Assigning occurrence data to cryptic taxa improves climatic niche assessments: Biodecrypt, a new tool tested on European butterflies

Abstract

Aim Occurrence data are fundamental to macroecology, but accuracy is often compromised when multiple units are lumped together (e.g., in recently separated cryptic species or in citizen science records). Using amalgamated data leads to inaccuracy in species mapping, to biased beta‐diversity assessments and to potentially erroneously predicted responses to climate change. We provide a set of R functions (biodecrypt) to objectively attribute unidentified occurrences to the most probable taxon based on a subset of identified records. Innovation Biodecrypt assumes that unidentified occurrences can only be attributed at certain distances from areas of sympatry. The function draws concave hulls based on the subset of identified records; subsequently, based on hull geometry, it attributes (or not) unidientified records to a given taxon. Concavity can be imposed with an alpha value and sea or land areas can be excluded. A cross‐validation function tests attribution reliability and another function optimises the parameters (alpha, buffer, distance ratio between hulls). We applied the procedure to 16 European butterfly complexes recently separated into 33 cryptic species for which most records were amalgamated. We compared niche similarity and divergence between cryptic taxa, and re‐calculated and contributed updated climatic niche characteristics of the butterflies in Europe (CLIMBER). Main conclusions Biodecrypt showed a cross‐validated correct attribution of known records always ≥ 98% and attributed more than 80% of unidientified records to the most likely taxon in parapatric species. The functions determined where records can be assigned even for largely sympatric species, and highlighted areas where further sampling is required. All the cryptic taxa showed significantly diverging climatic niches, reflected in different values of mean temperature and precipitation compared to the values originally provided in the CLIMBER database. The substantial fraction of cryptic taxa existing across different taxonomic groups and their divergence in climatic niches highlight the importance of using reliably assigned occurrence data in macroecology.

High resolution DNA barcode library for European butterflies reveals continental patterns of mitochondrial genetic diversity

“…Range size has been used as a proxy for both effective population size and as a proxy for the likelihood that a species possesses disjunct populations 6 . Range size estimates for most European butterflies are available in the CLIMBER dataset ( 72 and update in 73 ) as the number of 30 × 30 km square cells occupied in Europe. A relationship between haplotype richness and range size can be due to the fact that species with a wider range are also more heavily sampled although the asymptotic values obtained by iNEXT should be relatively independent of the number of sequenced specimens.…”

Two ways to be endemic. Alps and Apennines are different functional refugia during climatic cycles

“…For SE and LE all occurrences were attributed to the original species while for ME and LME, we attributed species occurrence to their most probable ESU by using “biodecrypt” (“recluster” R package, https://rdrr.io/github/leondap/recluster/). The function creates concave hulls based on the distribution of the sequences attributed to a given entity and uses the relative hull geometries to attribute unknown occurrence data to a given entity (Platania et al, 2020) (see Appendix : Figures S1 and S2 for details). The “biodecrypt” function also provides a measure for hull overlap as an evaluation of sympatry among cryptic entities.…”

“…The traits of species which entities belong to were compared between endemics from different centres and between endemics and nonendemics from the same centre. We used a series of 10 ecological traits for European butterflies (Middleton‐Welling et al, 2020; Platania et al, 2020). These traits were used to describe both the alpha niche (i.e., functional traits describing the primary functions of invertebrates), and the beta niche (features related to distributional and environmental preferences; Table 1).…”

Butterfly communities track climatic variation over space but not time in the Iberian Peninsula

“…We used these data to calculate the mean annual temperature (°C) and precipitation (mm) for each period (1901–1979 and 1980–2016) in each 10 km grid square. This allowed us to compute for each species at 10 km resolution in the Iberian Peninsula the same metrics that have been calculated in the CLIMBER database (Climatic niche characteristics of the butterflies in Europe) using butterfly records at 50 km resolution for 1981–2000 (Schweiger et al ., 2014; Platania et al ., 2020). For each species, we calculate the Species Temperature Index (STI) as the mean temperature (°C), and the Species Precipitation Index (SPI) as the mean precipitation (mm), in 10 km grid squares where the species was recorded in 1980–2016, the period when distributions were sampled more completely.…”