “Sternidius sp. A” is treated the same as “Sternidius”).

# Sample 3 records per BIN-taxon combination
bin_reps <- get_bin_reps(
  bold.search.res = cerambycidae_search,
  Nreps = 3,
  by.tax = TRUE,
  enforce.scientific = TRUE,
  criteria = list(
    seq_length = 658,
    id_method = "Morphology",
    vouchered = TRUE
  )
)

# Compare sample to full dataset
n_bins <- length(unique(bin_reps$bin_uri))
cat(
  "Sampled", nrow(bin_reps), "representatives from", n_bins, "BINs",
  "(out of", tot_records, "total records)."
)
#> Sampled 1020 representatives from 311 BINs (out of 3330 total records).

The actual number of sampled records varies according to BIN size and taxonomy, as we can see in the collected results (Please note only 50 records are shown here). In the case of BINs with multiple species, further analysis of barcode sequences often reveals small but consistent differences between them.

DT::datatable(
  head(bin_reps, 50),
  options = list(
    pageLength = 10,
    scrollX = TRUE
  )
)

7. Generate a DNAStringset object for the genus Clytus

bcdm_to_dnastringset converts the search results into a DNAStringSet object. First, bold_parquet_search is used to filter the COI-5P Clytus records from Canada. Additional search parameters are put in to return only full length barcodes having very low (or none) ambiguous bases (Only top three results displayed) (Please note: The bcdm_to_dnastringset function requires Biostrings to be installed before use).

library(Biostrings)
# Filter for genus Clytus
clytus_search <- bold_parquet_search(
  input.parquet = parquet_file,
  taxonomy = "Clytus",
  geography = "Canada",
  marker = "COI-5P",
  basecount = 658,
  ambi.base.cutoff = "<1%"
)

# Collect the filtered data
clytus_data <- bcdm_to_dnastringset(clytus_search,
  cols_for_seq_names = c("processid", "bin_uri", "species")
)
head(clytus_data, 3)
#> DNAStringSet object of length 3:
#>     width seq                                               names               
#> [1]   658 GACCCTTTACTTTGTATTTGGAG...CAGTTCTATATCAACATCTATTT ASALC013-12|BOLD:...
#> [2]   658 GACCCTTTACTTTGTATTTGGAG...CAGTTCTATATCAACATCTATTT ASALC197-12|BOLD:...
#> [3]   658 AACCCTTTACTTCGTCTTTGGGG...CAATTCTATATCAACATCTATTT ASALC224-12|BOLD:...

The DNAStringSet object can then be used in various downstream analyses using packages like Biostrings, muscle, ape etc.

7a. Obtain base proportions from the sequences (using external package Biostrings)

library(Biostrings)
# Count bases across all sequences
base_counts <- colSums(alphabetFrequency(clytus_data, baseOnly = TRUE))
# Proportions
base_props <- base_counts / sum(base_counts)
base_props
#>            A            C            G            T        other 
#> 0.2986929131 0.2062370207 0.1674432403 0.3266321135 0.0009947123

7b. Multiple sequence alignment using ‘Muscle’ (using external packages muscle and Biostrings)

library(Biostrings)
library(muscle)
alignment_muscle <- muscle(clytus_data)
#> 
#> MUSCLE v3.8.31 by Robert C. Edgar
#> 
#> http://www.drive5.com/muscle
#> This software is donated to the public domain.
#> Please cite: Edgar, R.C. Nucleic Acids Res 32(5), 1792-97.
#> 
#> file9149555b46f4 87 seqs, max length 658, avg  length 658
#> 1034 MB(6%)00:00:00                Iter   1    0.03%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   13.09%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   26.15%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   39.21%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   52.27%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   65.33%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   78.40%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1   91.46%  K-mer dist pass 11034 MB(6%)00:00:00                Iter   1  100.00%  K-mer dist pass 1
#> 1034 MB(6%)00:00:00                Iter   1    0.03%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   13.09%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   26.15%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   39.21%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   52.27%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   65.33%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   78.40%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1   91.46%  K-mer dist pass 21034 MB(6%)00:00:00                Iter   1  100.00%  K-mer dist pass 2
#> 1034 MB(6%)00:00:00                Iter   1    1.16%  Align node       1035 MB(6%)00:00:00                Iter   1    2.33%  Align node1035 MB(6%)00:00:00                Iter   1    3.49%  Align node1035 MB(6%)00:00:00                Iter   1    4.65%  Align node1036 MB(6%)00:00:00                Iter   1    5.81%  Align node1036 MB(6%)00:00:00                Iter   1    6.98%  Align node1036 MB(6%)00:00:00                Iter   1    8.14%  Align node1036 MB(6%)00:00:00                Iter   1    9.30%  Align node1037 MB(6%)00:00:00                Iter   1   10.47%  Align node1037 MB(6%)00:00:00                Iter   1   11.63%  Align node1037 MB(6%)00:00:00                Iter   1   12.79%  Align node1037 MB(6%)00:00:00                Iter   1   13.95%  Align node1038 MB(6%)00:00:00                Iter   1   15.12%  Align node1038 MB(6%)00:00:00                Iter   1   16.28%  Align node1038 MB(6%)00:00:00                Iter   1   17.44%  Align node1039 MB(6%)00:00:00                Iter   1   18.60%  Align node1039 MB(6%)00:00:00                Iter   1   19.77%  Align node1039 MB(6%)00:00:00                Iter   1   20.93%  Align node1040 MB(6%)00:00:00                Iter   1   22.09%  Align node1040 MB(6%)00:00:00                Iter   1   23.26%  Align node1040 MB(6%)00:00:00                Iter   1   24.42%  Align node1041 MB(6%)00:00:00                Iter   1   25.58%  Align node1041 MB(6%)00:00:00                Iter   1   26.74%  Align node1041 MB(6%)00:00:00                Iter   1   27.91%  Align node1042 MB(6%)00:00:00                Iter   1   29.07%  Align node1042 MB(6%)00:00:00                Iter   1   30.23%  Align node1042 MB(6%)00:00:00                Iter   1   31.40%  Align node1043 MB(6%)00:00:00                Iter   1   32.56%  Align node1043 MB(6%)00:00:00                Iter   1   33.72%  Align node1043 MB(6%)00:00:00                Iter   1   34.88%  Align node1043 MB(6%)00:00:00                Iter   1   36.05%  Align node1044 MB(6%)00:00:00                Iter   1   37.21%  Align node1044 MB(6%)00:00:00                Iter   1   38.37%  Align node1044 MB(6%)00:00:00                Iter   1   39.53%  Align node1045 MB(6%)00:00:00                Iter   1   40.70%  Align node1045 MB(6%)00:00:00                Iter   1   41.86%  Align node1046 MB(6%)00:00:00                Iter   1   43.02%  Align node1046 MB(6%)00:00:00                Iter   1   44.19%  Align node1046 MB(6%)00:00:00                Iter   1   45.35%  Align node1047 MB(6%)00:00:00                Iter   1   46.51%  Align node1047 MB(6%)00:00:00                Iter   1   47.67%  Align node1047 MB(6%)00:00:00                Iter   1   48.84%  Align node1048 MB(6%)00:00:00                Iter   1   50.00%  Align node1048 MB(6%)00:00:00                Iter   1   51.16%  Align node1049 MB(6%)00:00:00                Iter   1   52.33%  Align node1049 MB(6%)00:00:00                Iter   1   53.49%  Align node1049 MB(6%)00:00:00                Iter   1   54.65%  Align node1049 MB(6%)00:00:00                Iter   1   55.81%  Align node1049 MB(6%)00:00:00                Iter   1   56.98%  Align node1049 MB(6%)00:00:00                Iter   1   58.14%  Align node1049 MB(6%)00:00:00                Iter   1   59.30%  Align node1049 MB(6%)00:00:00                Iter   1   60.47%  Align node1050 MB(6%)00:00:00                Iter   1   61.63%  Align node1050 MB(6%)00:00:00                Iter   1   62.79%  Align node1050 MB(6%)00:00:00                Iter   1   63.95%  Align node1050 MB(6%)00:00:00                Iter   1   65.12%  Align node1050 MB(6%)00:00:00                Iter   1   66.28%  Align node1051 MB(6%)00:00:00                Iter   1   67.44%  Align node1051 MB(6%)00:00:00                Iter   1   68.60%  Align node1051 MB(6%)00:00:00                Iter   1   69.77%  Align node1051 MB(6%)00:00:00                Iter   1   70.93%  Align node1051 MB(6%)00:00:00                Iter   1   72.09%  Align node1052 MB(6%)00:00:00                Iter   1   73.26%  Align node1052 MB(6%)00:00:00                Iter   1   74.42%  Align node1052 MB(6%)00:00:00                Iter   1   75.58%  Align node1052 MB(6%)00:00:00                Iter   1   76.74%  Align node1052 MB(6%)00:00:00                Iter   1   77.91%  Align node1053 MB(6%)00:00:00                Iter   1   79.07%  Align node1053 MB(6%)00:00:00                Iter   1   80.23%  Align node1054 MB(6%)00:00:00                Iter   1   81.40%  Align node1054 MB(6%)00:00:00                Iter   1   82.56%  Align node1054 MB(6%)00:00:00                Iter   1   83.72%  Align node1055 MB(6%)00:00:00                Iter   1   84.88%  Align node1055 MB(6%)00:00:00                Iter   1   86.05%  Align node1056 MB(6%)00:00:00                Iter   1   87.21%  Align node1056 MB(6%)00:00:00                Iter   1   88.37%  Align node1057 MB(6%)00:00:00                Iter   1   89.53%  Align node1057 MB(6%)00:00:00                Iter   1   90.70%  Align node1057 MB(6%)00:00:00                Iter   1   91.86%  Align node1057 MB(6%)00:00:00                Iter   1   93.02%  Align node1058 MB(6%)00:00:00                Iter   1   94.19%  Align node1059 MB(6%)00:00:00                Iter   1   95.35%  Align node1059 MB(6%)00:00:00                Iter   1   96.51%  Align node1059 MB(6%)00:00:00                Iter   1   97.67%  Align node1060 MB(6%)00:00:00                Iter   1   98.84%  Align node1060 MB(6%)00:00:00                Iter   1  100.00%  Align node1060 MB(6%)00:00:00                Iter   1  100.00%  Align node
#> 1060 MB(6%)00:00:00                Iter   1    1.15%  Root alignment1060 MB(6%)00:00:00                Iter   1    2.30%  Root alignment1060 MB(6%)00:00:00                Iter   1    3.45%  Root alignment1060 MB(6%)00:00:00                Iter   1    4.60%  Root alignment1060 MB(6%)00:00:00                Iter   1    5.75%  Root alignment1060 MB(6%)00:00:00                Iter   1    6.90%  Root alignment1060 MB(6%)00:00:00                Iter   1    8.05%  Root alignment1060 MB(6%)00:00:00                Iter   1    9.20%  Root alignment1060 MB(6%)00:00:00                Iter   1   10.34%  Root alignment1060 MB(6%)00:00:00                Iter   1   11.49%  Root alignment1060 MB(6%)00:00:00                Iter   1   12.64%  Root alignment1060 MB(6%)00:00:00                Iter   1   13.79%  Root alignment1060 MB(6%)00:00:00                Iter   1   14.94%  Root alignment1060 MB(6%)00:00:00                Iter   1   16.09%  Root alignment1060 MB(6%)00:00:00                Iter   1   17.24%  Root alignment1060 MB(6%)00:00:00                Iter   1   18.39%  Root alignment1060 MB(6%)00:00:00                Iter   1   19.54%  Root alignment1060 MB(6%)00:00:00                Iter   1   20.69%  Root alignment1060 MB(6%)00:00:00                Iter   1   21.84%  Root alignment1060 MB(6%)00:00:00                Iter   1   22.99%  Root alignment1060 MB(6%)00:00:00                Iter   1   24.14%  Root alignment1060 MB(6%)00:00:00                Iter   1   25.29%  Root alignment1060 MB(6%)00:00:00                Iter   1   26.44%  Root alignment1060 MB(6%)00:00:00                Iter   1   27.59%  Root alignment1060 MB(6%)00:00:00                Iter   1   28.74%  Root alignment1060 MB(6%)00:00:00                Iter   1   29.89%  Root alignment1060 MB(6%)00:00:00                Iter   1   31.03%  Root alignment1060 MB(6%)00:00:00                Iter   1   32.18%  Root alignment1060 MB(6%)00:00:00                Iter   1   33.33%  Root alignment1060 MB(6%)00:00:00                Iter   1   34.48%  Root alignment1060 MB(6%)00:00:00                Iter   1   35.63%  Root alignment1060 MB(6%)00:00:00                Iter   1   36.78%  Root alignment1060 MB(6%)00:00:00                Iter   1   37.93%  Root alignment1060 MB(6%)00:00:00                Iter   1   39.08%  Root alignment1060 MB(6%)00:00:00                Iter   1   40.23%  Root alignment1060 MB(6%)00:00:00                Iter   1   41.38%  Root alignment1060 MB(6%)00:00:00                Iter   1   42.53%  Root alignment1060 MB(6%)00:00:00                Iter   1   43.68%  Root alignment1060 MB(6%)00:00:00                Iter   1   44.83%  Root alignment1060 MB(6%)00:00:00                Iter   1   45.98%  Root alignment1060 MB(6%)00:00:00                Iter   1   47.13%  Root alignment1060 MB(6%)00:00:00                Iter   1   48.28%  Root alignment1060 MB(6%)00:00:00                Iter   1   49.43%  Root alignment1060 MB(6%)00:00:00                Iter   1   50.57%  Root alignment1060 MB(6%)00:00:00                Iter   1   51.72%  Root alignment1060 MB(6%)00:00:00                Iter   1   52.87%  Root alignment1060 MB(6%)00:00:00                Iter   1   54.02%  Root alignment1060 MB(6%)00:00:00                Iter   1   55.17%  Root alignment1060 MB(6%)00:00:00                Iter   1   56.32%  Root alignment1060 MB(6%)00:00:00                Iter   1   57.47%  Root alignment1060 MB(6%)00:00:00                Iter   1   58.62%  Root alignment1060 MB(6%)00:00:00                Iter   1   59.77%  Root alignment1060 MB(6%)00:00:00                Iter   1   60.92%  Root alignment1060 MB(6%)00:00:00                Iter   1   62.07%  Root alignment1060 MB(6%)00:00:00                Iter   1   63.22%  Root alignment1060 MB(6%)00:00:00                Iter   1   64.37%  Root alignment1060 MB(6%)00:00:00                Iter   1   65.52%  Root alignment1060 MB(6%)00:00:00                Iter   1   66.67%  Root alignment1060 MB(6%)00:00:00                Iter   1   67.82%  Root alignment1060 MB(6%)00:00:00                Iter   1   68.97%  Root alignment1060 MB(6%)00:00:00                Iter   1   70.11%  Root alignment1060 MB(6%)00:00:00                Iter   1   71.26%  Root alignment1060 MB(6%)00:00:00                Iter   1   72.41%  Root alignment1060 MB(6%)00:00:00                Iter   1   73.56%  Root alignment1060 MB(6%)00:00:00                Iter   1   74.71%  Root alignment1060 MB(6%)00:00:00                Iter   1   75.86%  Root alignment1060 MB(6%)00:00:00                Iter   1   77.01%  Root alignment1060 MB(6%)00:00:00                Iter   1   78.16%  Root alignment1060 MB(6%)00:00:00                Iter   1   79.31%  Root alignment1060 MB(6%)00:00:00                Iter   1   80.46%  Root alignment1060 MB(6%)00:00:00                Iter   1   81.61%  Root alignment1060 MB(6%)00:00:00                Iter   1   82.76%  Root alignment1060 MB(6%)00:00:00                Iter   1   83.91%  Root alignment1060 MB(6%)00:00:00                Iter   1   85.06%  Root alignment1060 MB(6%)00:00:00                Iter   1   86.21%  Root alignment1060 MB(6%)00:00:00                Iter   1   87.36%  Root alignment1060 MB(6%)00:00:00                Iter   1   88.51%  Root alignment1060 MB(6%)00:00:00                Iter   1   89.66%  Root alignment1060 MB(6%)00:00:00                Iter   1   90.80%  Root alignment1060 MB(6%)00:00:00                Iter   1   91.95%  Root alignment1060 MB(6%)00:00:00                Iter   1   93.10%  Root alignment1060 MB(6%)00:00:00                Iter   1   94.25%  Root alignment1060 MB(6%)00:00:00                Iter   1   95.40%  Root alignment1060 MB(6%)00:00:00                Iter   1   96.55%  Root alignment1060 MB(6%)00:00:00                Iter   1   97.70%  Root alignment1060 MB(6%)00:00:00                Iter   1   98.85%  Root alignment1060 MB(6%)00:00:00                Iter   1  100.00%  Root alignment1060 MB(6%)00:00:00                Iter   1  100.00%  Root alignment
#> 1060 MB(6%)00:00:00                Iter   2    1.18%  Refine tree   1060 MB(6%)00:00:00                Iter   2    2.35%  Refine tree1060 MB(6%)00:00:00                Iter   2    3.53%  Refine tree1060 MB(6%)00:00:00                Iter   2    4.71%  Refine tree1060 MB(6%)00:00:00                Iter   2    5.88%  Refine tree1060 MB(6%)00:00:00                Iter   2    7.06%  Refine tree1060 MB(6%)00:00:00                Iter   2    8.24%  Refine tree1060 MB(6%)00:00:00                Iter   2    9.41%  Refine tree1060 MB(6%)00:00:00                Iter   2   10.59%  Refine tree1060 MB(6%)00:00:00                Iter   2   11.76%  Refine tree1060 MB(6%)00:00:00                Iter   2   12.94%  Refine tree1060 MB(6%)00:00:00                Iter   2   14.12%  Refine tree1060 MB(6%)00:00:00                Iter   2   15.29%  Refine tree1060 MB(6%)00:00:00                Iter   2   16.47%  Refine tree1060 MB(6%)00:00:00                Iter   2   17.65%  Refine tree1060 MB(6%)00:00:00                Iter   2   18.82%  Refine tree1060 MB(6%)00:00:00                Iter   2   20.00%  Refine tree1060 MB(6%)00:00:00                Iter   2   21.18%  Refine tree1060 MB(6%)00:00:00                Iter   2   22.35%  Refine tree1060 MB(6%)00:00:00                Iter   2   23.53%  Refine tree1060 MB(6%)00:00:00                Iter   2   24.71%  Refine tree1061 MB(6%)00:00:00                Iter   2   25.88%  Refine tree1061 MB(6%)00:00:00                Iter   2   27.06%  Refine tree1061 MB(6%)00:00:00                Iter   2   28.24%  Refine tree1061 MB(6%)00:00:00                Iter   2   29.41%  Refine tree1061 MB(6%)00:00:00                Iter   2   30.59%  Refine tree1061 MB(6%)00:00:00                Iter   2  100.00%  Refine tree
#> 1061 MB(6%)00:00:00                Iter   2    1.15%  Root alignment1061 MB(6%)00:00:00                Iter   2    2.30%  Root alignment1061 MB(6%)00:00:00                Iter   2    3.45%  Root alignment1061 MB(6%)00:00:00                Iter   2    4.60%  Root alignment1061 MB(6%)00:00:00                Iter   2    5.75%  Root alignment1061 MB(6%)00:00:00                Iter   2    6.90%  Root alignment1061 MB(6%)00:00:00                Iter   2    8.05%  Root alignment1061 MB(6%)00:00:00                Iter   2    9.20%  Root alignment1061 MB(6%)00:00:00                Iter   2   10.34%  Root alignment1061 MB(6%)00:00:00                Iter   2   11.49%  Root alignment1061 MB(6%)00:00:00                Iter   2   12.64%  Root alignment1061 MB(6%)00:00:00                Iter   2   13.79%  Root alignment1061 MB(6%)00:00:00                Iter   2   14.94%  Root alignment1061 MB(6%)00:00:00                Iter   2   16.09%  Root alignment1061 MB(6%)00:00:00                Iter   2   17.24%  Root alignment1061 MB(6%)00:00:00                Iter   2   18.39%  Root alignment1061 MB(6%)00:00:00                Iter   2   19.54%  Root alignment1061 MB(6%)00:00:00                Iter   2   20.69%  Root alignment1061 MB(6%)00:00:00                Iter   2   21.84%  Root alignment1061 MB(6%)00:00:00                Iter   2   22.99%  Root alignment1061 MB(6%)00:00:00                Iter   2   24.14%  Root alignment1061 MB(6%)00:00:00                Iter   2   25.29%  Root alignment1061 MB(6%)00:00:00                Iter   2   26.44%  Root alignment1061 MB(6%)00:00:00                Iter   2   27.59%  Root alignment1061 MB(6%)00:00:00                Iter   2   28.74%  Root alignment1061 MB(6%)00:00:00                Iter   2   29.89%  Root alignment1061 MB(6%)00:00:00                Iter   2   31.03%  Root alignment1061 MB(6%)00:00:00                Iter   2   32.18%  Root alignment1061 MB(6%)00:00:00                Iter   2   33.33%  Root alignment1061 MB(6%)00:00:00                Iter   2   34.48%  Root alignment1061 MB(6%)00:00:00                Iter   2   35.63%  Root alignment1061 MB(6%)00:00:00                Iter   2   36.78%  Root alignment1061 MB(6%)00:00:00                Iter   2   37.93%  Root alignment1061 MB(6%)00:00:00                Iter   2   39.08%  Root alignment1061 MB(6%)00:00:00                Iter   2   40.23%  Root alignment1061 MB(6%)00:00:00                Iter   2   41.38%  Root alignment1061 MB(6%)00:00:00                Iter   2   42.53%  Root alignment1061 MB(6%)00:00:00                Iter   2   43.68%  Root alignment1061 MB(6%)00:00:00                Iter   2   44.83%  Root alignment1061 MB(6%)00:00:00                Iter   2   45.98%  Root alignment1061 MB(6%)00:00:00                Iter   2   47.13%  Root alignment1061 MB(6%)00:00:00                Iter   2   48.28%  Root alignment1061 MB(6%)00:00:00                Iter   2   49.43%  Root alignment1061 MB(6%)00:00:00                Iter   2   50.57%  Root alignment1061 MB(6%)00:00:00                Iter   2   51.72%  Root alignment1061 MB(6%)00:00:00                Iter   2   52.87%  Root alignment1061 MB(6%)00:00:00                Iter   2   54.02%  Root alignment1061 MB(6%)00:00:00                Iter   2   55.17%  Root alignment1061 MB(6%)00:00:00                Iter   2   56.32%  Root alignment1061 MB(6%)00:00:00                Iter   2   57.47%  Root alignment1061 MB(6%)00:00:00                Iter   2   58.62%  Root alignment1061 MB(6%)00:00:00                Iter   2   59.77%  Root alignment1061 MB(6%)00:00:00                Iter   2   60.92%  Root alignment1061 MB(6%)00:00:00                Iter   2   62.07%  Root alignment1061 MB(6%)00:00:00                Iter   2   63.22%  Root alignment1061 MB(6%)00:00:00                Iter   2   64.37%  Root alignment1061 MB(6%)00:00:00                Iter   2   65.52%  Root alignment1061 MB(6%)00:00:00                Iter   2   66.67%  Root alignment1061 MB(6%)00:00:00                Iter   2   67.82%  Root alignment1061 MB(6%)00:00:00                Iter   2   68.97%  Root alignment1061 MB(6%)00:00:00                Iter   2   70.11%  Root alignment1061 MB(6%)00:00:00                Iter   2   71.26%  Root alignment1061 MB(6%)00:00:00                Iter   2   72.41%  Root alignment1061 MB(6%)00:00:00                Iter   2   73.56%  Root alignment1061 MB(6%)00:00:00                Iter   2   74.71%  Root alignment1061 MB(6%)00:00:00                Iter   2   75.86%  Root alignment1061 MB(6%)00:00:00                Iter   2   77.01%  Root alignment1061 MB(6%)00:00:00                Iter   2   78.16%  Root alignment1061 MB(6%)00:00:00                Iter   2   79.31%  Root alignment1061 MB(6%)00:00:00                Iter   2   80.46%  Root alignment1061 MB(6%)00:00:00                Iter   2   81.61%  Root alignment1061 MB(6%)00:00:00                Iter   2   82.76%  Root alignment1061 MB(6%)00:00:00                Iter   2   83.91%  Root alignment1061 MB(6%)00:00:00                Iter   2   85.06%  Root alignment1061 MB(6%)00:00:00                Iter   2   86.21%  Root alignment1061 MB(6%)00:00:00                Iter   2   87.36%  Root alignment1061 MB(6%)00:00:00                Iter   2   88.51%  Root alignment1061 MB(6%)00:00:00                Iter   2   89.66%  Root alignment1061 MB(6%)00:00:00                Iter   2   90.80%  Root alignment1061 MB(6%)00:00:00                Iter   2   91.95%  Root alignment1061 MB(6%)00:00:00                Iter   2   93.10%  Root alignment1061 MB(6%)00:00:00                Iter   2   94.25%  Root alignment1061 MB(6%)00:00:00                Iter   2   95.40%  Root alignment1061 MB(6%)00:00:00                Iter   2   96.55%  Root alignment1061 MB(6%)00:00:00                Iter   2   97.70%  Root alignment1061 MB(6%)00:00:00                Iter   2   98.85%  Root alignment1061 MB(6%)00:00:00                Iter   2  100.00%  Root alignment1061 MB(6%)00:00:00                Iter   2  100.00%  Root alignment
#> 1061 MB(6%)00:00:00                Iter   2  100.00%  Root alignment
#> 1061 MB(6%)00:00:00                Iter   3    1.17%  Refine biparts1061 MB(6%)00:00:00                Iter   3    1.75%  Refine biparts1061 MB(6%)00:00:00                Iter   3    2.34%  Refine biparts1061 MB(6%)00:00:00                Iter   3    2.92%  Refine biparts1061 MB(6%)00:00:00                Iter   3    3.51%  Refine biparts1061 MB(6%)00:00:00                Iter   3    4.09%  Refine biparts1061 MB(6%)00:00:00                Iter   3    4.68%  Refine biparts1061 MB(6%)00:00:00                Iter   3    5.26%  Refine biparts1061 MB(6%)00:00:00                Iter   3    5.85%  Refine biparts1061 MB(6%)00:00:00                Iter   3    6.43%  Refine biparts1061 MB(6%)00:00:00                Iter   3    7.02%  Refine biparts1061 MB(6%)00:00:00                Iter   3    7.60%  Refine biparts1061 MB(6%)00:00:00                Iter   3    8.19%  Refine biparts1061 MB(6%)00:00:00                Iter   3    8.77%  Refine biparts1061 MB(6%)00:00:00                Iter   3    9.36%  Refine biparts1061 MB(6%)00:00:00                Iter   3    9.94%  Refine biparts1061 MB(6%)00:00:00                Iter   3   10.53%  Refine biparts1061 MB(6%)00:00:00                Iter   3   11.11%  Refine biparts1061 MB(6%)00:00:00                Iter   3   11.70%  Refine biparts1061 MB(6%)00:00:00                Iter   3   12.28%  Refine biparts1061 MB(6%)00:00:00                Iter   3   12.87%  Refine biparts1061 MB(6%)00:00:00                Iter   3   13.45%  Refine biparts1061 MB(6%)00:00:00                Iter   3   14.04%  Refine biparts1061 MB(6%)00:00:00                Iter   3   14.62%  Refine biparts1061 MB(6%)00:00:00                Iter   3   15.20%  Refine biparts1061 MB(6%)00:00:00                Iter   3   15.79%  Refine biparts1061 MB(6%)00:00:00                Iter   3   16.37%  Refine biparts1061 MB(6%)00:00:00                Iter   3   16.96%  Refine biparts1061 MB(6%)00:00:00                Iter   3   17.54%  Refine biparts1061 MB(6%)00:00:00                Iter   3   18.13%  Refine biparts1061 MB(6%)00:00:00                Iter   3   18.71%  Refine biparts1061 MB(6%)00:00:00                Iter   3   19.30%  Refine biparts1061 MB(6%)00:00:00                Iter   3   19.88%  Refine biparts1061 MB(6%)00:00:00                Iter   3   20.47%  Refine biparts1061 MB(6%)00:00:00                Iter   3   21.05%  Refine biparts1061 MB(6%)00:00:00                Iter   3   21.64%  Refine biparts1061 MB(6%)00:00:00                Iter   3   22.22%  Refine biparts1061 MB(6%)00:00:00                Iter   3   22.81%  Refine biparts1061 MB(6%)00:00:00                Iter   3   23.39%  Refine biparts1061 MB(6%)00:00:00                Iter   3   23.98%  Refine biparts1061 MB(6%)00:00:00                Iter   3   24.56%  Refine biparts1061 MB(6%)00:00:00                Iter   3   25.15%  Refine biparts1061 MB(6%)00:00:00                Iter   3   25.73%  Refine biparts1061 MB(6%)00:00:01                Iter   3   26.32%  Refine biparts1061 MB(6%)00:00:01                Iter   3   26.90%  Refine biparts1061 MB(6%)00:00:01                Iter   3   27.49%  Refine biparts1061 MB(6%)00:00:01                Iter   3   28.07%  Refine biparts1061 MB(6%)00:00:01                Iter   3   28.65%  Refine biparts1061 MB(6%)00:00:01                Iter   3   29.24%  Refine biparts1061 MB(6%)00:00:01                Iter   3   29.82%  Refine biparts1061 MB(6%)00:00:01                Iter   3   30.41%  Refine biparts1061 MB(6%)00:00:01                Iter   3   30.99%  Refine biparts1061 MB(6%)00:00:01                Iter   3   31.58%  Refine biparts1061 MB(6%)00:00:01                Iter   3   32.16%  Refine biparts1061 MB(6%)00:00:01                Iter   3   32.75%  Refine biparts1061 MB(6%)00:00:01                Iter   3   33.33%  Refine biparts1061 MB(6%)00:00:01                Iter   3   33.92%  Refine biparts1061 MB(6%)00:00:01                Iter   3   34.50%  Refine biparts1061 MB(6%)00:00:01                Iter   3   35.09%  Refine biparts1061 MB(6%)00:00:01                Iter   3   35.67%  Refine biparts1061 MB(6%)00:00:01                Iter   3   36.26%  Refine biparts1061 MB(6%)00:00:01                Iter   3   36.84%  Refine biparts1061 MB(6%)00:00:01                Iter   3   37.43%  Refine biparts1061 MB(6%)00:00:01                Iter   3   38.01%  Refine biparts1061 MB(6%)00:00:01                Iter   3   38.60%  Refine biparts1061 MB(6%)00:00:01                Iter   3   39.18%  Refine biparts1061 MB(6%)00:00:01                Iter   3   39.77%  Refine biparts1061 MB(6%)00:00:01                Iter   3   40.35%  Refine biparts1061 MB(6%)00:00:01                Iter   3   40.94%  Refine biparts1061 MB(6%)00:00:01                Iter   3   41.52%  Refine biparts1061 MB(6%)00:00:01                Iter   3   42.11%  Refine biparts1061 MB(6%)00:00:01                Iter   3   42.69%  Refine biparts1061 MB(6%)00:00:01                Iter   3   43.27%  Refine biparts1061 MB(6%)00:00:01                Iter   3   43.86%  Refine biparts1061 MB(6%)00:00:01                Iter   3   44.44%  Refine biparts1061 MB(6%)00:00:01                Iter   3   45.03%  Refine biparts1061 MB(6%)00:00:01                Iter   3   45.61%  Refine biparts1061 MB(6%)00:00:01                Iter   3   46.20%  Refine biparts1061 MB(6%)00:00:01                Iter   3   46.78%  Refine biparts1061 MB(6%)00:00:01                Iter   3   47.37%  Refine biparts1061 MB(6%)00:00:01                Iter   3   47.95%  Refine biparts1061 MB(6%)00:00:01                Iter   3   48.54%  Refine biparts1061 MB(6%)00:00:01                Iter   3   49.12%  Refine biparts1061 MB(6%)00:00:01                Iter   3   49.71%  Refine biparts1061 MB(6%)00:00:01                Iter   3   50.29%  Refine biparts1061 MB(6%)00:00:01                Iter   3   50.88%  Refine biparts1061 MB(6%)00:00:01                Iter   3   51.46%  Refine biparts1061 MB(6%)00:00:01                Iter   3   52.05%  Refine biparts1061 MB(6%)00:00:01                Iter   3   52.63%  Refine biparts1061 MB(6%)00:00:01                Iter   3   53.22%  Refine biparts1061 MB(6%)00:00:01                Iter   3   53.80%  Refine biparts1061 MB(6%)00:00:01                Iter   3   54.39%  Refine biparts1061 MB(6%)00:00:01                Iter   3   54.97%  Refine biparts1061 MB(6%)00:00:01                Iter   3   55.56%  Refine biparts1061 MB(6%)00:00:01                Iter   3   56.14%  Refine biparts1061 MB(6%)00:00:01                Iter   3   56.73%  Refine biparts1061 MB(6%)00:00:01                Iter   3   57.31%  Refine biparts1061 MB(6%)00:00:01                Iter   3   57.89%  Refine biparts1061 MB(6%)00:00:01                Iter   3   58.48%  Refine biparts1061 MB(6%)00:00:01                Iter   3   59.06%  Refine biparts1061 MB(6%)00:00:01                Iter   3   59.65%  Refine biparts1061 MB(6%)00:00:01                Iter   3   60.23%  Refine biparts1061 MB(6%)00:00:01                Iter   3   60.82%  Refine biparts1061 MB(6%)00:00:01                Iter   3   61.40%  Refine biparts1061 MB(6%)00:00:01                Iter   3   61.99%  Refine biparts1061 MB(6%)00:00:01                Iter   3   62.57%  Refine biparts1061 MB(6%)00:00:01                Iter   3   63.16%  Refine biparts1061 MB(6%)00:00:01                Iter   3   63.74%  Refine biparts1061 MB(6%)00:00:01                Iter   3   64.33%  Refine biparts1061 MB(6%)00:00:01                Iter   3   64.91%  Refine biparts1061 MB(6%)00:00:01                Iter   3   65.50%  Refine biparts1061 MB(6%)00:00:01                Iter   3   66.08%  Refine biparts1061 MB(6%)00:00:01                Iter   3   66.67%  Refine biparts1061 MB(6%)00:00:01                Iter   3   67.25%  Refine biparts1061 MB(6%)00:00:01                Iter   3   67.84%  Refine biparts1061 MB(6%)00:00:01                Iter   3   68.42%  Refine biparts1061 MB(6%)00:00:01                Iter   3   69.01%  Refine biparts1061 MB(6%)00:00:01                Iter   3   69.59%  Refine biparts1061 MB(6%)00:00:01                Iter   3   70.18%  Refine biparts1061 MB(6%)00:00:01                Iter   3   70.76%  Refine biparts1061 MB(6%)00:00:01                Iter   3   71.35%  Refine biparts1061 MB(6%)00:00:01                Iter   3   71.93%  Refine biparts1061 MB(6%)00:00:01                Iter   3   72.51%  Refine biparts1061 MB(6%)00:00:01                Iter   3   73.10%  Refine biparts1061 MB(6%)00:00:01                Iter   3   73.68%  Refine biparts1061 MB(6%)00:00:01                Iter   3   74.27%  Refine biparts1061 MB(6%)00:00:01                Iter   3   74.85%  Refine biparts1061 MB(6%)00:00:01                Iter   3   75.44%  Refine biparts1061 MB(6%)00:00:01                Iter   3   76.02%  Refine biparts1061 MB(6%)00:00:01                Iter   3   76.61%  Refine biparts1061 MB(6%)00:00:01                Iter   3   77.19%  Refine biparts1061 MB(6%)00:00:01                Iter   3   77.78%  Refine biparts1061 MB(6%)00:00:01                Iter   3   78.36%  Refine biparts1061 MB(6%)00:00:01                Iter   3   78.95%  Refine biparts1061 MB(6%)00:00:01                Iter   3   79.53%  Refine biparts1061 MB(6%)00:00:01                Iter   3   80.12%  Refine biparts1061 MB(6%)00:00:01                Iter   3   80.70%  Refine biparts1061 MB(6%)00:00:01                Iter   3   81.29%  Refine biparts1061 MB(6%)00:00:01                Iter   3   81.87%  Refine biparts1061 MB(6%)00:00:01                Iter   3   82.46%  Refine biparts1061 MB(6%)00:00:01                Iter   3   83.04%  Refine biparts1061 MB(6%)00:00:01                Iter   3   83.63%  Refine biparts1061 MB(6%)00:00:01                Iter   3   84.21%  Refine biparts1061 MB(6%)00:00:01                Iter   3   84.80%  Refine biparts1061 MB(6%)00:00:01                Iter   3   85.38%  Refine biparts1061 MB(6%)00:00:01                Iter   3   85.96%  Refine biparts1061 MB(6%)00:00:01                Iter   3   86.55%  Refine biparts1061 MB(6%)00:00:01                Iter   3   87.13%  Refine biparts1061 MB(6%)00:00:01                Iter   3   87.72%  Refine biparts1061 MB(6%)00:00:01                Iter   3   88.30%  Refine biparts1061 MB(6%)00:00:01                Iter   3   88.89%  Refine biparts1061 MB(6%)00:00:01                Iter   3   89.47%  Refine biparts1061 MB(6%)00:00:01                Iter   3   90.06%  Refine biparts1061 MB(6%)00:00:01                Iter   3   90.64%  Refine biparts1061 MB(6%)00:00:01                Iter   3   91.23%  Refine biparts1061 MB(6%)00:00:01                Iter   3   91.81%  Refine biparts1061 MB(6%)00:00:01                Iter   3   92.40%  Refine biparts1061 MB(6%)00:00:01                Iter   3   92.98%  Refine biparts1061 MB(6%)00:00:01                Iter   3   93.57%  Refine biparts1061 MB(6%)00:00:01                Iter   3   94.15%  Refine biparts1061 MB(6%)00:00:01                Iter   3   94.74%  Refine biparts1061 MB(6%)00:00:01                Iter   3   95.32%  Refine biparts1061 MB(6%)00:00:01                Iter   3   95.91%  Refine biparts1061 MB(6%)00:00:01                Iter   3   96.49%  Refine biparts1061 MB(6%)00:00:01                Iter   3   97.08%  Refine biparts1061 MB(6%)00:00:01                Iter   3   97.66%  Refine biparts1061 MB(6%)00:00:01                Iter   3   98.25%  Refine biparts1061 MB(6%)00:00:01                Iter   3   98.83%  Refine biparts1061 MB(6%)00:00:01                Iter   3   99.42%  Refine biparts1061 MB(6%)00:00:01                Iter   3  100.00%  Refine biparts1061 MB(6%)00:00:01                Iter   3  100.58%  Refine biparts1061 MB(6%)00:00:01                Iter   3  100.00%  Refine biparts
7c. NJ tree visualization of the alignment (using external package ape and phangorn)
library(ape)
library(phangorn)
# Convert the alignment to DNABin
dna_bin <- as.DNAbin(alignment_muscle)

# Distance matrix using K80
dist_matrix <- dist.dna(
  dna_bin,
  model = "K80"
)

# Neighbor Joining tree
nj_tree <- nj(dist_matrix)

# midpoint rooting
nj_tree <- midpoint(nj_tree)

# Plot tree
plot(
  nj_tree,
  cex = 0.6,
  main = "Neighbor-Joining Tree"
)

A simple NJ tree visualization of the aligned data

8. Generate a sf object for spatial mapping of occurrences of the genus Monochamus

# Filter for genus Monochamus
monochamus_search <- bold_parquet_search(
  input.parquet = parquet_file,
  taxonomy = "Monochamus",
  geography = "Canada",
  marker = "COI-5P"
)

# Collect the filtered data
monochamus_data <- bcdm_to_sf(monochamus_search)

The sf object can be used directly for mapping occurrences with external packages like ggplot2. (Background maps can be created in many ways. The sf library has been used in this case).

library(sf)
# Creating a background map using the maps package; some map_data country names (ID column) are changed to suit the BCDM country.ocean names
map_data <- st_as_sf(maps::map("world",
  plot = FALSE,
  fill = TRUE
)) %>%
  filter(ID == "Canada")
# Convert the data to WGS84
map_data <- st_transform(
  map_data,
  4326
)
# Plot
map_plot <- ggplot() +
  geom_sf(
    data = map_data,
    alpha = 0.3,
    linewidth = 0.4
  ) +
  geom_point(
    data = monochamus_data,
    mapping = aes(
      x = lon,
      y = lat
    ),
    colour = "#011B26",
    fill = "#F78E1E",
    size = 3,
    pch = 21
  ) +
  theme_bw(base_size = 15) +
  theme(panel.grid.major = element_line(
    colour = "grey50",
    size = 0.3,
    linetype = 3
  )) +
  xlab("Longitude") +
  ylab("Latitude") +
  coord_sf(expand = FALSE) +
  ggtitle("Distribution map")

map_plot

Occurrence map of Monochamus

Performance and Integration Notes

Benchmarking

Benchmarking used three representative queries:

  1. ~1 million records (taxonomy = “Hemiptera”)
  2. ~4.5 million records (taxonomy = “Diptera”, geography = “Costa Rica”)
  3. ~10 million records (taxonomy = “Diptera”)

Search (bold_parquet_search) and data retrieval (bold_search_collect) were benchmarked separately using the rbenchmark package with three replications on the 30th June 2026 data releaseß(https://doi.org/10.5883/DP-BOLD_Public.30-Jun-2026). Tests were run on a MacBook Air M2 (8-core CPU, 16 GB RAM). Across the three datasets, bold_parquet_search completed in 1.1 - 1.3s, while bold_search_collect required approximately 4 - 12 min, with runtime increasing as dataset size increased.

BOLDNODE can efficiently search and retrieve very large datasets; however, the data retrieval (collect) step is constrained by the user’s available system memory, and collecting very large query results may exceed the machine’s memory capacity.

Integration with BOLDconnectR

Functions from BOLDNODE can be combined with the analysis functions from the BOLDconnectR package because BOLD data packages are based on the Barcode Core Data Model (BCDM) (https://github.com/boldsystems-central/BCDM). This shared data structure enables direct interoperability between packages without requiring additional data conversion.