DOI: 10.5061/dryad.2z34tmptv
title: Local climatic effects on colonisation and extinction drive changes in mountain butterfly communities

description: Aim: The capacity of cool refugia to protect cold-adapted species against
 climate change may depend on both their initial climatic conditions and
 how quickly these change. We test how local climatic conditions influence
 mountain butterfly communities via their effects on colonisation and local
 extinction. Location: Four mountain ranges in Central Spain. Methods: We
 used Community Temperature Index (CTI), based on the climatic niches of
 constituent species (Species Temperature Index, STI), to estimate thermal
 affinities for butterfly communities sampled in 1984-2005 to 2017-2022. We
 related CTI to local temperature, estimated using the model Microclima,
 and tested for changes to local temperature and CTI over time. We used
 standard deviation in CTI (CTISD) and species richness to detect effects
 of colonisation and local extinction on community change. Finally, we
 tested for differences in thermal affinity and thermal niche breadth
 (STISD) between species undergoing local extinction or colonisation at
 each site. Results: CTI was positively related to local temperature in
 both periods. However, there were regional differences in rates of change
 in CTI and local temperature. CTI increased overall, even though
 temperatures decreased at many sites; and CTI increases were greatest in
 historically cool sites. Neither CTISD nor species richness changed
 overall, suggesting that communities experienced equivalent numbers of
 colonisations and extinctions. Colonising species had warmer thermal
 affinities than those undergoing local extinction, and species with
 broader thermal niches increased their occupancy most over time. Main
 conclusions: Local climatic conditions influenced changes to community
 composition based on species thermal tolerances, resulting in the loss of
 communities where species with cool thermal affinities predominated, and a
 narrower range of community thermal affinities overall. Our results
 suggest that a regional perspective to identifying climate change refugia
 is needed to provide a wide range of local climate conditions and rates of
 change to help adapt conservation to climate change.
description: This dataset contains all data needed for the main statistical
 analysis and figures of the paper. It contains the data related to
 community composition using the Community Temperature Index (CTI),
 estimated species richness corrected using Sample Coverage with iNEXT R
 package and the fine-scale modelled temperature maxima in the study sites
 using Microclima R package.  The data comes from our
 field surveys in 2020 to 2022 (recent survey) compared to historical data
 processed from data presented in the following original
 sources:  Baz, A. (1987).
 Abundancia y riqueza de las comunidades forestales de mariposas
 (Lepidoptera: Rhopalocera) y su relación con la altitud en el Sistema
 Ibérico Meridional. Graellsia, 43, 179–192. 
 Gutiérrez-Illán, J., Gutiérrez, D., & Wilson, R. J.
 (2010). The contributions of topoclimate and land cover to species
 distributions and abundance: Fine-resolution tests for a mountain
 butterfly fauna: Determinants of butterfly distribution and abundance.
 Global Ecology and Biogeography, 19(2), 159–173. https://doi.org/10.1111/j.1466-8238.2009.00507.x Sánchez-Rodríguez, J. F., & Baz, A. (1995). The effects of elevation on the butterfly communities of a Mediterranean mountain, Sierra de Javalambre, central Spain. Journal of the Lepidopterists’ Society, 49(3), 192–207. Viejo, J. L., & Martín, J. (1988). Las mariposas del Macizo Central de Gredos (Lepidóptera: Hesperioidea et Papilionoidea). Actas de Gredos, Boletin Universitario 7, 81–93  
description: # **Local climatic effects on colonisation and extinction drive changes in
 mountain butterfly communities**
 [https://doi.org/10.5061/dryad.2z34tmptv](https://doi.org/10.5061/dryad.2z34tmptv) This dataset contains all data necessary to replicate all methods and results found in the manuscript with the title “Local climatic effects on colonisation and extinction drive changes in mountain butterfly communities” **Glossary:** STI: Species Temperature Index CTI: Community Temperature Index SC: Sample Coverage qD: estimated species richness **Description of the data and file structure** All data is stored in one Excel file with several sheets. Below there is an explanation of the use and information contained on each sheet: **1. Coordinates_list:** longitude and latitude coordinates for the 75 study sites to run Microclima in R and extract the modeled temperature values for each site. Also, the coordinates are used to conduct Moran's I test on spatial autocorrelation on model residuals. **2. iNEXT_spp_richness:** contains the size-based output from *iNEXT* function in R (see [https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.pdf](https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.pdf) for more information about iNEXT functionality). This data was used to extract estimated species richness (qD) for the several SC filters used in the paper. For each location and period (historical and recent) the model is iterated by equal increments on sampling size (column named m) up to double it.  Then qD is either rarefacted if its calculated for a sample size below the observed value or extrapolated if its calculated for a sample size above the observed value. All information included is listed below:  * Code: location code which can also be referred as a “community” * m: sample size of the assemblage or community * Method: Rarefaction, Observed or Extrapolation * Order.q: 0 refers to the hill number selected (q = 0 in our case because we are interested in species richness)  * qD: diversity estimate of order q or in this case, estimated species richness (q = 0) * qD.LCL: 95% lower boundary confidence limits of diversity * qD.UCL: 95% upper boundary confidence limits of diversity * SC: Sample Coverage * SC.LCL: 95% lower boundary confidence limits of sample coverage * SC.UCL: 95% upper boundary confidence limits of sample coverage * Survey: either Historical (1984-2005) or Recent (2017-2022) * Region: region of study (Guadarrama, Gredos, Meridional or Javalambre) **3. Main_analysis_data:** contains all variables used in the statistical analysis conducted for Figures 3, 4 and 5. All columns are explained below: * Code: location code which can also be referred as a “community” * Region: region of study (Guadarrama, Gredos, Meridional or Javalambre) * Variable: * *CTI_IB* for Iberian CTI (average across all STI of species in a community) * *CTI_EU* for European CTI * *CTI_sd_IB* for CTIsd for Iberian CTI (SD across all STI of species in a community) * *CTI_sd_EU* for CTIsd for European CTI * *STI_sd_IB* for STIsd for Iberian STI (average across STIsd for all species in a community) * *STI_sd_EU* for STIsd for European STI * Survey: * *Historical* (butterfly communities from 1984-2005 period) * *Recent* (buttferly communities from 2017-2022 period) * *diff* (difference between Recent and Historical) for each variable * *mean to q75*: values extracted from the randomisation process for each variable including the average (*mean*), standard error (*se*), the median (which is used in the main analysis), 25th percentile (*q25*) and 75th percentile (*q75*). * *recent_10y to diff_surv_y*: maximum temperature values extracted from Microclima R package for two different methods: 10-year periods (*10y*) 1980-89 and 2013-22 and for surveyed years (from September previous year of survey to August from the surveyed year). There are three columns for each method, either historical, recent and the difference (recent - historical Tmax). * Estimated species richness (qD): there are three sets of columns here, one for each SC filter used (*qD.LCL* refers to the lower bound of qD (95%),  *qD.UCL* refers to the upper bound of qD (95%) and *qD* refers to the estimated species richness which is used as an independent variable in the manuscript) **4. Persistence category analysis:** data used for figure 6A from main paper about the different persistence categories (colonize, persist and extinct): follows the same pattern as sheet 3 with main analysis data but includes a column with persistence category (if a species has colonised that particular site, persisted over time or became extinct). *Variable* column contains either *CTI* of all species in each persistence category and site; and *STIsd* of all species in each persistence category. Three variables of modelled temperature are added here to compute test for Figure 6A.  **5. Occupancy analysis:** data used for any occupancy analysis related to their historical occupancy (Nsites historical) and the temporal change in their occupancy (ΔNsites): * *Name*: Complete species name following Wiemers et al. (2018) nomenclature to be able to add the phylogenetic tree into the analysis. * *Survey*: this refers to which variable mean, se, median, q25 and q75 refer, in this case, these variables refer to temporal change in Nsites (e.g. occupancy) for each species (N = 129). Median is used in all analysis.  * *IBER_mean_temp* and *IBER_sd_temp*: contains Species Temperature Index (STI) and the standard deviation on STI based on Iberian Peninsula distributions of butterfly species (Mingarro et al. 2021).  * *EU_temp.mean* and *EU_temp.sd*: contains Species Temperature Index (STI) and the standard deviation on STI based on European distributions of butterfly species (Platania et al. 2020).  * *N_sites_hist*: contains historical occupancy of species as the number of sites where they were present.  **6. Paired test table:** contains historical, recent and the difference for each of the 11 variables used in the analysis. This table is used to compute paired test to check for significant differences over time. **7. Microclima data:** contains mean annual maximum temperatures for the two methods used to compute them: two 10-year period (1980-1989 and 2013-2022) and the surveyed years with butterfly data. This data is used to run spearman tests on microclima data and to compute Figure S1 to compare temperature data from both methods. **Sharing/Access information** Data was derived from the following sources: * Species nomenclature from Wiemers et al. (2018): [https://doi.org/10.3897/zookeys.811.28712](https://doi.org/10.3897/zookeys.811.28712) * Phylogenetic tree for European butterflies from Wiemers et al. (2019): [https://zenodo.org/records/3531555](https://zenodo.org/records/3531555) * Iberian Species Temperature Index from Mingarro et al. (2021): [https://doi.org/10.1111/icad.12498](https://doi.org/10.1111/icad.12498) * European Species Temperature Index from Platania et al. (2020): [https://doi.org/10.1111/geb.13154](https://doi.org/10.1111/geb.13154) More information about the methods behind estimated especies richness (*iNEXT*) and *Microclima* can be found in the following links:  * [https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.pdf](https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.pdf) * [https://rdrr.io/github/ilyamaclean/microclima/man/runauto.html](https://rdrr.io/github/ilyamaclean/microclima/man/runauto.html) **R code availability** **Script_manuscript_Ursul_etal_2025_DDI**: R code is available in this Dryad DOI to replicate all analyses of the paper. More code related to this work can be found at [https://github.com/guimursul/Public_MNCN](https://github.com/guimursul/Public_MNCN)