Data from: Wildfires induce a reduction in body size and morphological variation of an insular endemic insect
Data files
Feb 07, 2025 version files 95.30 KB
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Data_2021.xlsx
22.69 KB
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env_all.txt
1.18 KB
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Individual_Microsatellite.xlsx
22.54 KB
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morph_all.txt
3.53 KB
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Morphology_Data(2020_2021).xlsx
24.89 KB
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PCA_values_for_anova_and_variance.xlsx
13.24 KB
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README.md
7.24 KB
Apr 25, 2025 version files 95.30 KB
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Data_2021.xlsx
22.69 KB
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env_all.txt
1.18 KB
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Individual_Microsatellite.xlsx
22.54 KB
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morph_all.txt
3.53 KB
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Morphology_Data(2020_2021).xlsx
24.89 KB
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PCA_values_for_anova_and_variance.xlsx
13.24 KB
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README.md
7.23 KB
Abstract
Anthropogentic disturbance is known to affect population sizes and genetic population structure of many biotas. Wildfires are a major disturbance in many regions of the world, particularly in Mediterranean regions and on the Atlantic islands. Populations of many insects, such as the Madeiran Green Bush-Cricket (Psalmatophanes barretoi), are threated by wildfies. However, the effects of wildfires on genetic structure and diversity as well as morphological variation of the populations reamins little understood. Therefore, we studied genetic diversity, structure, and potential bottlenecks of this species using microsatellites. We also studied morphological variation and fluctuating asymmetry within and between populations to unravel potential effects of wildfires. We did not find any evidence for genetic differentiation of populations, but some populations had high heterozygosity excess, regardless of burning. Morphological variation in burnt areas was lower than in non-burnt areas. Flucturating Asymmetry of the wing length was significantly higher in burnt areas. Our results show that even genetically well-connected populations may suffer from bottlenecks leading to reduced morphological variation after disturbance.
https://doi.org/10.5061/dryad.sj3tx96f2
Description of the data and file structure
These are the data from Rhee et al. (2025).
Other information, including the software used, is available in detail in Rhee et al. (2025). The supplemental figures (Supplemental figures (Revised).pdf) contain box plots about the relationship between burn status and morphological traits, and the graphical results of STRUCTURE analysis. The supplemental tables (Supplemental table (Revised).pdf) include tables about information of microsatellite markers and the ANOVA results of the relationship between burnt status and morphological traits.
Rhee, H., Naber, S., Krehenwinkel, H. & Hochkirch, A. (2025) Wildfires induce a reduction in body size and morphological variation of an insular endemic insect. Ecological Entomology, 1–10. Available from: https://doi.org/ 10.1111/een.13418
Files and variables
File: Individual_Microsatellite.xlsx
Description: The genetic data from Rhee et al. (2025). Due to measuring heterozygosity, each locus has two alleles always. n/a indicates the samples from 2021.
Variables
- Specimen: A name of each specimen.
- Population: A sampling area of each specimen.
- M74(1): Fragment sizes of a microsatellite locus (M74) of each individual.
- M74(2): Fragment sizes of a microsatellite locus (M74) of each individual.
- M109(1): Fragment sizes of a microsatellite locus (M109) of each individual.
- M109(2): Fragment sizes of a microsatellite locus (M109) of each individual.
- M170(1): Fragment sizes of a microsatellite locus (M170) of each individual.
- M170(2): Fragment sizes of a microsatellite locus (M170) of each individual.
- M182(1): Fragment sizes of a microsatellite locus (M182) of each individual.
- M182(2): Fragment sizes of a microsatellite locus (M182) of each individual.
- M145(1): Fragment sizes of a microsatellite locus (M145) of each individual.
- M145(2): Fragment sizes of a microsatellite locus (M145) of each individual.
- M164(1): Fragment sizes of a microsatellite locus (M164) of each individual.
- M164(2): Fragment sizes of a microsatellite locus (M164) of each individual.
- M186(1): Fragment sizes of a microsatellite locus (M186) of each individual.
- M186(2): Fragment sizes of a microsatellite locus (M186) of each individual.
- Heterozygosity: The proportion of heterozygous of each individual from all loci.
File: morph_all.txt
Description: The morphological data for PCA.
Variables
- Pronotum_length: Pronotum length (mm) of each individual.
- Pronotum_Width: Pronotum width (mm) of each individual.
- Femur_length: Femur length (mm) of each individual.
- Wing_width: Wing width (mm) of each individual.
- Body_length: Body length (mm) of each individual.
- Body_weight: Body weight (g) of each individual.
File: env_all.txt
Description: The environmental data for PCA.
Variables
- Altitude: Altitude variation (m) of sampled areas of each individual.
- Burned: The burnt status of sampling areas with binormal values.
- Unburned: The burnt status of sampling areas with binormal values.
File: PCA_values_for_anova_and_variance.xlsx
Description: The first component PCA values of the morphological variables of each individual for ANOVA and variance test.
Variables
- Dim.1: The first component PCA values of the morphological variables of each individual.
- Area: Burnt status.
- Altitude: Altitude (m) variation of sampled areas of each individual.
File: Morphology_Data(2020_2021).xlsx
Description: The morphological data in 2020 and 2021 from Rhee et al. (2025)
Variables
- ID: An name of each individual
- GPS: Geographical coordinates of each individual.
- Pronotum length: Pronotum length (mm) of each individual.
- Pronotum width: Pronotum width (mm) of each individual.
- Femur length: Femur length (mm) of each individual.
- Wing width: Wing width (mm) of each individual.
- Body length first: The first measurement of body length (mm) of each individual.
- Body length second: The second measurement of body length (mm) of each individual 4 days later.
- Weight first: The first measurement of body weight (g) of each individual.
- Weight second: The second measurement of body weight (g) of each individual 4 days later.
- Population: A sampling area of each individual.
- Y: Longitude of geographical coordinates of each individual.
- X: Latitude of geographical coordinates of each individual.
- Altitude: Altitude variation (m) of sampling areas of each individual.
- Body length: The mean between Body length first and second.
- Body weight: The mean between Weight first and second.
- Burn status: Burnt status of each sampling area.
File: Data_2021.xlsx
Description: The morphological data in 2021 from Rhee et al. (2025)
Variables
- ID: An name of each individual
- GPS: Geographical coordinates of each individual.
- Pronotum length: Pronotum length (mm) of each individual.
- Pronotum width: Pronotum width (mm) of each individual.
- Femur length: Femur length (mm) of each individual.
- Wing width: Wing width (mm) of each individual.
- Wing left: Left wing length (mm) of each individual.
- Wing right: Right wing length (mm) of each individual.
- Body length first: The first measurement of body length (mm) of each individual.
- Body length second: The second measurement of body length (mm) of each individual 4 days later.
- Weight first: The first measurement of body weight (g) of each individual.
- Weight second: The second measurement of body weight (g) of each individual 4 days later.
- Population: A sampling area of each individual.
- Y: Longitude of geographical coordinates of each individual.
- X: Latitude of geographical coordinates of each individual.
- Altitude: Altitude variation (m) of sampling areas of each individual.
- Body length: The mean between Body length first and second.
- Body weight: The mean between Weight first and second.
- Asymmetry: Asymmetry between left and right wing of each individual.
- Burn status: Burn status of each sampling area.
Rhee, H., Naber, S., Krehenwinkel, H. & Hochkirch, A. (2025) Wildfires induce a reduction in body size and morphological variation of an insular endemic insect. Ecological Entomology, 1–10. Available from: https://doi.org/ 10.1111/een.13418
Code/software
The R file “Morpholgy_MANOVA and ANOVA.R” contains a script about statistical analysis for morphological data.
The R file “ PCA_testing.R” contains a script about statistical analysis relating to Principle Component Analysis (PCA).
The detailed information about statistical analysis is in Rhee et al. (2025).
All other software analysis are described in Rhee et al. (2025) in detail clearly.
Rhee, H., Naber, S., Krehenwinkel, H. & Hochkirch, A. (2025) Wildfires induce a reduction in body size and morphological variation of an insular endemic insect. Ecological Entomology, 1–10. Available from: https://doi.org/ 10.1111/een.13418
Geography of the sampling and studied regions
We sampled individuals of P. barretoi from the known populations of this species (Rhee et al., 2023) in burnt and unburnt areas across the whole island of Madeira (Figure 2). Data on wildfires between 2006 and 2019 were obtained from the Institute of Forests and Nature Conservation in Madeira (IFCN). We divided the sampling localities into three different categories according to their fire severities (“Unburnt”, “Partially burnt” and “Completely Burnt Regions”) based on their overlap with the fire polygons visually for genetic analysis (Figure 2). The study sites Machico and Santana, Seixal and Fanal did not experience any recent wildfires (“Unburnt Regions”), the study site Ribeira da Vacca and Amparo had wildfires (“Burnt Regions”), the sites Serra de Agua and Vagum, and Paul da Serra burnt partially (“Partially Burnt Regions”). We also assigned each individual to the fire history of its locality in two categories (burnt or not) based on their overlap with the fire polygons and the sampling coordinates in Arc GIS Pro for the morphological analysis (Esri, 2003, see Rhee et al. 2023). Altitude (m) above sea level was determined in EU-DEM v1.0 based on 25 m raster plots (Copernicus Land Monitoring Service) as a digital surface elevation model. All GIS and geographic visualisation were carried out with ArcGIS Pro (Esiri, 2023).
Sampling and morphology measurements
Specimens were sampled from 6 August to 2 September 2020 (n = 39) and from 12 July to 6 September 2021 (n = 86). Areas which burnt between 2016 and 2019 were not sampled, as these recent fires had stronger effects on the populations of this species than those between 2006 and 2015 (Rhee et al., 2023). As the species is listed as Vulnerable on the IUCN Red List of Threatened Species (Hochkirch et al., 2016), most individuals were released after measurement of morphological traits and a minimum invasive genetic sampling was conducted (single hind legs, which are readily autotomized when catching bush-crickets). Pronotum length, width, hind femur length, body weight, and body length are widely used to measure body size and fitness in Orthoptera (Del Castillo and Gwynne, 2007; Gwynne and Bailey, 1988; Montealegre‐Z, 2009; Montealegre‐Z et al., 2017) (Figure 1). Therefore, we measured these traits in all specimens (n = 125). As wing dimensions may be crucial for recolonisation and play an important role for sound production, we also measured length and width of the left fore wing. As a measure of fluctuating asymmetry, we measured the length of both fore wings in the 2021 samples (n = 83 after exclusion of one population with a low sample size of wing length data). Wing length was measured after bending both wings vertically straight from the pronotum. All traits were measured by a digital sliding calliper (Digital Caliper DC01, Tack Life). Fluctuating asymmetry (FA) between the left and right wing was calculated using the modified formula by Anciães and Marini (2000) and Henriques and Cornelissen (2019):
FA = (Right wing - Left wing)/ [(Right wing – Left wing)/2]
The body weight was measured with a spring balance (PESOLA micro-line 20010). We reared animals to measure body size and body weight twice (once on the capture day and once four days later) and later calculated the average, as these two traits are very flexible and depend upon feeding and oviposition.
Genetic analysis
We extracted DNA from the hind leg tissues of 123 samples using the DNeasy Blood & Tissue Kit, following the manufacturers protocol (Qiagen, Leipzig in Germany). Microsatellite markers were not available for this and related species. Therefore, we identified microsatellites in the species and designed the primers first.
To design primers, we created the genomic library of the species by using the Illuminia miseq 500 cycle Nano kit (Illumina, San Diego, California, the United States of America) for shotgun sequencing (i.e. Tru seq sequencing). After that, we filtered microsatellites in the library with the Msatcommander program (Faircloth, 2008). Only tri- and tetranucleotide repeats were searched and used, as these nucleotides are easier to score (Tsukagoshi and Abe, 2023). From these nucleotides, we screened twenty-four loci and found seven polymorphic primers (M74, M109, M170, M182, M145, M164 and M186) based on five individuals (Supplemental Table 1). For the rest of the samples, we amplified these seven microsatellite markers with two multiplex PCR reactions (Supplemental Table 1). For the PCR reactions, we mixed the multiplex master mix following the protocol per each individual and primer: 15 ml multiplex mix kit, 7.8 ml water, 3 ml primer (10 µmol concentration) and 4.5 ml DNA. The first multiplex reaction was for three markers (M145, M164 and M186, annealing temperature 56°C) with 35 cycles and the second reaction was for four markers (M74, M109, M170 and M182, annealing temperature 59°C) with 34 cycles (Supplemental Table 1). All primers were labelled with the dye FAM, except for M186 and M182 labelled with PET (Supplemental Table 1). All fragment analyses were carried out by Macrogene (Macrogene Europe B.V.). For scoring the genotypes of each marker and individual, we used the GeneMapper software 5 (Applied Biosystem).