Seagrass meadows support key ecosystem services, via provision of food directly for herbivores, and indirectly to their predators. The importance of herbivores in seagrass meadows has been well-documented, but the links between food webs and ecosystem services in seagrass meadows have not previously been made explicit. Herbivores interact with ecosystem services – including carbon sequestration, cultural values, and coastal protection. Interactions can be positive or negative and depend on a range of factors including the herbivore identity and the grazing type and intensity. There can be unintended consequences from management actions based on a poor understanding of trade-offs that occur with complex seagrass-herbivore interactions. Tropical seagrass meadows support a diversity of grazers spanning the meso-, macro-, and megaherbivore scales. We present a conceptual model to describe how multiple ecosystem services are influenced by herbivore pressure in tropical seagrass meadows. Our model suggests that a balanced ecosystem, incorporating both seagrass and herbivore diversity, is likely to sustain the broadest range of ecosystem services. Our framework suggests the pathway to achieve desired ecosystem services outcomes requires knowledge on four key areas: (1) how size classes of herbivores interact to structure seagrass; (2) desired community and management values; (3) seagrass responses to top–down and bottom–up controls; (4) the pathway from intermediate to final ecosystem services and human benefits. We suggest research should be directed to these areas. Herbivory is a major structuring influence in tropical seagrass systems and needs to be considered for effective management of these critical habitats and their services.

Structural habitat complexity is a fundamental attribute influencing ecological food webs. Simplification of complex habitats occurs due to both natural and anthropogenic pressures that can alter productivity of food webs. Relationships between food web structure and habitat complexity may be influenced by multiple mechanisms, and untangling these can be challenging. We investigated whether (1) size spectra vary across a gradient of habitat complexity in seagrass meadows and (2) structural complexity changes the importance of different primary producers supporting the food web (determined using stable isotope analysis) in the Great Barrier Reef World Heritage Area. We found that moderately complex meadows had much steeper size spectra slopes, caused by a higher abundance of smaller animals and fewer larger animals, while meadows on either end of the complexity scale (low and a single meadow with very high complexity) had shallower slopes, indicative of a more balanced distribution of animal sizes across the spectrum. We also found that the importance of epiphytic algae as a food source was high in most meadows, despite the increase in seagrass surface area on which epiphytes could grow. The consistent importance of epiphytic algae suggests that the changes in the availability of different potential food sources did not affect food web structure. Our findings indicate that food web structure may change with variations in structural complexity because of changes in the abundance of smaller and/or larger animals. Food web structure and food sources are important determinants of the dynamic stability of food webs. Size spectra analysis is already used as a monitoring tool for assessing populations of key fisheries species in commercial fishing operations, and thus, we recommend using size spectra as a proxy for assessing the structure of the food webs in different types of seagrass meadows. Size spectra may be a useful indicator of how different meadows provide for ecosystem services such as fisheries.

The ongoing need to sustainably manage fishery resources can benefit from fishery-independent monitoring of fish stocks. Camera systems, particularly baited remote underwater video system (BRUVS), are a widely used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning (DL) models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g., 1 h). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often obscure other fish. We build upon existing DL methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australasian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: (1) varying confidence thresholds (CTs), (2) on/off use of sequential non-maximum suppression (Seq-NMS), and (3) statistical correction equations. Output from the DL model was more accurate at low abundances of snapper than at higher abundances (>15 fish per frame) where the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1–2 of manual counts (R2 = 88%), used Seq-NMS, a 45% CT, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in the BRUV footage on which it was tested. Additional evaluation will be required to test and refine the procedure so that automated counts of snapper are accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here could help to increase the usefulness of BRUVS.

Connectivity is fundamentally important for shaping the resilience of complex human and natural networks when systems are disturbed. Ecosystem resilience is, in part, shaped by the spatial arrangement of habitats, the permeability and fluxes between them, the stabilising functions performed by organisms, their dispersal traits, and the interactions between functions and stressor types. Controlled investigations of the relationships between these phenomena under multiple stressors are sparse, possibly due to logistic and ethical difficulties associated with applying and controlling stressors at landscape scales. Here we show that grazing performance, a key ecosystem function, is linked to connectivity by manipulating the spatial configuration of habitats in microcosms impacted by multiple stressors. Greater connectivity enhanced ecosystem function and reduced variability in grazing performance in unperturbed systems. Improved functional performance was observed in better connected systems stressed by harvesting pressure and temperature rise, but this effect was notably reversed by the spread of disease. Connectivity has complex effects on ecological functions and resilience, and the nuances should be recognised more fully in ecosystem conservation.

Seagrass meadows are an important habitat for a variety of animals, including ecologically and socioeconomically important species. Seagrass meadows are recognised as providing species with nursery grounds, and as a migratory pathway to adjacent habitats. Despite their recognised importance, little is known about the species assemblages that occupy seagrass meadows of different depths in the coastal zone. Understanding differences in the distribution of species in seagrass at different depths, and differences in species diversity, abundance, biomass, and size spectra, is important to fully appreciate both the ecological significance and economic importance of these seagrass meadows. Here, we assess differences in the assemblage characteristics of fish, crustacea, and cephalopods (collectively, nekton) between deep (>9 m; Halophila spinulosa dominant) and shallow water (<2 m; Halodule uninervis and/or Zostera muelleri dominant) seagrass meadows of the central Great Barrier Reef coast of Queensland, Australia. Nekton assemblage structure differed between deep and shallow seagrass. Deeper meadows were typified by juvenile emperors (e.g., Lethrinus genivittatus), hairfinned leatherjacket (Paramonacanthus japonicus) and rabbitfish (e.g., Siganus fuscescens) in both biomass per unit effort (BPUE) and catch per unit effort (CPUE), whereas shallow meadows were typified by the green tiger prawn (Penaeus semisulcatus) and pugnose ponyfish (Secutor insidiator) in both BPUE and CPUE. Both meadow depths were distinct in their nekton assemblage, particularly for socioeconomically important species, with 11 species unique to both shallow and deep meadows. However, both meadow depths also included juveniles of socioeconomically important species found in adjacent habitats as adults. The total nekton CPUE was not different between deep and shallow seagrass, but the BPUE and body mass of individual animals were greater in deep than shallow seagrass. Size spectra analysis indicated that in both deep and shallow meadows, smaller animals predominated, even more so than theoretically expected for size spectra. Our findings highlight the unique attributes of both shallow and deeper water seagrass meadows, and identify the distinct and critically important role of deep seagrass meadows within the Great Barrier Reef World Heritage Area (GBRWHA) as a habitat for small and juvenile species, including those of local fisheries value.