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

Abstract 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.


| INTRODUC TI ON
Global declines in the abundance, richness, and distribution areas of insects have been reported in the last decades (Montgomery et al., 2020;Sánchez-Bayo & Wyckhuys, 2019;Wagner et al., 2021).
However, most of the declines are reported from geographically restricted studies, which makes it difficult to obtain reliable conclusions about the threats that affect species at different scales (Montgomery et al., 2020). Given the rapid loss of tropical forests each year (Curtis et al., 2018;Stokstad, 2018), there is an urgent need to collect data in tropical environments, where entomofauna are ecologically diverse and still little known (Stork, 2018). This can be achieved using species distribution modeling (SDM), which is a useful tool to address knowledge gaps about the distribution of species, based on species occurrences and environmental data (Peterson & Soberón, 2012).
Here, we present a new occurrence record of Amphidecta calliomma in the eastern Amazon (Carajás National Forest; southwestern Pará), expanding the known distribution of this species. We also aim to determine potential areas of occurrence of this species to support future field research.

| Sampling
From November 05 to 14, 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 h, following methodologies adapted from Freitas et al. (2014) and Uehara-Prado et al. (2005).
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" (https:// www.butte rflie sofam erica.com/L/Nymph alidae.htm, 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, 2022;www.gbif.org, accessed in November 2022; DOI: https://doi.org/10.15468/ dl.kgbph8) and SpeciesLink (https://speci eslink.net/, accessed in November 2022) and from published articles, totaling 52 records. We also removed duplicate and nongeoreferenced 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; Table S1).

| Climate information
We downloaded climate data with a resolution of 10 arc-minutes (~18 km × 18 km) from the WorldClim database version 2.1 (www. world clim.org, accessed in November 2022). We focused on noncorrelated climate data, based on ecological relevance. Butterflies are highly sensitive to climate as warm temperatures can stimulate their flight muscle efficiency and wind is a key component for flying animals and precipitation affects species richness (Checa et al., 2019;Turner et al., 1987). We downloaded and tested for correlation (coefficient threshold |ρ| < .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 -International Union for Conservation of Nature, 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 are a sample from the study area used to characterize its environmental conditions (Phillips et al., 2009). Distribution modeling methods using background data generally outperformed those using presence-absence or pseudo-absence information, especially when modeling mobile species (Fernandez et al., 2022). Also, background information methods are more flexible, producing more realistic and less overfitted 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 & ter 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 5%. 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 (De Ro et al., 2021;Gaston, 2009). 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 et al., 2017), gstat (Pebesma & Heuvelink, 2016), dismo , rJava (Urbanek, 2017), and SDMTools (VanDerWal et al., 2019).

| New occurrence
A specimen identified by AP as A. calliomma (Figure 1) was col- A total of 16 occurrences were used to build the models when included the new occurrence (15 occurrences without it; Figure S1).
We compared the results of modeling with and without the new occurrence point.

| Species distribution model
The correlation test including the seven downloaded historical climate variables resulted on the selection of the four least correlated ones: precipitation, solar radiation, wind speed, and maximum temperature ( Figure S2). Precipitation and wind speed were the most important environmental variables for our models, accounting for over 99% of contribution (Table S2)

| DISCUSS ION
The high suitability values obtained by our model for central Amazon show a range that seems consistent for the species' distribution area, but our models also show a large area for Brazilian Tropical Savannas and Atlantic Forest. Although the new occurrence adds little information to the distribution modeling, it is important for understanding the current distribution of the species, showing also the significant role of protected areas for the conservation of A. calliomma. Therefore, it is essential that new occurrences would be recorded not only in the Amazon Forest but also outside its domain, to determine a more accurate distribution area.
Our predictive models also showed that A. calliomma can be dis-  Saout et al., 2013). Our study used only climate data to build an environmental suitability model, but other information can be included in distribution modeling to improve predictions, such as land-use data and biotic interactions (Sobral-Souza et al., 2021;Wisz et al., 2013).
For example, the abundance of species of the subfamily Satyrinae is dependent on the distribution pattern of its host plants, since the larval stage of these butterflies usually develops in monocotyledons whose growth, abundance, and nutritional content are strongly seasonal (Sousa et al., 2019). However, there is still no information on the immature stages of A. calliomma, and consequently, there is no knowledge about its host plant. The only species within the genus for which a description of immature and host plants is available is Amphidecta reynoldsi Sharpe, 1890, which feeds on bamboo leaves (Merostachys) (Poaceae) (Freitas, 2004). Information on Amphidecta pignerator A. Butler, 1867, host plants is also lacking (DeVries, 1987).
The A. calliomma specimen was collected in an area of seasonal F I G U R E 1 Specimen of Amphidecta calliomma collected in the Carajás National Forest. (a) Ventral view; (b) dorsal view. Photograph by Amanda Paracampo. semideciduous forest, which is characterized by seasonal leaf loss that occurs during the driest months, causing high light incidence.
In addition to these issues, the previous records of this species do not include precise information on collection sites, sampling efforts, or sampling methods. For example, in studies by Salazar (2019) and Silva et al. (2020), a specimen of A. calliomma was collected, but there is no description of which capture method was used. Murray (2000) also collected an individual of A. calliomma but did not report in which stratum the capture occurred. Thus, more detailed data on the habitats in which specimens were collected are useful to calibrate the species distribution model. Although our distribution model did not include information on land-use and biotic interactions, we provided a primary evaluation of suitable areas for A. calliomma occurrence. This is the first step to help understanding the species distribution patterns, discovering populations and species limits, and developing

| CON CLUS ION
Our results contribute to the development of knowledge about the diversity of butterflies in the Amazon Forest and show that A. calliomma occurs further east in the biome than previously reported.
Although the species potentially occurs in a wide area in the Amazon, few collections of the species were made in the region. Even collections made over long periods resulted in few specimens; this may suggest that there is a low abundance of this butterfly in nature, which is possibly an indication that this species is rare or that the species has low detectability or, when it is in the process of identification, it can be easily confused with other similar species. It is important to highlight that the objective of the predictive model pre-

CO N FLI C T O F I NTE R E S T
No potential conflict of interest is reported by the authors.

DATA AVA I L A B I L I T Y S TAT E M E N T
All relevant data are within the manuscript and its Supporting Information files (two tables) are located in The Dryad Data Platform repository (https://doi.org/10.5061/dryad.mw6m9 061d).