Shark Research & Monitoring Software
How do you get more from every shark observation and project?
Shark research generates complex, valuable data. Observations can come from BRUVs, diver surveys, photo identification, fisheries observers, logbooks, electronic monitoring, catch and bycatch records, tagging, acoustic receivers, tourism operators, citizen science, stranding networks, and other research programs. Each method captures a different part of the picture, with its own sampling design, effort, metadata, biases, and analytical requirements.
The challenge is rarely just collecting the observations. Researchers then need to organize and validate the data, account for sampling effort, reconcile species and terminology, analyze abundance or distribution, examine interactions, produce maps and figures, compare locations or time periods, and turn the results into publications, reports, assessments, or management advice.
When those workflows are built separately for every project, a substantial amount of scientific capacity can be consumed by data management and repeated analysis rather than interpretation.
eOceans automates the data science for shark research so researchers can spend more time understanding sharks and less time rebuilding the workflow.
Study sharks using the methods that make sense for your question
Different shark questions require different methods. A reef survey may use diver observations or BRUVs. A fisheries study may depend on catch, effort, bycatch, gear, observer, or electronic-monitoring data. A population study may combine photo identification, tagging, acoustic detections, or repeated surveys. A tourism program may generate thousands of observations from operators and guests.
eOceans is designed to accommodate these different sources rather than forcing every shark project into the same sampling approach.
Keep the methodology, effort, location, timing, observer, species identification, and other relevant metadata connected to each observation. This makes it possible to understand not only what was observed, but also how, where, when, and under what conditions it was observed.
That context is essential when comparing observations across sites, years, species, programs, or methods.
Understand abundance, distribution, and change
Shark monitoring needs to move beyond presence and absence. Depending on the study design, researchers may need to examine abundance, encounter rates, density, distribution, species richness, community composition, size structure, sex, life stage, habitat associations, temporal change, or spatial patterns.
The same underlying observations may support multiple analyses. A dataset collected to monitor shark abundance may later be used to investigate changes in species composition, range, habitat use, or interactions with other species.
Instead of repeatedly rebuilding the dataset for each question, a structured and continuously updated workflow allows new observations to build on the existing record.
Account for effort and sampling design
A count of sharks is rarely meaningful without understanding the effort that produced it.
Was the observation generated from a 30-minute BRUV deployment, a two-hour dive, a fishing trip, a standardized transect, an observer program, or an opportunistic encounter? Were sites surveyed equally? Did the sampling method change? Were environmental conditions different?
eOceans keeps observations connected to the activities and metadata needed to understand the sampling process. This allows researchers to work with measures such as encounter rates and catch per unit effort and to distinguish changes in observations from changes in sampling effort.
Better data structure means more defensible analysis.
Study sharks in relation to other species
Sharks do not exist independently of the rest of the ecosystem.
A shark study may require information about prey, competitors, predators, habitat-forming species, fisheries, seabirds, marine mammals, or environmental conditions. A question about shark distribution may become a question about temperature, habitat, prey availability, fishing pressure, or protected-area status.
eOceans can connect observations across species and environmental variables while retaining their original context.
That makes it possible to ask questions such as:
Where are sharks being observed, and how does that change over time?
Which species occur together?
How does shark abundance vary with habitat?
Are changes in shark observations associated with changes in fishing activity?
Where are sharks interacting with fisheries?
What happens to shark communities inside and outside protected areas?
Are changes in shark populations occurring alongside changes in other species?
The important distinction is that connecting datasets does not automatically mean assuming that one variable causes another. The system helps researchers bring the evidence together so those relationships can be investigated appropriately.
Study shark–fishery interactions
Many shark research questions sit directly at the intersection of wildlife and fisheries.
Researchers may need to understand shark catch and bycatch, fishing effort, gear interactions, mortality, release outcomes, depredation, or hooking rates. They may need to examine where interactions occur, which species are involved, how rates vary among gears or locations, or whether management measures are associated with changes.
For example, shark depredation can be studied as an interaction between sharks and fishing activity: where sharks remove or damage hooked catch, how frequently interactions occur, which species are involved, and how those interactions vary spatially or temporally.
The same infrastructure can support the analysis of sharks as bycatch, sharks interacting with specific gears, or changes in shark observations associated with fishing activity.
Evaluate shark sanctuaries and protected areas
Protected areas and shark sanctuaries create another important research need: determining what changes after protection and whether those changes are consistent with the objectives of the intervention.
That can require baseline observations, ongoing monitoring, information about protection status and boundaries, fishing activity, species distributions, abundance or encounter rates, and observations from inside and outside protected areas.
eOceans can keep those observations and spatial boundaries connected over time, allowing researchers to build a record rather than repeatedly reconstructing the evidence for each assessment.
This is particularly useful when protection needs to be evaluated across multiple species or when shark conservation is part of a broader MPA or fisheries assessment.
Bring together long-term shark observations
Some of the most valuable shark datasets are also the hardest to manage because they accumulate over many years.
The Great Fiji Shark Count, for example, brought together observations from tourism operators and researchers across Fiji. It ultimately included 30,668 dives, 146,304 shark observations, 592 sites, 39 operators, and 11 shark species.
Long-term datasets like this can reveal patterns that are impossible to see from a single survey or short-term project. But their value depends on maintaining consistent structure, metadata, taxonomy, locations, methods, and quality control as the dataset grows.
eOceans was built from this kind of real-world research problem: how to make large, heterogeneous observational datasets useful not just once, but continuously.
Scale from one study site to many regions
Shark research can range from a single reef or coastal area to national monitoring programs and global datasets.
The same underlying workflow can support a local study while allowing researchers to add sites, species, years, observers, methods, or regions as the project grows. Spatial tools can support boundaries, sampling locations, distributions, and comparisons across management areas.
This makes it possible to start with a defined research question without designing an entirely new data system if the research later expands.
Keep research reproducible as the project grows
Scientific reproducibility depends on more than retaining the final dataset. Researchers need to understand where observations came from, how they were collected, what quality-control processes were applied, what transformations occurred, and how analytical results were produced.
eOceans keeps observations connected to their methods, metadata, validation, analyses, maps, and reports. As new observations are added, established workflows can be rerun rather than recreated manually.
That makes it easier to update results, investigate new questions, onboard new researchers, and preserve institutional knowledge when people or projects change.
From field observation to publication
Shark researchers should not have to move the same data through a chain of disconnected systems every time they need a new result.
A typical workflow might look like:
Collect → Organize → Validate → Standardize → Analyze → Map → Visualize → Report → Publish → Update
eOceans connects these stages so that the work done during data management contributes to everything that follows.
The goal isn't to replace statistical expertise, ecological interpretation, or scientific judgement. Those remain essential. The goal is to automate the repetitive work around them.
In one California monitoring application, a workflow estimated at six months was completed in two weeks, using one part-time scientist. The project included 247 surveys and 245,908 animal observations, replaced eight separate tools, and reduced analysis work by approximately 80%.
That is one demonstrated workflow, not a promise that every shark project will achieve the same result. But it illustrates what becomes possible when repetitive data-management and analytical tasks are connected and automated.
Built from shark science
eOceans began as a tool specifically intended to automate shark science.
Years of shark and ray research helped define what the system needed to handle: observations from different methods, large numbers of records, spatial and temporal analysis, species-level information, fisheries interactions, protected areas, changing populations, and the need to turn observations into defensible scientific evidence.
Work such as the Great Fiji Shark Count, eManta, shark sanctuary reviews and evaluations, research into shark declines on reefs, and research into shark mortality and fisheries interactions all contributed to that evolution.
The more shark science was examined, the clearer it became that sharks could not be studied in isolation. Sharks interact with prey, predators, fisheries, habitats, other wildlife, environmental conditions, and people.
So the system expanded.
From sharks to rays. From rays to fish and other wildlife. From species to habitats and ecosystems. From conservation monitoring to fisheries assessments, environmental impact assessments, coral reefs, coral bleaching, oil spills, cetaceans, and other environmental questions.
Testing then moved beyond the ocean, including freshwater and terrestrial applications. Researchers and organizations began using the same infrastructure for species and problems as varied as salmon, moose, caribou, ticks, bird mortality, and environmental hazards in cities.
The original question was how to automate shark science. The answer became a system for automating environmental science.
Shark research, without rebuilding the workflow
Whether the question is abundance on a reef, shark–fishery interactions, bycatch, depredation, sanctuary effectiveness, population change, distribution, behaviour, habitat use, or interactions with other species, the underlying need is similar: turn observations into reliable evidence and keep that evidence useful as the work continues.
eOceans provides the infrastructure around the science—so researchers can collect the data their study requires, retain the context, automate repeatable processing, analyze and visualize results, produce reports and publications, and continue building on the work as new observations arrive.
Study sharks. Understand their interactions. Keep the evidence growing.