This tutorial was developed during OceanHackWeek2023 to provide a simple workflow to developing a marine Species Distribution Model (SDM) using R programming. To see the OHW23 project at the end of OHW23, go to the ohw23_proj branch or see the ohw23_proj release.
Species Distribution Modelling (SDM) also known as niche/environmental/ecological modelling uses an algorithm to predict the distribution of a species across space and time using environmental data. An understanding of the relationship between the species of interest and the physical environment they occupy will inform the selection of relevant environmental factors that will be included in the model.
Biotic information is also needed by SDMs and at the very least locations of individuals are needed. Abundance or densities can also be used as inputs, but are not compulsory. It is worth noting that absences, that is, the locations where individuals of a species are NOT present is just as important because it provides information about the environmental conditions where individuals are not usually sighted. Often absences are not recorded in biological data, but we can use background points (also known as pseudo-absences), which provide information about the full range of environmental conditions available for the species interest in our study area.
For a review of the performance of different SDM algorithms, see the following publications:
-
Valavi, Guillera-Arroita, Lahoz-Monfort, Elith (2021). Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. DOI: 10.1002/ecm.1486
-
Elith et al (2006). Novel methods improve prediction of species’ distributions from occurrence data. DOI: 10.1111/j.2006.0906-7590.04596.x
For a discussion on the impact of background data on SDMs see: Phillips et al (2009). Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. DOI: 10.1890/07-2153.1. For a background sample generation refer to work by Valavi.
Our area of interest is the Indian Ocean, where four species of sea turtles have been reported to occupy this area:
- Loggerhead, Caretta caretta
- Green, Chelonia mydas
- Olive Ridley, Lepidochelys olivacea
- Hawksbill, Eretmochelys imbricata
For this tutorial, we will focus on predicting the areas occupied by loggerhead sea turtles. To do this, we will use presence-only data from 2000 until present, which have been sourced from the Ocean Biodiversity Information System (OBIS) via the robis package.
This tutorial focuses on regions in the northern Indian Sea, specifically the western Arabian Sea, Persian Gulf, Gulf of Oman, Gulf of Aden and Red Sea. Environmental predictor variables were sourced via the sdmpredictors R package. The package give access to the https://bio-oracle.org/ and http://marspec.org/ high-resolution layers of various marine variables. Note these variables are location specific but not time specific: they are average values over time periods.
This tutorial is based on the notes by Ben Tupper (Bigelow Lab, Maine), and highlights modeling presence-only data via maxnet R package.
Tutorial roadmap
- Presence Data
-- obtain Loggerhead sea turtle (C. caretta) presence data from OBIS via
robis - Background Points -- shows two methods to create random background points within our area of interest
- Environmental Data
-- obtain environmental predictors of interest using
SDMpredictors - Model
-- run species distribution model and predict using
maxnet - Data Visualizations
-
Bosch S, Fernandez S (2022). sdmpredictors: Species Distribution Modelling Predictor Datasets. R package version 0.2.14, http://lifewatch.github.io/sdmpredictors/.
-
OBIS (2023) Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Accessed: 2023-08-08.
-
Steven J. Phillips, Miroslav Dudík, Robert E. Schapire. [Internet] Maxent software for modeling species niches and distributions (Version 3.4.1). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2023-08-10.
- Eli Holmes: Research Fisheries Biologist, Northwest Fisheries Science Center, NOAA Fisheries.
- Catherine Courtier:
- Mackenzie Fiss: Fifth-year PhD student at Northeastern University studying carbon cycling and microbial interactions in salt marshes.
- Denisse Fierro Arcos: PhD candidate at the Institute for Marine and Antarctic Studies (IMAS) and Data Officer at the Integrated Marine Observing System (IMOS)
- Paulo Freire: PhD candidate at the University of North Carolina at Charlotte (UNCC) studying marine microbial ecology.
- Jade Hong: Recently finished ungraduate studies majoring Biology and Marine Science at Boston University.
- Tylar Murray: USF IMaRS Software Engineer - code whisperer, data viz enthusiast, scientific generalist, compulsive overengineerer, & UX PhD
- Caitlin O'Brien: Research Scientist, Columbia Basin Research, School of Aquatic Fishery and Sciences, University of Washington
- Mary Solokas: John A. Knauss Marine Policy Fellow, National Oceanic and Atmospheric Administration
- Laura Tsang: Recent Master's graduate from Northeastern University
- Ben Tupper: Senior Research Associate at Bigelow Laboratory for Ocean Science
Some experience programming in R is needed to make the most of this tutorial. To run this tutorial make sure you clone this repository into your local machine by creating a new project that uses version control (git).
The tutorial content was developed in a R version 4.2.2 for Linux.
If you need additional support with R programming, you can check the following resources:
- Ben Best's labs on SDMs
Rfor Data Science - 2nd edition by Wickham, Çetinkaya-Rundel and Grolemund.- Data analysis and visualisation in
Rfor ecologists - For information on how to use
gitandGitHubwithR, Happy Git and GitHub for the useR by Jenny Bryan is a great resource.