An integrative method for the evaluation, monitoring, and comparison of seagrass habitat structure

Review of ecological valuation and equivalency analysis methods for assessing temperate nearshore submerged aquatic vegetation

Nearshore seagrass, kelp, and other macroalgae beds (submerged aquatic vegetation [SAV]) are productive and important ecosystems. Mitigating anthropogenic impacts on these habitats requires tools to quantify their ecological value and the debits and credits of impact and mitigation. To summarize and clarify the state of SAV habitat quantification and available tools, we searched peer-reviewed literature and other agency documents for methods that either assigned ecological value to or calculated equivalencies between impact and mitigation in SAV. Out of 47 tools, there were 11 equivalency methods, 7 of which included a valuation component. The remaining valuation methods were most commonly designed for seagrasses and rocky intertidal macroalgae rather than canopy-forming kelps. Tools were often designed to address specific resource policies and associated habitat evaluation. Frequent categories of tools and methods included those associated with habitat equivalency analyses and those that scored habitats relative to reference or ideal conditions, including models designed for habitat suitability indices and the European Union's Water and Marine Framework Directives. Over 29 tool input metrics spanned 3 spatial scales of SAV: individual shoots or stipes, bed or site, and landscape or region. The most common metric used for both seagrasses and macroalgae was cover. Seagrass tools also often employed density measures, and some categories used measures of tissue content (e.g., carbon, nitrogen). Macroalgal tools for rocky intertidal habitats frequently included species richness or incorporated indicator species to assess habitat. We provide a flowchart for decision-makers to identify representative tools that may apply to their specific management needs.

Assessing Seagrass Restoration Actions through a Micro-Bathymetry Survey Approach (Italy, Mediterranean Sea)

Underwater photogrammetry provides a means of generating high-resolution products such as dense point clouds, 3D models, and orthomosaics with centimetric scale resolutions. Underwater photogrammetric models can be used to monitor the growth and expansion of benthic communities, including the assessment of the conservation status of seagrass beds and their change over time (time lapse micro-bathymetry) with OBIA classifications (Object-Based Image Analysis). However, one of the most complex aspects of underwater photogrammetry is the accuracy of the 3D models for both the horizontal and vertical components used to estimate the surfaces and volumes of biomass. In this study, a photogrammetry-based micro-bathymetry approach was applied to monitor Posidonia oceanica restoration actions. A procedure for rectifying both the horizontal and vertical elevation data was developed using soundings from high-resolution multibeam bathymetry. Furthermore, a 3D trilateration technique was also tested to collect Ground Control Points (GCPs) together with reference scale bars, both used to estimate the accuracy of the models and orthomosaics. The root mean square error (RMSE) value obtained for the horizontal planimetric measurements was 0.05 m, while the RMSE value for the depth was 0.11 m. Underwater photogrammetry, if properly applied, can provide very high-resolution and accurate models for monitoring seagrass restoration actions for ecological recovery and can be useful for other research purposes in geological and environmental monitoring.

Evaluation of a popular spatial cumulative impact assessment method for marine systems: A seagrass case study

Human activities put stress on our oceans and with a growing global population, the impact is increasing. Stressors rarely act in isolation, with the majority of marine areas being impacted by multiple, concurrent stressors. Marine spatial cumulative impact assessments attempt to estimate the collective impact of multiple stressors on marine environments. However, this is difficult given how stressors interact with one another, and the variable response of ecosystems. As a result, assumptions and generalisations are required when attempting to model cumulative impacts. One fundamental assumption of the most commonly applied, semi-quantitative cumulative impact assessment method is that a change in modelled cumulative impact is correlated with a change in ecosystem condition. However, this assumption has rarely been validated with empirical data. We tested this assumption using a case study of seagrass in a large, inverse estuary in South Australia (Spencer Gulf). We compared three different seagrass condition indices, based on survey data collected in the field, to scores from a spatial cumulative impact model for the study area. One condition index showed no relationship with cumulative impact, whilst the other two indices had very small, negative relationships with cumulative impact. These results suggest that one of the most commonly used methods for assessing cumulative impacts on marine systems is not robust enough to accurately reflect the effect of multiple stressors on seagrasses; possibly due to the number and generality of assumptions involved in the approach. Future methods should acknowledge the complex relationships between stressors, and the impact these relationships can have on ecosystems. This outcome highlights the need for greater evaluation of cumulative impact assessment outputs and the need for data-driven approaches. Our results are a caution for marine scientists and resource managers who may rely on spatial cumulative impact assessment outputs for informing policy and decision-making.

Seagrass ecosystem trajectory depends on the relative timescales of resistance, recovery and disturbance

Seagrass ecosystems are inherently dynamic, responding to environmental change across a range of scales. Habitat requirements of seagrass are well defined, but less is known about their ability to resist disturbance. Specific means of recovery after loss are particularly difficult to quantify. Here we assess the resistance and recovery capacity of 12 seagrass genera. We document four classic trajectories of degradation and recovery for seagrass ecosystems, illustrated with examples from around the world. Recovery can be rapid once conditions improve, but seagrass absence at landscape scales may persist for many decades, perpetuated by feedbacks and/or lack of seed or plant propagules to initiate recovery. It can be difficult to distinguish between slow recovery, recalcitrant degradation, and the need for a window of opportunity to trigger recovery. We propose a framework synthesizing how the spatial and temporal scales of both disturbance and seagrass response affect ecosystem trajectory and hence resilience.

Error, Power, and Blind Sentinels: The Statistics of Seagrass Monitoring

We derive statistical properties of standard methods for monitoring of habitat cover worldwide, and criticize them in the context of mandated seagrass monitoring programs, as exemplified by Posidonia oceanica in the Mediterranean Sea. We report the novel result that cartographic methods with non-trivial classification errors are generally incapable of reliably detecting habitat cover losses less than about 30 to 50%, and the field labor required to increase their precision can be orders of magnitude higher than that required to estimate habitat loss directly in a field campaign. We derive a universal utility threshold of classification error in habitat maps that represents the minimum habitat map accuracy above which direct methods are superior. Widespread government reliance on blind-sentinel methods for monitoring seafloor can obscure the gradual and currently ongoing losses of benthic resources until the time has long passed for meaningful management intervention. We find two classes of methods with very high statistical power for detecting small habitat cover losses: 1) fixed-plot direct methods, which are over 100 times as efficient as direct random-plot methods in a variable habitat mosaic; and 2) remote methods with very low classification error such as geospatial underwater videography, which is an emerging, low-cost, non-destructive method for documenting small changes at millimeter visual resolution. General adoption of these methods and their further development will require a fundamental cultural change in conservation and management bodies towards the recognition and promotion of requirements of minimal statistical power and precision in the development of international goals for monitoring these valuable resources and the ecological services they provide.

Response of multi-metric indices to anthropogenic pressures in distinct marine habitats: the need for recalibration to allow wider applicability

Sustainable exploitation of coastal ecosystems is facilitated by tools which allow reliable assessment of their response to anthropogenic pressures. The Infaunal Quality Index (IQI) and Multivariate-AMBI (M-AMBI) were developed to classify the ecological status (ES) of benthos for the Water Framework Directive (WFD). The indices respond reliably to the impacts of organic enrichment in muddy sand habitats, but their applicability across a range of pressures and habitats is less well understood. The ability of the indices to predict changes in response to pressures in three distinct habitats, intertidal muddy sand, maerl and inshore gravel, was tested using pre-existing datasets. Both responded following the same patterns of variation as previously reported. The IQI was more conservative when responding to environmental conditions so may have greater predictive value in dynamic habitats to provide an early-warning system to managers'. Re-calibration of reference conditions is necessary to reliably reflect ES in different habitats.