{"numFound":27,"page":1,"rows":20,"url":"http://catalogue.ceh.ac.uk/eidc/documents?facet=recordType%7CModel+code","nextPage":"http://catalogue.ceh.ac.uk/eidc/documents?page=2&facet=recordType%7CModel+code","results":[{"publicationDate":"2022-08-31T00:00:00.000+00:00","authorAffiliation":["University of Cambridge","University of Exeter"],"authorGivenName":["R.A.","F.J.","M.A"],"authorFamilyName":["Johnstone","Thompson","Cant"],"authorFullName":["Johnstone, R.A.","Thompson, F.J.","Cant, M.A."],"authorOrcid":["https://orcid.org/0000-0001-7581-2204","https://orcid.org/0000-0002-1530-3077"],"catalogue":"eidc","description":"This is a theoretical model of leadership in warfare by exploitative individuals who reap the benefits of conflict while avoiding the costs. In this model we extend the classic hawk-dove model to consider pairwise interactions between groups in which a randomly chosen leader decides whether the group will collectively adopt aggressive or peaceful tactics. We allow for unequal sharing of fitness payoffs among group members such that the leader can obtain either a larger share of the benefits, or pay a reduced share of costs, from fighting compared to their followers. Our model shows that leadership of this kind can explain the evolution of severe collective violence in certain animal societies.","documentType":"GEMINI_DOCUMENT","identifier":"7aab999e-cef9-41c2-8400-63f10af798ec","incomingCitationCount":0,"givenName":["R.A.","F.J.","M.A","F. J."],"familyName":["Johnstone","Thompson","Cant"],"fullName":["Johnstone, R.A.","Thompson, F.J.","Cant, M.A."],"keyword":["Collective violence","Intergroup conflict","Social evolution","Game theory","Aggression"],"keywordsOther":["Collective violence","Intergroup conflict","Social evolution","Game theory","Aggression"],"licence":"Open government licence","lineage":"Our model extends the classic hawk-dove model to consider pairwise encounters between groups of size n. In each encounter, an individuals is randomly selected as leader and the rest of the group assigned as followers. The leader decides whether the group collectively adopts either aggressive (hawk) or peaceful (dove) tactics, via their disproportionate influence on group behaviour. Total fitness payoffs to groups are identical to that of individuals in the classic model, except that we also allow for unequal sharing. Each follower’s share of the benefit obtained from fighting is reduced compared to that of their leader, while the leader’s share of any costs incurred is reduced compared to that of a follower. Our model predicts that extreme levels of intergroup aggression will evolve when a subset of group members can initiate conflicts that involve the whole group and when initiators gain a disproportionate benefit (or suffer lower costs) than others. It suggests that destructive warfare can result from the decoupling of leaders from the costs that they incite.","orcid":["https://orcid.org/0000-0001-7581-2204","https://orcid.org/0000-0002-1530-3077"],"organisation":["University of Cambridge","University of Exeter","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/7aab999e-cef9-41c2-8400-63f10af798ec","10.5285/7aab999e-cef9-41c2-8400-63f10af798ec"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["University of Exeter"],"ror":["https://ror.org/04xw4m193"],"state":"published","title":"Theoretical model of exploitative leadership in collective violence  in animal societies","version":1.0,"view":["public","crimar"],"shortenedDescription":"This is a theoretical model of leadership in warfare by exploitative individuals who reap the benefits of conflict while avoiding the costs. In this model we extend the classic hawk-dove model to consider pairwise interactions between groups in which a randomly chosen..."},{"publicationDate":"2024-01-11T00:00:00.000+00:00","authorAffiliation":["Charles University","University of St Andrews","Instituto de Investigaciones de la Amazonía Peruana","University of Edinburgh","University of Leeds","Servicio Nacional Forestal y de Fauna Silvestre","University of Liverpool","Center for International Forestry Research (CIFOR)","Karlsruhe Institute for Technology","Sam Houston State University Natural History Museum","Unaffiliated","University of Leicester","San Diego Zoo"],"authorGivenName":["A.","E.H.","J.","E.T.","C.","T.R.","L.E.S.","C.J.","G.C.","N.","E.C.","D.","R.","F.C.","G.","K.","J.E.","J.P.","O.","P.","K.H.","M.","C.E.","I.T."],"authorFamilyName":["Hastie","Honorio Coronado","Reyna Huaymacari","Mitchard","Åkesson","Baker","Cole","Cordova Oroche","Dargie","Dávila","De Grandi","del Aguila-Pasquel","Del Castillo Torres","De la Cruz Paiva","Draper","Flores","Grandez Ríos","Hergoualc’h","Householder","Janovec","Lähteenoja","Reyna","Rodríguez-Veiga","Roucoux","Tobler","Wheeler","Williams","Lawson"],"authorFullName":["Hastie, A.","Honorio Coronado, E.H.","Reyna Huaymacari, J.","Mitchard, E.T.","Åkesson, C.","Baker, T.R.","Cole, L.E.S.","Cordova Oroche, C.J.","Dargie, G.C.","Dávila, N.","De Grandi, E.C.","del Aguila-Pasquel, J.","Del Castillo Torres, D.","De la Cruz Paiva, R.","Draper, F.C.","Flores, G.","Grandez Ríos, J.","Hergoualc’h, K.","Householder, J.E.","Janovec, J.P.","Lähteenoja, O.","Reyna, D.","Rodríguez-Veiga, P.","Roucoux, K.H.","Tobler, M.","Wheeler, C.E.","Williams, M.","Lawson, I.T."],"authorOrcid":["https://orcid.org/0000-0003-2098-3510","https://orcid.org/0000-0002-3251-1679","https://orcid.org/0000-0002-0692-3186","https://orcid.org/0000-0002-3547-2425"],"authorRor":["https://ror.org/024d6js02","https://ror.org/02wn5qz54","https://ror.org/010ywy128","https://ror.org/01nrxwf90","https://ror.org/024mrxd33","https://ror.org/04xs57h96","https://ror.org/01rt0fh70","https://ror.org/04t3en479","https://ror.org/00yh3cz06","https://ror.org/04h699437","https://ror.org/04q1yyt92"],"catalogue":"eidc","description":"Two scripts for classifying remotely sensed data used to produce maps of peatland distribution and predicted peat thickness, using random forest classification and regression. Written in JavaScript for use with Google Earth Engine.\n\nThese are versions of the scripts used in Hastie et al. (2022), https://doi.org/10.1038/s41561-022-00923-4. Users should also cite Rodríguez-Veiga et al. (2020), https://doi.org/10.3390/rs12152380 .","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/R000751/1"],"identifier":"e337de58-df5e-4412-8aef-28875870f965","incomingCitationCount":0,"givenName":["A.","E.H.","J.","E.T.","C.","T.R.","L.E.S.","C.J.","G.C.","N.","E.C.","D.","R.","F.C.","G.","K.","J.E.","J.P.","O.","P.","K.H.","M.","C.E.","I.T."],"familyName":["Hastie","Honorio Coronado","Reyna Huaymacari","Mitchard","Åkesson","Baker","Cole","Cordova Oroche","Dargie","Dávila","De Grandi","del Aguila-Pasquel","Del Castillo Torres","De la Cruz Paiva","Draper","Flores","Grandez Ríos","Hergoualc’h","Householder","Janovec","Lähteenoja","Reyna","Rodríguez-Veiga","Roucoux","Tobler","Wheeler","Williams","Lawson"],"fullName":["Hastie, A.","Honorio Coronado, E.H.","Reyna Huaymacari, J.","Mitchard, E.T.","Åkesson, C.","Baker, T.R.","Cole, L.E.S.","Cordova Oroche, C.J.","Dargie, G.C.","Dávila, N.","De Grandi, E.C.","del Aguila-Pasquel, J.","Del Castillo Torres, D.","De la Cruz Paiva, R.","Draper, F.C.","Flores, G.","Grandez Ríos, J.","Hergoualc’h, K.","Householder, J.E.","Janovec, J.P.","Lähteenoja, O.","Reyna, D.","Rodríguez-Veiga, P.","Roucoux, K.H.","Tobler, M.","Wheeler, C.E.","Williams, M.","Lawson, I.T."],"keyword":["Modelling","peat","Peru","mapping","Random Forest","Regression","remote sensing","model"],"keywordsTheme":["Modelling"],"keywordsOther":["peat","Peru","mapping","Random Forest","Regression","remote sensing","model"],"licence":"Open government licence","lineage":"The scripts were as part of the NERC-funded project “Carbon Storage in Amazonian Peatlands: Distribution and Dynamics” (grant ref. NE/R000751/1).\nTo run the model code the user needs to first have a Google Earth Engine account. They should also familiarise themselves with the basics of running GEE code editor. GEE uses JavaScript.\n","locations":["POLYGON((-77.78 -13.65, -77.78 -0.04, -68.65 -0.04, -68.65 -13.65, -77.78 -13.65))"],"orcid":["https://orcid.org/0000-0003-2098-3510","https://orcid.org/0000-0002-3251-1679","https://orcid.org/0000-0002-0692-3186","https://orcid.org/0000-0002-3547-2425"],"organisation":["Charles University","University of St Andrews","Instituto de Investigaciones de la Amazonía Peruana","University of Edinburgh","University of Leeds","Servicio Nacional Forestal y de Fauna Silvestre","University of Liverpool","Center for International Forestry Research (CIFOR)","Karlsruhe Institute for Technology","Sam Houston State University Natural History Museum","Unaffiliated","University of Leicester","San Diego Zoo","NERC EDS Environmental Information Data Centre","University of St Andrews ","University of Edinburgh "],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/e337de58-df5e-4412-8aef-28875870f965","10.5285/e337de58-df5e-4412-8aef-28875870f965"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["University of St Andrews ","University of Edinburgh "],"ror":["https://ror.org/024d6js02","https://ror.org/02wn5qz54","https://ror.org/010ywy128","https://ror.org/01nrxwf90","https://ror.org/024mrxd33","https://ror.org/04xs57h96","https://ror.org/01rt0fh70","https://ror.org/04t3en479","https://ror.org/00yh3cz06","https://ror.org/04h699437","https://ror.org/04q1yyt92","https://ror.org/04xw4m193"],"state":"published","supplementalName":["Hastie, A. et al. (2022). Risks to carbon storage from land-use change revealed by peat thickness maps of Peru. Nature Geoscience 15: 369-374. ","Rodríguez-Veiga, P. et al. (2020).  Carbon stocks and fluxes in Kenyan forests and wooded grasslands derived from Earth observation and model–data fusion. Remote Sensing 12: 2380"],"title":"Model code of peatland distribution and predicted peat thickness in lowland Amazonian Peru","topic":["0/Modelling/"],"version":1.0,"view":["public","margla"],"shortenedDescription":"Two scripts for classifying remotely sensed data used to produce maps of peatland distribution and predicted peat thickness, using random forest classification and regression. Written in JavaScript for use with Google Earth Engine.\n\nThese are versions of the scripts..."},{"publicationDate":"2008-09-30T00:00:00.000+00:00","authorAffiliation":["University of Oxford"],"authorGivenName":["S."],"authorFamilyName":["Whatmore"],"authorFullName":["Whatmore, S."],"authorRor":["https://ror.org/052gg0110"],"catalogue":"eidc","description":"This dataset consists of computer code transcripts for two proprietary flood risk models from a study as part of the NERC Rural Economy and Land Use (RELU) programme. This project was conceived in order to address the public controversies generated by the risk management strategies and forecasting technologies associated with diffuse environmental problems such as flooding and pollution. Environmental issues play an ever-increasing role in all of our daily lives. However, controversies surrounding many of these issues, and confusion surrounding the way in which they are reported, mean that sectors of the public risk becoming increasingly disengaged. To try to reverse this trend and regain public trust and engagement, this project aimed to develop a new approach to interdisciplinary environmental science, involving non-scientists throughout the process. Examining the relationship between science and policy, and in particular how to engage the public with scientific research findings, a major diffuse environmental management issue was chosen as a focus - flooding. As part of this approach, non-scientists were recruited alongside the investigators in forming Competency Groups - an experiment in democratising science. The Competency Groups were composed of researchers and laypeople for whom flooding is a matter of particular concern. The groups worked together to share different perspectives - on why flooding is a problem, on the role of science in addressing the problem, and on new ways of doing science together. We aimed to achieve four substantive contributions to knowledge: 1. To analyse how the knowledge claims and modelling technologies of hydrological science are developed and put into practice by policy makers and commercial organisations (such as insurance companies) in flood risk management. 2. To develop an integrated model for forecasting the in-river and floodplain effects of rural land management practices. 3. To experiment with a new approach to public engagement in the production of interdisciplinary environmental science, involving the use of Competency Groups. 4. To evaluate this new approach to doing public science differently and to identify lessons learnt that can be exported beyond this particular project to other fields of knowledge controversy. This dataset consists of computer code transcripts for two proprietary flood risk models. Flood risk and modelling interview transcripts from this study are available at the UK Data Archive under study number 6620 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).","documentType":"GEMINI_DOCUMENT","funder":["RELU"],"grant":["RES-227-25-0018"],"identifier":"0a385f48-9ae8-44be-af02-39fbebac1bf5","incomingCitationCount":0,"givenName":["S.","Stuart","Gillian"],"familyName":["Whatmore","Lane","Willis"],"fullName":["Whatmore, S.","Lane, S.","Willis, G."],"keyword":["Rural Economy and Land Use Programme","Understanding Environmental Knowledge Controversies: the Case for Flood Risk Management","Malton","North yorkshire","Pickering","Ryedale (district)","Academic personnel","Environmental engineering","Environmental management","Environmental policy","Floods","Land drainage","Modelling","Public opinion","Rural planning","Science policy","Specialists","Specialization","2007-2010"],"keywordsProject":["Rural Economy and Land Use Programme","Understanding Environmental Knowledge Controversies: the Case for Flood Risk Management"],"keywordsOther":["Malton","North yorkshire","Pickering","Ryedale (district)","Academic personnel","Environmental engineering","Environmental management","Environmental policy","Floods","Land drainage","Modelling","Public opinion","Rural planning","Science policy","Specialists","Specialization","2007-2010"],"licence":"Non-Open government licence","lineage":"Research funded by Economic and Social Research Council, Natural Environment Research Council and Biotechnology and Biological Sciences Research Council. Award Number: RES-224-26-0041 Development of two new models was undertaken with the basic aim of estimating the effects of rural land management measures upon downstream flood risk in two separate case studies; Pickering ('bunding' model) and Uckfield ('overflow' model). The focus of these modelling approaches was on harnessing a greater depth of the information available to the modeller than perhaps traditionally used. In addition, the model development process was steered by Competency Groups in each respective area. This collaborative process drew on three identifiable knowledge sources: 1. experiential, as held by both academics and local members of the CGs 2. theoretical and encoded to the ways in which we tend to see the world as Newtonian (e.g. flood water has to go somewhere; it must conserve mass) 3 data-based, in terms of the records of rainfall and water level that we had obtained and which were used to develop and to test assumptions The two models developed were specifically tailored to the regional flooding problem by the innovative development process.","locations":["POLYGON((-6.375 49.9, -6.375 55.82, 1.79 55.82, 1.79 49.9, -6.375 49.9))"],"organisation":["University of Oxford","Durham University","NERC EDS Environmental Information Data Centre","NERC Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["1414489256812","https://catalogue.ceh.ac.uk/id/0a385f48-9ae8-44be-af02-39fbebac1bf5"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["University of Oxford"],"ror":["https://ror.org/052gg0110","https://ror.org/01v29qb04","https://ror.org/04xw4m193"],"state":"published","supplementalName":["RELU Data Archive"],"title":"Flood model scripts - RELU Understanding environmental knowledge controversies","version":1.0,"view":["public","rstu"],"shortenedDescription":"This dataset consists of computer code transcripts for two proprietary flood risk models from a study as part of the NERC Rural Economy and Land Use (RELU) programme. This project was conceived in order to address the public controversies generated by the risk management..."},{"publicationDate":"2024-05-01T00:00:00.000+00:00","authorAffiliation":["Queen Mary University of London"],"authorGivenName":["J.C.D.","J.D.","A.G. "],"authorFamilyName":["Terry","O'Sullivan","Rossberg"],"authorFullName":["Terry, J.C.D.","O'Sullivan, J.D.","Rossberg, A.G."],"authorOrcid":["https://orcid.org/0000-0002-0626-9938","https://orcid.org/0000-0003-2386-6635"],"authorRor":["https://ror.org/026zzn846"],"catalogue":"eidc","description":"This resource contains the R code and core results of a study seeking to identify whether there are global patterns in whether larger or smaller bodied species are showing different population trends within communities. This used the global BioTIME database (including community time series from hundreds of studies of mostly covering the last 20-50 years) and several large trait databases to gain a very large sample size (12,956 assemblage time series from 144 studies, incorporating 2,109,593 observations of 10,286 species, of which 7,234 could be linked to at least one size trait). \n\nData resources in this deposit include matched trait values for each species, several population trends of each species, and community level correlations between population trends and body-size trait values. Additionally, html files describing the R markdown code to produce the data resources are included. This resource does not contain the raw population and trait data, which are openly available from various sources that are listed in the supporting documentation.\n\nThe matching between global databases required a large amount of initial cleaning and filtering steps. Although the data was subject to a number of checks, with tens of thousands of species, it was too large to check all alignments manually, and trait matches assume a single bodysize value for a species across its range. The original purpose was to generate a relative rank within a community, but caution is needed for more fine-grained analyses using this approach. ","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/T003510/1"],"identifier":"1f7687de-d68e-4349-b4d3-b7d3a127a7df","incomingCitationCount":0,"givenName":["J.C.D.","J.D.","A.G. "],"familyName":["Terry","O'Sullivan","Rossberg"],"fullName":["Terry, J.C.D.","O'Sullivan, J.D.","Rossberg, A.G."],"keyword":["Biodiversity","body size","community ecology"],"keywordsTheme":["Biodiversity"],"keywordsOther":["body size","community ecology"],"licence":"Open government licence","lineage":"To generate the assemblage time series, we downloaded all studies available in the ‘open' component of the BioTIME database of community time series from Zenodo. We identified studies as ‘multi-site' or ‘single-site' based on the number of coordinates in the BioTIME database. Single-site studies were considered as one combined assemblage, whilst widely dispersed ‘multi-site' studies were portioned into assemblages based on a global hexagonal grid of 96 km2 cells using the dggridR package in R. We retained records from assemblages with abundance or biomass data of at least 10 distinct species and at least 5 years between the first and last record.\nWe used four separate trait databases that include some measure of organism size, but we did not mix information between databases. For amniotes, an amniote life history database was downloaded. For plants, we used the TRY database. For fish, we downloaded a curated database of fish traits, which in turn is largely based on data from the FishBase database. It is focused on the North Atlantic and Pacific continental shelf, but this represents the majority of the relevant BioTIME studies. For marine species, we downloaded size data from the WoRMS database. \n\nData cleaning was performed in order to match up species names in the trait databases and BioTIME database.\n\nWe assessed each assemblage–trait combination where ≥40% and ≥5 of the species had data for that trait and >80% of year samples contained at least 5 species. We excluded transitory species within each assemblage by including only those species that were seen in over half of the year samples. Where this filtering left data from less than 1% of the cells in the original study, we removed the whole study. Where a study included both ‘abundance' and ‘biomass' data, we preferentially used the abundance data. Studies with only presence–absence data were not used.\nTo calculate the relative change in abundance of each species, we fitted the square-root transformed and scaled species totals as a function of year for each assemblage using ordinary least-squares regression models and calculate the slope β for each species.\n\nThe main response variable τ for each assemblage was then computed as Kendall's rank correlation coefficient between size trait values and the set of βs. Species with missing trait values were excluded from the calculation of τ. Where there were multiple assemblages per study, study-level τ was taken as a simple arithmetic mean of all assemblage-level τ values. We also test two alternative transformations of the population data.\nTo examine study-level determinates of τ within each size trait, for each study we calculated: (1) the mean total species richness of each assemblage over the time frame, (2) the mean assemblage-level trait data completeness, (3) the mean number of years from which there were data, (4) the mean span of years from which there were data, (5) the log10-transformed number of assemblages within the study (that is, the spatial extent), (6) the absolute latitude of the centre of the study and (7) the range of traits in the assemblage (log10(max) − log10(min)). We fitted a set of linear models to assess whether these factors could predict either τ or τ2.\n\nAll analysis used the R language, and scripts are included in the KnittedScripts folder. More information is provided in the supporting information.","orcid":["https://orcid.org/0000-0002-0626-9938","https://orcid.org/0000-0003-2386-6635"],"organisation":["Queen Mary University of London","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/1f7687de-d68e-4349-b4d3-b7d3a127a7df","10.5285/1f7687de-d68e-4349-b4d3-b7d3a127a7df"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["Queen Mary University of London"],"ror":["https://ror.org/026zzn846","https://ror.org/04xw4m193"],"state":"published","supplementalDescription":["Terry, J.C.D., O'Sullivan, J.D., & Rossberg, A.G.  (2022). No pervasive relationship between species size and local abundance trends. Nat Ecol Evol, 6, 140–144. ","Biotime - bodysize: archived files on Zenodo"],"title":"Code and synthetic data for analysis of relation between species sizes and local abundances trends","topic":["0/Biodiversity/"],"version":1.0,"view":["public","elsdhi"],"shortenedDescription":"This resource contains the R code and core results of a study seeking to identify whether there are global patterns in whether larger or smaller bodied species are showing different population trends within communities. This used the global BioTIME database (including..."},{"publicationDate":"2022-03-24T00:00:00.000+00:00","authorAffiliation":["UK Centre for Ecology & Hydrology"],"authorGivenName":["C.-H.M."],"authorFamilyName":["Tso"],"authorFullName":["Tso, C.-.M."],"authorOrcid":["https://orcid.org/0000-0002-2415-0826"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"This model code provides an example to demonstrate a new application of the 'learnr' R package to help authors to make elements of their research analysis more readily reproducible to users. It turns a R Markdown document to guided, editable, isolated, executable, and resettable code sandboxes where users can readily experiment with altering the codes exposed","documentType":"GEMINI_DOCUMENT","identifier":"df57b002-2a42-4a7d-854f-870dd867618c","incomingCitationCount":2,"givenName":["Michael","C.-H.M."],"familyName":["Tso"],"fullName":["Tso, M.","Tso, C.-.M."],"keyword":["Modelling","R shiny","Rainfall chemistry","Web app","Reproducible research"],"keywordsTheme":["Modelling"],"keywordsOther":["R shiny","Rainfall chemistry","Web app","Reproducible research"],"licence":"Open government licence","lineage":"The model code depends on the 'learnr' R package.","orcid":["https://orcid.org/0000-0002-2415-0826"],"organisation":["UK Centre for Ecology & Hydrology","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/df57b002-2a42-4a7d-854f-870dd867618c","10.5285/df57b002-2a42-4a7d-854f-870dd867618c"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"state":"published","supplementalDescription":["Lamb Weather Types (LWTs)","Reading daily Atlantic multidecadal oscillation (AMO) data"],"title":"R Shiny demonstrator application for advancing reproducible research","topic":["0/Modelling/"],"version":1.0,"view":["public","clwe"],"shortenedDescription":"This model code provides an example to demonstrate a new application of the 'learnr' R package to help authors to make elements of their research analysis more readily reproducible to users. It turns a R Markdown document to guided, editable, isolated, executable, and..."},{"publicationDate":"2024-06-17T00:00:00.000+00:00","authorAffiliation":["Newcastle University"],"authorGivenName":["D."],"authorFamilyName":["Pritchard"],"authorFullName":["Pritchard, D."],"authorOrcid":["https://orcid.org/0000-0002-9215-7210"],"authorRor":["https://ror.org/01kj2bm70"],"catalogue":"eidc","description":"This dataset contains the stochastic Rainfall and Weather GENerator (RWGEN) model and observational historical climate inputs for UK applications. The model simulates one or more stochastic realisations of any length for rainfall (mm), temperature (°C) and potential evapotranspiration (mm) at hourly or longer timesteps.\n\nRWGEN can be used for single site or spatial simulations of historical/reference or perturbed/future climate. The model version in this dataset is a snapshot of the RWGEN Github repository, which contains new releases and developments: https://github.com/rwgen1/rwgen.\n\nThe observational climate inputs consist of historical hourly rainfall and daily weather time series for selected UK Met Office (UKMO) station locations. The historical time series are derived from the UKMO Met Office Integrated Data Archive System (MIDAS) Open datasets for the period 1853 to 2020. These time series can be used to train the RWGEN model for UK locations or catchments. Note that the data coverage is not consistent throughout the 1853-2020 period, with lower data availability prior to the mid-twentieth century. A user may also choose to use alternative data for model input.\n\n","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/W007037/1"],"identifier":"44c577d3-665f-40de-adce-74ecad7b304a","incomingCitationCount":0,"givenName":["D.","David"],"familyName":["Pritchard"],"fullName":["Pritchard, D."],"keyword":["Climate and climate change","Modelling","RWGEN","stochastic","model","rainfall","precipitation","weather","temperature","potential evapotranspiration","python"],"keywordsTheme":["Climate and climate change","Modelling"],"keywordsOther":["RWGEN","stochastic","model","rainfall","precipitation","weather","temperature","potential evapotranspiration","python"],"licence":"Open government licence","lineage":"The model was developed within the UK Research and Innovation (UKRI)/UKMO UK Climate Resilience Programme as part of project NE/W007037/1. The model is provided as a Python package, with installation instructions, examples and documentation. The rainfall component of the model is based on the Neyman-Scott Rectangular Pulse process. The weather and potential evapotranspiration component is based on a regression model. It has been tested against other software (RainSim V3) and against observations (although like all models it has limitations and prospective users should ensure that the model is fit-for-purpose in their applications).\n\nThe historical climate time series were obtained by extracting data from the quality-controlled version (qc1) of the UKMO MIDAS-Open data collection. Further spot checks were undertaken to confirm that the extraction, processing and reformatting appear to have worked satisfactorily.\n\n","locations":["POLYGON((-8.648 49.864, -8.648 60.861, 1.768 60.861, 1.768 49.864, -8.648 49.864))"],"orcid":["https://orcid.org/0000-0002-9215-7210"],"organisation":["Newcastle University","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/44c577d3-665f-40de-adce-74ecad7b304a","10.5285/44c577d3-665f-40de-adce-74ecad7b304a"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["Newcastle University"],"ror":["https://ror.org/01kj2bm70","https://ror.org/04xw4m193"],"state":"published","title":"Code and UK observation-based historical climate input data for the Rainfall and Weather GENerator (RWGEN) stochastic model","topic":["0/Climate and climate change/","0/Modelling/"],"version":1.0,"view":["public","jenroe"],"shortenedDescription":"This dataset contains the stochastic Rainfall and Weather GENerator (RWGEN) model and observational historical climate inputs for UK applications. The model simulates one or more stochastic realisations of any length for rainfall (mm), temperature (°C) and potential..."},{"publicationDate":"2024-05-01T00:00:00.000+00:00","authorAffiliation":["Imperial College London","University of Oxford","Lawrence Berkeley National Laboratory"],"authorGivenName":["K.","A.","L.","R.","G."],"authorFamilyName":["Chrapkiewicz","Lipp","Barron","Barnes","Roberts"],"authorFullName":["Chrapkiewicz, K.","Lipp, A.","Barron, L.","Barnes, R.","Roberts, G."],"authorOrcid":["https://orcid.org/0000-0001-6949-9741","https://orcid.org/0000-0003-2130-8576","https://orcid.org/0000-0001-5986-3853","https://orcid.org/0000-0002-6487-8117"],"authorRor":["https://ror.org/041kmwe10","https://ror.org/052gg0110","https://ror.org/02jbv0t02"],"catalogue":"eidc","description":"This resource contains python code to carry out source apportionment of chemicals in river water. Convex optimisation is used to efficiently apportion tracer and pollutant sources from point concentration observations. A minimum working example of the python code and the open-source framework used for the analysis is also provided. Chemical data collected in 2019-2020 along the River Wandle (Thames, UK) were used to test the code and example results are also included. \n\nThe code assumes conservative mixing and hence is likely to be best suited to assessing chronic sources of pollution (i.e., sources that are temporally invariant, or as near as, during the sampling campaign).","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/X010805/1"],"identifier":"8ae3c419-611d-4bee-ad41-1c9081e79975","incomingCitationCount":0,"givenName":["K.","A.","L.","R.","G.","Gareth"],"familyName":["Chrapkiewicz","Lipp","Barron","Barnes","Roberts"],"fullName":["Chrapkiewicz, K.","Lipp, A.","Barron, L.","Barnes, R.","Roberts, G."],"keyword":["Thames","Pollution","Hydrology","source apportionment","chemicals","environmental contaminant","pollutant","waterway","river","stream"],"keywordsPlace":["Thames"],"keywordsTheme":["Pollution","Hydrology"],"keywordsOther":["source apportionment","chemicals","environmental contaminant","pollutant","waterway","river","stream"],"licence":"Open government licence","lineage":"The code was developed as part of a six-month project funded via a NERC Exploring the Frontiers grant. K. Chrapkiewicz was the principal code developer with A. Lipp. We made use of existing models developed by co-investigators A. Lipp and R. Barnes. This work built upon the optimisation techniques developed by A. Lipp, G. Roberts and colleagues. The data inverted in this study were generated L Barron and colleagues (Egli et al., 2023). \n\nTo run the code please see the readme files that accompany this repository. Briefly, once the code is downloaded and installed a minimum working example can be run using > python examples/mwe.py. Examples of data and their associated formats are included with this repository. The raw chemical data are also included. Examples of outputs in the form of figures (.png format) are also included. With regards to quality assurance, a synthetic data inversion was conducted and an Analysis of Variance and an assessment of the uncertainties were also carried out. Further details are provided in the supporting documentation and cited papers.   ","orcid":["https://orcid.org/0000-0001-6949-9741","https://orcid.org/0000-0003-2130-8576","https://orcid.org/0000-0001-5986-3853","https://orcid.org/0000-0002-6487-8117"],"organisation":["Imperial College London","University of Oxford","Lawrence Berkeley National Laboratory","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/8ae3c419-611d-4bee-ad41-1c9081e79975","10.5285/8ae3c419-611d-4bee-ad41-1c9081e79975"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["Imperial College London"],"ror":["https://ror.org/041kmwe10","https://ror.org/052gg0110","https://ror.org/02jbv0t02","https://ror.org/04xw4m193"],"state":"published","supplementalName":["A One-Health environmental risk assessment of contaminants of emerging concern in London’s waterways throughout the SARS-CoV-2 pandemic"],"title":"Python code to identify sources of chemical pollutants in waterways","topic":["0/Pollution/","0/Hydrology/"],"version":1.0,"view":["public","jenroe"],"shortenedDescription":"This resource contains python code to carry out source apportionment of chemicals in river water. Convex optimisation is used to efficiently apportion tracer and pollutant sources from point concentration observations. A minimum working example of the python code and..."},{"publicationDate":"2022-04-25T00:00:00.000+00:00","authorAffiliation":["Cranfield University"],"authorGivenName":["D."],"authorFamilyName":["Lefebvre"],"authorFullName":["Lefebvre, D."],"authorOrcid":["https://orcid.org/0000-0003-0281-8781"],"catalogue":"eidc","description":"This model combines the carbon footprint of a reforestation project in the Peruvian amazon with a biomass model of the growing trees and a soil carbon model. The script aims at estimating the net carbon capture potential of a growing forest located in the Peruvian amazon and on degraded sandy soil only. It compares the emissions associated with setting up a reforestation plot (from seed reception to seedling transplant) with the expected carbon capture by the growing trees and increased soil carbon stock at a desired timescale. The model includes the production, use, and degradation of biochar. This model was produced within the Soils-R-GGREAT project, funded by NERC. ","documentType":"GEMINI_DOCUMENT","identifier":"ef45a7de-035a-486c-9cef-ee7f78a8efcf","incomingCitationCount":0,"givenName":["D."],"familyName":["Lefebvre"],"fullName":["Lefebvre, D."],"keyword":["Modelling","RothC","Life Cycle Assessment","Monte Carlo Analysis","modelling"],"keywordsTheme":["Modelling"],"keywordsOther":["RothC","Life Cycle Assessment","Monte Carlo Analysis","modelling"],"licence":"Open government licence","lineage":"LCA activity and soil and characteristics were provided by the ‘Centro de Innovacion Cientifica Amazonica’ (CINCIA), a non-profit organization located in the Peruvian amazon. Emission factors were collected in the EcoInvent 3.7 and U.S. Life Cycle Inventory (USLCI) databases in SimaPro software v8.3. Soil carbon stock changes over time was modelled using the Rohamsted Carbon (RothC) model found inside the SoilR package in R. The above-ground carbon model uses the moist forest, non-plantation model reported in Busch et al. (2019) (https://doi.org/10.1038/s41558-019-0485-x), which simulates tropical secondary forests above-ground growth based on data from 829 tropical forest stands. The below-ground carbon model has an evolving root to shoot ratio over time, specific for tropical forests from Waring and Powers (2017). Meteorological data were extracted from the FAO, ClimWat database. All the data necessary to the model are embedded in the model script. ","locations":["POLYGON((-82.16 -21.483, -82.16 1.9, -64.23 1.9, -64.23 -21.483, -82.16 -21.483))"],"orcid":["https://orcid.org/0000-0003-0281-8781"],"organisation":["Cranfield University","NERC EDS Environmental Information Data Centre","Natural Environment Research Council"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/ef45a7de-035a-486c-9cef-ee7f78a8efcf","10.5285/ef45a7de-035a-486c-9cef-ee7f78a8efcf"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["Natural Environment Research Council"],"ror":["https://ror.org/04xw4m193"],"state":"published","supplementalName":["EcoInvent 3.7 database ","SoilR package ","Busch et al. (2019) ","ClimWat database "],"title":"Life cycle assessment carbon model to determine the net greenhouse gas balance of reforesting one hectare of goldmining-degraded soil in the Peruvian amazon","topic":["0/Modelling/"],"version":1.0,"view":["public","crimar"],"shortenedDescription":"This model combines the carbon footprint of a reforestation project in the Peruvian amazon with a biomass model of the growing trees and a soil carbon model. The script aims at estimating the net carbon capture potential of a growing forest located in the Peruvian amazon..."},{"publicationDate":"2023-12-14T00:00:00.000+00:00","authorAffiliation":["University of Oxford"],"authorGivenName":["T.W."],"authorFamilyName":["Scott"],"authorFullName":["Scott, T.W."],"authorOrcid":["https://orcid.org/0000-0002-5941-3905"],"catalogue":"eidc","description":"This code: (1) Generates equilibrium genotype frequency values. This is provided in the \"Script_to_generate_equilibrium_genotype_frequencies.m\" script. (2) Tests our relatedness expression with simulated data. This is provided in the \"Comparison_of_simulated_and_expected_relatedness.m\" script. ","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"identifier":"07af78a7-4022-43b1-b85f-b31caf596362","incomingCitationCount":0,"givenName":["T.W."],"familyName":["Scott"],"fullName":["Scott, T.W."],"keyword":["Social evolution","Evolutionary theory"],"keywordsOther":["Social evolution","Evolutionary theory"],"licence":"Open government licence","lineage":"This came into existence through the process of writing code for MATLAB, to numerically solve equations in the paper: \"Is cooperation favoured by horizontal gene transfer?\".","orcid":["https://orcid.org/0000-0002-5941-3905"],"organisation":["University of Oxford","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/07af78a7-4022-43b1-b85f-b31caf596362","10.5285/07af78a7-4022-43b1-b85f-b31caf596362"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["University of Oxford"],"ror":["https://ror.org/04xw4m193"],"state":"published","title":"Code for generating equilibrium genotype frequency values, and checking the analytical relatedness expression with simulation","version":1.0,"view":["public","matnic"],"shortenedDescription":"This code: (1) Generates equilibrium genotype frequency values. This is provided in the \"Script_to_generate_equilibrium_genotype_frequencies.m\" script. (2) Tests our relatedness expression with simulated data. This is provided in the \"Comparison_of_simulated_and_expected_relatedness.m\"..."},{"publicationDate":"2024-10-09T00:00:00.000+00:00","authorAffiliation":["University of Exeter"],"authorGivenName":["D.J."],"authorFamilyName":["Baker"],"authorFullName":["Baker, D.J."],"authorOrcid":["https://orcid.org/0000-0002-0466-8222"],"catalogue":"eidc","description":"The speciesRecordTools R package contains functions for examining the distribution of species records, understanding sampling trends and potential biases, and building correlative presence-background species distribution models for prediction of the distribution of species across the landscape. The package is built to work with the Environmental Record Centre for Cornwall and the Isles of Scilly's (ERCCIS) opportunistic species records.","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/V007726/1"],"identifier":"030b49f4-9e1f-46e9-ad98-157d8668a517","incomingCitationCount":0,"givenName":["D.J."],"familyName":["Baker"],"fullName":["Baker, D.J."],"keyword":["Biodiversity","Modelling","opportunistic records","spatial sampling bias","presence-background models","species distribution"],"keywordsTheme":["Biodiversity","Modelling"],"keywordsOther":["opportunistic records","spatial sampling bias","presence-background models","species distribution"],"licence":"Open government licence","lineage":"Code supplied here can be used to analyse data held by ERCCIS (Cornwall Wildlife Trust). The data is not included in the package but can be requested https://erccis.org.uk/requesting-data. The tools provided in this package were designed in collaboration with ERCCIS to overcome some of the limitations associated with these data when used to map the distribution of species. In particular, the methods attempt to address sparse sampling and spatial sampling bias associated with sampling for many species. Distribution data is required to support planning decisions and local nature recovery strategies within the region. ","locations":["POLYGON((-6.757 49.504, -6.757 51.052, -4.043 51.052, -4.043 49.504, -6.757 49.504))"],"orcid":["https://orcid.org/0000-0002-0466-8222"],"organisation":["University of Exeter","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/030b49f4-9e1f-46e9-ad98-157d8668a517","10.5285/030b49f4-9e1f-46e9-ad98-157d8668a517"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["University of Exeter"],"ror":["https://ror.org/04xw4m193"],"state":"published","title":"Tools for modelling species distributions using data from the Environmental Record Centre for Cornwall and the Isles of Scilly (ERCCIS)","topic":["0/Biodiversity/","0/Modelling/"],"version":1.0,"view":["public","tinzha"],"shortenedDescription":"The speciesRecordTools R package contains functions for examining the distribution of species records, understanding sampling trends and potential biases, and building correlative presence-background species distribution models for prediction of the distribution of..."},{"publicationDate":"2023-09-05T00:00:00.000+00:00","authorAffiliation":["UK Centre for Ecology & Hydrology","University of Manchester"],"authorGivenName":["E.","D.","C.-H.M."],"authorFamilyName":["Magee","Huxley","Tso"],"authorFullName":["Magee, E.","Huxley, D.","Tso, C.-.M."],"authorOrcid":["https://orcid.org/0000-0002-2415-0826"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"This resource comprises two Jupyter notebooks that includes the model code in python to train a random forest model to predict long-term seasonal nitrate and orthophosphate concentrations at each river reach in Great Britain. The input features considered are catchment descriptors and land cover matched to the reaches. The training data is obtained from the Environmental Agency Water Quality Archive, 2010-2020. This method provides an effective way to map water quality data from stations to the river network.\n\nA live demo of a web application to visualize the dataset can be viewed at https://moisture-wqmlviewer.datalabs.ceh.ac.uk/wqml_viewer","documentType":"GEMINI_DOCUMENT","identifier":"ba208b6c-6f1a-43b1-867d-bc1adaff6445","incomingCitationCount":0,"givenName":["Michael","E.","D.","C.-H.M."],"familyName":["Tso","Magee","Huxley"],"fullName":["Tso, M.","Magee, E.","Huxley, D.","Tso, C.-.M."],"keyword":["UK-SCAPE","Modelling","Water quality","Hydrology","Jupyter notebook","Python","random forest model","web application","orthophosphate","River network","Machine learning","river","nitrate","modelling"],"keywordsProject":["UK-SCAPE"],"keywordsTheme":["Modelling","Water quality","Hydrology"],"keywordsOther":["Jupyter notebook","Python","random forest model","web application","orthophosphate","River network","Machine learning","river","nitrate","modelling"],"licence":"Open government licence","lineage":"The machine learning model is trained on the following datasets:\n* Land cover map\n* NRFA Catchment descriptors\n* UKCEH digital river network of Great Britain (1:50,000)\n* Environmental Agency water quality archive\nMore information is provided in the supporting documentation accompanying this resource.\n\nPlease note that only a subset of the data (HA 72 Wyre and Lune) is provided for demonstration. This is because the underlying river network is separately licensed (https://catalogue.ceh.ac.uk/documents/7d5e42b6-7729-46c8-99e9-f9e4efddde1d). ","locations":["POLYGON((-8.648 49.864, -8.648 60.861, 1.768 60.861, 1.768 49.864, -8.648 49.864))"],"orcid":["https://orcid.org/0000-0002-2415-0826"],"organisation":["UK Centre for Ecology & Hydrology","University of Manchester","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/ba208b6c-6f1a-43b1-867d-bc1adaff6445","10.5285/ba208b6c-6f1a-43b1-867d-bc1adaff6445"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"state":"published","supplementalName":["A live demo of the web application to visualize the model","Environment Agency water quality data archive"],"title":"Random forest model to predict long-term seasonal nitrate and orthophosphate concentrations in British river reaches","topic":["0/Modelling/","0/Water quality/","0/Hydrology/"],"version":1.0,"view":["public","clwe"],"shortenedDescription":"This resource comprises two Jupyter notebooks that includes the model code in python to train a random forest model to predict long-term seasonal nitrate and orthophosphate concentrations at each river reach in Great Britain. The input features considered are catchment..."},{"publicationDate":"2019-12-17T00:00:00.000+00:00","authorAffiliation":["UK Centre for Ecology & Hydrology","University College London"],"authorGivenName":["C.L.R.","J.R."],"authorFamilyName":["Laize","Thompson"],"authorFullName":["Laize, C.L.R.","Thompson, J.R."],"authorOrcid":["https://orcid.org/0000-0002-7560-7769","https://orcid.org/0000-0002-8927-6462"],"authorRor":["https://ror.org/00pggkr55","https://ror.org/02jx3x895"],"catalogue":"eidc","description":"This application is an implementation of the Ecological Risk due to Flow Alteration (ERFA) method in R language. This method assesses the potential impact of flow change on river ecosystems.\n\nAlthough the code was developed with a geographical focus on southeast Asia (example datasets are provided for the Mekong River Basin), it can be applied for any location where baseline and scenario monthly river flow time series are available.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.","documentType":"GEMINI_DOCUMENT","identifier":"98ec8073-7ebd-44e5-aca4-ebcdefa9d044","incomingCitationCount":0,"givenName":["Cedric","C.L.R.","J.R."],"familyName":["Laize","Thompson"],"fullName":["Laize, C.","Laize, C.L.R.","Thompson, J.R."],"keyword":["Environmental risk","Modelling","Flow alteration","River ecosystem","Ecological risk","Ecohydrology","Hydroecology"],"keywordsTheme":["Environmental risk","Modelling"],"keywordsOther":["Flow alteration","River ecosystem","Ecological risk","Ecohydrology","Hydroecology"],"licence":"Non-Open government licence","lineage":"This application comprises a series of R scripts and also uses the Shiny R package. It was developed using R version 3.4.2 and Shiny version 1.0.5 and has been tested up to R version 3.6.1 and Shiny version 1.3.2 on both the Windows and MacOS operating systems. At a minimum it requires the installation of R but it can also run under R Studio.","orcid":["https://orcid.org/0000-0002-7560-7769","https://orcid.org/0000-0002-8927-6462"],"organisation":["UK Centre for Ecology & Hydrology","University College London","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/98ec8073-7ebd-44e5-aca4-ebcdefa9d044","10.5285/98ec8073-7ebd-44e5-aca4-ebcdefa9d044"],"resourceStatus":"Available","resourceType":"Application","ror":["https://ror.org/00pggkr55","https://ror.org/02jx3x895","https://ror.org/04xw4m193"],"state":"published","supplementalName":["TEFRIC Project web site"],"title":"R implementation of the Ecological Risk due to Flow Alteration (ERFA) method","topic":["0/Environmental risk/","0/Modelling/"],"version":1.0,"view":["public","nuri"],"shortenedDescription":"This application is an implementation of the Ecological Risk due to Flow Alteration (ERFA) method in R language. This method assesses the potential impact of flow change on river ecosystems.\n\nAlthough the code was developed with a geographical focus on southeast Asia..."},{"publicationDate":"2015-10-19T00:00:00.000+00:00","authorAffiliation":["Centre for Ecology & Hydrology"],"authorGivenName":["A.M.","P.A.","R.L.","N.P."],"authorFamilyName":["Keith","Henrys","Rowe","McNamara"],"authorFullName":["Keith, A.M.","Henrys, P.A.","Rowe, R.L.","McNamara, N.P."],"authorOrcid":["https://orcid.org/0000-0001-9619-1320","https://orcid.org/0000-0003-4758-1482","https://orcid.org/0000-0002-7554-821X","https://orcid.org/0000-0002-5143-5819"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"This is an application providing code for the non-parametric comparison of soil depth profiles, and testing for significant differences between soil depth profiles, using bootstrapped Loess (local) regressions (BLR). The BLR approach was developed to be able to compare and test for significant differences in potentially non-linear depth profiles of soil properties across land use transitions, which does not need to meet any parametric distribution assumptions, and is intended to be generally applicable regardless of specific contexts of land use and soil type. A small dataset is provided with the code to demonstrate the BLR approach and its outputs. The code was written using the R statistical programming language and provides two examples of the BLR approach. This application was created by the Centre for Ecology & Hydrology at Lancaster in 2015 during the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI).","documentType":"GEMINI_DOCUMENT","identifier":"d4f92cd8-43e8-49e4-8f9e-efcc0e3b2478","incomingCitationCount":1,"givenName":["Aidan","A.M.","P.A.","R.L.","N.P."],"familyName":["Keith","Henrys","Rowe","McNamara"],"fullName":["Keith, A.","Keith, A.M.","Henrys, P.A.","Rowe, R.L.","McNamara, N.P."],"keyword":["Soil","Modelling","loess","soil","model","R code","local regression"],"keywordsTheme":["Soil","Modelling"],"keywordsOther":["loess","soil","model","R code","local regression"],"licence":"Open government licence","lineage":"The code was written using the R statistical programming language at CEH Lancaster, and provides two examples of the BLR approach. This application was created by the Centre for Ecology & Hydrology at Lancaster in 2015 during the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI).","orcid":["https://orcid.org/0000-0001-9619-1320","https://orcid.org/0000-0003-4758-1482","https://orcid.org/0000-0002-7554-821X","https://orcid.org/0000-0002-5143-5819"],"organisation":["UK Centre for Ecology & Hydrology","Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/d4f92cd8-43e8-49e4-8f9e-efcc0e3b2478","10.5285/d4f92cd8-43e8-49e4-8f9e-efcc0e3b2478"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"state":"published","title":"Bootstrapped local regression (LOESS) for soil depth profile comparison","topic":["0/Soil/","0/Modelling/"],"version":1.0,"view":["public","clamw"],"shortenedDescription":"This is an application providing code for the non-parametric comparison of soil depth profiles, and testing for significant differences between soil depth profiles, using bootstrapped Loess (local) regressions (BLR). The BLR approach was developed to be able to compare..."},{"publicationDate":"2024-04-18T00:00:00.000+00:00","authorAffiliation":["University of Exeter"],"authorGivenName":["D.J."],"authorFamilyName":["Baker"],"authorFullName":["Baker, D.J."],"catalogue":"eidc","description":"This package contains a number of functions required to predict spatial patterns of encounter rate, the probability of encountering the species on a survey visit under specified conditions, around the south west (Cornwall) coast.","documentType":"GEMINI_DOCUMENT","identifier":"1b9a9a48-0402-4839-9e8a-3d8c4bc35154","incomingCitationCount":0,"givenName":["D.J."],"familyName":["Baker"],"fullName":["Baker, D.J."],"keyword":["Seaquest","marine megafauna","marine mammal","species distribution","encounter rate","Cornwall","UK","species distributions","marine mammals"],"keywordsOther":["Seaquest","species distributions","encounter rate","marine megafauna","marine mammals"],"licence":"Open government licence","lineage":"Code supplied here can be used to analyse data collected by the Seaquest Southwest monitoring scheme organised by the Cornwall Wildlife Trust. The data is not included in the package but can be requested https://erccis.org.uk/requesting-data and the code here can be used to model encounter rates for Common Dolphin, Grey Seal, and Harbour Porpoise using a variety of methods, including calibrated Random Forest models. Data may also be adequate for modelling encounter rates for Bottlenose Dolphin and Basking Shark, although further investigation is required. The package contains all the code necessary to process the species and environmental data, run models and plot results.","locations":["POLYGON((-5.845 49.951, -5.845 51.076, -3.933 51.076, -3.933 49.951, -5.845 49.951))"],"organisation":["University of Exeter","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/1b9a9a48-0402-4839-9e8a-3d8c4bc35154","10.5285/1b9a9a48-0402-4839-9e8a-3d8c4bc35154"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["University of Exeter"],"ror":["https://ror.org/04xw4m193"],"state":"published","title":"Tools for modelling species distributions using marine mammal data from Seaquest Southwest recording scheme","version":1.0,"view":["public","tinzha"],"shortenedDescription":"This package contains a number of functions required to predict spatial patterns of encounter rate, the probability of encountering the species on a survey visit under specified conditions, around the south west (Cornwall) coast."},{"publicationDate":"2023-12-08T00:00:00.000+00:00","authorAffiliation":["Queen Mary University of London","Haverford College"],"authorGivenName":["C.S.","M.","S."],"authorFamilyName":["Pui","Fumagalli","Mathieson"],"authorFullName":["Pui, C.S.","Fumagalli, M.","Mathieson, S."],"authorOrcid":["https://orcid.org/0009-0008-4956-1358","https://orcid.org/0000-0002-4084-2953","https://orcid.org/0000-0002-0484-0838"],"authorRor":["https://ror.org/026zzn846","https://ror.org/04fnrxr62"],"catalogue":"eidc","description":"A collection of python and bash scripts to implement, train and deploy a generative adversarial network for population genetic inferences.\n\nThe networks have been tuned to be deployed to genomic data from Anopheles mosquitoes. However, the general framework can be applied to other species.\n\nIt requires the input data to be in Variant Call Format (VCF) format and the simulations need to be in msprime format.\n","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/X009637/1"],"identifier":"3ae572f6-4862-47ae-b4a0-4b9c496b5b54","incomingCitationCount":0,"givenName":["C.S.","M.","S."],"familyName":["Pui","Fumagalli","Mathieson"],"fullName":["Pui, C.S.","Fumagalli, M.","Mathieson, S."],"keyword":["generative adversarial network","population genetics","demographic inference"],"keywordsOther":["generative adversarial network","population genetics","demographic inference"],"licence":"Open government licence","lineage":"The methodology can be applied to up to two populations at the same time. The code uses the keras python package and the scripts have been tested via simulations. Inferences were tested using simulations with known output, and the power to infer the ground truth was recovered. Simulations can be performed using msprime or SLiM.","orcid":["https://orcid.org/0009-0008-4956-1358","https://orcid.org/0000-0002-4084-2953","https://orcid.org/0000-0002-0484-0838"],"organisation":["Queen Mary University of London","Haverford College","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/3ae572f6-4862-47ae-b4a0-4b9c496b5b54","10.5285/3ae572f6-4862-47ae-b4a0-4b9c496b5b54"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["Queen Mary University of London"],"ror":["https://ror.org/026zzn846","https://ror.org/04fnrxr62","https://ror.org/04xw4m193"],"state":"published","title":"Python code for building and training a generative adversarial network for demographic inferences from genomic data","version":1.0,"view":["public","jenroe"],"shortenedDescription":"A collection of python and bash scripts to implement, train and deploy a generative adversarial network for population genetic inferences.\n\nThe networks have been tuned to be deployed to genomic data from Anopheles mosquitoes. However, the general framework can be applied..."},{"publicationDate":"2021-03-01T00:00:00.000+00:00","authorAffiliation":["UK Centre for Ecology & Hydrology"],"authorGivenName":["M.J."],"authorFamilyName":["Hollaway"],"authorFullName":["Hollaway, M.J."],"authorOrcid":["https://orcid.org/0000-0003-0386-2696"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"This application is an implementation of a Fuzzy changepoint based approach to evaluate how well numerical models capture local scale temporal shifts in environmental time series. A changepoint in a time series represents a change in the statistical properties of the time series (either mean, variance or mean and variance in this case). These can often represent important local events of interest that numerical models should accurately capture. The application detects the locations of changepoints in two time series (typically one representing observations and one representing a model simulation) and estimates uncertainty on the changepoint locations using a bootstrap approach. The changepoint locations and associated confidence intervals are then converted to fuzzy numbers and fuzzy logic is used to evaluate how well the timing of any changepoints agree between the time series. The app returns individual similarity scores for each changepoint with higher scores representing a better performance of the numerical model at capturing local scale temporal changes seen in the observed record.\n\nTo use this application, the user will upload a csv file containing the two time series to be compared.\n\nThis work was supported by Engineering and Physical Sciences Research Council (EPSRC) Data Science for the Natural Environment (DSNE) project (EP/R01860X/1) and the Natural Environment Research Council (NERC) as part the UK-SCAPE programme (NE/R016429/1).","documentType":"GEMINI_DOCUMENT","funder":["Engineering and Physical Sciences Research Council","Natural Environment Research Council"],"grant":["EP/R01860X/1","NE/R016429/1"],"identifier":"49d04d55-90a7-4106-b8fe-2e75aba228e4","incomingCitationCount":1,"givenName":["Michael","M.J."],"familyName":["Hollaway"],"fullName":["Hollaway, M.","Hollaway, M.J."],"keyword":["UK Status, Change and Projections of the Environment ","UK-SCAPE","Changepoints","Fuzzy logic","Data science","Uncertainty","Evaluation framework","Data analytics"],"keywordsProject":["UK Status, Change and Projections of the Environment ","UK-SCAPE"],"keywordsOther":["Changepoints","Fuzzy logic","Data science","Uncertainty","Evaluation framework","Data analytics"],"licence":"Open government licence","lineage":"This application comprises the source code of an R Shiny app (app.R) that implements the method described in https://doi.org/10.1016/j.envsoft.2021.104993. The application also provides access to the standalone functions used to execute the analysis (Fuzzy_CPT_functions.R). It can be run on any machine with R and the required packages installed. It has been tested up to R version 3.6.1 with more testing information and package dependencies found in Session_info.txt.","orcid":["https://orcid.org/0000-0003-0386-2696"],"organisation":["UK Centre for Ecology & Hydrology","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/49d04d55-90a7-4106-b8fe-2e75aba228e4","10.5285/49d04d55-90a7-4106-b8fe-2e75aba228e4"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"state":"published","title":"Fuzzy changepoint application to evaluate numerical model ability to capture important shifts in environmental time series","version":1.0,"view":["public","jualob"],"shortenedDescription":"This application is an implementation of a Fuzzy changepoint based approach to evaluate how well numerical models capture local scale temporal shifts in environmental time series. A changepoint in a time series represents a change in the statistical properties of the..."},{"publicationDate":"2024-09-30T00:00:00.000+00:00","authorAffiliation":["UK Centre for Ecology & Hydrology","British Trust for Ornithology"],"authorGivenName":["E.","R.A.","R.K.","S.A.","J.W.","I.","R.F.","J.M."],"authorFamilyName":["Gardner","Robinson","Broughton","Hinsley","Redhead","Woodward","Pywell","Bullock"],"authorFullName":["Gardner, E.","Robinson, R.A.","Broughton, R.K.","Hinsley, S.A.","Redhead, J.W.","Woodward, I.","Pywell, R.F.","Bullock, J.M."],"authorOrcid":["https://orcid.org/0000-0002-1669-7151","https://orcid.org/0000-0003-0504-9906","https://orcid.org/0000-0002-6838-9628","https://orcid.org/0000-0003-0820-2590"],"authorRor":["https://ror.org/00pggkr55","https://ror.org/03w54w620"],"catalogue":"eidc","description":"Bird4pop is a process-based model, written in R, that simulates the foraging, dispersal and population processes of nidicolous passerines. It accounts for both amount and configuration of habitat resources to predict spatially-explicit relative abundance and foraging rates, for a given input landscape. The input landcover information must be in raster format. The model can ingest a base landcover raster as well as edgecover rasters, which represent the locations of small habitat features that occupy only a fraction of a pixel. Model outputs are raster stacks with the same spatial extent and resolution as the input landcover raster.\n\nThe bird4pop model was co-developed by the UK Centre for Ecology & Hydrology and the British Trust for Ornithology. It has been parameterised for seven UK birds/bird groups (woodland specialists, woodland generalists, edge-nesting farmland birds, skylark (Alauda arvensis) – an open-nesting farmland bird, nuthatch (Sitta europaea) – a woodland specialist, robin (Erithacus rubecula) – a woodland generalist, yellowhammer (Emberiza citrinella) – an edge-nesting farmland bird). These parameter files (bird_guild_params.csv and bird_habitat_params.csv) are included. The model’s predictions have been validated against observations of these birds/bird groups collected across Great Britain through the BTO/RSPB/JNCC Breeding Bird Survey.\n\nData in bird_guild_params.csv includes movement ranges (breeding season foraging range, juvenile dispersal range in meters), population growth parameters (nest density per ha, mean number of chicks produced per year, growth parameters in arbitrary units) and survival parameters (yearly adult survival probability, yearly juvenile survival probability) for seven birds/bird groups (woodland specialists, woodland generalists, edge-nesting farmland birds, skylark – an open-nesting farmland bird, nuthatch – a woodland specialist, robin – a woodland generalist, yellowhammer – an edge-nesting farmland bird), used in bird4pop model.\n\nData in bird_habitat_params consists of scores representing the relative availability of suitable nest sites (on a scale from 0-1, where 0 = none and 1 = very high) and the relative availability of foraging resources during the breeding season (on a scale from 0-5, where 0 = none and 5 = very high) for four bird groups (woodland specialists, woodland generalists, edge-nesting farmland birds, skylark – an open-nesting farmland bird) and the 78 landcover classes, used in bird4pop model. Given scores are the mean values across n contributing experts. The data also include the number of contributing experts, the standard error on the mean score, and the alpha and beta values that describe a beta distribution consistent with the measured mean and variance for each score.\n\nThis model is intended to help support understanding of how birds might be using landscapes. No decisions should ever be made solely on the basis of this model’s predictions – on-the-ground ecological surveys and integration of local knowledge are always required before any landscape-level or site-level decisions are made, due to real-life bird activity levels potentially differing from predicted levels. Users are recommended to get in touch with the model developers to ensure they have the most up-to-date version of the code and to receive support on model use and interpretation.","documentType":"GEMINI_DOCUMENT","funder":["Natural Environment Research Council"],"grant":["NE/V007831/1"],"identifier":"985691ce-e66b-416a-bb85-fc9ad0eca6ed","incomingCitationCount":0,"givenName":["E.","R.A.","R.K.","S.A.","J.W.","I.","R.F.","J.M."],"familyName":["Gardner","Robinson","Broughton","Hinsley","Redhead","Woodward","Pywell","Bullock"],"fullName":["Gardner, E.","Robinson, R.A.","Broughton, R.K.","Hinsley, S.A.","Redhead, J.W.","Woodward, I.","Pywell, R.F.","Bullock, J.M."],"keyword":["Modelling","bird","Eurasian nuthatch","Sitta europaea","European robin","Erithacus rubecula","yellowhammer","Emberiza citrinella","nesting","animal habitat","land cover","model","United Kingdom","Animal behaviour","bird species","robin","nuthatch","skylark","foraging behavior","dispersal","animal population","breeding bird"],"keywordsInstrument":["model"],"keywordsPlace":["United Kingdom"],"keywordsTheme":["Animal behaviour"],"keywordsOther":["bird","bird species","yellowhammer","robin","nuthatch","skylark","foraging behavior","dispersal","animal population","breeding bird"],"licence":"Open government licence","lineage":"Bird4pop model code was developed from the poll4pop model (Gardner, et al., 2020; Yclough, 2020). It contains functions that are identical to those used in the poll4pop model, functions that are adapted from functions used in the poll4pop model, and new functions. \n\nData in bird_guild_params.csv is derived from literature data, with subsequent processing to convert units and calculate guild-level values. The growth parameters are set by selecting the values that best reproduced observed responses of woodland specialist birds to habitat fragmentation.\n\nData in bird_habitat_params is derived from an expert opinion questionnaire completed by four researchers. The mean scores and their standard error are weighted according to the self-reported certainty scores that experts gave alongside their habitat resource scores.","locations":["POLYGON((-8.648 49.864, -8.648 60.861, 1.768 60.861, 1.768 49.864, -8.648 49.864))"],"orcid":["https://orcid.org/0000-0002-1669-7151","https://orcid.org/0000-0003-0504-9906","https://orcid.org/0000-0002-6838-9628","https://orcid.org/0000-0003-0820-2590"],"organisation":["UK Centre for Ecology & Hydrology","British Trust for Ornithology","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/985691ce-e66b-416a-bb85-fc9ad0eca6ed","10.5285/985691ce-e66b-416a-bb85-fc9ad0eca6ed"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/03w54w620","https://ror.org/04xw4m193"],"state":"published","supplementalDescription":["Gardner E, Breeze TD, Clough Y, et al. Reliably predicting pollinator abundance: Challenges of calibrating process-based ecological models. Methods Ecol. Evol. 2020; 11: 1673–1689. https://doi.org/10.1111/2041-210X.13483","yclough. (2020). yclough/poll4pop: Gardner_etal_MEE (v1.1.1). Zenodo. https://doi.org/10.5281/zenodo.4001015"],"supplementalName":["Poll4Pop model","Poll4Pop model code"],"title":"Bird4pop model for the simulation of foraging, dispersal and population processes of UK passerine birds","topic":["0/Animal behaviour/"],"version":1.0,"view":["public","jashun"],"shortenedDescription":"Bird4pop is a process-based model, written in R, that simulates the foraging, dispersal and population processes of nidicolous passerines. It accounts for both amount and configuration of habitat resources to predict spatially-explicit relative abundance and foraging..."},{"publicationDate":"2020-07-22T00:00:00.000+00:00","authorAffiliation":["UK Centre for Ecology & Hydrology","University of Stirling"],"authorGivenName":["E.H.","D.S."],"authorFamilyName":["Waddell","Chapman"],"authorFullName":["Waddell, E.H.","Chapman, D.S."],"authorOrcid":["https://orcid.org/0000-0003-2555-6390","https://orcid.org/0000-0003-1836-4112"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"This code uses pathway modelling to look at correlations of exotic plant invasion in tropical rainforest remnants and continuous sites. Partial least squares path-modelling looks at correlations between latent variables that are informed by measured variables. The code examines the relative influence of landscape-level fragmentation, local forest disturbance, propagule pressure, soil characteristics and native community composition on invasion. The total native community is examined first. Then subsets of the native community are modelled separately, adult trees, tree saplings, tree seedlings and ground vegetation. The relationship between the native and exotic communities was tested in both directions. \n\n\n","documentType":"GEMINI_DOCUMENT","identifier":"adbf6d29-ee7b-4dd1-9730-11d2308d526c","incomingCitationCount":1,"givenName":["Emily","E.H.","D.S."],"familyName":["Waddell","Chapman"],"fullName":["Waddell, E.","Waddell, E.H.","Chapman, D.S."],"keyword":["Borneo","Agriculture","Biodiversity","Invasive species","Land cover","Land use","Soil","Agricultural landscapes","Roundtable on Sustainable Palm Oil","Sustainable agriculture","Forest fragmentation","Logging","Forest degradation","Non-native species","Oil palm","Conservation set-aside","tropical forest","flora (biology)","forest conservation","agriculture","environmental impact of agriculture","degradation"],"keywordsPlace":["Borneo"],"keywordsTheme":["Agriculture","Biodiversity","Invasive species","Land cover","Land use","Soil"],"keywordsOther":["Agricultural landscapes","Roundtable on Sustainable Palm Oil","Sustainable agriculture","Forest fragmentation","Logging","Forest degradation","Non-native species","Oil palm","Conservation set-aside","tropical forest","flora (biology)","forest conservation","agriculture","environmental impact of agriculture","degradation"],"licence":"Open government licence","lineage":"The fieldwork for this study took place in fragmented and continuous forest sites in Sabah, Malaysian Borneo from July to October 2017. Native and exotic plants were surveyed in a nested plot design to determine genera richness and abundance. Phylogenetic diversity was calculated for the total native community and separately for adult trees, tree saplings, tree seedlings and ground vegetation. Phylogenetic diversity, genera richness and abundance made up the 'Native community' latent variable for the pathway modelling. 'Invasion' latent variable was informed by the species richness and abundance of exotic plants found in each plot. 'Fragmentation' latent variable was informed by the number of years since first planting of oil palm, the amount of non-forest area and the forest edge to forest area ratio within a 2km radius buffer of each plot. 'Disturbance' latent variable was informed the number of large trees belonging to Dipterocarpaceae and the the average adult tree wood density per plot. \"Propagule pressure' latent variable was informed the number of exotic species and individuals found outside the forest over two 100m transect in the oil palm. 'Soil characteristics' latent variable was informed by soil pH and available Phosphorus in the topsoil of each plot. Connections between latent variables were informed by ecological theory and made up the full model. This included Fragmentation influencing Disturbance, Propagule pressure, Soil characteristics, Native community and Invasion.  Disturbance influenced Soil characteristics, Native community and Invasion.  Soil characteristics influenced the Native community and Invasion. Both the Native community and Propagule pressure influenced Invasion only. Invasion influenced just the Native community. Non-significant connections were removed in a step-wise manner before the final path-model was produced with significant correlations.\n","locations":["POLYGON((117.032 4.883, 117.032 6.381, 118.586 6.381, 118.586 4.883, 117.032 4.883))"],"orcid":["https://orcid.org/0000-0003-2555-6390","https://orcid.org/0000-0003-1836-4112"],"organisation":["UK Centre for Ecology & Hydrology","NERC EDS Environmental Information Data Centre","NERC Environmental Information Data Centre","University of Stirling"],"recordType":"Model code","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/adbf6d29-ee7b-4dd1-9730-11d2308d526c","10.5285/adbf6d29-ee7b-4dd1-9730-11d2308d526c"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"state":"published","title":"R code to reproduce analyses of exotic plant invasion in fragmented and continuous forest sites in Sabah, Malaysian Borneo, 2017","topic":["0/Agriculture/","0/Biodiversity/","0/Invasive species/","0/Land cover/","0/Land use/","0/Soil/"],"version":0.0,"view":["public","leav"],"shortenedDescription":"This code uses pathway modelling to look at correlations of exotic plant invasion in tropical rainforest remnants and continuous sites. Partial least squares path-modelling looks at correlations between latent variables that are informed by measured variables. The code..."},{"publicationDate":"2015-05-13T00:00:00.000+00:00","altTitle":["MultiMOVE Version 2.0.1","MultiMOVE: A Package of niche models for British vegetation v.2.0.1"],"authorAffiliation":["Centre for Ecology & Hydrology","Biomathematics & Statistics Scotland (BIOSS)"],"authorGivenName":["P.A.","A.","S.G.","S.M.","Z."],"authorFamilyName":["Henrys","Butler","Jarvis","Smart","Fang"],"authorFullName":["Henrys, P.A.","Butler, A.","Jarvis, S.G.","Smart, S.M.","Fang, Z."],"authorOrcid":["https://orcid.org/0000-0003-4758-1482","https://orcid.org/0000-0001-5382-5135","https://orcid.org/0000-0003-2750-7832"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 3.1.2 and depends on R packages 'leaps', 'earth', 'fields', 'mgcv', 'stringr', 'gsubfn', 'randomForest' and 'nnet'. ","documentType":"GEMINI_DOCUMENT","identifier":"94ae1a5a-2a28-4315-8d4b-35ae964fc3b9","incomingCitationCount":3,"givenName":["Pete","P.A.","A.","S.G.","S.M.","Z."],"familyName":["Henrys","Butler","Jarvis","Smart","Fang"],"fullName":["Henrys, P.","Henrys, P.A.","Butler, A.","Jarvis, S.G.","Smart, S.M.","Fang, Z."],"keyword":["Great Britain","Modelling","Vegetation science","ecology"],"keywordsPlace":["Great Britain"],"keywordsTheme":["Modelling"],"keywordsOther":["Vegetation science","ecology"],"licence":"Open government licence","lineage":"Modelling methods used: Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1990) Generalised Linear Models (GLMs) (McCullagh and Nelder, 1989) Random Forests (RF) (Liaw and Wiener, 2002) Multiple Adaptive Regression Splines (MARS) (Friedman, 1991). Neural Networks (NNet) (Venables and Ripley, 2002) Data sources used in the models: National Vegetation Classification (Rodwell 1991 et seq). Data collected at 4-250 m2 using targeted homogenous stands to census full range of plant community variation across Britain. Sampled from 1950-1980. The Key Habitats surveys of Britain (Hornung 1996). Data collected at 4m2 using random sampling within a random selection of 1-km squares stratified by three landscape GIS masks for upland, calcareous grassland and lowland heathland ecosystems. Sampled in 1992. Broadleaved woodland survey of Britain (Kirby et al. 2005; Corney et al. 2006). Data collected at 200 m2 using 16 plots located at random within each of 103 representative sites in Britain. Sampled in 1971. Countryside Survey of Great Britain (Smart et al. 2003). Data collected at 4-200 m2 using randomly located plots within a random selection of 569 1-km squares across Britain stratified by ITE Land Class. Data collected in 1998/1999. ","locations":["POLYGON((-9.227 49.837, -9.227 60.85, 2.6875999999999998 60.85, 2.6875999999999998 49.837, -9.227 49.837))"],"orcid":["https://orcid.org/0000-0003-4758-1482","https://orcid.org/0000-0001-5382-5135","https://orcid.org/0000-0003-2750-7832"],"organisation":["UK Centre for Ecology & Hydrology","Centre for Ecology & Hydrology","Biomathematics & Statistics Scotland (BIOSS)","NERC Environmental Information Data Centre","NERC EDS Environmental Information Data Centre"],"recordType":"Model code","resourceIdentifier":["1431527379383","10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9","https://catalogue.ceh.ac.uk/id/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9"],"resourceStatus":"Available","resourceType":"Application","rightsHolder":["UK Centre for Ecology & Hydrology"],"ror":["https://ror.org/00pggkr55","https://ror.org/04xw4m193"],"state":"published","title":"MultiMOVE Model: Ensemble niche modelling of British vegetation v2.0.1","topic":["0/Modelling/"],"version":1.0,"view":["public","clamw"],"shortenedDescription":"MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from..."},{"publicationDate":"2013-03-04T00:00:00.000+00:00","altTitle":["MultiMOVE Version 1.0"],"authorAffiliation":["Centre for Ecology & Hydrology","Biomathematics & Statistics Scotland (BIOSS)"],"authorGivenName":["P.","E.C.","C.D.","B.A.","S.M.","A."],"authorFamilyName":["Henrys","Rowe","Evans","Emmett","Smart","Butler"],"authorFullName":["Henrys, P.","Rowe, E.C.","Evans, C.D.","Emmett, B.A.","Smart, S.M.","Butler, A."],"authorOrcid":["https://orcid.org/0000-0003-4758-1482","https://orcid.org/0000-0002-2713-4389"],"authorRor":["https://ror.org/00pggkr55"],"catalogue":"eidc","description":"MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 2.10.1 and depends on R packages 'leaps', 'earth', 'fields' and 'mgcv'.","documentType":"GEMINI_DOCUMENT","identifier":"c4d0393e-ff0a-47da-84e0-09ca9182e6cb","incomingCitationCount":0,"givenName":["Peter","P.","E.C.","C.D.","B.A.","S.M.","A."],"familyName":["Henrys","Rowe","Evans","Emmett","Smart","Butler"],"fullName":["Henrys, P.","Rowe, E.C.","Evans, C.D.","Emmett, B.A.","Smart, S.M.","Butler, A."],"keyword":["Great Britain","Modelling","Vegetation science","ecology","vegetation","model"],"keywordsPlace":["Great Britain"],"keywordsTheme":["Modelling"],"keywordsOther":["Vegetation science","ecology","vegetation","model"],"licence":"Non-Open government licence","lineage":"Modelling methods used: Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1990) Generalised Linear Models (GLMs) (McCullagh and Nelder, 1989) Multiple Adaptive Regression Splines (MARS) (Friedman, 1991). 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