Data and code from: Moving towards better risk assessment for invertebrate conservation
Data files
Jun 06, 2025 version files 93.65 MB
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IBA_sim.zip
93.65 MB
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README.md
4.91 KB
Abstract
Among the most widely used information underpinning international conservation efforts is the IUCN Red List of endangered species. The Red List designates species extinction risk based on geographic range, population size, or declines in either. However, the Red-List has poor representation of invertebrates, which comprise the majority of animal diversity, and it has frequently been questioned whether Red List criteria are appropriate for these organisms. Due to their small size, difficulty in identification, and general rarity, many invertebrates are hard to study, making the Red List criteria difficult to apply. Here we discuss these criticisms in the context of empirical evidence from one of the largest terrestrial arthropod surveys to date, documenting the abundance and distribution of over 13,000 species in Sweden. Using simple empirical examples from these data, we argue that even the most ambitious monitoring efforts are unlikely to produce enough observations to reliably estimate population sizes and ranges for more than a fraction of species. Thus, there is likely to be substantial uncertainty in classifying most species according to current criteria. In response, we discuss the introduction of potential new IUCN criteria to more accurately capture the conservation needs of invertebrates, and to increase the representation of invertebrates on the IUCN Red List.
https://doi.org/10.5061/dryad.69p8cz987
The repository contains data and code needed to replicate figures and analysis for the article, DOI: https://doi.org/10.1002/ecog.07819.
Note that the full invertebrate data can be downloaded from the IBA figshare:
https://figshare.scilifelab.se/articles/dataset/Amplicon_sequence_variants_from_the_Insect_Biome_Atlas_project/25480681/5
Data: Contains data required to run analyses.
malaise_samples_metadata_SE_2019.tsv - Contains malaise trap metadata.
- sampleID_FIELD/LAB: the field/lab-specific ID number for a sample.
- trapID: ID of the trap a sample was collected from.
- lab_sample_type: the type of sample (e.g., field sample or extraction blank)
- biomass_grams: the biomass of the sample in grams.
- latitude/longitude_WGS84: the WGS84 coordinates of a trap.
- placing_time/date: the time/date a trap was placed.
- collecting_time/date: the time/date a trap was collected.
NA's in these metadata represent samples that were not collected from the field (e.g. extraction blanks)
filtered_arthropod_clusters.rds - Contains raw OTU cluster observations derived from sequence data. Columns are labelled as follows
- Cluster: the cluster ID of a detected organism.
- Order::Species: Taxonomic designations of OTU clusters.
- Lysate ID: The sample ID in which the organism was detected.
- reads: The raw reads of an organism in a sample
- contam_reads: The total number of reads for a given cluster found in all extraction blanks
- reads_adj: The raw reads minus the contamination reads
- pres: An indicator of whether an organism was present in a given sample.
species_incidence_abundance - contains a summary of the incidence and read numbers for each cluster.
- cluster: the cluster ID of an organism.
- mean_reads: average reads per cluster.
- total_reads: total reads per cluster.
- total_occ: total number of observations per cluster.
family_tree - contains a family-level taxonomic tree from which to plot the figure in box 1.
abundace/occupancy_sim_res.rds - contains results from the simulation exercises.
- iter: Simulation iteration
- preds: Predicted abundance/occupancy
- Ab/Occ: Simulated (real) abundance/occupancy
- pTrend : predicted trend
- rTrend : simulated trend
- pBeta : predicted trend effect size.
- rBeta: simulated trend effect size.
- pBeta_0: predicted intercept.
- rBeta_0: simulated intercept.
- N: Simulated sample size.
- conv_warn: Indicator of any convergence warnings (1 if convergence failed).
- fit_0 : Indicator of whether model failed to fit (1 if fitting failed).
- IUCN: IUCN trend category.
NA values in simulation results are a consequence of fitting or convergence failures, of which there were very few.
The two files needed to replicate the figures are not contained in this folder due to licensing rights:
red_list_assessments.csv - contains data on species occurring on the IUCN red list. Data were downloaded from https://www.iucnredlist.org/resources/spatial-data-download, where the relevant metadata can be found.
number_of_described_species.csv
A .csv summarising the number of described species, from: https://www.iucnredlist.org/resources/summary-statistics#Summary%20Tables
Both of which can be downloaded from the supplied links.
R: Contains scripts needed to run analyses, scripts contain a numerical prefix in the order they should be run (e.g. _1). Scripts without a suffix do not require running in any particular order.
1_filter_clusters.R: Filters clustered OTU data to only arthropods that match species level references, removes spike ins and potential contaminants. Note the original cluster data is not included in this repository and will be published in a separate data paper. This script is only provided for transparency.
2_get_incidence_abundace.R: Derive incidence and abundance distributions for species included in analysis.
3_simulate_occupancy_trends.R: Simulate occupancy trends.
4_simulate_abundance_trends.R: Simulate abundance trends.
functions.R: contains all functions for plotting and analysis
taxonomy.R: contains code to replicate figure in box 1.
distribution_simulation_figures.R: contains code to replicate figure in box 2.
plot_assessments.R: contains code to plot the number and type of Red List assessments.
RL_stats.R: contains code to calculate summary measures from IUCN Red List data.
The .Rproj file sets up the RStudio R project.
- Goodsell, Robert; Tack, Ayco; Ronquist, Fredrik et al. (2024). The rarity of Invertebrates prevents reliable application of IUCN Red-List criteria. [Preprint]. California Digital Library (CDL). https://doi.org/10.32942/x23g71
- Goodsell, Robert M.; Tack, Ayco J. M.; Ronquist, Fredrik et al. (2025). Moving towards better risk assessment for invertebrate conservation. Ecography. https://doi.org/10.1002/ecog.07819
