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 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. Full details about this application can be found at https://doi.org/10.5285/ef45a7de-035a-486c-9cef-ee7f78a8efcf

This model code for object oriented data analysis of surface motion time series in peatland landscapes provides the procedure to assess peatland condition using object oriented data analysis. The model code assesses peatland condition according to which cluster each surface motion time series is assigned, based on key measures capturing differences between the time series. It can be run on any machine with R. Full details about this application can be found at https://doi.org/10.5285/dbdb9f19-c039-4a73-b590-e1acc7f79df4

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. Full details about this application can be found at https://doi.org/10.5285/1b9a9a48-0402-4839-9e8a-3d8c4bc35154

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 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 January 1, 2025]. A collection of python and bash scripts to implement, train and deploy a generative adversarial network for population genetic inferences. The networks have been tuned to be deployed to genomic data from Anopheles mosquitoes. However, the general framework can be applied to other species. It requires the input data to be in Variant Call Format (VCF) format and the simulations need to be in msprime format. Full details about this application can be found at https://doi.org/10.5285/3ae572f6-4862-47ae-b4a0-4b9c496b5b54

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. Full details about this application can be found at https://doi.org/10.5285/7aab999e-cef9-41c2-8400-63f10af798ec

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

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 Full details about this application can be found at https://doi.org/10.5285/df57b002-2a42-4a7d-854f-870dd867618c