Our team has developed tools for quantifying human impacts on and benefits from marine systems, including conceptual frameworks, computational code, data products, and training materials. We believe stongly in open science, using open-source tools such as R and share our work on GitHub as much as possible. These tools are constantly being used, evaluated, improved and updated.
Ocean Health Index
The Ocean Health Index (OHI) is a framework to comprehensively and quantitatively evaluate ocean health. A healthy ocean is defined as one that sustainably delivers a range of benefits to people now and in the future. The framework was first published in Nature in 2012 and has two parts: a core part that is the same for all assessments and a tailored part that details what is assessed and how it will be represented for any specific assessment.
Assessments using the OHI framework can be conducted at different spatial scales and contexts. The OHI Science team uses the framework to conduct global assessments of ocean health annually. The first global assessment was published accompanying the OHI framework in Nature 2012, and certain methods were improved for the second annual assessment published in PLoS ONE in 2015. With each annual assessment, models incoporate the most updated input information and processing techniques are evaluated and improved.
Explore data and methods from OHI global assessments
Learn more about the science behind the global assessments by exploring and downloading methods, data, and code.
Please see publications about the OHI framework, assessments and supporting research, view presentations describing the framework, explore current and on-going assessments, and learn more about OHI goals.
The OHI Toolbox is used to calculate and visualize scores for assessments using the OHI framework. Like the framework, the Toolbox has two parts: the core engine behind calculating and visualizing scores, which is an R package called ohicore, and a tailored repository to organize, store, and share information and write goal model equations specific to the local context.
You can explore these repositories on GitHub. Core OHI functions are in ohicore. For examples of the tailored repositories, explore the global assessments, which includes the data used to calculate OHI scores, the goal models, and the final scores and figures. Repositories for independently-led assessments (called ‘OHI+’ assessments) are listed in Projects. Published assessments are listed in Publications.
The OHI+ page provides instruction for leading OHI+ assessments. If you are interested in beginning an OHI+ assessment, please contact us at firstname.lastname@example.org.
Cumulative Human Impacts
Cumulative Human Impacts is a framework to evaluate the comprehensive effect of human stressors on global oceans. The framework and analysis at the global scale was first published in Science in 2008 and a five-year comparison of the global analysis was published in Nature Communications in 2015. In addition to global scales, this framework has been used to assess cumulative impacts at smaller spatial scales.
The high resolution spatial data from the 2015 analysis are currently available from the [Knowledge Base Network KNB as .tif global rasters (Mollweide wgs84 coordinate reference system at ~ 1km resolution). Download a figure that illustrates the workflow for calculating Cumulative Human Impacts and the data that are available from KNB.
Use in OHI analyses
Cumulative Human Impacts “raw stressor data” was used to obtain pressure information for the OHI global assessments, and these data can be similarly processed for use at different spatial scales. For OHI+ assessments, these data can be extracted based on pre-defined Regions within the Assessment Area. Our Spatial analysis in R: Rasters tutorial is good resource for how to extract raster data by polygonal regions.
Some of the stressor data, and some methods, has been updated since the publication of the 2015 Cumulative Human Impacts data. The updated data were used in the global OHI 2015 analysis to generate pressures data. The data sources and methods used to process these data are available from GitHub for the following stressors:
- Ocean Acidification
- Sea Level Rise
- Sea Surface Temperature
- Ultraviolet Radiation
- Commercial Fishing
- Marine Plastics
Data Science References
These are technical data science references that are helpful for many uses, including OHI assessments.
Git and Github
R and RStudio
- Spatial analysis in R: Rasters
- Spatial analysis in R: Vectors
- Data visualization using ggmap - cheatsheet
- A primer on coordinate reference systems
- Dealing with color in R