Citizen-Science for the Future: Advisory Case Studies From Around the Globe

The democratization of ocean observation has the potential to add millions of observations every day. Though not a solution for all ocean monitoring needs, citizen scientists offer compelling examples showcasing their ability to augment and enhance traditional research and monitoring. Information they are providing is increasing the spatial and temporal frequency and duration of sampling; reducing time and labor costs for academic and government monitoring programs; providing hands-on STEM learning related to real-world issues; and increasing public awareness and support for the scientific process. Examples provided here demonstrate the wide range of people who are already dramatically reducing gaps in our global observing network while at the same time providing unique opportunities to meaningfully engage in ocean observing and the research and conservation it supports. While there are still challenges to overcome before widespread inclusion in projects requiring scientific rigor, the growing organization of international citizen science associations is helping to reduce barriers. The case studies described support the idea that citizen scientists should be part of an effective global strategy for a sustained, multidisciplinary and integrated observing system.


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Logistical considerations and the high costs of deploying traditional in situ ocean observing 57 systems limit their density and thus ability to accurately monitor fine-scale environmental 58 conditions. In the coming years, the combination of youth who are increasingly globally 59 connected and a growing population of retired professionals, poses an opportunity to create a "K 60 to gray" network of citizen scientists with capacity that spans multiple cross-cutting and societal   Svalbard is a Norwegian archipelago that is one of the world's northernmost inhabited areas.   Another example of citizens engaging in the scientific process involves assessing the impacts of 110 climate change on zoogeography. Two species of amphipods, Gammarus setosus and G. 111 oceanicus, occur on Svalbard. The former is a local Arctic species and the latter is a boreal 112 species. Both are found in the littoral zone, are about two to three centimeters in size, and are 113 relatively easy to spot. They dwell in sheltered sites, almost exclusively under flat, loose stones, 114 making specimens readily available to volunteers at low tide. Scientists had previously 115 conducted research demonstrating that the two species compete for space (Weslawski, 1990). 116 However, in that study only a single fjord was investigated. A large scale survey was desired to 117 determine if increasing temperature was resulting in the northward dispersal of the boreal 118 I n r e v i e w species, G. oceanicus, hence creating more competition with the Arctic species. Tourists visiting 119 remote areas of the Svalbard archipelago were asked to participate in the study, given 120 instructions on how to collect samples, and given small vials with alcohol to preserve samples.

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Three seasons of collection provided sufficient data for scientists to analyze. Results indicate that 122 the range of G. oceanicus is being extended poleward (Weslawski et al. 2018).

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Volunteers in these citizen science initiatives enthusiastically collected data and were recognized 125 for their participation with acknowledgements in research publications and on project web pages.   This has limited the acquisition of the high density spatial and temporal data needed to develop 138 dynamic predictive models and ruled out sampling remote habitats which are often necessary for 139 establishing baseline 'pristine habitat' data.  There are thousands of manned vessels cruising the world's oceans every day. Most follow long-         generating random null data distributions to assess the significance of patterns observed.

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Additionally, for many habitats, professional scientists collect samples to ground-truth collection 229 methods. IVE has conducted three transects of the Indian Ocean to validate community patterns.

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The IHO has a history of encouraging innovative ways to gather data and data maximizing 287 initiatives so that we can better understand the bathymetry of the seas, oceans and coastal waters.

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In 2014, the IHO, at its Fifth Extraordinary International Hydrographic Conference, recognized 289 that traditional survey vessels alone could not be relied upon to solve data deficiency issues and 290 agreed there was a need to encourage and support all mariners in an effort to "map the gaps".

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One outcome of the conference was an initiative to support and enable mariners and 292 professionally manned vessels to collect crowdsourced bathymetry (CSB). The information 293 I n r e v i e w would be used to supplement the more rigorous and scientific bathymetric coverage done by 294 hydrographic offices, industry, and researchers around the world.

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While CSB data may not meet accuracy requirements for charting areas of critical under-keel 297 clearance, it does hold limitless potential for myriad other uses. If vessels collect and donate 298 depth information while on passage, the data can be used to identify uncharted features, assist in 299 verifying charted information, and to help confirm that existing charts are appropriate for the 300 latest traffic patterns. This is especially relevant considering that many soundings on charting 301 products are pre-1950. In some cases, CSB data can fill gaps where bathymetric data are scarce, 302 such as unexplored areas of polar regions, around developing maritime nations, and the open 303 ocean. CSB also has potential uses along shallow, complex coastlines that are difficult for 304 traditional survey vessels to access. These areas may be more frequently visited by recreational 305 boaters whose data could help illustrate seafloor and shoaling trends from the repeated trips they 306 make along their favorite routes. CSB will also be invaluable in providing ground-truthing data 307 to validate Satellite Derived Bathymetry (SDB). SDB is a necessary technology in the Arctic yet 308 has a serious validation problem irrespective of the model (empirical, semi-empirical or physics).

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Finally, crowdsourced bathymetry can provide vital information to support national and regional  waters and an ever-increasing number of vessels can also take measurements in deeper water.

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The CSB vision is to tap into volunteer enthusiasm for mapping the ocean floor. Enabling trusted 326 mariners to easily contribute data will augment current bathymetric coverage and enhance 327 charting capabilities of the bathymetric initiative.  The intent is that these data, like all bathymetric data submitted to the DCDB, would not 354 necessarily be "harmonised" or reviewed but would reside in the DCDB "as is". It would remain 355 up to the end users to determine their value and utility for their own purpose. In this way, the 356 fundamental data that reside in the DCDB will serve as the world reference raw bathymetric data 357 set which can be used as the basis for refined and processed products.

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The GPS data points without attached photos are used to compare with contour data for the 555 bathtub tidal prediction models and interpret slope of water throughout the river and bay basins.  (Figure 7). In more 565 severe cases, once volunteers are safely comfortable that adverse conditions have passed, 566 inundation markers such as debris lines are recorded similar to USGS file reports (but with less 567 sophisticated surveying equipment) to capture flood features that are likely to be removed before 568 official survey crews come by to document the event more than 24 hours later.

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Water levels were compared with nearby water level gauges to estimate depths relative to a root 593 mean squared error in cm.

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Project Summary 596 The new water level sensor data and the crowd-sourced high water marks from the king tide 597 were initially filtered for relative location accuracy and timing, interpolated with the use of   The Gulf Citizen Science Portal (GCSP) described here has been structured to accommodate data