BON in a Box provides a toolbox for countries and organizations to calculate variables and indicators using local and global data. It is not meant to provide ready-made global results.
The indicators and variables pipelines are contributed by the scientific community. A first set of pipelines come from Humboldt Institute in Colombia and QCBS in Quebec. There are more to come, and any further contributions are welcome.
Pipelines can run with global data, local data, or a mix of both. In order to run a pipeline with local data, you will need an instance of the web platform on a computer or server that you contol. See the github repository for more details of the setup.
Below are examples of reports that can come out of BON in a Box pipelines.
Authors: Maria Isabel Arce-Plata, Guillaume Larocque, Jaime Burbano-Girón, Maria Camila Díaz, Timothée Poisot.
This pipeline takes the outputs from the Species Habitat Score (SHS) pipeline and measures the Species Habitat Index for the species used as inputs, following the methodology proposed for Jetz et al. 2022 (https://cdn.mol.org/static/files/indicators/habitat/WCMC-species_habitat_index-15Feb2022.pdf). The index has two componentes an Area Score and a Connectivity score that are measured for the habitat of the required species (Species Habitat Score), and the average between those scores for the study area is the Species Habitat Index. It can also have weight values assigned according to the proportion of the area of the habitat of the species that is located in the study area.
View the pipeline results for Saguinus Oedipus (Colombia) or Martes americana and Vulpes vulpes (Québec)
Species distribution modeling with Maxent
Authors: Sarah Valentin, Guillaume Larocque, François Rousseu.
This pipeline generates predictions for a species distribution model using the
Maxent algorithm. Several background methods are possible, including randomly
distributed pseudo-absences throughout the region, background thickening
([Vollering et al. 2019](https://doi.org/10.1111/ecog.04503)) and
target-group background selection ([Phillips et al.
2009](https://doi.org/10.1890/07-2153.1)). Bias correction is achieved using
the target-group background selection method. A variance map to represent the
prediction uncertainty is generated through bootstraping.
View the pipeline results for Acer Saccharum
Species Distribution Modelling with Boosted Regression Trees
Authors: Michael Catchen, Timothée Poisot.
This pipeline performs species distribution modeling for a given species and
region from GBIF data with Boosted Regression Trees in the Julia programming
language.
View the pipeline results for Acer Saccharum
Sampling prioritization based on species distribution modeling
Authors: Michael D Catchen
This is a pipeline that contains two pipelines, first to compute and SDM using BRTs,
and second to take the SDM uncertainty and weigh it with accessibility, climate uniqueness,
and climate velocity to produce a sampling priority map for a selected species.
View the pipeline results for Acer Saccharum
If you wish to contribute your indicator or EBV code, please let us know at web@geobon.org.
The recommended method is to setup an instance of BON in a Box somewhere you can easily play with the script files, using the local or remote setup (see instructions on GitHub).
You can create a branch or fork from the github repository to save your work.
Make sure that the code is general, and will work when used with various parameters, such as in different regions around the globe.
Once the integration of the new scripts or pipelines are complete, open a pull request to the pipelines repository.
The pull request will be peer-reviewed before acceptation.