Multiple dilemmas confound social-ecological modelling. This review paper focuses on two: a modeller's dilemma associated with determining appropriate levels of model simplification, and a dilemma of decision-making relating to the use of models that were never designed to predict. We analyse approaches for addressing these dilemmas as they relate to shallow coastal systems and conclude that wicked problems cannot be adequately addressed using traditional disciplinary or systems engineering modelling. Simplified inter- and trans-disciplinary models have the potential to identify directions of system change, challenge thinking in disciplinary silos, and ultimately confront the dilemmas of social-ecological modelling.
Tools and Data
Electronic tags are significantly improving our understanding of aquatic animal behavior and are emerging as key sources of information for conservation and management practices. Future aquatic integrative biology and ecology studies will increasingly rely on data from electronic tagging. Continued advances in tracking hardware and software are needed to provide the knowledge required by managers and policymakers to address the challenges posed by the world's changing aquatic ecosystems. We foresee multiplatform tracking systems for simultaneously monitoring the position, activity, and physiology of animals and the environment through which they are moving. Improved data collection will be accompanied by greater data accessibility and analytical tools for processing data, enabled by new infrastructure and cyberinfrastructure. To operationalize advances and facilitate integration into policy, there must be parallel developments in the accessibility of education and training, as well as solutions to key governance and legal issues.
Field data are still recorded on paper in many worldwide beach surveys of nesting marine turtles. The data must be subsequently transferred into an electronic database, and this can introduce errors in the dataset. To minimize such errors, the “Turtles” software was developed and piloted to record field data by one software user accompanying one Tortuguero in Akumal beaches, Quintana Roo, Mexico, from June 1st to July 31st during the night patrols. Comparisons were made between exported data from the software with the paper forms entered into a database (henceforth traditional). Preliminary assessment indicated that the software user tended to record a greater amount of metrics (i.e., an average of 18.3 fields ± 5.4 sd vs. 8.6 fields ± 2.1 sd recorded by the traditional method). The traditional method introduce three types of “errors” into a dataset: missing values in relevant fields (40.1%), different answers for the same value (9.8%), and inconsistent data (0.9%). Only 5.8% of these (missing values) were found with the software methodology. Although only tested by a single user, the software may suggest increased efficacy and warrants further examination to accurately assess the merit of replacing traditional methods of data recording for beach monitoring programmes.
Coastline degradation, as well as subsequent ecosystem loss, has long been attributed to anthropogenic stress and is an all too familiar issue affecting coastal habitats. Should management and conservation efforts fail to improve the quality of coastal ecosystems and the services they provide, they may be irrevocably damaged. A significant limitation to conservation efforts is often the ability to track change in seagrass meadows due to the significant time and cost of monitoring efforts in underwater habitats. Remote sensing is often a tool used to improve our knowledge of habitat status, however, ground-truthing remote sensing results is difficult when historical data is required. We apply an innovative and resourceful approach to the attainment of data to check the status of seagrass meadows from resources that are available in many areas due to the collection of other data sets. We employ the use of underwater digital photographs originally taken for monitoring sediment movement patterns. We were successfully able to develop a method to critically and easily evaluate these photographs for habitat status, enabling the generation of a data set unable to be obtained in other ways. This method can further be utilised in a citizen science project, for other underwater digital photographs, to support the assessment of coastal submerged ecosystem habitat status.
One of the more challenging tasks in Marine Spatial Planning (MSP) is identifying critical areas for management and conservation of fish stocks. However, this objective is difficult to achieve in data-poor situations with different sources of uncertainty. In the present study we propose a combination of hierarchical Bayesian spatial models and remotely sensed estimates of environmental variables to be used as flexible and reliable statistical tools to identify and map fish species richness and abundance hot-spots. Results show higher species aggregates in areas with higher sea floor rugosity and habitat complexity, and identify clear richness hot-spots. Our findings identify sensitive habitats through essential and easy-to-use interpretation tools, such as predictive maps, which can contribute to improving management and operability of the studied data-poor situations.
While marine protected areas (MPAs) can simultaneously contribute to biodiversity conservation and fisheries management, the global network is biased towards particular ecosystem types, as it was largely established in an ad hoc fashion. The optimization of trade-offs between biodiversity benefits and socio-economic values increases implementation success and minimizes enforcement costs in the long run, but is often neglected in marine spatial planning (MSP). Although the acquisition of spatially explicit socioeconomic data is often perceived as a costly/secondary step in MSP, it is critical to account for lost opportunities by people whose activities will be restricted, especially fishers. Here we present an easily-reproducible habitat-based approach to estimate the spatial distribution of opportunity cost to fishers in data poor regions, assuming that the most accessible areas have higher values and their designation as no-take zones represents increased loss of fishing opportunities. Our method requires only habitat and bathymetric maps, a list of target species, the location of ports, and the relative importance for each port and/or vessel/gear type. The potential distribution of fishing resources is estimated from bathymetric ranges and benthic habitat distribution, while the relative importance of the different resources is estimated for each port, considering total catches (kg), revenues and/or stakeholder perception. Finally, the model can combine different cost layers to produce a comprehensive cost layer, and also allows for the evaluation of tradeoffs. The development of FishCake was based on data from a contentious conservation-planning arena (Abrolhos Bank, Brazil) in which attempts to expand MPA coverage failed due to fishers’ resistance. The opportunity cost approach that we introduce herein allows for the incorporation of economic interests of different stakeholders and evaluation of tradeoffs among different stakeholder groups. The novel approach can be directly used to support conservation planning, in Abrolhos and elsewhere, and is expected to facilitate community consultation.
Environmental DNA (eDNA) can be a powerful method for assessing the presence and the distribution of aquatic species. We used this tool in order to detect and quantify eDNA from the elusive species Octopus vulgaris, using qPCRs (SybrGreen protocol). We designed species-specific primers, and set up an experimental aquarium approach to validate the new molecular tool in different controlled conditions. Field validation was conducted from sea water samples taken from 8 locations within an octopus fishery area in the Cantabrian Sea during February–March 2016. A significant positive correlation between the total biomass (g of O. vulgaris within thanks) and the amount of O. vulgaris eDNA detected (p-value = 0.01261) was found in aquarium experiments. The species was also detected by PCR in 7 of the 8 water samples taken at sea, and successfully quantified by qPCR in 5 samples. This preliminary study and innovative method opens very promising perspectives for developing quick and cheap tools for the assessment of O. vulgaris distribution and abundance in the sea. The method could help in a close future for quantifying unseen and elusive marine species, thus contributing to establish sustainable fisheries.
As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances.
Increasing numbers of people are living in and using coastal areas. Combined with the presence of pervasive coastal threats, such as flooding and erosion, this is having widespread impacts on coastal populations, infrastructure and ecosystems. For the right adaptive strategies to be adopted, and planning decisions to be made, rigorous evaluation of the available options is required. This evaluation hinges on the availability and use of suitable datasets. For knowledge to be derived from coastal datasets, such data needs to be combined and analysed in an effective manner. This paper reviews a wide range of literature relating to data-driven approaches to coastal risk evaluation, revealing how limitations have been imposed on many of these methods, due to restrictions in computing power and access to data. The rapidly emerging field of ‘Big Data’ can help overcome many of these hurdles. ‘Big Data’ involves powerful computer infrastructures, enabling storage, processing and real-time analysis of large volumes and varieties of data, in a fast and reliable manner. Through consideration of examples of how ‘Big Data’ technologies are being applied to fields related to coastal risk, it becomes apparent that geospatial Big Data solutions hold clear potential to improve the process of risk based decision making on the coast. ‘Big Data’ does not provide a stand-alone solution to the issues and gaps outlined in this paper, yet these technological methods hold the potential to optimise data-driven approaches, enabling robust risk profiles to be generated for coastal regions.
Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high-resolution imaging and associated machine-learning image-scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free-to-use machine-learning software to semi-automatically generate dense and widespread abundance records of a habitat-forming algae over ~5,000 m2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver-based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10–20 transects (50 × 1 m) were required to obtain reliable results. This represents 2–20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine-resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi-automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.