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

Abstract

The Great Barrier Reef World Heritage Area (GBRWHA) in north eastern Australia spans 2500 km of coastline and covers an area of ~ 350,000 km2. It includes one of the world’s largest seagrass resources. To provide a foundation to monitor, establish trends and manage the protection of seagrass meadows in the GBRWHA we quantified potential seagrass community extent using six random forest models that include environmental data and seagrass sampling history. We identified 88,331 km2 of potential seagrass habitat in intertidal and subtidal areas: 1111 km2 in estuaries, 16,276 km2 in coastal areas, and 70,934 km2 in reef areas. Thirty-six seagrass community types were defined by species assemblages within these habitat types using multivariate regression tree models. We show that the structure, location and distribution of the seagrass communities is the result of complex environmental interactions. These environmental conditions include depth, tidal exposure, latitude, current speed, benthic light, proportion of mud in the sediment, water type, water temperature, salinity, and wind speed. Our analysis will underpin spatial planning, can be used in the design of monitoring programs to represent the diversity of seagrass communities and will facilitate our understanding of environmental risk to these habitats.

Seagrass Biodiversity and its Drivers in the Kepulauan Banyak Marine Nature Park, Indonesia

“…Specifically, the organic content found within the mud substrate at the Orongan and Matahari sites appeared to be beneficial for seagrass diversity. This finding aligns with previous research that has identified substrate type as a key determinant of seagrass diversity across various spatial scales and bioregions (Carter et al 2021;Short et al, 2007).…”

“…These findings align with the influence of geographical factors on seagrass diversity discussed in the Great Barrier Reef study (Carter et al, 2021). This study highlighted the strong effect of latitude on the probability of seagrass presence (Short et al, 2007), with seagrass being more likely to occur at latitudes >18° S (Carter et al, 2021).…”

“…However, none of these variables had a significant impact on Shannon's H diversity index, which measures species abundance and evenness. The only exception was the CDFL driver, which almost reached statistical significance (P= 0.052), explaining 40% of the variation (R² = 0.4) in Shannon's H' index, indicating that seagrass species coexist and share similar ecological niches and tolerances in the study area (Carter et al, 2021).…”

Seagrass spatial data synthesis from north‐east Australia, Torres Strait and Gulf of Carpentaria, 1983 to 2022

“…The immediate scientific value of projects like this have been demonstrated on Queensland's east coast, where the Great Barrier Reef World Heritage Area (GBRWHA) data synthesis (Carter et al 2021 a ) has been used to answer a number of key ecological questions, including the probability of seagrass distribution and communities (Carter et al 2021 d ), defining the desired state of seagrass communities in the Townsville region (Collier et al 2020) and GBRWHA (Carter et al 2022 b ), examining management targets for rivers influencing seagrass habitat (Collier et al 2021; Lambert et al 2021), and in designing a Great Barrier Reef‐scale monitoring program (Udy et al 2019). Seagrass data has also been used on the Great Barrier Reef to model risk exposure (Grech et al 2011; Grech et al 2012; Bainbridge et al 2018); propagule distribution (Grech et al 2016; Schlaefer et al 2022); developing a National Ocean Account in Australia (https://www.abs.gov.au/articles/towards-national-ocean-account); and connectivity among meadows (Tol et al 2017; Grech et al 2018).…”

The Risk of Multiple Anthropogenic and Climate Change Threats Must Be Considered for Continental Scale Conservation and Management of Seagrass Habitat

“…The generated and freely available maps for each state are available at https://portal.tern.org.au/robert-franklin-ccanto/19365 and all have the acronym ACEAS at the end of the file name (Canto et al, 2014a,b,c,d,e,f,g). Then additional sources were added to this map (QDPI-F, 2009;McKenzie et al, 2014;TropWATER, 2016). The deep water seagrass map (QDPI-F, 2009) was categorized as the probability of seagrass occurrence.…”