DOI: 10.5061/dryad.q2bvq83xm
title: Recent and rapid assembly of an island species–area relationship threatened by human disturbance

description: The island species–area relationship (ISAR) describes how larger islands
 support more species. Studies on oceanic archipelagos have shown that
 ISARs assembled over millions of years have predictable shapes. However,
 it remains unclear how rapidly “classic” ISARs develop, and how they are
 formed on much younger systems. Here, we compile a dataset for the fish
 communities of 79 postglacial peri-Alpine lakes, and report that an ISAR
 with a classical shape has formed de novo in less than 15,000 years.
 Despite their very young age, these lakes exhibit an ISAR mirroring older
 systems, with a characteristic asymptotic shape. Immigration responds
 primarily to area and saturates, whereas speciation is primarily driven by
 lake depth. This young ISAR has been reshaped by anthropogenic activities,
 with species introductions erasing its upper limit. We demonstrate that
 ISARs can develop rapidly after habitat formation in semi-isolated
 systems, offering insights into the assembly of ecological patterns.
description: # Recent and rapid assembly of an island species–area relationship
 threatened by human disturbance Dataset DOI:
 [10.5061/dryad.q2bvq83xm](10.5061/dryad.q2bvq83xm) ## Description of the
 data and file structure These files contain the raw data and R codes used
 in the manuscript for the: 1. **Analyses**: Multimodel analysis to test
 for the best mathematical function to describe the lake ISAR. 2. **GLMM**:
 Mixed model to identify the lake features related to the richness metrics.
 3. **Power Function**: Power function fit to extract the parameters *z*
 (slope) and *c* (intercept). ## Files and variables ### File:
 Supporting_information_S1.pdf **Description:** Additional description of
 the methods used in the manuscript. ### File:
 Supp_info_S2_lakeFeat_richn.csv **Description:** Table containing the lake
 features and richness metrics used in the analyses. #### Variables *
 *Lake* | Name of the lake. * *Catchment* | River basin or drainage system
 to which the lake belongs. * *Long* | Longitude (decimal degrees) of the
 lake. * *Lat* | Latitude (decimal degrees) of the lake. * *Elevation_m* |
 Elevation of the lake surface above sea level (meters). *
 *Surface_area_km2* | Lake surface area (square kilometers). *
 *Maximum_depth_m* | Maximum recorded depth of the lake (meters). *
 *Average_depth_m* | Average depth of the lake (meters). *
 *Volume_10.6_m3_GL* | Lake volume (10^6 cubic meters; gigaliters). *
 *Shore_length_hydrolake* | Total shoreline length of the lake
 (kilometers). * *Dis_avg_hydrolake* | Average discharge (water outflow)
 from the lake (cubic meters per second). * *Wshd_area_hydrolake* |
 Watershed (catchment) area contributing to the lake (square kilometers). *
 *avg_surface_temp_domischetal2015* | Average surface water temperature
 (°C) based on Domisch et al. (2015). * *avg_air_temperature_chelsa* |
 Average annual air temperature (°C) from CHELSA climate data. *
 *air_temp_range_chelsa* | Annual range of air temperatures (°C) from
 CHELSA climate data. * *surf_temp_range_domischetal2015* | Annual range of
 surface water temperatures (°C) from Domisch et al. (2015). *
 *distance_to_river_mouth* | Distance from the lake to the nearest river
 mouth entering the ocean (kilometers). * *distance_to_refugium* | Distance
 from the lake to the nearest hypothetical glacial refugium (kilometers). *
 *mean_steepness* | Mean terrain steepness of the lake’s surrounding
 watershed (degrees or percent, specify if known). * *range_steepness* | Or
 slope; range (maximum - minimum) of terrain steepness in the watershed. *
 *D1.Total_native_Richness* | Total richness of native species originally
 present in the lake. * *D1.Native.Salmonidae.richness* | Richness of
 native Salmonidae species originally present in the lake. *
 *D2.Total.richness* | Total species richness including both native and
 introduced species. * *D3.Total_Native_extant.richness* | Richness of
 extant (currently surviving) native species. * *D5.Endemic* | Number of
 endemic species. * *D6.Total.Extant.richness* | Total extant species
 richness, including surviving native and introduced species. *
 *Endemism_proportion* | Proportion of endemic species among the original
 native community. ### File: Supp_info_S3_species_list.csv
 **Description:** List of fish species #### Variables * *Class* | Taxonomic
 class of the species (e.g., Actinopterygii for ray-finned fishes). *
 *Order* | Taxonomic order of the species. * *Family* | Taxonomic family of
 the species. * *Genus* | Taxonomic genus of the species. * *Species* |
 Scientific species name (binomial nomenclature: Genus + species epithet).
 * *Lake* | Name of the lake where the species was recorded. * *Drainage* |
 Name of the drainage basin or river system associated with the lake. *
 *Origin* | Species' biogeographic origin relative to the lake (e.g.,
 native, introduced). * *Endemicity* | Endemism status of the species in
 the lake (e.g., endemic, non-endemic). * *Extinction status* | Current
 extinction or conservation status of the species within the lake (e.g.,
 extant, extinct, locally extinct). ### File: R.code.for.Fig1.zip
 **Description:** R code to generate the Figure 1: Map of the lakes. This
 folder contains the following subfolders or files: * *Figure1.Rmd:* R
 script with all steps to generate Figure 1. * *richness.csv*: dataframe
 containing the lake features and richness information. That is the same
 as *Supp_info_S2_lakeFeat_richn.csv* Note that to run this code, one will
 need to download data from HydroSHEDS (Messager et al. 2016; Lehner &
 Grill 2013). The code for that is provided. ### File: R.code.for.Fig2.zip
 **Description:** R code to generate the Figure 2: Mixed models. This
 folder contains the following files: 1. *Supporting_information_S2.csv*:
 This is the same file as in *Supp_info_S2_lakeFeat_richn.csv.* Please,
 report to the variable description provided above. 2.
 *Supporting_informatio_S4_GLMM.Rmd*. R script to run the mixed models and
 generate Figure 2. ### File: R.code.for.Fig3.zip **Description:** R code
 to generate the Figure 3: Parameters of the power function. This folder
 contains the following files: 1. *richness.csv*: This file also contains
 the same data available in *Supp_info_S2_lakeFeat_richn.csv.*  Report to
 the variable description provided above. 2. *Figure3_Power_f.Rmd*: This R
 script contains de code to generate Figure 3. ### File:
 R.code.multimodel.zip **Description:** R code to test for the best
 mathematical function for the distinct richness metrics. This folder
 contains the following files: 1. *R.code.for.multimodel_analyses_S5.Rmd*:
 This is a R script to test for the best mathematical function to describe
 the relationship between lake area and species richness.  2.
 *R.code.for.multimodel_analyses_S5Ext.Rmd*: In this R script we extended
 the analyses of the previous script to include sensitive tests, where a
 subset of the lakes were used. 3. *multimodel_equations.csv*: This file
 contains a list of the mathematical functions tested. Note that this is an
 adaptation of the R package developed by Rampal Etienne for this work
 ([https://github.com/rsetienne/isars](https://github.com/rsetienne/isars))
 ### File: R.code.HydroLakes_CHELSA_EarthEnv.zip **Description:** R code to
 extract lake features from HydroLakes, CHELSA bioclimatic data, and
 freshwater EarthEnv bioclimatic data. This folder contains the following
 files: 1. *Lake_list.csv*: List of the 79 peri-Alpine lakes with their
 geographical coordinates in decimal degrees. 2.
 *Data_from_Hydrolakes.Rmd*. R script to obtain the bioclimatic variables
 for each peri-Alpine lake. Note that to run this script, one needs to
 obtain extra data from CHELSA (Karger et al. 2017), EarthEnv (Domisch et
 al. 2015), and HydroLAKES (Messager et al. 2016). The script is equipped
 with the code to download the necessary data. ### File:
 R.code.Refugium_Ocean_Steepness.zip **Description:** R code to calculate
 lake isolation metrics: distance to the ocean, distance to hypothetical
 glacial refugia, and steepness (*aka* **slope**). This folder contains the
 following files: 1. *R_code_Distance_To_Glacial_Refugia.Rmd*. R script
 used to compute distance of the peri-Alpine lakes to the ocean, to glacial
 refugia, and their steepness (i.e., slope). 2. *lakes_coords.csv*: list of
 the peri-Alpine lakes with their geographical coordinates in decimal
 degrees. To run this script, it will be necessary to download an elevation
 map of Europe (European Environment Agency, 2023), a shapefile of the
 European Rivers (Copernicus, 2023), and a shape file of the
 lakes (Messager et al. 2016). ## Code/software ##### *Language and
 Environment* R Environment for Statistical Computing ##### *Version* R
 4.3.3 ##### *Dependencies* * AICcmodavg * car * corrplot * cowplot *
 DHARMa * dplyr * ggeffects * ggnewscale * ggplot2 * glmmTMB * isars *
 knitr * MuMIn * raster * readxl * rmarkdown * rnaturalearth * riverdist *
 sars * scales * sf * sjPlot * terra ## Access information Data used here
 derivesfrom the following sources: **Bioclimatic variables**: *CHELSA
 v1.2* and EarthEnv: * Karger, D. N., Conrad, O., Böhner, J., Kawohl, T.,
 Kreft, H., Soria‐Auza, R. W., Zimmermann, N. E., Linder, H. P., &
 Kessler, M. (2017). Climatologies at high resolution for the earth’s land
 surface areas. *Scientific Data*, 4, 170122.
 [https://doi.org/10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122) * Domisch, S., Amatulli, G., and Jetz, W. (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2:150073 doi:10.1038/sdata.2015.73. **Lake features**: *HydroLAKES, HydroBASINS and HydroRIVERS* * Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. *Nature Communications*, 7, 13603. [https://doi.org/10.1038/ncomms13603](https://doi.org/10.1038/ncomms13603) * Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. **Fish species richness** * Alexander, T. and Seehausen, O. 2021. Diversity, distribution and community composition of fish in perialpine lakes. “Projet Lac” synthesis report.: 282. * Freyhof, J. and Kottelat, M. 2007. Handbook of European freshwater fishes. * Gassner, H., Achleitner, D. and Luger, M. 2015. Guidance on srveying the biological quality elements Part B1 – Fish.: 40. * Luger, M., Kammerlander, B., Pamminger-Lahnsteiner, B., Achleitner, D. and Gassner, H. 2025. Die Fischgemeinschaften in österreichischen Seen &gt;50 ha: Erhebung und Bewertung nach EU-WRRL und ALFI (Austrian Lake Fish Index). - Österr. Wasser- Abfallwirtsch. in press. **Other dataset that were used in the process** * Copernicus, European Union's Copernicus Land Monitoring Service information. 2023. EU-Hydro River Network Database 2006-2012. European Union's Copernicus Land Monitoring Service information, DOI: [10.2909/393359a7-7ebd-4a52-80ac-1a18d5f3db9c](https://doi.org/10.2909/393359a7-7ebd-4a52-80ac-1a18d5f3db9c). * European Environment Agency, 2023. Elevation map of Europe, 1 km grid. [d9cd252baa831ac4469ae055dbd8a8c1.](https://www.eea.europa.eu/ds_resolveuid/d9cd252baa831ac4469ae055dbd8a8c1 "d9cd252baa831ac4469ae055dbd8a8c1")