Ocean Scientists Create Global Network to Help Save Biodiversity
Ocean scientists have been busy creating a global network to understand and measure changes in ocean life. The system will aggregate data from the oceans, climate and human activity to better inform sustainable marine management practices.
EcoWatch sat down with some of the scientists spearheading the collaboration to learn more.
"There is the Global Ocean Observing System (GOOS) and the Marine Biodiversity Observation Network (MBON). We're trying to put them together," explained Frank Muller-Karger, a professor at the University of South Florida studying phytoplankton, a member of the Biology & Ecosystems Panel (BioEco Panel) of GOOS and a co-founder of MBON.
GOOS recommends what kinds of essential measurements should be collected to address particular problems. Over the last few decades, physical variables (salinity, temperature, oxygen, etc.) have helped improved weather forecasts and understanding of how the ocean redistributes heat around the world, Muller-Karger said.
Climate models are predicting faster warming of the North Atlantic Ocean, which will shift the Gulf Stream. NASA
MBON has established biodiversity variables (genetics, number of species at a certain depth, ecosystem structure, etc.) to understand the structures of life in the oceans, explained Patricia Miloslavich, executive director of the Scientific Committee on Oceanic Research at the University of Delaware and member of both the BioEco Panel and MBON.
Now, scientists are trying to bridge the gap between the two observing networks by creating one "language" both systems can speak and in which all data can be collected and shared. A major goal is to be more efficient and effective in solving issues that affect people around the world by leveraging the oceans' resources sustainably. The effort will be a key contribution to the United Nations Decade of Ocean Science for Sustainable Development (2021 - 2030), an international effort under which top researchers have been collaborating to reverse ocean decline.
"Data have to be formatted and have to carry a measure of precision if you want any hope to understand how the world is changing," Muller-Karger said. "It's so difficult to make measurements in the sea, that if we don't follow standards, we end up with things we cannot compare. It complicates things even further."
Woody Turner, who works with NASA and remotely sensed data that will be incorporated into the new system, said the entire endeavor is about "coordination at a global scale to get a global view of the planet." He noted that people have been looking at phenomena in the ocean for centuries and that the hard part has always been stitching these diverse observations together.
"That's why the U.N. Decade is such a big deal. It's an effort to bring together natural and social scientists from around the world to deliberately tie things together and create a standardized global observation system for it all," he said.
The system will also link into a similar global climate observation system that has been in place for over 40 years, as well as account for human intervention. In this way, scientists hope to more fully understand how the two biggest stressors on the oceans — climate change and human activity — affect biodiversity and abundance of life in the oceans, Miloslavich said.
"This can be used by policymakers to better manage how we use the oceans," she added.
For example, through the system, scientists could explain the fact that the oceans off the northeast U.S. are warming and document the how and why of a northward-migration of lobsters to forecast whether specific action may prevent the impending collapse of current fisheries and the resultant shift towards new species in the ecosystem.
Traditional climate data would show the effect of increased emissions of greenhouse gases, the GOOS might measure higher water temperatures, and the MBON might separately register the disappearing lobster populations, but the new system would hopefully be able to connect it all. Fisheries managers could then anticipate shortening or shifting a catch season accordingly, making the fishery more effective and profitable.
"We really need to be measuring life and the diversity of life, because that's ultimately what we depend on," Muller-Karger said. "We don't eat bulk carbon; we eat fish and potatoes."
Fundamentally, the scientists are trying to create a way to link physical and chemical measurements to the changes in biodiversity and life in the oceans.
"We need to be able to harness all that information so we can profit more from the info we're collecting," said Nic Bax, co-chair of the BioEco Panel. "That multi-disciplinarity is really needed now."
Critically, the new integrated observing system also shows how human actions can increase biodiversity in a particular region, so that abundance and productivity can be used to improve human life in various ways, Bax explained.
For example, Turner and Maury Estes apply space-based satellite imagery of the Earth with sea-based measurements to improve aquaculture practices in Palau. This combats overfishing while sustaining the health of the people and the economy of the island nation. They are also applying such technologies to mangrove conservation and restoration efforts in Kenya, where carbon credits are purchased to maintain mangroves as essential fish spawning grounds, increasing biological resilience while reducing human poverty.
"As we understand more, how we talk about sustainably developing the environment won't be just about how to not lose diversity, but about how to bring it back," Bax said. "A lot of things just require someone to invest in them, and financial markets need to be informed about what's going on in the larger system. This is a way to generate profit while improving the lives of people."
Turner added, "It's not only about saving life on the planet, it's about saving ourselves. It's a global problem, so we have to address it top-down, globally."
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By Eric Tate and Christopher Emrich
Disasters stemming from hazards like floods, wildfires, and disease often garner attention because of their extreme conditions and heavy societal impacts. Although the nature of the damage may vary, major disasters are alike in that socially vulnerable populations often experience the worst repercussions. For example, we saw this following Hurricanes Katrina and Harvey, each of which generated widespread physical damage and outsized impacts to low-income and minority survivors.
Mapping Social Vulnerability<p>Figure 1a is a typical map of social vulnerability across the United States at the census tract level based on the Social Vulnerability Index (SoVI) algorithm of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6237.8402002" target="_blank"><em>Cutter et al.</em></a> . Spatial representation of the index depicts high social vulnerability regionally in the Southwest, upper Great Plains, eastern Oklahoma, southern Texas, and southern Appalachia, among other places. With such a map, users can focus attention on select places and identify population characteristics associated with elevated vulnerabilities.</p>
Fig. 1. (a) Social vulnerability across the United States at the census tract scale is mapped here following the Social Vulnerability Index (SoVI). Red and pink hues indicate high social vulnerability. (b) This bivariate map depicts social vulnerability (blue hues) and annualized per capita hazard losses (pink hues) for U.S. counties from 2010 to 2019.<p>Many current indexes in the United States and abroad are direct or conceptual offshoots of SoVI, which has been widely replicated [e.g., <a href="https://link.springer.com/article/10.1007/s13753-016-0090-9" target="_blank"><em>de Loyola Hummell et al.</em></a>, 2016]. The U.S. Centers for Disease Control and Prevention (CDC) <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/index.html" target="_blank">has also developed</a> a commonly used social vulnerability index intended to help local officials identify communities that may need support before, during, and after disasters.</p><p>The first modeling and mapping efforts, starting around the mid-2000s, largely focused on describing spatial distributions of social vulnerability at varying geographic scales. Over time, research in this area came to emphasize spatial comparisons between social vulnerability and physical hazards [<a href="https://doi.org/10.1007/s11069-009-9376-1" target="_blank"><em>Wood et al.</em></a>, 2010], modeling population dynamics following disasters [<a href="https://link.springer.com/article/10.1007%2Fs11111-008-0072-y" target="_blank" rel="noopener noreferrer"><em>Myers et al.</em></a>, 2008], and quantifying the robustness of social vulnerability measures [<a href="https://doi.org/10.1007/s11069-012-0152-2" target="_blank" rel="noopener noreferrer"><em>Tate</em></a>, 2012].</p><p>More recent work is beginning to dissolve barriers between social vulnerability and environmental justice scholarship [<a href="https://doi.org/10.2105/AJPH.2018.304846" target="_blank" rel="noopener noreferrer"><em>Chakraborty et al.</em></a>, 2019], which has traditionally focused on root causes of exposure to pollution hazards. Another prominent new research direction involves deeper interrogation of social vulnerability drivers in specific hazard contexts and disaster phases (e.g., before, during, after). Such work has revealed that interactions among drivers are important, but existing case studies are ill suited to guiding development of new indicators [<a href="https://doi.org/10.1016/j.ijdrr.2015.09.013" target="_blank" rel="noopener noreferrer"><em>Rufat et al.</em></a>, 2015].</p><p>Advances in geostatistical analyses have enabled researchers to characterize interactions more accurately among social vulnerability and hazard outcomes. Figure 1b depicts social vulnerability and annualized per capita hazard losses for U.S. counties from 2010 to 2019, facilitating visualization of the spatial coincidence of pre‑event susceptibilities and hazard impacts. Places ranked high in both dimensions may be priority locations for management interventions. Further, such analysis provides invaluable comparisons between places as well as information summarizing state and regional conditions.</p><p>In Figure 2, we take the analysis of interactions a step further, dividing counties into two categories: those experiencing annual per capita losses above or below the national average from 2010 to 2019. The differences among individual race, ethnicity, and poverty variables between the two county groups are small. But expressing race together with poverty (poverty attenuated by race) produces quite different results: Counties with high hazard losses have higher percentages of both impoverished Black populations and impoverished white populations than counties with low hazard losses. These county differences are most pronounced for impoverished Black populations.</p>
Fig. 2. Differences in population percentages between counties experiencing annual per capita losses above or below the national average from 2010 to 2019 for individual and compound social vulnerability indicators (race and poverty).<p>Our current work focuses on social vulnerability to floods using geostatistical modeling and mapping. The research directions are twofold. The first is to develop hazard-specific indicators of social vulnerability to aid in mitigation planning [<a href="https://doi.org/10.1007/s11069-020-04470-2" target="_blank" rel="noopener noreferrer"><em>Tate et al.</em></a>, 2021]. Because natural hazards differ in their innate characteristics (e.g., rate of onset, spatial extent), causal processes (e.g., urbanization, meteorology), and programmatic responses by government, manifestations of social vulnerability vary across hazards.</p><p>The second is to assess the degree to which socially vulnerable populations benefit from the leading disaster recovery programs [<a href="https://doi.org/10.1080/17477891.2019.1675578" target="_blank" rel="noopener noreferrer"><em>Emrich et al.</em></a>, 2020], such as the Federal Emergency Management Agency's (FEMA) <a href="https://www.fema.gov/individual-disaster-assistance" target="_blank" rel="noopener noreferrer">Individual Assistance</a> program and the U.S. Department of Housing and Urban Development's Community Development Block Grant (CDBG) <a href="https://www.hudexchange.info/programs/cdbg-dr/" target="_blank" rel="noopener noreferrer">Disaster Recovery</a> program. Both research directions posit social vulnerability indicators as potential measures of social equity.</p>
Social Vulnerability as a Measure of Equity<p>Given their focus on social marginalization and economic barriers, social vulnerability indicators are attracting growing scientific interest as measures of inequity resulting from disasters. Indeed, social vulnerability and inequity are related concepts. Social vulnerability research explores the differential susceptibilities and capacities of disaster-affected populations, whereas social equity analyses tend to focus on population disparities in the allocation of resources for hazard mitigation and disaster recovery. Interventions with an equity focus emphasize full and equal resource access for all people with unmet disaster needs.</p><p>Yet newer studies of inequity in disaster programs have documented troubling disparities in income, race, and home ownership among those who <a href="https://eos.org/articles/equity-concerns-raised-in-federal-flood-property-buyouts" target="_blank">participate in flood buyout programs</a>, are <a href="https://www.eenews.net/stories/1063477407" target="_blank" rel="noopener noreferrer">eligible for postdisaster loans</a>, receive short-term recovery assistance [<a href="https://doi.org/10.1016/j.ijdrr.2020.102010" target="_blank" rel="noopener noreferrer"><em>Drakes et al.</em></a>, 2021], and have <a href="https://www.texastribune.org/2020/08/25/texas-natural-disasters--mental-health/" target="_blank" rel="noopener noreferrer">access to mental health services</a>. For example, a recent analysis of federal flood buyouts found racial privilege to be infused at multiple program stages and geographic scales, resulting in resources that disproportionately benefit whiter and more urban counties and neighborhoods [<a href="https://doi.org/10.1177/2378023120905439" target="_blank" rel="noopener noreferrer"><em>Elliott et al.</em></a>, 2020].</p><p>Investments in disaster risk reduction are largely prioritized on the basis of hazard modeling, historical impacts, and economic risk. Social equity, meanwhile, has been far less integrated into the considerations of public agencies for hazard and disaster management. But this situation may be beginning to shift. Following the adage of "what gets measured gets managed," social equity metrics are increasingly being inserted into disaster management.</p><p>At the national level, FEMA has <a href="https://www.fema.gov/news-release/20200220/fema-releases-affordability-framework-national-flood-insurance-program" target="_blank">developed options</a> to increase the affordability of flood insurance [Federal Emergency Management Agency, 2018]. At the subnational scale, Puerto Rico has integrated social vulnerability into its CDBG Mitigation Action Plan, expanding its considerations of risk beyond only economic factors. At the local level, Harris County, Texas, has begun using social vulnerability indicators alongside traditional measures of flood risk to introduce equity into the prioritization of flood mitigation projects [<a href="https://www.hcfcd.org/Portals/62/Resilience/Bond-Program/Prioritization-Framework/final_prioritization-framework-report_20190827.pdf?ver=2019-09-19-092535-743" target="_blank" rel="noopener noreferrer"><em>Harris County Flood Control District</em></a>, 2019].</p><p>Unfortunately, many existing measures of disaster equity fall short. They may be unidimensional, using single indicators such as income in places where underlying vulnerability processes suggest that a multidimensional measure like racialized poverty (Figure 2) would be more valid. And criteria presumed to be objective and neutral for determining resource allocation, such as economic loss and cost-benefit ratios, prioritize asset value over social equity. For example, following the <a href="http://www.cedar-rapids.org/discover_cedar_rapids/flood_of_2008/2008_flood_facts.php" target="_blank" rel="noopener noreferrer">2008 flooding</a> in Cedar Rapids, Iowa, cost-benefit criteria supported new flood protections for the city's central business district on the east side of the Cedar River but not for vulnerable populations and workforce housing on the west side.</p><p>Furthermore, many equity measures are aspatial or ahistorical, even though the roots of marginalization may lie in systemic and spatially explicit processes that originated long ago like redlining and urban renewal. More research is thus needed to understand which measures are most suitable for which social equity analyses.</p>
Challenges for Disaster Equity Analysis<p>Across studies that quantify, map, and analyze social vulnerability to natural hazards, modelers have faced recurrent measurement challenges, many of which also apply in measuring disaster equity (Table 1). The first is clearly establishing the purpose of an equity analysis by defining characteristics such as the end user and intended use, the type of hazard, and the disaster stage (i.e., mitigation, response, or recovery). Analyses using generalized indicators like the CDC Social Vulnerability Index may be appropriate for identifying broad areas of concern, whereas more detailed analyses are ideal for high-stakes decisions about budget allocations and project prioritization.</p>
By Jessica Corbett
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