An evidence-based approach for setting desired state in a complex Great Barrier Reef seagrass ecosystem: A case study from Cleveland Bay

A spatial analysis of seagrass habitat and community diversity in the Great Barrier Reef World Heritage Area

“…Understanding the factors that support the resilience of important coastal marine habitats at large regional scales is difficult. Challenges include describing diversity, distribution and connectivity within ecosystems, defining desired state and assessing ecosystem condition to understand long-term trends and in evaluating short-term impact-recovery cycles 7 . This is exacerbated in Australia’s Great Barrier Reef World Heritage Area (GBRWHA) by the vaguely defined objective and high bar set by the reef management authority in 2015 to “maintain diversity of species and ecological habitats in at least a good condition and with a stable to improving trend” 8 and updated in 2018 to “[facilitate] adaptive management for the Reef that is effective, efficient and evolving” 9 .…”

“…The ability of these maps to capture the diversity of environmental features that support biological complexity provides the foundation for large-scale spatial assessments of where habitats and communities are likely located 11 . Spatial maps also support an analysis of connectivity 12 , an understanding of spatial and temporal change 13 , and an analysis that defines a desired state 7 . They are a critical component of marine spatial planning, to resolve conflicts, incorporate indigenous knowledge, define management units, and to design representative monitoring programs 14 – 17 .…”

“…Four broad classifications have been applied to describe seagrasses in the GBRWHA: estuarine, coastal, deep-water (subtidal), and reef, and the dominant environmental factors influencing seagrasses identified as terrigenous runoff, physical disturbance, low light, and low nutrients 41 , 42 . Seagrass communities within each of these four classes are diverse and complex 7 . Previous GBRWHA-scale seagrass spatial models have focussed on overall seagrass distribution or on the distribution of single species.…”

Synthesizing 35 years of seagrass spatial data from the Great Barrier Reef World Heritage Area, Queensland, Australia

“…Key to addressing these challenges is access to data for analysis and comparison at appropriate spatial and temporal scales in a user‐friendly format. Such data can be used for describing the present condition of ecosystems; understanding long‐term trends while accounting for short‐term impact‐recovery cycles; defining the desired state of the diversity of habitats; establishing ecologically relevant targets that can be used to maintain resilience; and implementing appropriate management frameworks that maintain resilience (Levin and Möllmann 2015; Hallett et al 2016; Brodie et al 2017; O'Brien et al 2017; York et al 2017; Collier et al 2020). To this end, there is an increasing use of Geographic Information Systems (GIS) to record, synthesize, and analyze data and to benchmark previous states to inform research, conservation, ecosystem‐based management, and marine spatial planning (St. Martin 2004; St. Martin and Hall‐Arber 2008).…”

“…Spatial data are an increasingly important tool in the assessment and management of the marine environment (Hughes et al 2005; Rajabifard et al 2005; St. Martin and Hall‐Arber 2008). The immediate scientific value of this project and its approach already have been widely demonstrated, with earlier subsets of this synthesis used to answer a range of key ecological questions including probability modeling of seagrass distributions in the GBRHWA's deep‐water lagoon (Coles et al 2009) and inshore region (Grech and Coles 2010); seagrass risk exposure modeling (Grech et al 2011, 2012); propagule distribution (Grech et al 2016); connectivity among meadows (Tol et al 2017; Grech et al 2018); understanding changes in seagrass meadow health using MODIS imagery (Petus et al 2014); and defining the desired state of seagrass communities in the Townsville region (Lambert et al 2019; Collier et al 2020). We now make available the data behind these analyses and data updated to 2018 for the global community to use and compare.…”