MarineSDMs

Marine Species Distribution Models

Author

Affiliations

Benjamin D. Best

EcoQuants LLC

MarineBON

1 Introduction

1.1 Background

The best available global distributions are presently AquaMaps (Kaschner et al. 2006; Ready et al. 2010) with supplementation by IUCN RedList range maps¹. These have been used to calculate the biodiversity within national waters (Halpern et al. 2012) as well as beyond in the high seas (Visalli et al. 2020).

1.2 Goals

This book aims to capture the overview and details of modeling species distributions in the marine environment for the purposes of advancing the status quo of global and U.S. national species distributions along the following dimensions:

1. Space
   The current AquaMaps distributions are $1/2$º (~55 km at equator), whereas the best available global bathymetry is $1/240$º (< 0.5 km).

2. Time
   The current AquaMaps distributions are based on static climatic averages over all seasons, which does not capture temporal dynamics: seasonally within a year, nor long-term climate change trends. This will necessitate sampling the environment contemporaneously with species observations before fitting the model and predicting to different environmental snapshots.

3. Environment
   Other environmental variables besides the initial physiographic (depth) and oceanographic (temperature, chlorophyll, primary productivity and ice) may elicit an improved statistical fit, related to species’ environmental niche. Some candidates include: temperature fronts, eddy kinetic energy, distance from shore, distance from shelf.

4. Biology
   Where sufficient observations exist, additional models should be developed highlighting differences between:

   • Life stage, e.g. larval vs adult.

   • Gender where varies, such as male sperm whales being more cosmopolitan.

   • Subpopulations for understanding metapopulation dynamics

   • Behavior, such as migrating, feeding or breeding.

By definition MBONMarine Biodiversity Observation Network; see MarineBON.org is a network, so this is inclusive of and meant for all participants.

1.3 Motivations

• AquaMaps.org
  AquaMaps (Kaschner et al. 2006; Ready et al. 2010) represents a massive amount of work to gather parameters for >33.5K marine species, including areas to mask out.

• OBIS.org
  The Ocean Biogeographic Information System (Klein et al. 2019; Grassle 2000) is the central portal for continuously added observations with extra flags for quality control, all of which makes marine SDMs possible.

• Modeling methods have dramatically improved over time and are ripe for fresh application. The R package dismo originally came came out with an SDM vignette as a practical supplement to their excellent review of SDMs (Elith and Leathwick 2009) and using the Maxent algorithm (Elith et al. 2011). The raster package furthered that (raster sdm) and now there’s terra sdm. Alongside these developments has been a boon of cloud-computing, particularly Google Earth Engine (Gorelick et al. 2017; Campos et al. 2023), allowing for dense global raster processing.

• The world is quickly moving towards a future trying to conserve 30% of the oceans by 2030, so called “30 by 30”. In the U.S., this is America the Beautiful initiative (Carroll, Noss, and Stein 2022) for which MBONMarine Biodiversity Observation Network; see MarineBON.org is well poised to inform (Fautin et al. 2010; Muller-Karger et al. 2018). We need biodiversity indicators to track progress. This push for conservation is driven by increasing impacts of climate change, as evidenced by marine heatwaves and shifts in population distributions.

1.4 Process

Figure 1.1: Diagram of SDM data preparation and model fitting.

1.5 Contribute

We very much welcome your feedback, contributions and collaboration. As soon as you contribute, we will add you to to the authors list. Here are a few ways to contribute from least to most involved:

1. Email Ben (ben@ecoquants.com) with any suggestions, including suggested revisions of this online book.

   Note

   Note that you can download this entire book as:

   • Adobe Acrobat pdf to add annotations; or

   • Microsoft Word docx to edit with Track Changes on.

   These are available in the upper left navigation menu by clicking the download icon .

2. Submit a New Issue on Github.

3. Click on “ Edit this Page” in the upper right. If you have a Github account, then you can fork this repository from owner “marinebon” to your username, edit the page(s) and submit a pull request. See Hello World - GitHub Docs.

4. If you are a regular contributor, you can be added to the collaborators of this repository to push changes directly (without needing a pull request).

1.6 Features

This Quarto book has a few cool features:

• Multiple formats
  From the singe set of source Quarto documents (*.qmd), several output formats are rendered: html, pdf, docx. This is particularly helpful when suggesting changes. It also lends itself well to being carved into manuscripts.

• Self-rendering
  Github hosts the web pages (*.html), which get rendered from the source code (*.qmd) using a Github Action. So edits can be made simply through the web interface and all outputs get updated (html, pdf, docx). It also ensures the reproducibility of the document with a common setup environment.

• Mermaid diagrams
  e.g., Figure 1.1, Figure 3.1, Figure 6.1

• Quarto document listings

• References

• Glossary

• Search

Campos, João C., Nuno Garcia, João Alírio, Salvador Arenas-Castro, Ana C. Teodoro, and Neftalí Sillero. 2023. “Ecological Niche Models Using MaxEnt in Google Earth Engine: Evaluation, Guidelines and Recommendations.” Ecological Informatics 76 (September): 102147. https://doi.org/10.1016/j.ecoinf.2023.102147.

Carroll, C., R. F. Noss, and Bruce A. Stein. 2022. “US Conservation Atlas Needs Biodiversity Data.” Science 376 (6589): 144–45. https://doi.org/10.1126/science.abo0526.

Elith, Jane, and John R. Leathwick. 2009. “Species Distribution Models: Ecological Explanation and Prediction Across Space and Time.” Annual Review of Ecology, Evolution, and Systematics 40 (1): 677–97. https://doi.org/10.1146/annurev.ecolsys.110308.120159.

Elith, Jane, Steven J. Phillips, Trevor Hastie, Miroslav Dudík, Yung En Chee, and Colin J. Yates. 2011. “A Statistical Explanation of MaxEnt for Ecologists.” Diversity and Distributions 17 (1): 43–57. http://dx.doi.org/10.1111/j.1472-4642.2010.00725.x.

Fautin, Daphne, Penelope Dalton, Lewis S. Incze, Jo-Ann C. Leong, Clarence Pautzke, Andrew Rosenberg, Paul Sandifer, et al. 2010. “An Overview of Marine Biodiversity in United States Waters.” PLoS ONE 5 (8): e11914. https://doi.org/10.1371/journal.pone.0011914.

Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment, Big remotely sensed data: Tools, applications and experiences, 202 (December): 18–27. https://doi.org/10.1016/j.rse.2017.06.031.

Grassle, J. Frederick. 2000. “The Ocean Biogeographic Information System (OBIS): An on-Line, Worldwide Atlas for Accessing, Modeling and Mapping Marine Biological Data in a Multidimensional Geographic Context.” Oceanography 13 (3): 5–7. https://www.jstor.org/stable/43924357.

Halpern, Benjamin S., Catherine Longo, Darren Hardy, Karen L. McLeod, Jameal F. Samhouri, Steven K. Katona, Kristin Kleisner, et al. 2012. “An Index to Assess the Health and Benefits of the Global Ocean.” Nature. https://doi.org/10.1038/nature11397.

Kaschner, K., R. Watson, A. W. Trites, and D. Pauly. 2006. “Mapping World-Wide Distributions of Marine Mammal Species Using a Relative Environmental Suitability (RES) Model.” Marine Ecology Progress Series 316 (July): 285–310. https://doi.org/10.3354/meps316285.

Klein, Eduardo, Ward Appeltans, Pieter Provoost, Hanieh Saeedi, Abigail Benson, Lenore Bajona, Ana Carolina Peralta, and R. Sky Bristol. 2019. “OBIS Infrastructure, Lessons Learned, and Vision for the Future.” Frontiers in Marine Science 6. https://www.frontiersin.org/articles/10.3389/fmars.2019.00588.

Muller-Karger, Frank E., Patricia Miloslavich, Nicholas J. Bax, Samantha Simmons, Mark J. Costello, Isabel Sousa Pinto, Gabrielle Canonico, et al. 2018. “Advancing Marine Biological Observations and Data Requirements of the Complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) Frameworks.” Frontiers in Marine Science 5. https://doi.org/10.3389/fmars.2018.00211.

Ready, Jonathan, Kristin Kaschner, Andy B. South, Paul D. Eastwood, Tony Rees, Josephine Rius, Eli Agbayani, Sven Kullander, and Rainer Froese. 2010. “Predicting the Distributions of Marine Organisms at the Global Scale.” Ecological Modelling 221 (3): 467–78. https://doi.org/10.1016/j.ecolmodel.2009.10.025.

Visalli, Morgan E., Benjamin D. Best, Reniel B. Cabral, William W. L. Cheung, Nichola A. Clark, Cristina Garilao, Kristin Kaschner, et al. 2020. “Data-Driven Approach for Highlighting Priority Areas for Protection in Marine Areas Beyond National Jurisdiction.” Marine Policy, March, 103927. https://doi.org/10.1016/j.marpol.2020.103927.

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1. IUCN RedList range maps: https://www.iucnredlist.org/resources/spatial-data-download