The data comprise measurements of abundance of invasive species, illuminance, air temperature, air humidity, specific leaf area, species richness, species diversity, vegetation cover, biomass, decomposition rate and, carbon efflux that was made during filed campaigns to assess the impact of an invasive grass (Urochloa decumbens) on a tropical savanna (Cerrado) in two nature reserve areas in Brazil. The two experimental areas were located within natural reserves in Southeast and Central Brazil; Estação Ecológica de Itirapina in the municipalities of Brotas and Itirapina in São Paulo State, and Parque Nacional de Brasília in the Distrito Federal. Data were collected during multiple field excursions between March and September 2019. Full details about this dataset can be found at https://doi.org/10.5285/abcabfe2-612c-4cab-b626-641002fc442e

[THIS DATASET HAS BEEN WITHDRAWN]. The data comprise measurements of the impact of two prescribed annual burnings on the ecological properties of an invasion gradient by Urochloa decumbens in a tropical savanna (Cerrado). The ecological properties measured before (2019) and after the experiment (2021) were the abundance of invasive species, illuminance, air temperature, air humidity, specific leaf area, species richness, species diversity, vegetation cover, biomass, decomposition rate and carbon efflux. The two experimental areas were located within two reserves in Southeast and Central Brazil: Estação Ecológica de Itirapina in the municipalities of Brotas and Itirapina in São Paulo State, and Parque Nacional de Brasília in the Distrito Federal. Data were collected during multiple field excursions between March 2019 and September 2021. Full details about this dataset can be found at https://doi.org/10.5285/8d3d252c-8fa2-493e-ba8e-ebae9fee4f99

The data provided here are model iteration objects and rasters needed to run the multi-scale modelling process and predict how the host condition affects probability of Hendra virus shedding. The dataset contains predictions of three proxies for host conditions (including food shortage, rehabilitation admissions and formation of a new roost) across eastern Australia in 2008-2019. The Roost Species Distribution Model (SDM) has predictions of roost suitability. These are monthly, spatially explicit predictions of particular conditions or probability of roost occupations. The model objects are iterations of models that were initially trained on data held in figshare (https://figshare.com/s/ddb5a1584609b20f6596). These data objects are linked with code provided at https://github.com/hanlab-ecol/BatOneHealth to be able to run the models and analyses. This includes comparisons of virus predictions of seven different multiscale model structures to observed Hendra virus shedding in field surveys. The purpose of this study was to determine if quantifying and incorporating host condition into epidemiological models improves predictions of virus shedding in space and time. The data objects relate to the 1,000 iterations run of this process to better able to account for uncertainty. Full details about this dataset can be found at https://doi.org/10.5285/93bb37c6-ef86-4386-945d-c1a3d1e2683c

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. Full details about this application can be found at https://doi.org/10.5285/030b49f4-9e1f-46e9-ad98-157d8668a517

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