The R code "carbon_stock_calculations.R" estimates aboveground carbon stocks for 49 plots in 14 fragmented forest sites and 4 continuous forest sites in Sabah, Malaysian Borneo, using the vegetation dataset ‘Vegetation and habitat data for fragmented and continuous forest sites in Sabah, Malaysian Borneo, 2017’. The 14 fragmented sites were all in Roundtable on Sustainable Palm Oil-certified oil palm plantations, and are hereafter termed 'conservation set-asides'. The code also estimates the aboveground carbon stocks of oil palm plantations for comparison. The R code "analyses_and_figures.R" runs analyses and makes figures of aboveground carbon stocks and associated plant diversity for these sites, as presented in Fleiss et al. (2020) This R code was created in order to investigate the following: (1) to establish the value of conservation set-asides for increasing oil palm plantation aboveground carbon stocks; (2) to establish whether set-asides with high aboveground carbon stocks can have co-benefits for plant diversity; (3) to compare the carbon stocks and vegetation structure of conservation set-asides with that of continuous forest, including assessing tree regeneration potential by examining variation in seedling density; (4) to examine potential drivers of variation in aboveground carbon stocks of conservation set-asides (topography, degree of fragmentation, and soil parameters); (5) to scale-up the estimates of the aboveground carbon stocks of conservation set-asides, in order to predict average carbon stocks of oil palm plantations with and without set-asides, and for varying coverage of set-asides across the plantation. Full details about this application can be found at https://doi.org/10.5285/9ff5cdca-b504-4994-8b07-5912ee6aff47

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. Full details about this application can be found at https://doi.org/10.5285/adbf6d29-ee7b-4dd1-9730-11d2308d526c

[This application is embargoed until March 31, 2022]. This dataset contains a water resource systems model for the Sutlej-Beas system in western Himalayas. It includes all the files required to run the model for the historical period 1989-2008 and climate change scenarios for the middle (2032-2050) and end of the century (2082-2100) considering the uncertainty associated to different Representative Concentration Pathways and Global Climate Models. The WEAP model was built within the “Sustaining Himalayan Water Resources in a Changing Climate” (SusHi-Wat) project (NE/N015541/1), funded by the UK Natural Environment Research Council and the Indian Ministry of Earth Sciences through the Newton-Bhabha Fund. Full details about this application can be found at https://doi.org/10.5285/715db0b2-1d63-4842-ab80-f0f33b39e5e0

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'. Full details about this application can be found at https://doi.org/10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9

This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data. To use this application, the user will need to input two separate csv files: one for state variables and the other for observations. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this application can be found at https://doi.org/10.5285/1de712d3-081e-4b44-b880-b6a1ebf9fcd8

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. To use this application, the user will upload a csv file containing the two time series to be compared. This 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). Full details about this application can be found at https://doi.org/10.5285/49d04d55-90a7-4106-b8fe-2e75aba228e4

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).

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). Full details about this application can be found at https://doi.org/10.5285/d4f92cd8-43e8-49e4-8f9e-efcc0e3b2478

[THIS APPLICATION HAS BEEN WITHDRAWN]. 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'. Full details about this application can be found at https://doi.org/10.5285/c4d0393e-ff0a-47da-84e0-09ca9182e6cb

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. Although 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. This 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. Full details about this application can be found at https://doi.org/10.5285/98ec8073-7ebd-44e5-aca4-ebcdefa9d044