Geospatial Applications in Aquaculture and Fisheries Management

(8/13/2019 12:00:00 AM)

Ø Fish Distribution, Abundance, and Movement



Geospatial applications can contribute to fish distribution, abundance, and movement analyses.  They are particularly useful for spatial management strategies for fisheries.  However, their feasibility must be assessed to determine utility.  Geospatial applications cannot address all management questions and often their large data demands are significant limitations.

“Managing fisheries is hard: It’s like managing a forest, in which the trees are invisible and keep moving around.” -Professor John Shepherd, University of Southampton


Spatial Management Strategies

Spatial management strategies are a tool to help optimize fisheries management and are often used as a complement to more traditional harvest management techniques such as catch quotas (e.g., total allowable catch [TAC]), fishing effort limits (e.g., limited number of boats, gear, or trips, and size limits on fish harvested (e.g., minimum length).  Spatial management strategies include fishery designation zones (e.g., no trawl areas), closed seasons for particular locations, and closed areas (e.g., marine protected areas [MPAs]).  Key issues to consider for successful implementation of spatial management strategies include: (1) all relevant jurisdictions should be engaged; (2) local communities should be involved; (3) enforcement must be feasible and functional.

Spatial strategies can be optimized, and geospatial applications can be used to assist in this process.  They can help estimate fish distribution, abundance, and movement.  Spatial analyses for fish populations can range from qualitative, such as visualizations, basic mapping, and multi-parameter overlays, to quantitative, such as georeferenced population dynamics and assessment and space-based fisheries management and prediction.


Feasibility questions for spatial management of fisheries:

      What are the key issues relating to the management and further development of the fisheries and/or aquaculture sector?

      Which of these issues are fully or partially spatial?

      What geographic area should be covered?

      What georeferenced data do you have?  What data are needed?

      To what extent will GIS satisfy information needs?

      Would funding be available for any longer-term GIS work?

      Are there better ways of achieving the information that is needed?


Fish Distribution

Analyses of fish distribution ask the (often not so) simple question of where fish are located.  These studies can examine fish across a geographic region (e.g., a species range) or within a given environment (e.g., zones of a lake).  Geospatial tools can help define fish distribution.  Habitat modeling can visualize spatial distributions of quantity and quality of fish habitat (for more information, see habitat modeling section).  Species distribution modeling can produce predictive maps of where species are likely to occur and not likely to occur based on field observations.  FishTail ( is an example tool that examines fish distribution projections with climate change.  It is a decision-support mapper that integrates land use, habitat fragmentation, water quality, and future climate conditions to provide risk estimates of changes in fish habitat for stream fish in the Northeastern United States.

Feasibility questions for geospatial applications for fish distribution:

      Which species do you manage with uncertain distributions?

      What data do you need? (e.g., primary data or secondary data)

      Are there implications for changing conditions? (e.g., climate, dams, land use)


Fish Abundance

Analyses of fish distribution examine how many fish are present in a given spatial context.  Often, these studies reference the density of fish within a given geographic area.  Geospatial tools can provide spatial interpolations to examine correlations between fish abundance and environmental variables (e.g., habitat types).  Dueri et al. 2014, for example, used the relationship between climate and skipjack tuna abundance and distribution to project changes in biomass of the species with climate change.

Feasibility questions for geospatial applications for fish abundance:

      How can geospatial data be used to examine abundance estimates?

      What data do you need? (e.g., primary data or secondary data)

      Why might accounting for spatial variability improve stock management?


Fish movement

Analyses of fish movement assess where fish are going.  These studies can track fine-scale daily behavior of fish and can examine large-scale migrations, depending on the monitoring equipment utilized.  Pop-up satellite archival tags (PSATs) record information such as temperature, light level, oxygen levels and pressure over a researcher defined interval of seconds to hours.  After a predetermined duration, the tag releases from the animal and, once at the surface, transmits the information to researchers via satellite.  These tags are principally for large oceanic species because they are expensive and can be lost due to biofouling or premature release.  Passive Integrated Transponder (PIT) tags, cannot provide environmental information like PSATs, but use a small, injectable, radio transponder to “barcode” fish.  The fish retain this acoustic tracking tag for life and transmit their individual identifying code when they pass an acoustic receiver.  PIT tags are considerably cheaper than PSATs and well-placed arrays of acoustic receivers can be used to examine movement patterns and habitat usage.  The information provided from fish tags can be analyzed to visualize timing and location of migration and important life history events as well as identify critical locations for conservation and protection. 

Feasibility questions for geospatial applications for fish movement:

      Which migratory species do you manage?

      What data do you need? (e.g., primary data or secondary data)

      How might geospatial analyses guide management? (e.g., fishing restrictions by season or location?)

Ø Habitat Mapping and Modeling



Habitat mapping and modeling are important for effective land and resource management. Defining and identifying critical habitat as well as threats to those habitats are also important for effective management. Habitat maps may be created via digitizing and field work or modeling within a GIS. The basic modeling process comprises six steps: 1) Define objectives and purpose, 2) State assumptions, 3) Identify model variables, 4) Locate GIS data representing the model variables at the desired scale, 5) Implement the model, 6) Evaluate model results. A well-designed model and GIS tools allow for re-running of models with adjusted inputs to reflect changing conditions or management objectives.


Why Map Habitat?

Knowledge of surrounding habitats and the species that live within them is critical for effective land and resource management and provides a basis for ecological research. Understanding where different ecological systems are and how they function is an important guide for intelligent management decisions that promote sustainability. Figure 6 diagrams the interplay between habitat mapping for management and for ecological understanding.


Figure 6. Comparison of reasons for habitat mapping for management and research.


Critical Habitats and Threats

Crucial or critical habitat are those habitats known to be important for either ecological or economic reasons. In many cases, economic reasons may depend on underlying ecological health but whether economic or ecological, identifying critical habitat helps prioritize management decisions.

The United States Fish and Wildlife Service maintains geospatial data of critical habitat ( These data are often used in a variety of project planning and geospatial modeling.

In addition to mapping habitats, it is important to also map threats to habitats; which are also threats to the species using the habitats. Maps of threats compared in a GIS to maps of habitat--especially critical habitat--provide a geographic focus for management decisions by showing where habitats are under threat. Examples of threats to species and habitats may include industrial areas, resource extraction, transportation networks and urbanization. Development of a holistic threat map may require the creation and mapping a host of features on the landscape.


Creating Habitat Maps


A commonly used method of habitat mapping is simply drawing in a GIS on top of imagery, or digitizing. A skilled person with knowledge of the landscape may be able to create GIS data just by looking at imagery and zooming in and out and identifying difference in the imagery that equates to changes in habitat. Existing paper maps with valuable geodata can be “heads-up digitized” into a GIS. A GIS specialist simply copies by hand and eye the locations, line work and areas by comparing the hard copy map to similar maps in the GIS and using tools in the GIS to recreate the features. Existing maps may also be digitized by being scanned or photographed to be loaded into a GIS. Spatial data on scanned maps can then be traced using data creation tools in a GIS. Any of the digitization processes is usually accompanied by field work to “ground truth” habitat areas identified in imagery.


Modeling Habitat

If direct mapping of habitat via field work and digitizing is not possible, a predictive model of areas likely to contain certain habitats can be built in a GIS. The modeling process includes the following steps:

1.     Define objectives and purpose

2.     State assumptions

3.     Identify model variables

4.     Locate GIS data representing the model variables at the desired scale

5.     Implement the model

6.     Evaluate model results

Habitat Suitability Index (HSI) models were developed by the U.S. Fish and Wildlife Service (FWS) in the early 1980’s as part of the Habitat Evaluation Program. Handbooks and policy developed by the FWS is available at

Example -- Fish Habitat Suitability Modeling in Florida Estuaries

Scientists at the Florida Fish & Wildlife Research Institute developed spatial habitat suitability models (HSM) using GIS to predict spatial distributions and relative abundance of fish species at various life stages in two major Florida estuaries. Data from sample locations were interpolated to produce seasonal habitat maps for dissolved oxygen, salinity, temperature. Model inputs for bathymetry and bottom type were also produced.

Fish catch data were compared to the different habitat parameters to build a predictive model across an estuary. The model algorithm was then applied to a second estuary and catch data from there were used to test the model. This process of transferring models between catch data from two estuaries refined the model and increased understanding of fish habitat for several species. The refined model information on preferable fish habitat conditions allows the model to be applied in regions for which catch data are not available.

Well-designed models and associated GIS processes can be used to test multiple scenarios. Specific input layers and the underlying algorithm used in the model can be adjusted and the model re-executed to derive results for different expected conditions. In the case of climate change, habitat models are often re-run with updated, predicted temperature conditions. Species distribution changes expected as a result of climate change can be produced by re-modeling a current distribution map but with a predicted temperature regime layer. Changing management objective can also be reflected in re-modeling to provide guidance on proposed management actions.

Ø Aquaculture Site Suitability


Any GIS modeling process includes five major steps, define the objectives and purpose, identify the model variables, locate GIS data representing the model variables at the desired scale, implement the model, and evaluate the model results.  The figure below lists some aquaculture suitability criteria variables:


Three different papers were presented that demonstrate aquaculture site suitability, two were located in Vietnam and the last one is in Tenerife Canary Islands of the north east coast of Africa.


Example 1: GIS for land evaluation for shrimp farming in Haiphong of Vietnam


This study was conducted to identify appropriate sites for shrimp farming development in Haiphong province of Vietnam using geographical information systems (GIS). Thirteen base layers (thematic maps) were grouped into four main land use requisites for aquaculture, namely, (1) potential for pond construction (slope, land use type, soil thickness, elevation), (2) soil quality (soil type, soil texture, soil pH), (3) water availability (distance to sea, and water source), and (4) infrastructure and socio-economic status (population density, distance to roads, local markets, and hatcheries). A constraint layer was used to exclude areas from suitability maps that were not allowed to implement shrimp farming. A series of GIS models was developed to identify and prioritize the most suitable areas for shrimp farming.

This study shows that the land evaluation model is useful for identifying suitable areas for shrimp farming and for allocating land for efficient income generation, effective conservation, and sustainable land management. It was estimated that about 31% (2604 ha) of the total land area (8281 ha) in Haiphong was highly suitable for shrimp farming. Since existing shrimp farms cover only 1690 ha of land in the study area, the potential for expanding shrimp farms should take into consideration further political and environmental issues.


Example 2: Investigation of a novel approach for aquaculture site selection


This study investigated the potential use of two “species distribution models” (SDMs), Mahalanobis Typicality and Maxent, for aquaculture site selection. SDMs are used in ecological studies to predict the spatial distribution of species based on analysis of conditions at locations of known presence or absence. Here the input points are aquaculture sites, rather than species occurrence, thus the models evaluate the parameters at the sites and identify similar areas across the rest of the study area. This is a novel approach that avoids the need for data reclassification and weighting which can be a source of conflict and uncertainty within the commonly used multi-criteria evaluation (MCE) technique. Using pangasius culture in the Mekong Delta, Vietnam, as a case study, Mahalanobis Typicality and Maxent SDMs were evaluated against two models developed using the MCE approach. Mahalanobis Typicality and Maxent assess suitability based on similarity to existing farms, while the MCE approach assesses suitability using optimal values for culture. Mahalanobis Typicality considers the variables to have equal importance whereas Maxent analyses the variables to determine those which influence the distribution of the input data. All of the models indicate there are suitable areas for culture along the two main channels of the Mekong River which are currently used to farm pangasius and also inland in the north and east of the study area. The results show the Mahalanobis Typicality model had more high scoring areas and greater overall similarity than Maxent to the MCE outputs, suggesting, for this case study, it was the most appropriate SDM for aquaculture site selection. With suitable input data, a combined SDM and MCE model would overcome limitations of the individual approaches, allowing more robust planning and management decisions for aquaculture, other stakeholders and the environment.


Example 3: Geographical information systems-based models for offshore floating marine fish cage aquaculture site selection in Tenerife, Canary Islands


The present study focuses on the development of a standard methodology for selection of suitable sites for offshore (exposed) marine fish-cage farming (floating cages) of seabream (Sparus aurata) and seabass (Dicentrarchus labrax) in an island environment, using Tenerife as an example. Site selection is a key factor in any aquaculture operation, affecting both success and sustainability and can solve conflicts between different activities, making a rational use of the coastal space. Site selection was achieved by using geographical information systems (GIS)-based models and related technology to support the decision-making process. The framework for spatial multi-criteria decision analysis used in this study began with a recognition and definition of the decision problem. Subsequently, 31 production functions (factors and constraints) were identified, defined and subdivided into eight submodels. These were then integrated into a GIS database in the form of thematic layers and later scored for standardization. At this stage, the database was verified by field sampling to establish the quality of data used. The decision maker’s preferences were incorporated into the decision model by assigning weights of relative importance to the evaluation under consideration. These, together with the thematic layers, were incorporated using multi-criteria evaluation techniques and simple overlays to provide an overall assessment of possible alternatives. The integration, manipulation and presentation of the results by means of GIS-based models in this sequential and logical flow of steps proved to be very effective for helping the decision-making process of site selection. Tenerife has very favorable environmental conditions for culture of marine fish and there are no totally unsuitable sites for cage farming identified in this study. On the other hand, there are few very suitable sites (high scores) either, principally due to the heavy use of the coastline and the conflicts between different users. From the 228 km2 of available area for siting cages in the coastal regions with depth less than 50m, the total area suitable for siting cages (scores 6-8) was 37 km2. There are only 0.51km2 of very suitable areas (score 8) and approximately 5.37 km2 of suitable (score 7), most of these being located in the southeast of the island. These relatively small areas of suitability should be put into the context of the wider use of the coastal environment around Tenerife.

Ø Watershed Management and Monitoring



Watershed management is a comprehensive strategy to protect and improve water quality and natural resources within a watershed.  All activities within a watershed affect the watershed’s natural resources and water quality.  Geospatial applications can contribute to watershed management and monitoring.

Watershed Basics

A watershed is the area of land where the water that drains off it goes to a common outlet.  Depending on scale, both basin (large-scale) and catchment (small-scale) are terms often used interchangeably with watershed.  In general, a basin references a large river system and a catchment references a small stream.  Watershed management is the land use and water use practices used to protect and improve the quality of water and other natural resources within a watershed by managing in a comprehensive manner.  Ultimately, the goal of watershed management is sustainable development and management to preserve natural resource functions and biodiversity for ecological health, economic prosperity, and social security.

Importance of Watershed Management

Watershed management is important because all activities within a watershed affect the watershed’s natural resources and water quality.  These activities include land development, runoff from urban areas, and agricultural activities.  Management within these systems can control, reduce, and even eliminate pollution sources from these activities within a watershed.  Watershed management is often an exercise in political diplomacy as watershed boundaries do not coincide with political boundaries.  But, for that reason, watershed management efforts are particularly important to ensure effective implementation of restoration and conservation measures.  What happens in the headwaters of a river affects the delta.  In the Mekong River, for example, 16% of the mean annual flow is from China, 2% from Myanmar, 35% from Lao PDR, 18% from Cambodia, 18% from Thailand, and 11% from Viet Nam.  However, the majority of inputs from China, Myanmar, Lao PDR, Cambodia, and Thailand, all end up down in the delta region of Viet Nam.

How Watershed Management Works

Watersheds are most effectively implemented as a participatory process.  The most productive examples include local partnerships of, for example, residents, landowners, governments, developers, and agricultural users.  Often, negotiations between these different stakeholders with diverse interests is necessary.  And effective implementation should be adaptive and capable of responding to feedback from monitoring to ensure progress towards meeting the intended goals.  Monitoring often results in iterative adjustments to guidelines and policies to make greater strides in achieving watershed management goals. 

Priorities for management action can include infrastructure improvements, reductions in impervious surfaces, improved agricultural practices, habitat restoration, improved waste management, and increased riparian buffers.  Considering a hypothetical watershed example, taking a watershed management approach could result in decreased sources and transport of nutrients, sediments, and contaminants; decreased nutrients from atmospheric deposition and wastewater discharge; increased wetlands and forest habitat; control of water withdrawals; and promotion of sustainable fisheries harvest.  These management actions could result in improved oxygen concentrations; fewer algal blooms and improved water clarity; increased submerged aquatic vegetation; sustainable water availability; and improved fish and bird populations.

Geospatial Applications of Watershed Management

Because watershed management is an inherently spatial process, geospatial tools can help implement strategies effectively.  Geospatial tools can delineate and map watershed boundaries and smaller drainages basins within a watershed.  They can inventory and map natural resources, land use and land cover, soils (and areas of erosion), and pollution (both point and nonpoint) within a watershed.  Using this geospatial information, the watershed management process can more appropriately identify targets for action and effective implementation. 

Chesapeake STAT is one example of a geospatial tool used for watershed monitoring.  The Chesapeake Bay is the largest estuary in the United States.  The Chesapeake Bay watershed is 64,000 miles2 and 17 million people reside within its boundaries.  Consequently, it is subject to many different nutrient and pollution sources and coordinating restoration and management efforts across the multijurisdictional watershed is challenging.  Chesapeake STAT is a tool developed by the Chesapeake Bay Foundation to improve information sharing and decision making within the watershed.  The online portal houses a range of environmental data time series that can be used to help track progress towards meeting watershed restoration and management objectives.

For more information and training in watershed management, please visit the U.S. Environmental Protection Agency (EPA)’s online training in watershed management.  This is an online learning program that offers self-paced training modules as a basic introduction to watershed management including watershed ecology, watershed change, analysis and planning, and management practices.

 VIFEP (USAID workshop)

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