STEVEN MANA‘OAKAMAI JOHNSON, PHD
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Climate Change | Conservation | Cooperation

GIS, coral reefs, and resilience

3/11/2018

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I'm currently enrolled in a GIS course for my PhD program. We had the option of a final or a course project - I opted for the latter option. I decided to combine work from here and here. We needed to create a poster or website, so I'm posting my work here.

Show me the money: using spatial interpolation of coral reef ecosystem indicators to support management decision making.
Steven M. Johnson, Geography - CEOAS, Oregon State University.
GEOG 561 - GIScience II: Analysis & Applications

Introduction
Coral reefs are some of the most diverse and dynamic ecosystems on the planet. Home to over 1 million species, they cover less than 1% of the worlds surface. However, coral reefs around the world are threatened by pollution [1], overfishing [2], and global climate change [3]. A challenge for coastal communities who rely on these ecosystems for their livelihoods is finding ways to maintain ecosystem resilience [4]. While the ultimate goal is to keep the planet within the habitable boundaries of reefs [5], managing for resilience seems to be our best bet[6].

Managing for resilience can take many shapes, including fisheries regulations [7] or marine protected areas [8]. However, a challenge remains in measuring resilience and identifying where these reefs are located. Coral reef resilience is typically assessed using underwater visual surveys. The inherent complexity of coral reefs makes quantifying resilience challenging [9], requiring the use of other analytical techniques.

Spatial statistical techniques have been gaining increased utility in coral reef management and conservation [10–12]. Spatial interpolation techniques can help identify potential areas for management and conservation support. Using underwater census data on ecosystem health, I used two interpolation techniques (inverse distance weighted (IDW) and kriging) to “fill-in” data between monitoring stations on Yap, Micronesia (Fig. 1).
Picture
Figure 1. Map of survey stations on Yap, Micronesia . Triangles: marine protected areas; circles - no protection.
Methods
Ecosystem condition
Ecosystem condition indicators were defined based on13 (Fig. 2). These indicators capture many of the key functional components of ecosystem health. Condition score is assumed to be indicative of resilience. Trained teams of divers collected data from sites between 5-10 m depth. Latent variables (e.g., fish condition, coral condition) are aggregate scores from individual metrics (e.g., fish length, coral colony size).
Picture
Figure 2. Ecosystem condition evaluation process depicting the contribution of individual biological metrics on their respective latent variables, and the weighting of latent variables on overall condition scores (Houk et al. 2015, PLoS ONE).
Spatial interpolation
Ecosystem condition for each survey station was designated as an attribute of each point. Using a reef layer for Micronesia, the barrier reef surrounding Yap was extracted and a 200 m buffer around the polygon was created. I conducted an IDW and kriging for survey sites across the 3 latent variables (fish, coral, and benthic habitat), as well as the aggregate ecosystem condition score. Using the clip function in ArcGIS, I extracted interpolated values for reef areas with no data.
Picture
Figure 3. Example of inverse distance weighting interpolation from points to raster grid. (Source: gisgeography.com)
Results & Discussion
General trends
Spatial interpolation of coral reef indicators highlighted a gradient of reef health. Overall reef health showed a general decline from north to south, with a secondary gradient moving from east to west (Fig. 4). Similar patterns exist for fish condition (Fig. 5) and coral condition (Fig. 6). Benthic substrate condition exhibited a stronger east-west pattern.
Methodological comparison
Kriging and IDW tools in ArcGIS detected similar broad patterns. Generally, kriging approaches are more robust models that account for prediction error. Additionally, these models are less influenced by a single point., where IDW results (right panel in Fig. 4 - 7) can be prone to the bullseye effect. However, the patchiness of reef condition and presence of MPAs is best captured by IDW.

Conservation/management prioritization
Priority areas for conservation and management were identified using binary calculations for areas beyond 1 standard deviation of mean values. Three locations with high values for fisheries management were identified (Fig. 8). Two of the sites were located within existing MPAs, while the third is the most geographically isolated location on the island. The existing MPAs may provide adjacent areas with fisheries replenishment via spillover. Maintaining support for these areas should remain a top priority. The last location may serve as a defacto reserve, due to its isolation. Formal protection might be a consideration to prevent potential poaching. Three locations were also identified for potential coral restoration/rehabilitation efforts (Fig. 9). Two locations are on the northwest of the island, while the third is adjacent to the main shipping channel into the lagoon.
Conclusion
Coral reef communities exhibit high levels of heterogeneity [14,15]. In addition, management tools for coral reefs can be spatially explicit (i.e., MPAs), recapitulating heterogeneity [16]. IDW spatial interpolation should be used as a preferred GIS tool as it best captures this effect. Identifying potential locations for conservation has been shown to maximize costs and utility of decisions [17]. Spatial tools can provide reef managers with both tools to support further conservation, as well as inform policy [18].

References
1. Wear, S. L. & Vega Thurber, R. Sewage pollution: Mitigation is key for coral reef stewardship. Ann. N. Y. Acad. Sci. 1355, 15–30 (2015).
2. MacNeil, M. A. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).
3. Spalding, M. D. & Brown, B. E. Warm-water coral reefs and climate change. Science (80-. ). 350, 769–771 (2015).
4. West, J. M. & Salm, R. V. Resistance and resilience to coral bleaching: implications for coral reef conservation and management. Conserv. Biol. 17, 956–967 (2003).
5. Norström, A. V. et al. Guiding coral reef futures in the Anthropocene. Front. Ecol. Environ. 14, 490–498 (2016).
6. McClanahan, T. R. et al. Prioritizing Key Resilience Indicators to Support Coral Reef Management in a Changing Climate. PLoS One 7, e42884 (2012).
7. Nash, K. L., Graham, N. A. J., Jennings, S., Wilson, S. K. & Bellwood, D. R. Herbivore cross-scale redundancy supports response diversity and promotes coral reef resilience. J. Appl. Ecol. 646–655 (2015). doi:10.1111/1365-2664.12430
8. Hughes, T. P., Bellwood, D. R., Folke, C. S., McCook, L. J. & Pandolfi, J. M. No-take areas, herbivory and coral reef resilience. Trends Ecol. Evol. 22, 1–3 (2007).
9. Venegas-Li, R., Cros, A., White, A. T. & Mora, C. Measuring conservation success with missing Marine Protected Area boundaries: A case study in the Coral Triangle. Ecol. Indic. 60, 119–124 (2016).
10. Heron, S. F. et al. Validation of Reef-Scale Thermal Stress Satellite Products for Coral Bleaching Monitoring. Remote Sens. 8, 59 (2016).
11. Rowlands, G. P. et al. Satellite imaging coral reef resilience at regional scale. A case-study from Saudi Arabia. Mar. Pollut. Bull. 64, 1222–1237 (2012).
12. Knudby, A., Jupiter, S. D., Roelfsema, C., Lyons, M. & Phinn, S. R. Mapping coral reef resilience indicators using field and remotely sensed data. Remote Sens. 5, 1311–1334 (2013).
13. Houk, P. et al. The Micronesia Challenge: Assessing the Relative Contribution of Stressors on Coral Reefs to Facilitate Science-to-Management Feedback. PLoS One 10, e0130823 (2015).
14. van Woesik, R. Processes Regulating Coral Communities. Comments Theor. Biol. 7, 201–214 (2002).
15. Dornelas, M., Connolly, S. R. & Hughes, T. P. Coral reef diversity refutes the neutral theory of biodiversity. Nature 440, 80–82 (2006).
16. Russ, G. R. & Alcala, A. C. Do marine reserves export adult fish biomass? Evidence from Apo Island, central Philippines. Mar. Ecol. Prog. Ser. 132, 1–9 (1996).
17. Scholz, A. J., Steinback, C., Kruse, S. A., Mertens, M. & Silverman, H. Incorporation of Spatial and Economic Analyses of Human-Use Data in the Design of Marine Protected Areas. Conserv. Biol. 25, 485–492 (2011).
18. Wedding, L. M. et al. Advancing the integration of spatial data to map human and natural drivers on coral reefs. PLoS One 13, e0189792 (2018).
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> 'hello world'

3/5/2018

15 Comments

 
...now that's a good coding joke for you!

Hi everyone! Welcome to my personal website. I'm a first-year PhD student in geography at Oregon State University. I'll be using this space as a place to share my thoughts and communicate my science. Hopefully you find my musings thought provoking and I hope to engage with you all.

Mahalo,
Steven
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    Steven M. Johnson

    Aloha and Hafa Adai! I'm an Assistant Professor at Cornell University. As a researcher and knowledge enthusiast,  I enjoy learning about social-ecological systems, the ocean, and the people who rely on it.

    When not buried in the joys of science, I enjoy running long distances, reading comics of the superhero variety, and consuming all types of music.

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