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Data from: New geographic record in eastern Amazon Forest and potential distribution of Amphidecta calliomma (Lepidoptera: Nymphalidae)

Cite this dataset

Paracampo, Amanda et al. (2023). Data from: New geographic record in eastern Amazon Forest and potential distribution of Amphidecta calliomma (Lepidoptera: Nymphalidae) [Dataset]. Dryad.


Amphidecta calliomma is a butterfly species that occurs in Colombia, Bolivia, Peru, Venezuela, Ecuador, Panama and Brazil (in the states of Mato Grosso, Mato Grosso do Sul, Rondônia and Pará). Here, we present a new occurrence of A. calliomma in the Carajás National Forest (Pará, eastern Amazon), expanding the known distribution of the species. We also provide Species Distribution Model comparing the contribution of the new occurrence to species area of occurrence projections, supporting future field research. The projections reveal an expansion of area of occurrence for A. calliomma located mainly in the southeast portion of Amazon Forest. Despite its wide distribution, the small number of records of A. calliomma may indicate that the species has a low detectability in surveys. This study provides support for new surveys and reduces the knowledge gap about A. calliomma, thus supporting its conservation.



From 05 to 14 November 2019, we conducted a campaign to collect frugivorous butterflies in the Carajás National Forest (southwestern Pará state, Brazil). Butterflies were collected using Van Someren-Rydon traps baited with a mixture of banana and beer (instead of sugarcane), which was fermented for 48 hours, following methodologies adapted from Uehara-Prado et al. (2005) and Freitas et al. (2014). The individuals captured in the traps were collected (SISBIO license number: 68977-1) and identified based on literature resources and with the help of the website “Butterflies of America” (, accessed in November 2020) (Warren et al., 2013). After identification and preparation, the specimen of A. calliomma was incorporated into the entomological collection of the Museu Paraense Emílio Goeldi (MPEG.HLE 04045043) (MPEG, Pará, Brazil).

Occurrence records

In addition to field collection, we retrieved data from Global Biodiversity Information Facility (GBIF;, accessed in November 2022; DOI: and SpeciesLink (, accessed in November 2022) and from published articles, totaling 52 records. We also removed duplicate and non-georeferenced data. We removed inconsistencies using a conservative pipeline (Gomes et al., 2018). Thus, our final database totaled 16 occurrence records (11 from the digital databases, 4 from articles and 1 occurrence from our field collections) (Supporting Information Table 1).

Climate information

We downloaded climate data with a resolution of 10 arc-minutes (~ 18 km x 18 km) from the WorldClim database version 2.1 (, accessed in November 2022). We focused on non-correlated climate data, based on ecological relevance. Butterflies are highly sensitive to climate as warm temperatures can stimulate their flight muscles efficiency and wind is a key component for flying animals and precipitation affects species richness (Turner et al. 1987; Checa et al. 2019). We downloaded and tested for correlation (coefficient threshold |ρ| < 0.7) seven historical climate variables: precipitation, water vapor pressure, solar radiation, wind speed, maximum temperature, minimum temperature and average temperature.

Species Distribution Model

We used an algorithm based on maximum entropy (MaxEnt) to produce models of species potential distribution to estimate A. calliomma area of occurrence (AOO) (Phillips et al., 2004; IUCN, 2022). We followed Gomes et al. (2019) and used background information to calibrate MaxEnt predictions based on data of tree species from Amazon forest since most of the occurrences of the A. calliomma are located in this biome. Background data is a sample from the study area used to characterize its environmental conditions (Phillips et al., 2009). Distribution modelling methods using background data generally outperformed those using presence-absence or pseudo-absence information, especially when modelling mobile species (Fernandez et al., 2022). Also, background information methods are more flexible, producing more realistic and less over-fitted predictions (Peterson et al., 2011). Since A. calliomma has little occurrence information available, we used a more flexible approach to understand the general distribution pattern of the species. We used product, threshold and hinge features of MaxEnt (Boucher-Lalonde et al., 2012; Merow et al., 2013). To evaluate the models, we used a null model approach (Raes & Steege, 2007). We tested the predictive performance of the A. calliomma models as estimated by the area under the ROC curve (AUC) against the predictive performance of 99 null models generated using the same number of occurrences of A. calliomma generated randomly. If the AUC of the models scores higher than the 95th best null models, this means that the chance of a model generated randomly showing a better performance is less than five percent. The models were converted in binary maps by using the 10th percentile training presence threshold, which omits the regions with environmental suitability lower than the lowest 10% of occurrence records (Gomes et al., 2018). We then clipped the binary maps by using the extent of occupancy (EOO) of the species plus a buffer of 300 km, based on the notion that the EOO is restricted by dispersal capabilities (Gaston, 2009; De Ro et al., 2021). We estimated A. calliomma AOO using the new occurrence sampled and comparing with the AOO estimation with no new occurrence. All calculations and analyses were performed with R version 3.6.3, including the R packages raster (Hijmans & van Etten, 2016), rgdal (Bivand, Keitt, & Rowlingson, 2017), gstat (Pebesma & Heuvelink, 2016), dismo (Hijmans et al., 2016), rJava (Urbanek, 2017) and SDMTools (VanDerWal et al., 2019).

Usage notes

Our data is a .xlsx extension so we recommend opening it with Excel for better reading and visualizations.


Coordenação de Aperfeicoamento de Pessoal de Nível Superior, Award: 88882.444580/2019-01

Vale Technological Institute, Award: RBRS000603.81