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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

Multimedia component 1: Tables and Figures., Peer reviewed

MMC. - Figure S1: EELS comparison before and after transfer of the aC nanofilm. - Figure S2: Preparation of a 2nd similar in-situ specimen. - Figure S3: Electrical characterization. - Figure S4-S7: Details of EELS data analysis for Fig. 9. - Video 1. Supplementary movie S1: Evolution of TEM image appearance and electrical current and resistance over time during the nucleation phase. Scale bar is 90 nm. - Video 2. Supplementary movie S2: Evolution of TEM image appearance and electrical resistance over time during the annealing phase. Scale bar is 8 nm. - Video 3. Supplementary movie S3: Evolution of TEM image appearance and electrical current and resistance over time during the diffusion/evaporation phase. Scale bar is 90 nm. - Video 4. Supplementary movie S4: Evolution of TEM image appearance and electrical current and resistance over time during the 2nd annealing phase. Scale bar is 20 nm., Peer reviewed

AFM data, hBN TEM analyses, HAADF-STEM imaging and STEM-EELS analyses of Pt_hBN, powder XRD analyses, XPS and EDS analyses, more details of HER LSV analyses, Raman spectra of catalysts, ICP-OES analyses of hBN_Au_Pt, TGA analyses, and comparison chart of present study with other reported Pt based catalysts., Peer reviewed

Code and preprocessed dataset used in the work "A data-driven approach for the assessment of the thermal stratification of reservoirs based on readily available data." Original data were obtained from the web page of the Ebro Automatic Water Quality Information System (SAICA Ebro) and the web page of the Ebro Automatic Hydrographic Information System (SAIH Ebro), in particular from the weather station of El Val (code EM71) and the monitoring station of the reservoir storage state (code E071). The data were preprocessed as explained in the article., Peer reviewed

Figure S1: UV-VIS spectra of the SERS substrates herein studied: Glass_ Ag, Glass_AuNPs, Glass_Ag_AuNPs, 1G_Ag_AuNPs, 3G_Ag_AuNPs; Figure S2: SERS spectra of 6 spots recorded on bottom and top of 1G_Ag_AuNPs sample upon exposure to 1.2 ppmV DMMP in gas phase., Peer reviewed

Additional proposed models and more STEM images along different crystallographic orientations together with the simulated data are included., Peer reviewed

Supplementary Note 1: Mobility data.-- Supplementary Note 2: The statistical description of empirical trajectories.-- Supplementary Note 3: The geographical city center as RP.-- Supplementary Note 4: Relationship between ρ and the field.-- Supplementary Note 5: Unbalance of empirical trajectories in NYC and further Chinese cities.-- Supplementary Note 6: Rand model flux results.-- Supplementary Note 7: Sensitivity analysis with the d-rand model for different D(r).-- Supplementary Note 8: d-rand-d model ρ results.-- Supplementary Note 9: Sensitivity analysis for the d-mix model for R and L.-- Supplementary Note 10: The empirical mobility field.-- Supplementary Note 11: Features of mobility field with Foursquare check-ins data.-- Supplementary Note 12: Fit of the average distance ℓc for the d-mix model., Peer reviewed

Data are values of mass loss, ergosterol content, sporulation rates, fungal richness, shredder biomass, abundance, and richness, associated with leaves decomposing in three exposure scenarios to determine the effect of mixing litter (1:1 mixture of Alnus glutinosa (L.) Gaertn. and Populus × canadensis Moench) on decomposition Exposure scenario. The litterbags were incubated in three terrestrial-aquatic exposure scenarios. 0:100 – 0 days terrestrial; 56 days aquatic (stream) 25:75 – 14 days terrestrial; followed by 42 days in the stream. 50:50 – 28 days terrestrial followed by 28 days in the stream. RME. Relative Mixture Effect: comparison of the values in the mixture with the average of the two single species. RIP. Relative Individual Performance: comparison of a species in the mixture with that species alone. n/a. There are no values because there was not enough leaf material to determine fungal colonization parameters: ergosterol (estimate of fungal biomass) and sporulation rates., [Description of the data and file structure] There are two sheets, one with the data for the Relative Mixture Effect (RME) and the other with the data for the Relative Individual Performance (RIP)., [Usage notes] Data are provided in an open access spreadsheet (ods)., [Methods] The study site and the litter decomposition study are described in detail in Abelho, M. & Descals, E. (2019). Litter movement pathways across terrestrial–aquatic ecosystem boundaries affect litter colonization and decomposition in streams. Functional Ecology, 33, 1785–1797. https://doi.org/10.1111/1365-2435.13356(opens in new window). Mean deviation between observed values in the mixture and the average of the single species was assessed with the relative mixture effect (RME). Mean deviation between the values of a species in the mixture and that single species was assessed with the relative individual performance (RIP)., The effect of mixing litter on decomposition has received considerable attention in terrestrial and aquatic (but rarely in both) ecosystems, with a striking lack of consensus in the obtained results. We studied the decomposition of a mixture of poplar and alder in three terrestrial:aquatic exposures to determine (1) if the effect of mixing litter on mass loss, associated decomposers (fungal biomass, sporulation rates, and richness) and detritivores (abundance, biomass, and richness of invertebrate shredders) differs between the stream (fully aquatic exposure) and when litter is exposed to a period of terrestrial exposure prior to immersion and (2) the effect of the mixture across exposure scenarios. The effect of the mixture was additive on mass loss and synergistic on decomposers and detritivores across exposure scenarios. Within scenarios, mass loss and decomposers showed synergistic effects only in the fully aquatic exposure, detritivores showed synergistic effects only when the period of terrestrial was shorter than the period of aquatic exposure, and when the period of terrestrial was equal to the period of aquatic exposure the effect of the mixture was additive on mass loss, decomposers, and detritivores. The species-specific effects also differed among exposure scenarios. Alder affected poplar only when there was a period of terrestrial exposure, with increased sporulation rates and fungal richness in exposure 25:75, and increased mass loss in exposure 50:50. Poplar affected alder only under fully aquatic exposure, with increased mass loss. In conclusion, the synergistic effects of the mixture changed with a period of terrestrial exposure prior to immersion. These results provide a cross-boundary perspective on the effect of mixing litter, showing a legacy effect of exposure to terrestrial decomposition on the fate of plant litter in aquatic ecosystems and highlighting the importance of assessing the effect of mixing litter on the associated biota and not only on mass loss., Peer reviewed

The data contains GPS locations of 20 individuals of yellow-legged gulls Larus michahellis and other parameters which were used to develop the seed dispersal models in this study., [Description of the Data and file structure] This is a csv file available for R packages used in this study. Parameter list: fid: ID of each GPS point location.long: Longitude of each GPS point location.lat: Latitude of each GPS point individual.local.identifier: ID of gull individual tags Body.weight: Body weight of gulls (g) study.local.timestamp: Timestamp of GPS positioning (yyyy/mm/dd hh:mm:ss) Wild.olive.distribution: Presence (1) or absence (0) of wild olive vegetation in each GPS point Night.stay: Nighttime GPS positioning (1) and daytime positioning (0) Year: Year of data collection individual_id: Individual ID separated by years dist: Distances from the previous GPS point (m) timediff: Time difference from the previous GPS point (s) speed: Gulls' speed s/h at each GPS point (m/s) inland: GPS position inland (1) or sea (0) ID_LOC: Land use ID of CORINE Land Cover 2018 islands: Name of islands in each GPS point Missing data is shown as "NA"., Aim: To analyse the role of non-frugivorous birds on seed dispersal, seed dispersal by gulls is expected to be especially instrumental in island ecosystems, as these have a smaller subset of frugivores when compared to the mainland, and because long-distance dispersal is required for plant colonization. Here we investigated the seed dispersal of olives by gulls among ten islands of the same archipelago to reveal if gulls contribute to long-distance seed dispersal including different islands, and how gulls’ adaptation to domestic olives and individual differences in foraging activities affect their seed dispersal pattern., Location: Balearic Islands in the Western Mediterranean Sea, Spain, Taxon: Yellow-legged gulls ( Larus michahellis), Domestic and wild Olives ( Olea europaea and O. europaea var. sylvestris), Methods: We developed seed dispersal models of the two ecotypes of olives dispersed by gulls across an archipelago, based on GPS tracking data, gut passage time, and seed viability., Results: Mean dispersal distances were 7.67 (±12.48) km in wild and 12.57 (±13.08) km in domestic olives. Seven-point one percent of wild and 8.5% of domestic olives were dispersed among islands. Among these, 8.2% of domestic seeds were transported from large to small islands where gull colonies are located, whereas wild olives were dispersed in more variable directions. Such dispersal pattern of two olive ecotypes were consistent despite the differences in dispersal distances among individuals., Main conclusions: Gulls contributed to long-distance olive seed dispersal including different islands. The seed dispersal of domestic olives to longer distances with specific directions may facilitate colonization and expansion of that variant if the conditions of seed deposition sites are suitable. Our findings indicate that gulls are relevant vectors for long-distance dispersal of large fleshy fruits in island ecosystems where specialist large frugivores are absent., Peer reviewed