Local climatic effects on colonisation and extinction drive changes in mountain butterfly communities
Data files
Dec 10, 2024 version files 3.83 MB
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Phylogenetic_tree_butterflies_cropped.nwk
4.92 KB
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README.md
7.88 KB
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Script_manuscript_Ursul_etal_2025_DDI.R
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supp_material_Ursul_etal_2025_DDI.xlsx
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Abstract
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.
README: Local climatic effects on colonisation and extinction drive changes in mountain butterfly communities
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 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
- Phylogenetic tree for European butterflies from Wiemers et al. (2019): https://zenodo.org/records/3531555
- Iberian Species Temperature Index from Mingarro et al. (2021): https://doi.org/10.1111/icad.12498
- European Species Temperature Index from Platania et al. (2020): 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://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
Methods
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:
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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