DOI: 10.5061/dryad.j0zpc86qk
title: Supporting information from: Chorotypes, zones for the conservation of Scarabaeoidea, and representativity in protected areas of El Salvador

description: Ecological niche models and species distribution models (SDM) are tools
 that can define a species’ niche based on presence records and identify
 those areas where appropriate ecological conditions converge as zones of
 potential distribution for the species. SDMs help to optimize conservation
 efforts, particularly important in countries with ecological and
 economical problems. We constructed SDMs by means of the Maxent algorithm
 for 160 species of scarab beetles (Scarabaeoidea) to identify chorotypes
 and important zones for conservation in El Salvador, and to evaluate
 richness representativity within the System of Protected Natural Areas
 (SANP). The chorotypes were identified with UPGMA method, with a matrix of
 presence-absence in ≈3 km², and important zones for conservation were
 identified with the Zonation algorithm. The richness representation in the
 SANP was done with EstimateS, comparing the estimated by Chao 2 ±
 confidence intervals (95%) in the same number of cells (n = 811). We
 identified five chorotypes: C1) lowland and middleland species; C2)
 species widely distributed; C3) species with an affinity for
 mid-elevations; C4) species limited to lowlands; and C5) species limited
 to high montane areas. Most important conservation zones are located in
 montane regions. As in other tropical areas, the volcanic and montane
 slopes of El Salvador represent key areas for conserving biodiversity.
 Species richness in the SANP was not significantly greater than that
 outside the system, therefore, we recommend continuous prioritization and
 amplification of the protected natural heritage of the country, with
 emphasis on the mountainous area of the territory.
description: # Supporting information: Chorotypes, zones for the conservation of
 Scarabaeoidea, and representativity in protected areas of El Salvador
 [https://doi.org/10.5061/dryad.j0zpc86qk](https://doi.org/10.5061/dryad.j0zpc86qk) ## Description of the data and file structure This is the detailed description of the methods to cull species from the study group, process information, and select the calibration area, and the detailed description of the methods for construction, calibration, evaluation, and selection of species distribution models of 168 scarab species from El Salvador. Besides the list of the Candidate variables for generating the species distribution models for Scarabaeoidea in El Salvador and the variables used to model the potential distribution of each species and the detailed results obtained through this process. Finally, three complementaries results are presented: Detailed results of the ecological niche and species distribution modelling of each species, list of species in each of the five chorotypes and the important conservation zones for Scarabaeoidea in El Salvador. ### Files and variables #### File: MethodsS1\_S2.docx (Zenodo - Supplemental Information) **Description:** **Methods S1:** Detailed description of the methods to cull species from the study group, process information, and select the calibration area. **Methods S2:** Detailed description of the methods for construction, calibration, evaluation, and selection of species distribution models. #### File: TableS1.csv **Description:** **Table S1.** List of 168 species of Scarabaeoidea included in the study of chorotypes in El Salvador: Geotrupidae (*n* = 7); Passalidae (*n* = 20); Trogidae (*n* = 2); Lucanidae (*n* = 1); Ochodaeidae (*n* = 2); Hybosoridae (*n* = 1); Scarabaeidae:  Scarabaeinae (*n* = 48); Dynastinae (*n* = 60); Cetoniinae (*n* = 25). #### File: TableS2.csv **Description:**  **Table S2.** Candidate variables for generating the species distribution models for Scarabaeoidea in El Salvador. Preliminarily, species distribution models were created using two groups. The first group used only the 15 variables of WolrdClim v2.1 (Fick & Hijmans, 2017) that do not present anomalies related to the discontinuity of the values recorded between nearby cells (Booth, 2022; Escobar et al., 2015). The second group used the 15 WolrdClim v2.1 variables in addition to another six environmental variables and five relief variables, the latter producing the best distribution models. All environmental and relief layers used a spatial resolution of 30 seconds (approximately 1 km²) and were delimited based on the calibration area of each species. #### File: TableS3.csv **Description:** **Table S3.** Variables used to model the potential distribution of each species. | Variables | Abbreviation | | :--------------------------------------------------------- | :----------- | | Annual mean temperature | bio01 | | Mean diurnal range (Mean of monthly (max temp - min temp)) | bio02 | | Isothermality (bio2/bio7) (×100) | bio03 | | Temperature seasonality (standard deviation ×100) | bio04 | | Max temperature of warmest month | bio05 | | Min temperature of coldest month | bio06 | | Temperature annual range (bio5-bio6) | bio07 | | Mean temperature of warmest quarter | bio10 | | Mean temperature of coldest quarter | bio11 | | Annual precipitation | bio12 | | Precipitation of wettest month | bio13 | | Precipitation of driest month | bio14 | | Precipitation seasonality (Coefficient of variation) | bio15 | | Precipitation of wettest quarter | bio16 | | Precipitation of driest quarter | bio17 | | Monthly maximum solar radiation (kJ m-2 day-1) | sr\_max | | Monthly minimum solar radiation (kJ m-2 day-1) | sr\_min | | Mean solar radiation (kJ m-2 day-1) | sr\_mn | | Mean topographic position index | tpi | | Mean terrain ruggedness index | tri | | Monthly maximum evapotranspiration | et\_max | | Monthly minimum evapotranspiration | et\_min | | Mean of roughness | rough | | Mean of slope | slope | | Mean of aridity Index | ai | | Mean of evapotranspiration | et | #### File: TableS4.csv **Description:** **Table S4.** Detailed results of the ecological niche and species distribution modelling of each species. | NM=Not modeled | | :------------------------------------------ | | AIC=Akaike Information Criterion | | AICc=Corrected Akaike Information Criterion | | AUC=Area Under the Curve | | ROC=Receiver Operating Characteristic | #### File: TableS5.csv **Description:** **Table S5.** List of species in each of the five chorotypes. C1 = Chorotype 1 lowland species that can penetrate mid-elevations; C2 = Chorotype 2 species with broad distribution; C3 = Chorotype 3 mid-elevation species that can penetrate higher elevations; C4 = Chorotype 4 lowland species; C5 = Chorotype 5 montane species. #### File: TableS6.csv **Description:** **Table S6.** Important conservation zones for Scarabaeoidea in El Salvador. Status: P = protected; NP = not protected. ## Access information Data was derived from the following sources: * Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015). spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 38(5), 541–545. Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5, 1–16.  Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A. T., Soberón, J., & Villalobos, F. (2011). The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222(11), 1810–1819. Booth, T. H. (2022). Checking bioclimatic variables that combine temperature and precipitation data before their use in species distribution models. Austral Ecology, 47(7), 1506–1514.  Cobos, M. E., Peterson, A. T., Barve, N., & Osorio-Olvera, L. (2019). kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ, 7, e6281.  Dellacasa, G., Bordat, P., & Dellacasa, M. (2001). A revisional essay of world genus group taxa of Aphodiinae. Memorie Della Società Entomologica Italiana, 79, 1–482. Escobar, L. E., Awan, M. N., & Qiao, H. (2015). Anthropogenic disturbance and habitat loss for the red-listed Asiatic black bear (Ursus thibetanus): Using ecological niche modeling and nighttime light satellite imagery. Biological Conservation, 191, 400–407.  Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315.  Google Earth. (2023). Google Earth. Hijmans, R. J. (2023). Package ‘raster’: Geographic Data Analysis and Modeling (Version 3.6-13) [Computer software]. Hijmans, R. J., Phillips, S., Leathwick, J., & Elith, J. (2022). Package ‘dismo’: Species Distribution Modeling (1.3-9) [Computer software]. [https://rspatial.org/raster/sdm/](https://rspatial.org/raster/sdm/) Kadej, M., Zając, K., Smolis, A., Tarnawski, D., Tyszecka, K., Malkiewicz, A., Pietraszko, M., Warchałowski, M., & Gil, R. (2017). The great capricorn beetle Cerambyx cerdo L. in south-western Poland – the current state and perspectives of conservation in one of the recent distribution centres in Central Europe. Nature Conservation, 19, 111–134.  Kie, J. G., Baldwin, J. A., & Evans, C. J. (1996). CALHOME: a program for estimating animal home ranges. Wildlife Society Bulletin, 24(2), 342–344. Morueta-Holme, N., Fløjgaard, C., & Svenning, J.-C. (2010). Climate Change Risks and Conservation Implications for a Threatened Small-Range Mammal Species. PLoS ONE, 5(4), e10360.  Morón, M. A., & Ramírez-Ponce, A. (2012). Mesoamerican genera of Anomalini (Coleoptera: Melolonthidae: Rutelinae): A brief review. Trends in Entomology, 8, 97–114. Mota-Vargas, C., & Rojas-Soto, O. R. (2016). Taxonomy and ecological niche modeling: Implications for the conservation of wood partridges (genus Dendrortyx). Journal for Nature Conservation, 29, 1–13. Noriega, J. A., Floate, K. D., Génier, F., Reid, C. A. M., Kohlmann, B., Horgan, F. G., Davis, A. L. V., Forgie, S. A., Aguilar, C., Ibarra, M. G., Vaz‐de‐Mello, F., Ziani, S., & Lobo, J. M. (2020). Global distribution patterns provide evidence of niche shift by the introduced African dung beetle Digitonthophagus gazella. Entomologia Experimentalis et Applicata, 168(10), 766–782. Pablo-Cea, J. D., Velado-Cano, M. A., Fuentes, R., Cruz, M., & Ari Noriega, J. (2017). Primer registro de Digitonthophagus gazella (Fabricius, 1787) y nuevo registro para Euoniticellus intermedius (Reiche, 1849) (Coleoptera: Scarabaeidae Latreille, 1802) en El Salvador. Acta Zoológica Mexicana (Nueva Serie), 33(3), 527–531. Pablo-Cea, J.D., Cave, R.D., Serrano-Peraza, S.A., Alvarado-Larios, R., Deloya, C., Serrano-Chicas, K.A., Alfaro, E., Chinchilla- Rodríguez, A.C., Girón-Segoviag, D., & Noriega, J.A. (2023) Catalog and distribution atlas of the Scarabaeoidea (Insecta: Coleoptera) of El Salvador. Revista Mexicana de Biodiversidad, e945117. Papeş, M., & Gaubert, P. (2007). Modelling ecological niches from low numbers of occurrences: Assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents: Ecological niche modelling of poorly known viverrids. Diversity and Distributions, 13(6), 890–902. Pebesma, E., & Bivand, R. (2022). Package ‘sp’: Classes and methods for spatial data (Version 1.5-1) [Computer software]. Phillips, S. J., Dudík, M., & Schapire, R. E. (2023). Maxent software for modeling species niches and distributions (Version 3.4.1).  R Core Team. (2020). A language and environment for statistical computing. R Foundation for Statistical Computing [Computer software]. Ramírez-Ponce, A. (2015). Moroniella, un nuevo género americano de Anomalini. Dugesiana, 22(2), 261–266. Ratcliffe, B. C. (2013). A Revision of the Neotropical Genus Amithao Thomson, 1878 (Coleoptera: Scarabaeidae: Cetoniinae: Gymnetini). The Coleopterists Bulletin, 67(3), 265–292. Soberón, J. (2007). Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10(12), 1115–1123. Trabucco, A., & Zomer, R. (2022). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v3 (p. 6086879943 Bytes) [dataset]. figshare.  Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecological Applications, 21(2), 335–342.