Essential ocean variables for global sustained observations of biodiversity and ecosystem changes

Abstract

Sustained observations of marine biodiversity and ecosystems focused on specific conservation and management problems are needed around the world to effectively mitigate or manage changes resulting from anthropogenic pressures. These observations, while complex and expensive, are required by the international scientific, governance and policy communities to provide baselines against which the effects of human pressures and climate change may be measured and reported, and resources allocated to implement solutions. To identify biological and ecological essential ocean variables (EOVs) for implementation within a global ocean observing system that is relevant for science, informs society, and technologically feasible, we used a driver-pressure-state-impact-response (DPSIR) model. We (1) examined relevant international agreements to identify societal drivers and pressures on marine resources and ecosystems, (2) evaluated the temporal and spatial scales of variables measured by 100+ observing programs, and (3) analysed the impact and scalability of these variables and how they contribute to address societal and scientific issues. EOVs were related to the status of ecosystem components (phytoplankton and zooplankton biomass and diversity, and abundance and distribution of fish, marine turtles, birds and mammals), and to the extent and health of ecosystems (cover and composition of hard coral, seagrass, mangrove and macroalgal canopy). Benthic invertebrate abundance and distribution and microbe diversity and biomass were identified as emerging EOVs to be developed based on emerging requirements and new technologies. The temporal scale at which any shifts in biological systems will be detected will vary across the EOVs, the properties being monitored and the length of the existing time-series. Global implementation to deliver useful products will require collaboration of the scientific and policy sectors and a significant commitment to improve human and infrastructure capacity across the globe, including the development of new, more automated observing technologies, and encouraging the application of international standards and best practices.

Marine top predators as climate and ecosystem sentinels

“…For this reason, metrics derived from monitoring top predators have been proposed as essential ocean variables that can contribute to the Global Ocean Observing System (Miloslavich et al . ; http://www.goosocean.org).…”

“…Traditional observing systems -including ship-and shorebased sampling, satellite-borne sensors, moorings, autonomous floats, and underwater vehicles -are capable of monitoring a wide range of physical and environmental properties (Constable et al 2016;Miloslavich et al 2018;Harcourt et al 2019). But understanding how and when physical changes cascade through ecosystems remains difficult, and ecosystem sentinels can play an important role in elucidating ecosystem responses.…”

“…While one can hypothesize how and when environmental changes (eg a delay in upwelling or an increase in temperature) will affect the ecosystem more broadly, top predator sentinels can indicate when and where these broad-scale impacts occur, and can help identify physical thresholds or tipping points when physical processes translate to broad-scale implications for the ecosystem (WebTable 1). For this reason, metrics derived from monitoring top predators have been proposed as essential ocean variables that can contribute to the Global Ocean Observing System (Miloslavich et al 2018; www. gooso cean.org).…”

Experimental evidence of spatial signatures of approaching regime shifts in macroalgal canopies

“…Increasing use of remote-sensing vehicles and aerial drones will make highresolution data on the distribution of benthic species increasingly available (Ling et al 2016), paving the way for the wide application of spatial indicators to monitor regime shifts in the marine environment. The possibility to derive cost-effective spatial indicators from remote-sensing data will further strengthen the role of macroalgal canopy cover as an essential ocean variable to employ in global observing networks of the status and trends of coastal ecosystems (Miloslavich et al 2018).…”

Changing how we approach fisheries: A first attempt at an operational framework for ecosystem approaches to fisheries management

“…,Nicholson & Jennings (2004),Link, Burnett, Kostovick, & Galbraith (2008),McClatchie et al (2014),Lynch et al (2018) IVOther physical measures to consider as part of monitoring/observing systemMugo, Saitoh, Nihira, & Kuroyama (2010),Manderson et al (2011),Kohut et al (2012),Borja et al (2013),Malone et al (2014),McClatchie et al (2014),Alin et al (2015), NOC (2016),Benson et al (2018),Miloslavich et al (2018), Muller-Karger et al,Peterman & Anderson (1999),Hilborn, Maguire, Parma, & Rosenberg (2001),Stobutzki, Miller, & Brewer (2001),Peterman (2004Peterman ( , 2009, Hobday, Smith, and Stobutzki (2004), Fletcher (2005), Smith et al (2007), Patrick et al (2010), Collie, Peterman, & Zuehlke (2012), Cormier, Kannen, Elliott, Hall, and Davies (2013), Micheli et al (2014), Holsman et al (2017), Lockerbie et al (2018), Stelzenmüller et al (2018), Lynch et al (2018) II Assessing risk to LMRs due to climate change Williams, Shoo, Isaac, Hoffmann, & Langham (2008), Cochrane, Young, Soto, & Bahri (2009), Preston, Yuen, & Westaway (2011), Foden et al (2013), Gaichas, Link, & Hare (2014), Gaichas et al (2016), Hare et al (2016) III Assessing risk to ecological dynamics Kolar & Lodge (2002), Forbes et al (2011), Hobday et al (2007, 2011), Le Quesne & Jennings (2012), Lockerbie et al (2018) IV Assessing risk to habitat loss Penney & Guinotte (2013), Arkema et al (2014), Seitz, Wennhage, Bergström, Lipcius, & Ysebaert (2014), Gaichas et al (2016) (1995), Dulvy et al (2004), Graham et al (2011), Burgess, Polasky, & Tilman (2013), Cormier et al (2013), Zhou, Hobday, Dichmont, & Smith (2016), Holsman et al (2017), Lynch et al (2018) VI Management of risk an important part of fisheries management Francis (1992), Smith, Hunt, & Rivard (1993), Peterman & Anderson (1999), Hilborn et al (2001), Stobutzki et al (2001), Peterman (2004, 2009), Hobday et al (2007, 2004, 2011), Fletcher (2005), Smith et al (2007), Patrick et al (2010), Collie et al (2012), Cormier et al (2013), Hare et al (2016), Holsman et al (2017), Lockerbie et al (2018), Stelzenmuller et al (2018) VII Many extant methods to evaluate risk for LMRs Dulvy et al (2004), Fletcher (2005), Jiao, Chen, & Wroblewski (2005), Hobday et al (2011), Burgess et al (2013), Cormier et al (2013), Hare et al (2016), Zhou et al (2016), Holsman et al (2017), Lockerbie et al (2018) flow charts to determine possible management actions. The overall schema of how we view major factors influencing LMR populations is as a range of possible influencing factors, which once iden...…”