Dataset.
Regional differences in thermoregulation between two European butterfly communities [Dataset]
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/361101
Digital.CSIC. Repositorio Institucional del CSIC
- Toro-Delgado, Eric
- Vila, Roger
- Talavera, Gerard
- Turner, Edgar
- Hayes, Matthew
- Horrocks, Nicholas
- Bladon, Andrew
This dataset contains the data and scripts required to reproduce the analyses in Toro-Delgado et al. (J. Anim. Ecol.). It contains the temperature measurements from Catalonia, the R scripts to conduct the statistical analyses, and Python scripts to download the solar radiation data from the Copernicus Atmospheric Monitoring Service (CAMS). The temperature data from Great Britain was released in Bladon et al. (2020)., [Description of the data and file structure] The "Data" folder contains the file "Butterfly_Thermoregulation_Data.txt", a tab-separated file containing the data gathered from the field in Catalonia. The column meanings are as follows:
Order: a numbered column, to be able to recover the original order of the rows.
Time: Hour of the measurement (in CEST, 24-hour format).
Species: the species of butterfly being measured (scientific name).
Code: field code used to identify the specimen.
GPS_Y (): the latitude, in decimal format.
GPS_X(): the longitude, in decimal format.
Slope: categorical variable classifying the slope of the terrain in ranges.
Aspect: the orientation of the slope on a compass (direction of ascent). Cells containing N/A are those for which slope is of 0 degrees, i.e. flat areas, and so no aspect can be applied to these (there is no direction of ascent if the ground is flat).
Veg.Type: categorical variable describing the vegetation type.
Manage: variable indicating if there are signs of some type of human management. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts.
Shelter: variable indicating if the spot in which the butterfly was detected was fully exposed to the wind (1) or fully sheltered (5), measured at chest height and 5m from the point.
Sunny: variable indicating how sunny it was; either fully sunny (S), mostly sunny (SSN), half sunny (SN), mostly cloudy (SNN) or fully cloudy (N).
Tair: temperature of the air, at waist height and in the shade, at the moment of capture of the butterfly. In degrees Celsius. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts.
Tbody1: first measurement of butterfly thoracic temperature at the time of capture; in degrees Celsius. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts.
Pbody1: position from which the thoracic temperature was measured; on the side of the thorax (L) or on the dorsal part (D).
Tperch: temperature of the substrate the butterfly was on (measured only when it was not flying). Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded.
Tair_perch: temperature of the air surrounding the substrate on which the butterfly was place (approximately 2cm above it). Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded.
Activity: categorical variable indicating what the butterfly was doing when first sighted.
Height(cm): the height at which the butterfly was.
Plant: species of plant OR type of substrate on which the butterfly was found. Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded.
Sp.Chase: if the butterfly was chasing/being chased by another, the species of the second butterfly. Empty cells correspond to observations of butterflies that were not chasing nor being chased by another, so this variable did not apply to these observations and hence it could not be recorded. As most butterflies were not not in a chase, this column consist mostly of empty cells.
Sex: sex of the specimen being measured (if known). Empty cells correspond to specimens for which sex could not be determined.
Wing_length: length of the forewing, measured from the base (“shoulder”) to the tip. In millimetres. Empty cells correspond to missing data, as this variable was not recorded for that particular observation.
Location: whether the locality is in the lowland or in the mountains (Pyrenees).
Country: the country in which the butterfly was captured and measured.
Locality: the locality in which the butterfly was captured and measured.
Captured: whether the butterfly was captured and taken to the lab for other studies or released after measuring the temperature measure.
Empty cells are left deliberately this way, instead of being filled with N/A, to ensure proper functioning with the provided scripts., [Sharing/Access information] Temperature data for the British butterflies is available at: https://datadryad.org/stash/dataset/doi:10.5061/dryad.z08kprr9n(opens in new window)
The data provided here (temperature measurements of Spanish butterfly populations) was collected by the authors, [Code/Software] The “Scripts” folder contains:
The “Analysis” folder, with several R scripts with the required code to reproduce all statistical analyses. This was originally done with R 4.1.3. The "worldclim_analysis.R" script can be used to reproduce the analysis of WorldClim data to characterise the climate of both Catalonia and Great Britain; the "GAM_models_buffering_mechanisms" can be used to reproduce the GAM models; the "Master_script.R" script contains the rest of analyses.
The “CAMS_solar_radiation_data_download” folder, with multiple Python scripts used to automate the download of the solar radiation data from CAMS. Note that, to be able to use these, the reader will have to register on the CAMS website and obtain access credentials, and then replace the “credentials_file” variable at the beginning of each Python script with the path to the file containing the credentials. More details are available at: https://ads.atmosphere.copernicus.eu/api-how-to, Understanding how different organisms cope with changing temperatures is vital for predicting future species’ distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood.
We investigated which factors influence thermoregulatory ability in a subset of a Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species’ demographic trends in Catalonia.
The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour, from heat-seeking to heat avoidance, at approximately 22 °C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations.
Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future., Spanish National Research Council, Award: JAEINT_20_00248, Departament de Recerca i Universitats, Award: FI-1 00556, Ministerio de Ciencia, Innovación, Award: PID2020-117739GA-I00 MCIN / AEI / 10.13039/501100011033, Isaac Newton Trust, Award: RG89529, Wellcome Trust, Award: RG89529, University of Cambridge, Award: RG89529, Natural Environment Research Council, Award: NE/V007173/1, Ministerio de Ciencia, Innovación y Universidades, Award: FPU22/02358, European Social Fund Plus, Award: FI-1 00556, Peer reviewed
DOI: http://hdl.handle.net/10261/361101
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/361101
HANDLE: http://hdl.handle.net/10261/361101
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/361101
Ver en: http://hdl.handle.net/10261/361101
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/361101
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1 Documentos relacionados
1 Documentos relacionados
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/349889
Artículo científico (article). 2024
REGIONAL DIFFERENCES IN THERMOREGULATION BETWEEN TWO EUROPEAN BUTTERFLY COMMUNITIES
Digital.CSIC. Repositorio Institucional del CSIC
- Toro-Delgado, Eric
- Vila, Roger
- Talavera, Gerard
- Turner, Edgar
- Hayes, Matthew
- Horrocks, Nicholas
- Bladon, Andrew
Understanding how different organisms cope with changing temperatures is vital for predicting future species' distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood.
We investigated which factors influence thermoregulatory ability in a subset of the Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species' demographic trends in Catalonia.
The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour from heat-seeking to heat avoidance at approximately 22°C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations.
Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future., This work was funded by the Spanish National Research Council (CSIC) with a JAE-Intro fellowship for the introduction to research (reference numbers JAEINT_20_00248 and JAEINT20_EX_0638), a Joan Oró fellowship by the Department of Research and Universities of Generalitat de Catalunya and the European Social Fund Plus (grant 2023 FI-1 00556), and an FPU fellowship for the formation of university faculty (grant FPU22/02358) by the Spanish Ministry of Science, Innovation and Universities to E.T.-D.; the grants PID2020-117739GA-I00 MCIN/AEI/10.13039/501100011033 and 2021-SGR-01334 to G.T.; the Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grants Scheme (RG89529) to E.C.T. and A.J.B.; and the NERC Highlight topic GLiTRS project NE/V007173/1 to A.J.B, 1 INTRODUCTION2 MATERIALS AND METHODS2.1 Data collection2.2 Statistical analyses2.2.1 Comparison of buffering ability between Catalan and British populations2.2.2 Thermal buffering mechanisms2.2.3 Catalan demographic trends3 RESULTS3.1 Comparison of buffering ability between Catalan and British populations3.2 Comparison of thermal buffering mechanisms between Catalan and British populations3.3 Catalan demographic trends4 DISCUSSION4.1 Comparison of buffering ability between Catalan and British populations4.2 Comparison of thermal buffering mechanisms between Catalan and British populations4.3 Catalan demographic trends5 CONCLUSIONSAUTHOR CONTRIBUTIONSACKNOWLEDGEMENTSCONFLICT OF INTEREST STATEMENT, Peer reviewed
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1 Versiones
1 Versiones
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/361101
Dataset. 2023
REGIONAL DIFFERENCES IN THERMOREGULATION BETWEEN TWO EUROPEAN BUTTERFLY COMMUNITIES [DATASET]
Digital.CSIC. Repositorio Institucional del CSIC
- Toro-Delgado, Eric
- Vila, Roger
- Talavera, Gerard
- Turner, Edgar
- Hayes, Matthew
- Horrocks, Nicholas
- Bladon, Andrew
This dataset contains the data and scripts required to reproduce the analyses in Toro-Delgado et al. (J. Anim. Ecol.). It contains the temperature measurements from Catalonia, the R scripts to conduct the statistical analyses, and Python scripts to download the solar radiation data from the Copernicus Atmospheric Monitoring Service (CAMS). The temperature data from Great Britain was released in Bladon et al. (2020)., [Description of the data and file structure] The "Data" folder contains the file "Butterfly_Thermoregulation_Data.txt", a tab-separated file containing the data gathered from the field in Catalonia. The column meanings are as follows:
Order: a numbered column, to be able to recover the original order of the rows.
Time: Hour of the measurement (in CEST, 24-hour format).
Species: the species of butterfly being measured (scientific name).
Code: field code used to identify the specimen.
GPS_Y (): the latitude, in decimal format.
GPS_X(): the longitude, in decimal format.
Slope: categorical variable classifying the slope of the terrain in ranges.
Aspect: the orientation of the slope on a compass (direction of ascent). Cells containing N/A are those for which slope is of 0 degrees, i.e. flat areas, and so no aspect can be applied to these (there is no direction of ascent if the ground is flat).
Veg.Type: categorical variable describing the vegetation type.
Manage: variable indicating if there are signs of some type of human management. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts.
Shelter: variable indicating if the spot in which the butterfly was detected was fully exposed to the wind (1) or fully sheltered (5), measured at chest height and 5m from the point.
Sunny: variable indicating how sunny it was; either fully sunny (S), mostly sunny (SSN), half sunny (SN), mostly cloudy (SNN) or fully cloudy (N).
Tair: temperature of the air, at waist height and in the shade, at the moment of capture of the butterfly. In degrees Celsius. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts.
Tbody1: first measurement of butterfly thoracic temperature at the time of capture; in degrees Celsius. Empty cells correspond to missing data, as this variable was not recorded for that particular observation. They are purposefully left blank in order not to interfere with the scripts.
Pbody1: position from which the thoracic temperature was measured; on the side of the thorax (L) or on the dorsal part (D).
Tperch: temperature of the substrate the butterfly was on (measured only when it was not flying). Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded.
Tair_perch: temperature of the air surrounding the substrate on which the butterfly was place (approximately 2cm above it). Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded.
Activity: categorical variable indicating what the butterfly was doing when first sighted.
Height(cm): the height at which the butterfly was.
Plant: species of plant OR type of substrate on which the butterfly was found. Empty cells correspond to observations of butterflies that were flying, so this variable did not apply to these observations and hence it could not be recorded.
Sp.Chase: if the butterfly was chasing/being chased by another, the species of the second butterfly. Empty cells correspond to observations of butterflies that were not chasing nor being chased by another, so this variable did not apply to these observations and hence it could not be recorded. As most butterflies were not not in a chase, this column consist mostly of empty cells.
Sex: sex of the specimen being measured (if known). Empty cells correspond to specimens for which sex could not be determined.
Wing_length: length of the forewing, measured from the base (“shoulder”) to the tip. In millimetres. Empty cells correspond to missing data, as this variable was not recorded for that particular observation.
Location: whether the locality is in the lowland or in the mountains (Pyrenees).
Country: the country in which the butterfly was captured and measured.
Locality: the locality in which the butterfly was captured and measured.
Captured: whether the butterfly was captured and taken to the lab for other studies or released after measuring the temperature measure.
Empty cells are left deliberately this way, instead of being filled with N/A, to ensure proper functioning with the provided scripts., [Sharing/Access information] Temperature data for the British butterflies is available at: https://datadryad.org/stash/dataset/doi:10.5061/dryad.z08kprr9n(opens in new window)
The data provided here (temperature measurements of Spanish butterfly populations) was collected by the authors, [Code/Software] The “Scripts” folder contains:
The “Analysis” folder, with several R scripts with the required code to reproduce all statistical analyses. This was originally done with R 4.1.3. The "worldclim_analysis.R" script can be used to reproduce the analysis of WorldClim data to characterise the climate of both Catalonia and Great Britain; the "GAM_models_buffering_mechanisms" can be used to reproduce the GAM models; the "Master_script.R" script contains the rest of analyses.
The “CAMS_solar_radiation_data_download” folder, with multiple Python scripts used to automate the download of the solar radiation data from CAMS. Note that, to be able to use these, the reader will have to register on the CAMS website and obtain access credentials, and then replace the “credentials_file” variable at the beginning of each Python script with the path to the file containing the credentials. More details are available at: https://ads.atmosphere.copernicus.eu/api-how-to, Understanding how different organisms cope with changing temperatures is vital for predicting future species’ distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood.
We investigated which factors influence thermoregulatory ability in a subset of a Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species’ demographic trends in Catalonia.
The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour, from heat-seeking to heat avoidance, at approximately 22 °C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations.
Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future., Spanish National Research Council, Award: JAEINT_20_00248, Departament de Recerca i Universitats, Award: FI-1 00556, Ministerio de Ciencia, Innovación, Award: PID2020-117739GA-I00 MCIN / AEI / 10.13039/501100011033, Isaac Newton Trust, Award: RG89529, Wellcome Trust, Award: RG89529, University of Cambridge, Award: RG89529, Natural Environment Research Council, Award: NE/V007173/1, Ministerio de Ciencia, Innovación y Universidades, Award: FPU22/02358, European Social Fund Plus, Award: FI-1 00556, Peer reviewed
×
1 Versiones
1 Versiones
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/361107
Software de investigación. Software (Software). 2023
REGIONAL DIFFERENCES IN THERMOREGULATION BETWEEN TWO EUROPEAN BUTTERFLY COMMUNITIES [SOFTWARE]
Digital.CSIC. Repositorio Institucional del CSIC
- Toro-Delgado, Eric
- Vila, Roger
- Talavera, Gerard
- Turner, Edgar
- Hayes, Matthew
- Horrocks, Nicholas
- Bladon, Andrew
The “Scripts” folder contains: The “Analysis” folder, with several R scripts with the required code to reproduce all statistical analyses. This was originally done with R 4.1.3. The "worldclim_analysis.R" script can be used to reproduce the analysis of WorldClim data to characterise the climate of both Catalonia and Great Britain; the "GAM_models_buffering_mechanisms" can be used to reproduce the GAM models; the "Master_script.R" script contains the rest of analyses.
The “CAMS_solar_radiation_data_download” folder, with multiple Python scripts used to automate the download of the solar radiation data from CAMS. Note that, to be able to use these, the reader will have to register on the CAMS website and obtain access credentials, and then replace the “credentials_file” variable at the beginning of each Python script with the path to the file containing the credentials. More details are available at: https://ads.atmosphere.copernicus.eu/api-how-to, Understanding how different organisms cope with changing temperatures is vital for predicting future species' distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood.
We investigated which factors influence thermoregulatory ability in a subset of a Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species' demographic trends in Catalonia.
The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour, from heat-seeking to heat avoidance, at approximately 22 °C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations.
Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future., Funding provided by: Spanish National Research Council
Crossref Funder Registry ID: https://ror.org/02gfc7t72
Award Number: JAEINT_20_00248, Funding provided by: Departament de Recerca i Universitats
Crossref Funder Registry ID: https://ror.org/01gbnem66
Award Number: FI-1 00556, Funding provided by: Ministerio de Ciencia, Innovación*
Crossref Funder Registry ID:
Award Number: PID2020-117739GA-I00 MCIN / AEI / 10.13039/501100011033, Funding provided by: Isaac Newton Trust
Crossref Funder Registry ID: https://ror.org/02gn6ta77
Award Number: RG89529, Funding provided by: Wellcome Trust
Crossref Funder Registry ID: https://ror.org/029chgv08
Award Number: RG89529, Funding provided by: University of Cambridge
Crossref Funder Registry ID: https://ror.org/013meh722
Award Number: RG89529, Funding provided by: Natural Environment Research Council
Crossref Funder Registry ID: https://ror.org/02b5d8509
Award Number: NE/V007173/1, Funding provided by: Ministerio de Ciencia, Innovación y Universidades
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100014440
Award Number: FPU22/02358, Funding provided by: European Social Fund Plus
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004895
Award Number: FI-1 00556, Peer reviewed
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