rOceans: an R Package for integrating trends in biodiversity, human stressors and protected areas for the conservation of the global oceans

Proposed by Ignasi Montero-Serra personal website

Background

The current availability of large ecological datasets and the fast development of computational tools to have fostered the testing and development of theory at broad spatial and temporal scales. The interest on macroecology and biodiversity conservation has rapidly increased in the era of big data, but tools are still required allow integration of massive amounts of data accessible to a growing community of scientists using R for their data analyses.

Marine ecosystems are facing unprecedented impacts from multiple local and global-scales stressors. However, a lack of skills required to handle biodiversity data in R prevents many ecologists and conservation biologists from addressing relevant questions that advance global marine conservation efforts. Indeed, because users need to access datasets, assess data quality, standardize and analyze data from multiple sources and complex structures, only highly experienced users with a strong R programming background can attempt this. OceansCon will be an R Package that provides a unifying platform to integrate spatial trends in marine biodiversity, human stressors, climate change predictions and protected area networks at local, regional and global scales to address issues in macroecology, conservation biology, environmental policy and science communication.

Related work

Many applications on biogeography and macroecology have been developed in the last years (see packages modestR, dismo, biomod2, letsR). Yet, there is still no unifying platform that allows the integration and easy exploration of the three most crucial patterns for marine ecosystems: marine biodiversity, climate change and multiple human stressors, and marine protection coverage. MarineBioCon will be specifically suited for the analysis of conservation strategies for marine ecosystems and will add new functionalities to toolboxes of recent, primarily macroecological packages in the R-environment. It will differ from other packages by its focus on large-scale assessments of spatial conservation priorities under climatic change.

Details of your coding project

The main goal of the project is to develop an R package that serves a platform to integrate multiple spatial datasets on marine biodiversity, human-driven stressors and protected areas coverage to advance in marine conservation research and make complex global and regional-scale macroecological analysis easy for a wide range of users. The package will include updates from some previous functions to fit marine spatial datasets as well as develop novel functions to obtain, standardize, quality check, correct biases and analyze trends of multiple spatial data on sources, including species’ ranges and conservation status as provided by the OBIS, GBIF or the IUCN Red List online data base (see packages that provide GBIF data or UICN data Chamberlain et al. 2014 and Cardoso 2017), as well as ocean layers on multiple stressors, (Halpern et al. 2008, 2015), including present and future predictions of climate change (for example, Bio-Oracle Assis et al. 2018) and current conservation efforts (i. e. marine protected areas worldwide coverage). A secondary goal is to implement a user-friendly Shiny R app that will easily allow for multiple global analyses under different climatic and conservation scenarios to provide visualizations on expected outcomes for non-specialist users such as policy-makers, journalists, and students. Finally, an example will be provided to illustrate the package’s capability for conducting macroecological analyses under a single computer platform, potentially helping researchers to save time and effort in this endeavor.

Expected impact

Despite the exponential growth in global patterns of biodiversity and potential threats from climate change and other stressors, currently, no open source code is available that allows for easy explorations of the potential implications of different conservation strategies in marine ecosystems. Hence, conservation scientists, NGOs and policy-makers, professors and students wanting to explore this crucial aspect will have a reliable, comprehensible R package as well as a friendly-user R Shiny app. This will lower the entry barrier, facilitating advancements in the field.

Mentors

1. Katherine A. Kaplan, University of California at Davis. Dr. Kaplan is an expert on R coding and spatial analyses to assess marine protected areas performance and population modeling. She has developed the R package “MPApopulationmodels” and an R Shiny App (https://github.com/katherinekaplan/MPApopulationmodels).

2. Eneko Aspillaga, is a postdoc at the University of Barcelona and is an expert on R coding for spatial analyses in R. He has developed an R package that will be soon released providing novel methods to compute fish home ranges in three-dimensions (https://github.com/aspillaga).

3. Vijay Barve, is a postdoc at the Florida Museum of Natural History. He is an expert on bioinformatics and biodiversity and has contributed to several R packages including rgbif and bdvist (see http://vijaybarve.net/r-proj.html)

4. Narayani Barve is a postdoc at the University of Florida. She is an expert on spatial analysis of biodiversity and Niche Envelope Models (https://eeb.ku.edu/narayani-barve)

Tests

• Easy: something that any useR should be able to do, e.g. download some existing package listed in the Related Work, and run it on some example data. • Medium: something a bit more complicated. You can encourage students to write a script or some functions that show their R coding abilities. • Hard: Can the student write a package with Rd files, tests, and vignettes? If your package interfaces with non-R code, can the student write in that other language? Solutions of tests

Easy: Convex Hull

Medium & Hard: R Function for Multiple Spcies Range Calculations