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# Scottish mountain hares do not respond behaviorally to increased camouflage mismatch [https://doi.org/10.5061/dryad.hqbzkh1rc](https://doi.org/10.5061/dryad.hqbzkh1rc) **Abstract** Climate change has resulted in myriad stressors to wild organisms. Phenotypic plasticity, including behavioral plasticity, is hypothesized to play a key role in allowing animals to cope with rapid climate change and mitigate its negative fitness consequences. Camouflage mismatch resulting from decreasing duration of snow cover presents a stressor to species that undergo coat color molts to maintain camouflage against seasonally changing backgrounds. Winter white animals appear highly conspicuous against dark, snowless background and experience increased predation-induced mortality. Here, we evaluate the potential of behavioral plasticity to buffer against camouflage mismatch in mountain hares (*Lepus timidus*) in Scotland. We carried out field surveys in three populations over two years and found no evidence that hares modify their behaviors in response to increasing camouflage mismatch. Hares did not prefer to rest closer to light-colored rocks or farther from conspecifics with increasing color contrast. Furthermore, whiter hares did not seek to rest closer to snowy backgrounds; rather, hares preferred to sit farther from snow. These results suggest that behavioral plasticity might not be a universal, rapid mechanism facilitating adaptation to climate change. ## Description of the data and file structure This dataset contains the following variables: * Date: Date of stationary hare observation. * Area: Area where the observation was taken. * Dist_Rock: The distance a stationary hare was to the closest light-colored rock in meters. Measurements are in 1 m increments between 0 and 20 m. >20 marks occasions when a hare was farther than 20 m from the closest rock. 0 indicates occasions when a resting hare was immediately adjacent to a rock. 'n/a's signify that data was not collected. * Dist_Snow: The distance a stationary hare was to the closest snow field/patch in meters. Measurements are in 1 m increments between 0 and 20 m. >20 marks occasions when a hare was farther than 20 m from the closest snow field/patch. 0 indicates occasions when a hare was resting on snow field/patch. 'n/a's signify that data was not collected. * Dist_Hare: The distance a stationary hare was to the closest conspecific. Measurements are in 1 m increments between 1 and 20 m. >20 marks occasions when a hare was farther than 20 m from another hare. 'n/a's signify that data was not collected. * Snow_5m: The percent snow cover within 5-m radius circle centered at the hare's resting site. Estimated in 25% increments. 'n/a's signify that data was not collected. * Snow_10m: The percent snow cover within 10-m radius circle centered at the hare's resting site. Estimated in 25% increments. 'n/a's signify that data was not collected. * Hare_White: Hare's coat color measured in percent white in four categories ; 0% (completely dark), 25% (mostly dark), 50% (half-dark and half-white), 75% (mostly white) or 100% white (completely white). For detailed description of categories see Zimova et al. 2020 ProcB. 'n/a's signify that data was not collected.  Study Sites Field surveys were carried out at three sites (Lecht [57.193 ̊ N, −3.240 ̊ W], Findhorn High [57.235 ̊ N, −4.136 ̊ W], Findhorn Low [57.206 ̊ N, −4.102 ̊ W]) in the northeast and central highlands of Scotland, UK. All sites were located between 430-730 m a.s.l. and dominated by dwarf heath and subalpine plant communities. The Lecht site included areas of eroded peat, and both sites were scatted with occasional white/pale, sometimes lichen covered, hare-sized rocks. Field Surveys We surveyed mountain hares twice a month in fall (October–January) and spring (March–June) seasons during 2015 and 2016 for a total of 5–11 surveys per season (Zimova et al. 2020b). During each survey, one surveyor walked along a predetermined route (ca 3–6 km long) and observed hares as they were either flushed (moved from their resting site in response to disturbance), or less frequently, detected by the surveyor during the frequent and thorough binoculars scans of the landscape. Hares are largely inactive during the day when they sit at a resting site above ground. We only used observations during which the observer had a clear view that allowed coat color to be assessed and was confident of the hare’s original resting location. For all hares detected within 200 m of the observer, we photographed the hare and recorded coat color following (Watson 1963). Coat color was ranked into four categories ; 0% (completely dark), 25% (mostly dark), 50% (half-dark and half-white), 75% (mostly white) or 100% white (completely white) (for detailed description of categories see Zimova et al. 2020b). For observations accompanied by photographs (> 80% of all observations) field estimates of molt were later verified by one of us (MZ). Finally, for each hare’s original resting site, we visually estimated the minimum distance a hare was to 1) any light-colored rocks of equal or larger size than the size of a resting hare, 2) another hare, and 3) snow. All distances between 0 and 20 m were estimated in 1 m increments; all other distances were recorded as ‘>20 m’ as we were not able to accurately estimate distances beyond 20 m. Climate change has resulted in myriad stressors to wild organisms. Phenotypic plasticity, including behavioral plasticity, is hypothesized to play a key role in allowing animals to cope with rapid climate change and mitigate its negative fitness consequences. Camouflage mismatch resulting from decreasing duration of snow cover presents a stressor to species that undergo coat color molts to maintain camouflage against seasonally changing backgrounds. Winter white animals appear highly conspicuous against dark, snowless background and experience increased predation-induced mortality. Here, we evaluate the potential of behavioral plasticity to buffer against camouflage mismatch in mountain hares (Lepus timidus) in Scotland. We carried out field surveys in three populations over two years and found no evidence that hares modify their behaviors in response to increasing camouflage mismatch. Hares did not prefer to rest closer to light-colored rocks or farther from conspecifics with increasing color contrast. Furthermore, whiter hares did not seek to rest closer to snowy backgrounds; rather, hares preferred to sit farther from snow. These results suggest that behavioral plasticity might not be a universal, rapid mechanism facilitating adaptation to climate change.

TECHNICAL INFO No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign. This publication may include a number of files that contain the same measurements (duplicate files, "dupes") as multiple raw data backups were performed on the data. The duplicate data can be differentiated by the information found in the timestamp column and file header. ACKNOWLEDGEMENTS Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding COPYRIGHT NOTICE Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved. CREATOR NOTICE This work is owned by the Principal Investigators (PIs) of the Urbisphere project. ATTRIBUTION NOTICE The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). DISCLAIMER NOTICE The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected. MATERIAL NOTICE The notices cover data in databases, text and images contained in the work. MATERIAL URI Urbisphere project Original logger data files from radiometer intercomparison measurements at SIRTA Atmospheric Observatory in Palaiseau in the Greater Paris region.

Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

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.DAMIP.MPI-M.MPI-ESM1-2-LR' 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 MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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.HighResMIP.MOHC.HadGEM3-GC31-HH' 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 HadGEM3-GC3.1-N512ORCA12 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N512; 1024 x 768 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA12 tripolar primarily 1/12 deg; 4320 x 3604 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA12 tripolar primarily 1/12 deg; 4320 x 3604 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 50 km, atmos: 50 km, land: 50 km, ocean: 10 km, seaIce: 10 km.

Data repository for the paper 'Morocco's Coal to Clean Journey: Optimised Pathways for Decarbonisation and Energy Security' (https://doi.org/10.21203/rs.3.rs-2579435/v4). Six clic-SAND scenario files for analysis of decarbonisation and energy security in Morocco, 'Data Note describing the scenarios' file outlining the steps to replicate the analysis and rebuild the scenarios, 'Data Annex' listing the data sources and assumptions in the scenarios, 'Instructions for running the model' outlining the steps required to re-run the scenarios on OSeMOSYS Cloud, and 'U4RIA Compliance' describing the level of compliance of the study to U4RIA principles.

Figure 1data_Exp 2Figure 1 data: Condition of experimental seedlings in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS) during the warmest growing season (2011) and at the end of the experiment (2013). Seedling condition was defined as: healthy (< 50% of the needles turned yellow or brown) or unhealthy (> 50% of the needles turned yellow or brown). Seedlings were 1 month old at plantation time in the July 2010.Table 1_environmental conditions_Exp 1Table 1 data: Environmental conditions and vegetation characteristics in hummocks (circular and bands) and lawns for Experiment 1. Water table depth below surface is an average for the four growing seasons (2010-2013)Table 2_ photosynthesis data_Exp 1Table 2 photosynthesis data: Photosynthesis rates for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1.Table 2_seedling responses_Exp 1Table 2 data: Responses of experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns for Experiment 1 after 4 growing seasons. ST: Seeds inserted on top of moss; SB: Seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Emergence = % of planted seeds emerged after 1 year. Condition = % healthy seedlings. Stem growth corresponds to vertical stem growth for germinating (ST and SB) seedlings and new stem growth for older (small and large) seedlings.Table 3_regression seedling-environment_Exp 1Table 3 data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 during the whole experimental period (2010-2013). ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old at plantation time); Large seedling (2 months old at plantation time). Condition = % healthy seedlings. Growth = stem growth.Table 4_Environmental data_Exp 2Table 4: Environmental conditions in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS).Table 4 and Table S5a_seedling performance_Exp 2Table 4: Seedling performance in hummocks with contrasting shrub density and tree canopy in Experiment 2: No Trees - Low Shrub biomass (NTLS), No Trees - High Shrub biomass (NTHS), Present Trees - Low Shrub biomass (PTLS) and Present Trees - High shrub biomass (PTHS). Seedling emergence, condition and survival from seeds inserted below the moss (SB), and from small planted seedlings.Table S3_cox regression (survival analysis)_Exp 1Table S3: Data for Cox survival analysis for experimental pine seedlings in hummocks (circular and bands) versus adjacent lawns during 2010-2013. ST: Seedlings from seeds inserted on top of moss; SB: Seedlings from seeds inserted below moss; Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time).Table S4_ regression seedling-environment 2011_Exp 1Table S4: Data for generalized linear models assessing the responses of experimental pine seedlings in hummocks (circular and bands) and adjacent lawns for Experiment 1 in 2011. Small seedling (1 month old, 10 cm tall at plantation time); Large seedling (2 months old, 30 cm tall at plantation time). Condition = % healthy seedlings. Growth = stem growth. Boreal ecosystems are warming roughly twice as fast as the global average, resulting in woody expansion that could further speed up the climate warming. Boreal peatbogs are waterlogged systems that store more than 30% of the global soil carbon. Facilitative effects of shrubs and trees on the establishment of new individuals could increase tree cover with profound consequences for the structure and functioning of boreal peatbogs, carbon sequestration and climate. We conducted two field experiments in boreal peatbogs to assess the mechanisms that explain tree seedling recruitment and to estimate the strength of positive feedbacks between shrubs and trees. We planted seeds and seedlings of Pinus sylvestris in microsites with contrasting water-tables and woody cover and manipulated both shrub canopy and root competition. We monitored seedling emergence, growth and survival for up to four growing seasons and assessed how seedling responses related to abiotic and biotic conditions. We found that tree recruitment is more successful in drier topographical microsites with deeper water-tables. On these hummocks, shrubs have both positive and negative effects on tree seedling establishment. Shrub cover improved tree seedling condition, growth and survival during the warmest growing season. In turn, higher tree basal area correlates positively with soil nutrient availability, shrub biomass and abundance of tree juveniles. Synthesis. Our results suggest that shrubs facilitate tree colonization of peatbogs which further increases shrub growth. These facilitative effects seem to be stronger under warmer conditions suggesting that a higher frequency of warmer and dry summers may lead to stronger positive interactions between shrubs and trees that could eventually facilitate a shift from moss to tree-dominated systems.