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Research data keyboard_double_arrow_right Dataset 2022Embargo end date: 30 Jan 2022Publisher:Dryad Authors:Barreaux, Antoine;
Barreaux, Antoine
Barreaux, Antoine in OpenAIREHigginson, Andrew;
Higginson, Andrew
Higginson, Andrew in OpenAIREBonsall, Michael;
English, Sinead;Bonsall, Michael
Bonsall, Michael in OpenAIREHere, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 07 Dec 2022Publisher:Dryad Authors:Shao, Junjiong;
Zhou, Xuhui; van Groenigen, Kees; Zhou, Guiyao; +9 AuthorsShao, Junjiong
Shao, Junjiong in OpenAIREShao, Junjiong;
Zhou, Xuhui; van Groenigen, Kees; Zhou, Guiyao; Zhou, Huimin; Zhou, Lingyan; Lu, Meng; Xia, Jianyang; Jiang, Lin; Hungate, Bruce; Luo, Yiqi; He, Fangliang; Thakur, Madhav;Shao, Junjiong
Shao, Junjiong in OpenAIREAim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors:Neubauer, David;
Neubauer, David
Neubauer, David in OpenAIREFerrachat, Sylvaine;
Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 AuthorsFerrachat, Sylvaine
Ferrachat, Sylvaine in OpenAIRENeubauer, David;
Neubauer, David
Neubauer, David in OpenAIREFerrachat, Sylvaine;
Siegenthaler-Le Drian, Colombe; Stoll, Jens; Folini, Doris Sylvia;Ferrachat, Sylvaine
Ferrachat, Sylvaine in OpenAIRETegen, Ina;
Tegen, Ina
Tegen, Ina in OpenAIREWieners, Karl-Hermann;
Wieners, Karl-Hermann
Wieners, Karl-Hermann in OpenAIREMauritsen, Thorsten;
Stemmler, Irene; Barthel, Stefan; Bey, Isabelle;Mauritsen, Thorsten
Mauritsen, Thorsten in OpenAIREDaskalakis, Nikos;
Heinold, Bernd;Daskalakis, Nikos
Daskalakis, Nikos in OpenAIREKokkola, Harri;
Kokkola, Harri
Kokkola, Harri in OpenAIREPartridge, Daniel;
Rast, Sebastian; Schmidt, Hauke;Partridge, Daniel
Partridge, Daniel in OpenAIRESchutgens, Nick;
Stanelle, Tanja;Schutgens, Nick
Schutgens, Nick in OpenAIREStier, Philip;
Stier, Philip
Stier, Philip in OpenAIREWatson-Parris, Duncan;
Watson-Parris, Duncan
Watson-Parris, Duncan in OpenAIRELohmann, Ulrike;
Lohmann, Ulrike
Lohmann, Ulrike in OpenAIREProject: 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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 12 Sep 2023Publisher:Dryad Authors:Mason, Victoria;
Burden, Annette; Epstein, Graham; Jupe, Lucy; +2 AuthorsMason, Victoria
Mason, Victoria in OpenAIREMason, Victoria;
Burden, Annette; Epstein, Graham; Jupe, Lucy; Wood, Kevin; Skov, Martin;Mason, Victoria
Mason, Victoria in OpenAIRE# Data from: Blue Carbon Benefits from Global Saltmarsh Restoration [https://doi.org/10.5061/dryad.pc866t1vp](https://doi.org/10.5061/dryad.pc866t1vp) This README file was generated on 12th September 2023 by Victoria Mason. **Title of Dataset:** Blue carbon benefits from global saltmarsh restoration. **Author information:** * Victoria G. Mason, Bangor University/Royal Netherlands Institute for Sea Research (NIOZ), victoria.mason@nioz.nl (*Corresponding author*) * Annette Burden, UK Centre for Ecology & Hydrology * Graham Epstein, University of Exeter/University of Victoria * Lucy L. Jupe, Wildfowl & Wetlands Trust * Kevin A. Wood, Wildfowl & Wetlands Trust * Martin W. Skov, Bangor University **Summary of dataset:** These data include all data which were extracted or derived from relevant studies on global saltmarsh carbon storage and greenhouse gas flux. Data were obtained following screening of 29,182 peer reviewed published studies for relevant data, which were then extracted from 431 studies via text, tables and figures. We then used a meta-analysis to assess drivers of variation in global saltmarsh and greenhouse gas flux. * Date of literature search: 21st January 2022. * Date of data extraction: February - March 2022 * Literature search conducted via: Scopus + Web of Science ## Description of the data and file structure The contents of these data include: * **Full dataset (Aug2023\_GlobalCarbonReview\_FullDataset.xls):** All data extracted from 431 relevant studies and used in analysis. This includes a title page, metadata (with descriptions of column headers) and the full dataset. Response variables included: * Carbon stock * Percentage organic carbon * Bulk density * Sediment accretion rate * Carbon accumulation rate * Carbon dioxide flux * Methane flux * Nitrous oxide flux **\- Data on each included study \(Aug2023\_GlobalCarbonReview\_IncludedStudies\.xls\):** List of each study included in the final analysis, and its metadata. This includes a title page, metadata (with descriptions of column headers) and the dataset. All data include standard deviation (SD) and n (number of replicates) where provided by the original study, which were used to calculate Hedge's *g* effect sizes reported in the subsequent study. | Frequently used abbreviations: | | | ------------------------------ | --- | | C | carbon | | OC | organic carbon | | GHG | greenhouse gas | | bd | bulk density (g cm-3 dry sediment) | | Y/N | yes/no | | ref | reference | | lat | latitude | | long | longitude | | rest | restoration | | prec | precipitation | | sal | salinity | | acc | accretion | | resp | respiration | | SR | soil respiration (appears for CO2 flux) | | ER | ecosystem respiration (appears for CO2 flux) | | n | number of samples included in mean/standard deviation | | sd | standard deviation | All abbreviations used are outlined in the ‘Metadata’ worksheet of .xls files. **Data specific information for Aug2023\_GlobalCarbonReview\_FullDataset.xls:** Number of variables: 88 Number of cases/rows: 2055 Variables included: See 'Metadata' sheet **Data specific information for** **Aug2023\_GlobalCarbonReview\_IncludedStudies.xls:** Number of variables: 47 Number of cases/rows: 431 Variables included: See 'Metadata' sheet **Empty cells:** Cells are empty where data on that variable were not provided by the original study from which they were extracted. For example, where a study provided data on carbon stock variables, but not greenhouse gas flux. For further details, see the 'Metadata' sheets of each file. ## Sharing/Access information These data are available via Dryad, and described in ‘Blue Carbon Benefits from Global Saltmarsh Restoration’, in Global Change Biology. **DOI:** 10.1111/gcb.16943 Data were extracted from 431 published peer reviewed articles, the details of which can be found in the attached datasheets. Coastal saltmarshes are found globally, yet are 25–50% reduced compared to their historical cover. Restoration is incentivised by the promise that marshes are efficient storers of ‘blue’ carbon, although the claim lacks substantiation across global contexts. We synthesised data from 431 studies to quantify the benefits of saltmarsh restoration to carbon accumulation and greenhouse gas uptake. The results showed global marshes store approximately 1.41–2.44 Pg carbon. Restored marshes had very low greenhouse gas (GHG) fluxes and rapid carbon accumulation, resulting in a mean net accumulation rate of 64.70 t CO2e ha-1 y-1. Using this estimate and potential restoration rates, we find saltmarsh regeneration could result in 12.93–207.03 Mt CO2e accumulation per year, offsetting the equivalent of up to 0.51% global-energy-related CO2 emissions – a substantial amount, considering marshes represent <1% of Earth’s surface. Carbon accumulation rates and GHG fluxes varied contextually with temperature, rainfall and dominant vegetation, with the eastern costs of the USA and Australia being particular hotspots for carbon storage. Whilst the study reveals paucity of data for some variables and continents, suggesting a need for further research, the potential for saltmarsh restoration to offset carbon emissions is clear. The ability to facilitate natural carbon accumulation by saltmarshes now rests principally on the action of the management-policy community and on financial opportunities for supporting restoration.
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visibility 2visibility views 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 02 Jun 2020Publisher:NERC Environmental Information Data Centre Authors:Boulton, C.A.;
Boulton, C.A.
Boulton, C.A. in OpenAIRERitchie, P.D.L.;
Ritchie, P.D.L.
Ritchie, P.D.L. in OpenAIREThis dataset contains modelled vegetation carbon output from the land surface model JULES, along with the temperature and rainfall outputs (which were originally inputted) at a monthly, 1.5km resolution. There are four different JULES simulations, using two different climate projections (global climate sensitivity of 3.5K and highest global climate sensitivity of 7.1K) under a constant, present day atmospheric CO2 and a CO2 pathway that follows the SRES (Special Report on Emissions Scenarios) A1B scenario. JULES is a community developed land surface model, led by the UK Met Office and Centre for Ecology and Hydrology and is available for use after registering on the JULES repository (https://code.metoffice.gov.uk/trac/jules). The data produced using JULES was model version vn4.9 and the model configuration can be found on the Rose suite u-ao645 under the branch ‘transient_25km_drive’, available from https://code.metoffice.gov.uk/trac/roses-u (registration required).
https://dx.doi.org/1... arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors:Neubauer, David;
Neubauer, David
Neubauer, David in OpenAIREFerrachat, Sylvaine;
Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 AuthorsFerrachat, Sylvaine
Ferrachat, Sylvaine in OpenAIRENeubauer, David;
Neubauer, David
Neubauer, David in OpenAIREFerrachat, Sylvaine;
Siegenthaler-Le Drian, Colombe; Stoll, Jens; Folini, Doris Sylvia;Ferrachat, Sylvaine
Ferrachat, Sylvaine in OpenAIRETegen, Ina;
Tegen, Ina
Tegen, Ina in OpenAIREWieners, Karl-Hermann;
Wieners, Karl-Hermann
Wieners, Karl-Hermann in OpenAIREMauritsen, Thorsten;
Stemmler, Irene; Barthel, Stefan; Bey, Isabelle;Mauritsen, Thorsten
Mauritsen, Thorsten in OpenAIREDaskalakis, Nikos;
Heinold, Bernd;Daskalakis, Nikos
Daskalakis, Nikos in OpenAIREKokkola, Harri;
Kokkola, Harri
Kokkola, Harri in OpenAIREPartridge, Daniel;
Rast, Sebastian; Schmidt, Hauke;Partridge, Daniel
Partridge, Daniel in OpenAIRESchutgens, Nick;
Stanelle, Tanja;Schutgens, Nick
Schutgens, Nick in OpenAIREStier, Philip;
Stier, Philip
Stier, Philip in OpenAIREWatson-Parris, Duncan;
Watson-Parris, Duncan
Watson-Parris, Duncan in OpenAIRELohmann, Ulrike;
Lohmann, Ulrike
Lohmann, Ulrike in OpenAIREProject: 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.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' 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-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 28 Apr 2022Publisher:Dryad This spreadsheet contains nine tabs to present the data used in the article 'Microclimate-driven trends in spring-emergence phenology in a temperate reptile (Vipera berus): Evidence for a potential ‘climate trap’?' (Turner & Maclean, 2022; Ecology and Evolution). The first tab, labelled 'Metadata_README', contains metadata for the dataset, including identification and affliliations of the authors, a description of the tabs in the spreadsheet, and descriptions of data labels used in the spreadsheet tabs. The second tab, labelled 'adder_sightings', comprises of records of Vipera berus (adder) sightings in Cornwall, United Kingdom, sourced from the Environmental Records Centre for Cornwall and the Isles of Scilly (www.erccis.org.uk), the Record Pool (www.recordpool.org.uk) and the Cornish Biodiversity Network (www.cornishbiodiversitynetwork.org). Due to data sensitivities and issues associated with the General Data Protection Regulation, information pertaining to the locations and dates of adder sightings in some instances in the dataset have only be provided at reduced spatial and temporal resolutions. A unique identification code for locations has been attributed to records. For full-resolution access, contact the data custodian and corresponding author. The raw datasets of adder sightings were filtered prior to inclusion in the analysis in Turner and Maclean. See the main text for all filtering procedures and microclimate modelling. The remaining tabs contain data relating to each adder sighting location in 'adder_sightings' for each year 1983 - 2017 computed from microclimate models using the microclima R package (Maclean et al., 2019). The third tab, labelled 'total_spring_frost', contains annual rates of spring ground frost. The fourth, fifth and sixt tabs, labelled 'Cue1(i)', "Cue1(ii)', and 'Cue1(iii)', each contain predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using the 5th, 2.5th and 10th percentile thresholds, respectively, of an accumulated (degree-hours) temperature cue for adder emergence. The seventh tab, labelled 'Cue2', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a sharp rise in accumulated (degree-hours) temperature cue for adder emergence. The eighth tab, labelled 'Cue3', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a below-ground temperature gradient collapse cue for adder emergence. Lastly, the ninth tab, labelled 'Cue4', contains predicted annual adder emergence timing and computed rates of post-emergence spring ground frost using a critical air temperature (10°C) cue for adder emergence. The main text presents the analysis of adder emergence and spring ground frost data from the 'Cue1(i)'. Analysis of data from 'Cue1(ii)', 'Cue1(iii)', 'Cue2', 'Cue3', and 'Cue4' are presented in the Supplementary Information for Turner and Maclean. Climate change will increase the exposure of organisms to higher temperatures, but can also drive phenological shifts that alter their susceptibility to conditions at the onset of breeding cycles. Organisms rely on climatic cues to time annual life-cycle events, but the extent to which climate change has altered cue reliability remains unclear. Here, we examine the risk of a ‘climate trap’ – a climatically-driven desynchronisation of the cues that determine life-cycle events and fitness later in the season in a temperate reptile, the European adder (Vipera berus). During the winter, adders hibernate underground, buffered against sub-zero temperatures, and re-emerge in the spring to reproduce. We derived annual spring-emergence trends between 1983 and 2017 from historical observations in Cornwall, United Kingdom, and related these trends to the microclimatic conditions that adders experienced. Using a mechanistic microclimate model, estimates of below- and near-ground temperatures were used to derive accumulated degree-hour and absolute temperature thresholds that predicted annual spring-emergence timing. Trends in annual emergence timing and subsequent exposure to ground frost were then quantified. We found that adders have advanced their phenology towards earlier emergence. Earlier emergence was associated with increased exposure to ground frost and, contradicting the expected effects of macroclimate warming, increased post-emergence exposure to ground frost at some locations. The susceptibility of adders to this ‘climate trap’ was related to the rate at which frost risk diminishes relative to advancement in phenology, which depends on the seasonality of climate. We emphasise the need to consider exposure to changing microclimatic conditions when forecasting biological impacts of climate change.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2013 United KingdomPublisher:University of Exeter Authors: Vickers, Andrew;handle: 10871/13568
Readme File for folder PRIMaRE_NAS\ADCP_5Beam\Data By: Dr. Abdessalem Bouferrouk, Renewable Energy Research Group This folder has two main subfolders: \Raw_Data: As the name indicates, this is the raw data as output from the ADCP’s memory cards for each deployment. Data from the 5-Beam ADCP is organised as follows: data that comes from the four inclined beams is named as MAST0000.000 (or ‘master’ unit) while data from the 5th vertical beam is named SLAV0000.000 (or the ‘slave’ unit). Note: Each ‘master’ or ‘slave’ can output data from up to two memory cards. In this case please note that the output files will named as: MAST0000.000: for 1st memory card MAST0000.001: for second memory card The same applies for the ‘slave’ unit. The file called ‘Merge.000’ is the one that must be formed out of all master and slave raw data. The raw master and slave raw data can be merged into’Merge.000’ using the RDI utility ‘Merge’ which is available under the folder \Software. The file ‘Merge.000’ is the file that must be processed through the software ‘WavesMon’, see \Software folder, to produce time series (for further independent processing e.g. through MATLAB) or obtain wave and data as output by WavesMon which is actually quite reliable. You cannot process individual files MAST0000 or SLAV0000 via WavesMon! For users interested in the currents data only, not wave data, they have to take the ‘MAST0000’ files and read them through the software ‘WinADCP’ to get time series of u, v and w velocity components in the orthogonal earth coordinate system. Alternatively, users can read the ‘MAST0000’ files into the BBList utility to get current velocity data (no wave data). \Processed_Data This folder may contain examples of processed data typically time series from WavesMon software for those interested in independent processing say in MATLAB. However, users can generate such data themselves by following relevant instructions on using WavesMon software. Processed data may also include time series data for the current velocity field ...
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:NERC EDS Environmental Information Data Centre Authors:Harper, A.;
Harper, A.
Harper, A. in OpenAIREPowell, T.;
Powell, T.
Powell, T. in OpenAIRECox, P.;
Cox, P.
Cox, P. in OpenAIREComyn-Platt, E.;
+1 AuthorsComyn-Platt, E.
Comyn-Platt, E. in OpenAIREHarper, A.;
Harper, A.
Harper, A. in OpenAIREPowell, T.;
Powell, T.
Powell, T. in OpenAIRECox, P.;
Cox, P.
Cox, P. in OpenAIREComyn-Platt, E.;
Comyn-Platt, E.
Comyn-Platt, E. in OpenAIREHuntingford, C.;
Huntingford, C.
Huntingford, C. in OpenAIREJULES is a community model available for use after registering on the JULES repository (https://code.metoffice.gov.uk/trac/jules). The model version used here was r9448, which is a branch of vn4.8 with updates to represent harvesting of bioenergy crops and for running IMOGEN in inverse mode (using specified temperature pathways instead of specified concentration pathways). To use JULES-IMOGEN, patterns from 34 GCMs are needed, which are available from https://doi.org/10.5285/343885af-0f5e-4062-88e1-a9e612f77779. JULES requires a series of input files (initial conditions, model grid, CO2 concentration, land-use per grid cell, soil parameters, etc.). These are provided in the “ancillary_files” directory of the dataset. JULES can be run with a system called Rose, which stores model settings. We also include an excel spreadsheet with the suites used for the Harper et al. (2018) paper. The suites are available from https://code.metoffice.gov.uk/trac/roses-u (registration required). The original IMAGE land use data is available from https://data.knmi.nl/datasets?q=PBL. We used v17 land fractions from SSP2-SPA0-RCP1.9 and SSP2-SPA2-RCP2.6. These were regridded from half degree to the N48 resolution using ESMF Regrid patch interpolation method from NCAR Command Language (http://ncl.ucar.edu/Document/Functions/ESMF/ESMF_regrid.shtml). The actual fractions used in the simulations are in both the input and output files. For further details, see Harper et al. (https://doi.org/10.1038/s41467-018-05340-z). The experimental design is the same as in Comyn-Platt et al. (https://doi.org/10.1038/s41561-018-0174-9), which also has a linked dataset for the historical period. This dataset includes six sets of model output from JULES/IMOGEN simulations. Each set includes output from JULES (the Joint UK Land Environment Simulator) run with 34 climate change patterns from 2000-2099. The outputs provide carbon stocks and variables related to the surface energy budget to understand the implications of land-based climate mitigation.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 28 Jun 2024Publisher:Dryad Authors:Westley, Joseph;
García, Francisca C.; Warfield, Ruth; Yvon-Durocher, Gabriel;Westley, Joseph
Westley, Joseph in OpenAIRE# The community background alters the evolution of thermal performance ## GENERAL INFORMATION Corresponding author * Name: Joseph Westley * Institution: University of Exeter * Email: [jw1235@exeter.ac.uk](mailto:jw1235@exeter.ac.uk) Principal Investigator * Name: Prof. Gabriel Yvon-Durocher * Institution: University of Exeter * Email: [G.Yvon-Durocher@exeter.ac.uk](mailto:G.Yvon-Durocher@exeter.ac.uk) Co-author 1 * Name: Dr. Francisca C. García * Institution: King Abdullah University of Science and Technology (KAUST) * Email: [paquigrcgrc@gmail.com](mailto:paquigrcgrc@gmail.com) Co-author 2 * Name: Ruth Warfield * Institution: University of Exeter * Email: [R.Warfield@exeter.ac.uk](mailto:R.Warfield@exeter.ac.uk) Date of data collection: 2020 ## SHARING/ACCESS INFORMATION **Recommended citation for this dataset:** Westley, J., García, F. C., Warfield, R., Yvon-Durocher, G. (2024). Data from: The community background alters the evolution of thermal performance. Dryad Digital Repository. doi.org/10.5061/dryad.vq83bk41b **Publication associated with this dataset:** Westley, J., García, F. C., Warfield, R., Yvon-Durocher, G. (2024). The community background alters the evolution of thermal performance. Evolution Letters. ## Data description and file structure Contained within the directory "datafiles" are both the raw data files, partially processed data files, and a single processed data file used in all analyses. Raw and partially processed data files for ancestral and monoculture-evolved isolates are combined and are found in the directory "datafiles/monraw". Raw and partially processed data files for community-evolved isolates are located in "datafiles/comraw". The fully processed data file, "final_data.csv", used in all statistical analyses is in the "datafiles" directory (not within a sub-directory). ### Processed data * "final_data.csv" * Description: A single file containing all growth rate data for monoculture, community, and ancestral isolates * Number of columns/variables: 11 * Number of rows/observations: 1421 * Variable List: * r: maximum growth rate per hour r(h−1) * K: maximum optical density reached at a wavelength of 600nm (OD600) * T0.biom: The OD600 at the point at which cultures were inoculated * AIC: The Akaike information criterion for the fit of the logistic growth curve * Id: A variable containing the ID of the isolate. For example, in "OTU2-T1-15-2", "OTU2" means the taxonomic identity is OTU2 (Pseudomonas spp.), "T1" means it was evolved in treatment 1 (monoculture), "15" means it was evolved at 15°C, and "2" means this is biological replicate 2 * temp.c: This is the growth temperature in Celsius * temp: This is the growth temperature in kelvin * ID.a: A variable containing the ID but without distinguishing between biological replicates of the same experimental unit. For example, in "OTU2-T1-15", "OTU2" means the taxonomic identity is OTU2 (Pseudomonas spp.), "T1" means it was evolved in treatment 1 (monoculture), and "15" means it was evolved at 15°C. * evotemp: The temperature an isolate was evolved at in Celsius * OTU: The taxonomic identity of the isolate * Treatment: Whether the isolate is ancestral, monoculture-evolved, or community evolved * Specific abbreviations: * T0 = ancestral isolate, T1 = monoculture evolved isolate, T2 = community evolved isolate * OTU2 = *Pseudomonas* spp., OTU15 = *Serratia* spp., OTU18 = *Aeromonas* spp., OTU20 = *Herbaspirillum* spp., OTU23 = *Janthinobacterium* spp. * survival.csv * Taxon: The taxonomic identity of the isolate * Evolution_temperature: The temperature in Celsius at which the isolate was evolved * Replicates_survived: The count of biological replicates for which the respective isolate survived to the end of the community evolution experiment, out of a total of three ### Raw and partially processed data The following is an explanation of the structure of "datafiles/monraw", but the same file structure is used in "datafiles/comraw". Within "datafiles/monraw" there are the following files: * 192 Raw OD600 measurement files, following the naming format of "T0_15_P1.csv" * Description: These are raw OD600 files output by the Thermo Scientific Multiskan Sky Microplate Spectrophotometer recording at a wavelength of 600 nm. There is a file for all combinations of time points of the growth assay, temperature of the growth assay, and plate identity (plate 1 or plate 2). In the example file name, "T0_15_P1.csv", "T0" refers to timepoint 0 (when the culture was inoculated, not to be confused with the treatment factor level "T0", which denotes ancestral isolates), "15" denotes that the plate was grown at 15°C, and P1 denotes that the data is for plate one. * Dataframe structure: These files do not follow a typical "tidy" or "long form" data structure. Cells are populated by values in the shape of a 96-well plate, where the columns are numbered 1-12, and rows contain sequential letters A-H. For example, the value in row A, column 1, denotes the OD600 for well A1 of the plate being measured. * "Data_mon.csv" * Description: This file contains all data from all 192 raw data files described above collated into an R object in a "tidy" or "long" format. * Number of columns/variables: 12 * Number of rows/observations: 11520 * Variable List: * Replicate: A number designating the biological replicate for the respective experimental unit (note: in the analogous community datafile "Data_com.csv", the replicate variable is named community instead of Replicate) * od_cor: The OD600 measure was corrected to remove the absorbance of the culture media * OTU: The taxonomic identity of the isolate * Treatment: The treatment group that the isolate was evolved in. For example, in "T1-15", "T1" denotes that the isolate evolved in a monoculture, and "15" denotes that the isolates evolved at 15°C. * Timepoint: An integer value specifying the timepoint that the measure was taken, e.g., "0" means at inoculation, "1" is the first measure post-inoculation, etc. * growthtemp: The temperature the plate was grown at in Celsius. * timestampcode: A variable that contains a combination of the growth temperature and the timepoint, e.g., "0-15" denotes timepoint 0 and a growth temperature of 15°C. * Hours: The exact time in hours since culture inoculation that the OD600 reading was recorded * Specific abbreviations: * T0 = ancestral isolate, T1 = monoculture evolved isolate, T2 = community evolved isolate * OTU2 = *Pseudomonas* spp., OTU15 = *Serratia* spp., OTU18 = *Aeromonas* spp., OTU20 = *Herbaspirillum* spp., OTU23 = *Janthinobacterium* spp. * "mon_out.csv" * Description: A file containing all specific OD600 measurements to be removed from the "Data_mon.csv" data frame prior to construction of logistic growth curves (these are typically measurements occurring after carrying capacity has been reached) * Number of columns/variables: 3 * Number of rows/observations: 630 * Variable List: * t: The time in hours that the datapoint occurs * LOG10N: The OD600 measure of the datapoint * pa: A variable containing the ID of the isolate. For example, in "OTU2-T1-15-2", "OTU2" means the taxonomic identity is OTU2 (Pseudomonas spp.), "T1" means it was evolved in treatment 1 (monoculture), "15" means it was evolved at 15°C, and "2" means this is biological replicate 2 * Specific abbreviations: * T0 = ancestral isolate, T1 = monoculture evolved isolate, T2 = community evolved isolate * OTU2 = *Pseudomonas* spp., OTU15 = *Serratia* spp., OTU18 = *Aeromonas* spp., OTU20 = *Herbaspirillum* spp., OTU23 = *Janthinobacterium* spp. * "mon_curves_outrem.csv" * Description: A file containing maximum growth rate data for all monoculture evolved isolates across all growth and evolution temperatures "mon_curves_outrem.csv". Each curve is for a single biological replicate and is produced by fitting a logistic growth model to the OD600 measurements * Number of columns/variables: 6 * Number of rows/observations: 960 * Variable List: * pa: A variable containing the ID of the isolate. For example, in "OTU2-T1-15-2", "OTU2" means the taxonomic identity is OTU2 (Pseudomonas spp.), "T1" means it was evolved in treatment 1 (monoculture), "15" means it was evolved at 15°C, and "2" means this is biological replicate 2 * r: maximum growth rate per hour r(h−1) * k: maximum OD600 reached * T0.biom: The OD600 at the point at which cultures were inoculated * AIC: The Akaike information criterion for the fit of the logistic growth curve * quasi_r2: A "quasi" or "pseudo" r squared value for the fit of the logistic growth curve * "Montimestamps.csv" * Description: A file containing the actual length of time in hours since inoculation (timepoint 0) that each OD600 measurement was taken, e.g., OD600 measurements at timepoint 1 for plates grown at 15°C were taken 4.05 hours after inoculation * Number of columns/variables: 3 * Number of rows/observations: 96 * Variable List: * Timepoint: An integer value specifying the timepoint that the measure was taken, e.g., "0" means at inoculation, "1" is the first measure post-inoculation, etc. * growthtemp: The temperature the plate was grown at in Celsius. * Hours: The exact time in hours since culture inoculation that the OD600 reading was recorded ## Code In the scripts directory, all data processing steps are numbered sequentially. In summary, these scripts perform the following: #### Step 1: Collating raw OD600 data GRE_mon_step1.R and GRE_com_step1.R each collate the raw 96 well plate OD600 datafiles for all time points (e.g., files following the naming format of "T0_15_P1.csv") into an R object. #### Step 2: Converting data to 'tidy' format GRE_mon_step2.R and GRE_com_step2.R take the outputs from step 1 and convert them to a "tidy" format, producing the files "Data_mon.csv" and "Data_com.csv". #### Step 3: Creating logistic growth curves GRE_mon_logcurves_step3.R and GRE_com_logcurves_step3.R take the outputs from step2 ("Data_mon.csv" and "Data_com.csv" respectively) and produce individual logistic growth curves for each isolate ("mon_curves_outrem.csv" and "com_curves_outrem.csv" respectively). "mon_out.csv" and "com_out.csv" are also produced at this stage, and include OD measurements to be removed, prior to growth curve estimation (for example, OD measurements showing a decline occurring after carrying capacity is reached would be removed). Plots of these growth curves are also written to the 'plots_growth_curves' directory as mon_curves_outrem.pdf and com_curves_outrem.pdf (not included in this repository). #### Step 4: Consolidating monoculture-evolved, ancestral, and community-evolved data into a single .csv file GRE_data_consolidation_step4.R collates the monoculture and community growth curve data ("mon_curves_outrem.csv" and "com_curves_outrem.csv") into the single file, "final_data.csv". Curves where carrying capacity is not reached or there is no growth are removed at this stage. #### Step 5: Conducting growth rate analyses and creating Figures 1 and 2 GAMM_r_analysis_step5.R takes "final_data.csv" as input and produces generalised additive mixed-effects models (GAMMs), conducts model comparison via AICc to select the best models, and produces Figure 1 and Figure 2 for the manuscript, based on the predictions of these best models. Additionally, post-hoc analysis is conducted using R package emmeans to get effect sizes and the significance of pairwise differences. See the section 'Statistical analysis' within 'Materials and Methods' of the manuscript for more detailed methods. #### Step 6: Conducting survival analysis and creating Figure 3 Survival_analysis_step6.R also takes "final_data.csv" as input and creates a heatmap showing the number of replicates of each community member that survive to the end of the community experiment, at each evolution temperature. Additionally, a binomial model is used here to test if survival to the end of the community experiment depends on taxonomic identity and evolution temperature. Study taxa Study taxa were derived from biofilm samples collected in May 2016- May 2017 from rock surfaces in several freshwater streams in Hvergerdi Valley, Iceland (64.02, −21.18). These samples were frozen in a 17% glycerol solution after collection and were stored at -20°C. The freshwater streams from which they originated ranged in temperature from 7°C - 38°C, due to variation in the levels of geothermal warming at the site (O’Gorman et al., 2014). On return to the laboratory, samples were thawed at 20°C. The solution they were transported in was then diluted consecutively, and 10 µL of solution was spread onto agar plates and incubated for 10 days at 20°C. Samples were taken from a random selection of the resulting colonies and were placed into 200 µL of lysogeny broth and incubated for 48 hours. This inoculated lysogeny broth was then centrifuged, and the supernatant was discarded. The pellet of bacterial cells was then placed into a lysogeny broth containing 17% glycerol and was frozen at -80°C. 16S PCR was performed for these samples, and the resulting rRNA was sequenced using Sanger sequencing, and taxonomy was assigned by comparing these sequences with existing databases (see (García et al., 2018)). The specific methodology is as follows: A master-mix solution was created and consisted of 7.2 μl of DNA-free water, 0.4 μl of 27 forward primer, 0.4 μl of 1492 reverse primer and 10 μl of Taq polymerase, per sample. A template solution was prepared by adding 2 μl of the sample diluted 100 x in DNA free water, to 18 μl of master-mix solution. These samples were then placed in a thermal cycler (Applied Biosystems Veriti Thermal Cycler). The cycling protocol consisted of 1 cycle at 94°C for 4 minutes, 35 cycles at 94, 48 and 72°C for 1 minute, 30s, and 2 minutes, respectively, and finally, 1 cycle at 72°C for 8 minutes. The final product of the PCR was cleaned using Exonuclease I and Antarctic Phosphatase. Sanger sequencing was conducted on high-quality samples using the 27F, 1492R primers (Core Genomic Facility, University of Sheffield). Geneious (version 6.1.8, (Kearse et al., 2012) was used to trim the sequences, removing the bp from the 5' end and trimming the 3' end to a maximum length of 1000bp. Sequences longer than 974bp were then aligned to the Silva.Bacteria.Fasta database using Mothur version 1.39.5 (Schloss et al., 2009) and the RDP trainset 9 032012 was used as a reference database to assign taxonomy to the isolates. A total of 36 different taxa were identified, and from these five were chosen for use in this study. These five taxa were chosen as they differed in their thermal traits, and in their colony morphologies, the latter requirement being to facilitate visual identification when cultures consisting of more than one taxon were grown on agar. The five taxa chosen for this study and the Genbank accession number were: Pseudomonas spp. (w_Ic161A, MZ506751), Serratia spp. (h_Ic174, MZ506746), Aeromonas spp. (n_Ic167, MZ506748), Herbaspirillum spp. (j_Ic165, MZ506747), and Janthinobacterium spp. (h_Ic161A, MZ506745). Evolution of bacteria in monocultures and communities Bacterial communities comprising all five taxa, as well as monocultures of each taxon, were evolved at temperatures ranging from 15°C - 42°C for ~110 generations. We used 110 generations as past research suggests this would be ample time for the communities to reach an equilibrium. In a previous community evolution experiment conducted at 20°C, it was observed that the majority of communities reached stability after approximately 50 generations (García et al., 2023). Earlier investigations passaging natural communities indicated that around 60 generations were needed for most communities to achieve population equilibria in various instances (Goldford et al., 2018). In the current study, we collected ‘initial’ growth rate data following 2-3 transfers (~10 generations) to allow communities to acclimate to the temperature (mainly to avoid acute stress responses). We then subsequently gathered data at approximately 100 generations later (~110 generations total). The time to reach this number of generations was calculated for the colder evolution temperature groups, to ensure all treatment groups reached a minimum of ~100 generations. The specific methodology follows: An initial stock solution for each taxon was created from a single colony clone, using lysogeny broth, which was then incubated overnight at 20°C. These were then standardised to a common optical density with R2 media, and then a community stock solution was constructed by combining 100 µL of each of the five taxa. 40 µL of stock solution was then used to inoculate 5000 µL of R2 media. Three replicates of these inoculated media were then incubated at each of the following temperatures: 15°C, 20°C, 23°C, 27°C, 30°C, 33°C, 37°C, and 42°C. This was then repeated, but instead of inoculation with community stock solution, monoculture stock solution was used, ensuring the same starting biomass of each taxon for each treatment group. Every 48 hours during incubation, 40 µL was removed from each culture and was used to inoculate a fresh 5000 µL of R2 media, to prevent resource limitation from occurring. This was done 18 times, equating to ~110 generations. At the end of the experiment, serial dilutions of the resulting cultures were then grown on agar, and samples of individual taxa were isolated and frozen at -80°C in 17% glycerol. For the community cultures, individual taxa were identified based on colony morphology. Growth assay of evolved isolates From every evolution experiment a single clone was isolated. These isolates, as well as the original ancestral samples, were then grown at temperatures ranging from 15°C - 42°C. Maximum growth rates (r(h-1)) were calculated at each temperature. The specific methodology is as follows: Every evolved isolate, as well as the original ancestral taxa, were thawed in R2 growth media at 20°C for 24 hours. These cultures were then diluted with more R2 media until all cultures were at an optical density (OD600) of 0.05, measured using a Themo ScientificTM Multiskan Sky Microplate Spectrophotometer, at a wavelength of 600nm. 200 µL of each culture was then transferred into 96 well plates. Control ‘blank’ wells were filled with only R2 medium. The plate was then incubated at 15 °C until carrying capacity was reached (~54 hours), and OD600 measurements were taken every ~4 hours. This process was repeated for all isolates, at incubation temperatures of 20°C, 23°C, 27°C, 30°C, 33°C, 37°C, and 42°C. Due to handling time, there was some variation in measurement intervals, but in all analyses exact intervals calculated from timestamps are used. The mean OD600 value for blank wells in a plate was subtracted from all OD600 measurements. Fitting growth curves All modelling of growth curves was conducted in R version 4.0.2 (R Core Team, 2021), using the nlsLoop package (Padfield, 2016/2020). The maximum growth rates (r (h−1), hereafter simply r, or maximum growth rate) for each incubated culture (see section 2.3) were calculated by fitting the logistic growth equation to the OD600 measurements, using non-linear least squares regression. Nt = K/(1 +〖Ae〗^(-rt) ); A = (K - N_0)/N0 (1) In equation 1, Nt is the biomass at time, t, K is the carrying capacity, N0 is the starting biomass and r is the exponential population growth rate (r(h−1)). For some cultures, after reaching carrying capacity there would be a slow decline in cell density. As the above model cannot estimate this decline, these datapoints demonstrating a post-asymptote decline were removed. Statistical analysis All statistical analyses were conducted in R version 4.0.2 (R Core Team, 2021), and all plots were created using ggplot2, and other tidyverse (Wickham et al., 2019) packages were used for data handling. For all analyses, monoculture-evolved isolates were only included if their respective isolate had survived to the end of the community evolution experiment. For example, at an evolution temperature of 33°C, only Aeromonas spp. and Herbaspirillum spp. survived to the end of the evolution experiment, therefore only Aeromonas spp. and Herbaspirillum spp. isolates that were evolved at 33°C in monoculture were included in the analysis, and Pseudomonas spp., Serratia spp., and Janthinobacterium spp. isolates that were evolved at 33°C in monoculture were excluded from the analysis. This prevented a survivorship bias from confounding the results. For the within-taxon analysis, separate generalised additive mixed-effect models (GAMMs) were fit for each taxon, using the function uGamm from the R package MuMIn (Bartoń, 2022). The initial full model included maximum growth rate as the response variable, and the following fixed effects and smoothing terms: evolution temperature, treatment (monoculture, ancestral, or community evolved), an interaction between evolution temperature and treatment, and a smoothing term on growth temperature, which was allowed to vary by treatment. A single random effect encompassing taxon and biological replicate (at the level of each evolved lineage) was included in all models. All possible sub-models were created and compared by their sample-corrected Akaike information criterion (AICc) using the function AICc from the package MuMIn, although models without a smoothing term on growth temperature were not considered. The threshold for determining a significant difference between models was when ΔAICc was >2. Where there were one or more models falling within 2 ΔAICc of the lowest AICc model, the more minimal model was selected as the best model. The R package emmeans (Lenth, 2017/2023) was used to conduct post-hoc pairwise comparisons for model estimates across both evolution temperatures and treatment group. For the ‘all taxa combined’ analysis, model creation and selection was conducted in the same way as the within taxon analysis. Fixed and random effects were the same as in the within taxon analysis. However, due to issues with rank deficiency when trying to incorporate ancestral data into this model, a separate model for the effect of growth temperature on growth rate for the ancestor was constructed. This negates pairwise significance testing of differences between ancestral and evolved lineages but does allow both visual comparison of TPCs by superimposing the ancestral model predictions onto a figure displaying the predictions for the monoculture and community evolved isolates (Figure 1). For the community survival analysis, binomial Generalised linear models were fit using the base R glm function, and fixed effects taxon identify and evolution temperature. Fixed effects were determined to be significant if the ΔAICc of their removal was >2. Microbes are key drivers of global biogeochemical cycles and their functional roles are heavily dependent on temperature. Large population sizes and rapid turnover rates mean that the predominant response of microbes to environmental warming is likely to be evolutionary, yet our understanding of evolutionary responses to temperature change in microbial systems is rudimentary. Natural microbial communities are diverse assemblages of interacting taxa. However, most studies investigating the evolutionary response of bacteria to temperature change are focused on monocultures. Here we utilise high throughput experimental evolution of bacteria in both monoculture and community contexts along a thermal gradient to determine how interspecific interactions influence the thermal adaptation of community members. We found that community-evolved isolates tended towards higher maximum growth rates across the temperature gradient compared to their monoculture-evolved counterparts. We also saw little evidence of systematic evolutionary change in the shapes of bacterial thermal tolerance curves along the thermal gradient. However, the effect of community background and selection temperature on the evolution of thermal tolerance curves was variable and highly taxon-specific – with some taxa exhibiting pronounced changes in thermal tolerance, while others were less impacted. We also found that temperature acted as a strong environmental filter, resulting in the local extinction of taxa along the thermal gradient, implying that temperature-driven ecological change was a key factor shaping the community background upon which evolutionary selection can operate. These findings offer novel insight into how the community background impacts thermal adaptation.
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