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Description: The eddy covariance technique was used to record continuous, non-invasive measurements of CO2, H2O and energy exchange between the ecosystem and the atmosphere. The measuring system consists of a semi-open path infrared gas analyser LI-7200 (LI-COR, USA), and a CSAT3 Sonic Anemometer (Campbell Scientific, USA) at a measuring height of 52 m over a canopy height of ~25 m. Data were recorded at a frequency of 20 Hz that was treated using the post-processing software EddyPro® (v.7.0.6; www.licor.com/eddypro) to compute fluxes for each 30-minute averaging period. To treat the raw fluxes, primary data processing steps were applied, including spike removal (Vickers, 1997 J Atmos Ocean Technol), coordinate rotation, block averaging detrending of CO2, H2O and sonic temperature, time lag compensation using covariance maximisation detection method, random uncertainty estimation (Finkelstein et al. 2001 Journal of Geophysical Research Atmospheres), computation of turbulent fluxes and mean fluxes, spectral corrections (Moncrieff et al. 1997 J Hydrol Amst) using correction of low-pass filtering effects, planar fit rotation (Wilczak et al. 2001 Boundary Layer Meteorol) and quality flagging policy (Göckede et al. 2006 Boundary Layer Meteorol). Eddy covariance meteorological data from above and below canopy is available at DOI 10.5281/zenodo.3888374. Cells with -9999 represent not enough data collected, which can be regarded as NA. This data has been collected over a heavily logged landscape between 2012 - 2018, please note 2016 was removed from this dataset. Before 2015, the landscape was ~10 years recovering from it's previous round of logging (four times logged). During 2015 the landscape was salvaged logged, removing 75% of tree stand basal area. The first data sheet, named "Raw_data" contains all raw fluxes that have been treated by EddyPro, which have not been filtered or quality controlled. The second sheet, named "Daily_fluxes" contains daily mean fluxes of net ecosystem CO2 exchange (NEE), ecosystem respirationn (Reco) and gross primary productivity and their associated standard errors. Net ecosystem CO2 exchange (NEE) was calculated by adding the estimated CO2 storage flux to the observed CO2 flux. Data was subjecto quality control including the removal of quality flags 4 and 5 (Göckede et al. 2006 Boundary Layer Meteorol) and the application of a mean u* threshold of >0.29 m s-1 to the dataset, as established using the package "REddyProc" (v.1.2; (Wultzer et al. 2019 Biogeosciences)) in based on the Moving Point Method (Reichstein et al. 2005, GCB). Data was subsequently gap filled and partitioned, as descripted within the variable methods of this sheet. This data was part of an analysis of carbon fluxes within three periods of data collection: in 2012 – 2013, which captured the four-times logged ecosystem ~10 years after its previous round of logging, in 2015 during a new round of active salvage logging, and in 2017 – 2018 when the ecosystem was recovery 2-3 years after the salvage logging. Days with large standard errors for Reco (> ± 5 µmol m−2 s−1) were deemed as bad quality and removed from the dataset and we used only days that had four or more observed half-hourly values of NEE. Of the final dataset , 29.5% of the half-hourly values are original observed fluxes, and 70.5% gap-filled. Of the 455 days remaining after all filtering processes were applied, 65 days were during the 10-years recovery phase (2012-2013), 100 during the active salvage logging (2015) and 290 during the 2-3 years recovery from active salvage logging phase (2017-2018). Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_EC_byYear.xlsx SAFE_EC_byYear.xlsx This file contains dataset metadata and 6 data tables: Raw_data_2012_2013 (described in worksheet Raw_data_2012_2013) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. Number of fields: 105 Number of data rows: 24213 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_h2o_flux: Random error of CO2 flux (Field type: numeric) H_strg: Estimate of storage sensible heat flux (Field type: numeric) LE_strg: Estimate of storage latent heat flux (Field type: numeric) co2_strg: Estimate of storage CO2 flux (Field type: numeric) h2o_strg: Estimate of storage H20 flux (Field type: numeric) co2_v.adv: Estimate of vertical advection flux of CO2 (Field type: numeric) h2o_v.adv: Estimate of vertical advection flux of H20 (Field type: numeric) co2_molar_density: Measured or estimated molar density of gas (Field type: numeric) co2_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) co2_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) co2_time_lag: Time lag used to synchronize gas time series (Field type: numeric) co2_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) h2o_molar_density: Measured or estimated molar density of gas (Field type: numeric) h2o_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) h2o_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) h2o_time_lag: Time lag used to synchronize gas time series (Field type: numeric) h2o_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) sonic_temperature: Mean temperature of ambient air as measured by the anemometer (Field type: numeric) air_temperature: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric) air_pressure: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric) air_density: Density of ambient air (Field type: numeric) air_heat_capacity: Specific heat at constant pressure of ambient air (Field type: numeric) air_molar_volume: Molar volume of ambient air (Field type: numeric) ET: Evapotranspiration flux (Field type: numeric) water_vapor_density: Ambient mass density of water vapor (Field type: numeric) e: Ambient water vapor partial pressure (Field type: numeric) es: Ambient water vapor partial pressure at saturation (Field type: numeric) specific_humidity: Ambient specific humidity on a mass basis (Field type: numeric) RH: Ambient relative humidity (Field type: numeric) VPD: Ambient water vapor pressure deficit (Field type: numeric) Tdew: Ambient dew point temperature (Field type: numeric) u_unrot: Wind component along the u anemometer axis (Field type: numeric) v_unrot: Wind component along the v anemometer axis (Field type: numeric) w_unrot: Wind component along the w anemometer axis (Field type: numeric) u_rot: Rotated u wind component (mean wind speed) (Field type: numeric) v_rot: Rotated v wind component (should be zero) (Field type: numeric) w_rot: Rotated w wind component (should be zero) (Field type: numeric) wind_speed: Mean wind speed (Field type: numeric) max_wind_speed: Maximum instantaneous wind speed (Field type: numeric) wind_dir: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric) yaw: First rotation angle (Field type: numeric) pitch: Second rotation angle (Field type: numeric) u.: Friction velocity (Field type: numeric) TKE: Turbulent kinetic energy (Field type: numeric) L: Monin-Obukhov length (Field type: numeric) X.z.d..L: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric) bowen_ratio: Sensible heat flux to latent heat flux ratio (Field type: numeric) T.: Scaling temperature (Field type: numeric) model: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric) x_peak: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric) x_offset: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric) x_10.: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_30.: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_50.: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_70.: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_90.: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric) un_Tau: Uncorrected momentum flux (Field type: numeric) Tau_scf: Spectral correction factor for momentum flux (Field type: numeric) un_H: Uncorrected sensible heat flux (Field type: numeric) H_scf: Spectral correction factor for sensible heat flux (Field type: numeric) un_LE: Uncorrected latent heat flux (Field type: numeric) LE_scf: Spectral correction factor for latent heat flux (Field type: numeric) un_co2_flux: Uncorrected gas flux (Field type: numeric) co2_scf: Spectral correction factor for gas flux (Field type: numeric) un_h2o_flux: Uncorrected gas flux (Field type: numeric) h2o_scf: Spectral correction factor for gas flux (Field type: numeric) spikes_hf: Hard flags for individual variables for spike test (Field type: numeric) amplitude_resolution_hf: Hard flags for individual variables for amplitude resolution (Field type: numeric) drop_out_hf: Hard flags for individual variables for drop-out test (Field type: numeric) absolute_limits_hf: Hard flags for individual variables for absolute limits (Field type: numeric) skewness_kurtosis_hf: Hard flags for individual variables for skewness and kurtosis (Field type: numeric) skewness_kurtosis_sf: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric) discontinuities_hf: Hard flags for individual variables for discontinuities test (Field type: numeric) discontinuities_sf: Soft flags for individual variables for discontinuities test (Field type: numeric) timelag_hf: Hard flags for gas concentration for time lag test (Field type: numeric) timelag_sf: Soft flags for gas concentration for time lag test (Field type: numeric) attack_angle_hf: Hard flags for gas concentration for time lag test (Field type: numeric) non_steady_wind_hf: Soft flags for gas concentration for time lag test (Field type: numeric) u_spikes: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric) v_spikes: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric) w_spikes: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric) ts_spikes: Number of spikes detected and eliminated for ts variable (Field type: numeric) co2_spikes: Number of spikes detected and eliminated for co2 variable (Field type: numeric) h2o_spikes: Number of spikes detected and eliminated for h2o variable (Field type: numeric) Raw_data_2014 (described in worksheet Raw_data_2014) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. There is a significant data gap, with some intermittent records available during the daytime, between 17/2/2014-17/06/2014 due to the problems in the power supply. Number of fields: 105 Number of data rows: 17520 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best,

1. Anthropogenic structures are mainly known to have negative impacts on wildlife populations but sometimes arethey can be beneficial. Power lines are a main driver of bird mortality through collision or electrocution, but electricity pylons are also commonly used for nest building by some species. Birds and nests cause power outages that need to be tackled by electricity companies. However, the use of pylons by threatened species provides an opportunity for conservation purposes. 2. In this study, we described an empirical modelling approach to predict the circumstances under which circumstances nesting birds use electricity pylons are used by nesting birds. We focused on white storks Ciconia ciconia, a species that has been increasingly using electricity pylons for nesting across Europe. 3. In a country-level census in Portugal, we found a total of 1348 white stork nests in 668 of the 8680 very high-tension power line pylons occurring in the distribution range of this colonial species, with spatial clustering in pylon occupation up to a distance of 30 km. The number of nests in each used pylon ranged from 1 to 21 (mean±SD= 2.2±2.06). 4. The main drivers of pylon use by nesting storks were distance to major feeding areas (rice fields, landfills and large wetlands), with more intensive use closer to these features, followed by land cover type surrounding each pylon. Pylon type and age, and stork population density in the region, had comparatively less importance. 5. Synthesis and applications. Our approach can be used to plan both for species conservation and minimising damage to infrastructures. For power lines, we outline: (i) planning power line routes to take account of the probability of pylon use; (ii) applying nesting deterrent devices (to reduce bird mortality and power outage risk) and providing nesting platforms (to promote bird use) on suitable pylons; and (iii) selecting adequate pylon types to promote or inhibit nesting. White stork nests in power line pylons and associated variablesNumber of nests of white storks in very high tension power line pylons in Portugal, in 2007, and associated variables to model the drivers of pylon use. Information on nest occurrence collected in the field, through aerial surveys. Explanatory variables from land cover information data, stork censuses and information provided by the power line company (REN).Data for storks paper.xlsx

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CNRM-CERFACS.CNRM-ESM2-1.ssp434' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-ESM2-1 climate model, released in 2017, includes the following components: aerosol: TACTIC_v2, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km), atmosChem: REPROBUS-C_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: Pisces 2.s, seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now. The dataset is a synthesis of literature values selected based on criteria described in the parent paper’s narrative. The files can be opened in Microsoft Excel or any other spreadsheet that can load Excel-format files.

Birds modulate their flight paths in relation to regional and global airflows in order to reduce their travel costs. Birds should also respond to fine-scale airflows, although the incidence and value of this remains largely unknown. We resolved the three-dimensional trajectories of gulls flying along a built-up coastline, and used computational fluid dynamic models to examine how gulls reacted to airflows around buildings. Birds systematically altered their flight trajectories with wind conditions to exploit updraughts over features as small as a row of low-rise buildings. This provides the first evidence that human activities can change patterns of space-use in flying birds by altering the profitability of the airscape. At finer scales still, gulls varied their position to select a narrow range of updraught values, rather than exploiting the strongest updraughts available, and their precise positions were consistent with a strategy to increase their velocity control in gusty conditions. Ultimately, strategies such as these could help unmanned aerial vehicles negotiate complex airflows. Overall, airflows around fine-scale features have profound implications for flight control and energy use, and consideration of this could lead to a paradigm-shift in the way ecologists view the urban environment. Ornithodolite dataThis file contains the Ornithodolite data from gulls soaring over two sites that border Swansea Bay.Flight paths for CFD modellingThis workbook contains two worksheets that detail (i) wind data (collected from balloon ascents) and (ii) summary data from birds gliding over hotels that border Swansea Bay. These data were used to estimate the wind fields around these flight paths, when combined with CFD model outputs. This file is also described in the README file for the Ornithodolite data.WindDataThe excel file "WindData" includes the 2D (averaged wind vector components) along the Y,Z axis. Data are available for each session where the positions of gulls soaring over the seafront hotels were recorded.Glide_polarThis contains the MATLAB code to estimate the glide polar for a fixed wing gull.

The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis. Leibovici DG, Bylund H, Bj��rkman C, Tokarevich N, Thierfelder T, Eveng��rd B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. International Journal of Environmental Research and Public Health, 18(20):10963. doi:10.3390/ijerph182010963 Special Issue: https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects {"references": ["Leibovici DG, Bylund H, Bj\u00f6rkman C, Tokarevich N, Thierfelder T, Eveng\u00e5rd B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive\u00a0Infections: An Example on Tick-Borne Diseases in the Nordic Area.\u00a0International Journal of Environmental Research and Public Health\u00a0( https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects)"]}

Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.

Climate change and agricultural intensification are exposing insect pollinators to temperature extremes and increasing pesticide usage. Yet, we lack good quantification of how temperature modulates the sublethal effects of pesticides on behaviours vital for fitness and pollination performance. Consequently, we are uncertain if warming decreases or increases the severity of different pesticide impacts, and whether separate behaviours vary in the direction of response. Quantifying these interactive effects is vital in forecasting pesticide risk across climate regions and informing pesticide application strategies and pollinator conservation. This multi-stressor study investigated the responses of six functional behaviours of bumblebees when exposed to either a neonicotinoid (imidacloprid) or a sulfoximine (sulfoxaflor) across a standardised low, mid, and high temperature. We found the neonicotinoid had a significant effect on five of the six behaviours, with a greater effect at the lower temperature(s) when measuring responsiveness, the likelihood of movement, walking rate, and food consumption rate. In contrast, the neonicotinoid had a greater impact on flight distance at the higher temperature. Our findings show that different organismal functions can exhibit divergent thermal responses, with some pesticide-affected behaviours showing greater impact as temperatures dropped, and others as temperatures rose. We must therefore account for environmental context when determining pesticide risk. Moreover, we found evidence of synergistic effects, with just a 3°C increase causing a sudden drop in flight performance, despite seeing no effect of pesticide at the two lower temperatures. Our findings highlight the importance of multi-stressor studies to quantify threats to insects, which will help to improve dynamic evaluations of population tipping points and spatiotemporal risks to biodiversity across climate regions.