Site selection for aquaculture of extractive species in open ocean environments: a systematic review with focus on ecological carrying capacity

1 Introduction

The world’s population is projected to keep growing over the next several decades, reaching an estimated peak of approximately 10.3 billion people by the 2080s (UN, 2024). Food, as a fundamental human necessity, has therefore become an increasingly prominent topic in recent decades (Azra et al., 2021). The rapid population growth is intensifying the demand for food, with an estimated 821 million people suffering from food insecurity, as conventional food production systems struggle to meet the rising demand (Gul et al., 2024). Seafood (which includes finfish, shellfish, seaweed, and etc.) is an important contributor to global food security, with its consumption and production rising significantly faster than the rate of population growth (Azra et al., 2021: FAO, 2024). At the same time, wild fisheries have been unable to meet this rising demand on their own. The combined effect of overfishing and habitat degradation have severely depleted wild fish stocks, emphasizing the need for more sustainable alternatives (Galparsoro et al., 2020). Aquaculture has consequently emerged as a key strategy to meet the increasing global seafood demand (Brye, 2023; Bricker et al., 2016). Despite its rapid expansion, inland and coastal aquaculture faces mounting spatial and ecological constraints (Costello et al., 2020). Competition for limited nearshore areas and growing environmental concerns have intensified conflicts among stakeholders and limited further development (Sanchez-Jerez et al., 2016; Schultz-Zehden et al., 2018; Stelzenmüller et al., 2017). These challenges have driven a shift toward offshore aquaculture, which offers opportunities to expand production while potentially reducing environmental pressures and user conflicts in crowded coastal zones (Charalambides et al., 2024; Lester et al., 2018; Sclodnick et al., 2024).

Definitions of offshore aquaculture vary widely, incorporating factors such as distance from shore, water depth, wave exposure, fetch, and jurisdictional boundaries (Buck et al., 2024; Markus, 2024). Buck et al. (2024) provided a general definition, describing offshore aquaculture as farming operations situated at least three nautical miles away from the coastline. Offshore areas offer several advantages for aquaculture development, including greater availability of space, high carrying and assimilative capacity, fewer stakeholder conflicts, reduced exposure to land-based pollution, opportunities to mitigate environmental impacts associated with coastal aquaculture, and favorable conditions for cultivating diverse marine species (Froehlich et al., 2017; Holm et al., 2017). Despite these advantages, offshore aquaculture also introduces substantial costs and operational challenges that require careful assessment. A key concern associated with offshore aquaculture is that sites located farther from land are often subject to more dynamic and potentially harsh oceanographic conditions, including strong currents, high wave energy, and limited accessibility (Heasman et al., 2024a; Heasman et al., 2024b). However, offshore sites are not necessarily fully exposed, as exposure reflects environmental forcing rather than location, and exposed conditions may also occur in nearshore environments (Buck et al., 2025). Additionally, greater distances from shore make it difficult to secure reliable energy supplies, further complicating farm operations (Charalambides et al., 2024). These factors can significantly impact the technical, economic, and environmental viability of offshore aquaculture operations (Xue et al., 2023; Karathanasi et al., 2022).

A critical requirement for sustainable aquaculture development is the integration of ecological considerations into the siting process (Gentry et al., 2016). In particular, aquaculture of extractive, low-trophic species (such as bivalves and seaweeds) has gained attention for its potential to supply food while simultaneously providing ecosystem services, including nutrient removal, water filtration, and habitat enhancement (Buck et al., 2017a; Buck et al., 2017b; Barrett et al., 2022). Additionally, their compatibility with multi-use settings, including co-location within offshore wind farms, further enhances this potential (Horn et al., 2025; Maar et al., 2023).

Site selection is widely recognized as one of the most critical determinants of the long-term viability of offshore aquaculture. It requires careful evaluation of environmental conditions, water quality, hydrodynamics, accessibility, and socio-economic factors (Buck et al., 2024; Chentouf et al., 2023; Galparsoro et al., 2020). Successful site selection typically follows a stepwise process: identifying broad potentially suitable areas, excluding zones with environmental or regulatory constraints, and evaluating competing marine uses. Initial screening often utilizes available maps, charts, and satellite images or Google Earth (Buck et al., 2018; Benetti et al., 2010). Building on these foundational steps, researchers have developed more sophisticated methodologies for determining site suitability. Geographic information System (GIS)-based multi-criteria evaluation (MCE) frameworks are among the most widely applied, allowing users to integrate diverse environmental and socio-economic variables into spatial decision-making (Chentouf et al., 2023; Gimple et al., 2015). Additional approaches include wave climate analyses (Karathanasi et al., 2022), assessments of physico-chemical water parameters (Mosqueira et al., 2022), and Marine Spatial Planning (MSP) principles (Gentry et al., 2016; Stelzenmüller et al., 2017). Together, these methods support the integration of technical, environmental, and social considerations necessary to ensure the development of sustainable offshore aquaculture.

The Ecosystem Approach to Aquaculture (EAA) provides an overarching framework that supports such integration by ensuring that aquaculture develops within the capacity of ecosystems, sustains ecosystem services, and accounts for social and governance considerations (Byron et al., 2024; Ross et al., 2013). Although originally applied to finfish farming, EAA principles are readily transferable to low-trophic aquaculture (LTA) systems (Byron et al., 2024). A central pillar of EAA is the concept of carrying capacity, which is applied across multiple contexts. Carrying capacity is commonly categorized into four complementary dimensions: physical, production, ecological, and social carrying capacity, each addressing different aspects of aquaculture sustainability (McKindsey et al., 2006). Ecological carrying capacity (ECC) refers to the maximum biomass that can be farmed without compromising ecosystem functioning (Fisher et al., 2023; Weitzman and Filgueira, 2019). Operating within ECC is therefore essential for maintaining ecosystem integrity and aligning aquaculture practices with EAA principles. However, ECC is inherently scale and site dependent, as excessive farming intensity or expansion can exceed system limits, leading to ecological degradation through food-web disruptions. Quantifying ECC often requires extensive ecosystem-level datasets, including time-series data on hydrodynamics, nutrients, phytoplankton, and seston. It also requires species-specific bioenergetic measures for the farmed species, together with information on farm operations. Consequently, ECC assessments are data-intensive and require specialized modeling expertise, which limits the implementation of ECC estimations (Filgueira et al., 2020; Ross et al., 2013).

The degree to which ECC is incorporated into offshore aquaculture site-selection studies remains unclear, despite the growing interest in the field. A systematic understanding of existing criteria, methodologies, and modeling approaches is therefore needed. This review synthesizes peer-reviewed literature on site selection for aquaculture of extractive species in open-ocean environments, with an emphasis on how ECC is assessed and operationalized. By identifying current practices, knowledge gaps, and opportunities for methodological advancement, this work aims to support the development of ecologically informed, sustainable offshore aquaculture planning.

We addressed the following research questions within the review:

• What criteria and models have been used in existing studies for offshore aquaculture site selection?
• To what extent do current site selection criteria and models incorporate considerations of ECC?
• What gaps and limitations exist in the current research on offshore aquaculture site selection?

2 Methods

2.1 Overview of the systematic review

A systematic literature review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Liberati et al., 2009). The PRISMA framework provides a widely recognized methodology to ensure transparent, rigorous, and replicable reporting of systematic reviews. A review protocol was developed to guide the study selection process, which followed PRISMA’s four-phase flow: identification, screening, eligibility, and inclusion (Chentouf et al, 2023), as illustrated in Figure 1. The electronic literature search for this review was undertaken in October 2025.

2.1.1 Resources and identification

The literature search was restricted to peer-reviewed journal articles published in English between 2000 and 2025. Review articles, conference proceedings, book chapters, and grey literature were excluded. Although we know that this grey literature contains a wealth of valuable information, we want to maintain a high standard of scientific reliability. Relevant literature was retrieved from the Scopus and Web of Science databases using keyword combinations structured with Boolean operators. These keywords were designed to capture the most relevant publications related to the research topic. A set of trial searches was carried out to fine-tune the keyword selection. The complete list of search terms is provided in Table 1, and the inclusion and exclusion criteria are summarized in Table 2.

Table 1. Summary of search keywords to identify relevant literature using Boolean operators.

Table 2. Inclusion and exclusion criteria.

2.1.2 Screening, eligibility, and inclusion

Following the initial search, duplicate records were removed using Mendeley Reference Manager, resulting in a total of 88 unique records. These records underwent a preliminary screening based on their titles and abstracts to assess their relevance to the research objectives. During this phase, 40 records were excluded for being either outside the scope of the study or not aligned with the review’s thematic focus. The remaining 48 records proceeded to the eligibility assessment. Full-text analysis was conducted to determine their alignment with the study’s aims and methodological criteria. During this phase, 34 publications were excluded after being identified as irrelevant or insufficiently aligned with the research objectives (e.g., studies focused on fish or other non–LTA systems, or studies conducted in non-offshore environments). Ultimately, 14 studies met all criteria and were included in the qualitative synthesis.

2.2 Identification of ECC indicators

To ensure consistent classification of ECC integration across studies, ecological indicators were systematically identified and categorized based on their relevance to ecosystem functioning and environmental sustainability. ECC indicators were defined as measurable environmental or biological variables that reflect ecosystem responses to aquaculture activities or the capacity of the system to assimilate ecological pressures. These indicators included: (1) water quality parameters (e.g., dissolved oxygen, nutrient concentrations, Chlorophyll-a (Chl-a), turbidity); (2) benthic condition indicators (e.g., sediment organic content, benthic biomass, biodiversity indices); (3) biological productivity or ecosystem dynamics (e.g., phytoplankton abundance, filtration rates, nutrient removal); (4) biogeochemical fluxes (e.g., nitrogen cycling, oxygen consumption, biodeposition); (5) ecosystem interaction indicators (e.g., food availability, trophic competition, ecosystem feedback mechanisms). Studies were considered to incorporate ECC when they measured, modeled, or evaluated one or more indicators directly linked to ecosystem functioning or environmental limits. In contrast, studies focusing solely on physical suitability or growth performance without assessing ecosystem responses were classified as not explicitly integrating ECC. This classification approach aligns with recent frameworks that propose standardized ecological indicators for aquaculture carrying capacity assessment (Byron et al., 2024; Filgueira et al., 2020).

3 Results and discussion

3.1 Overview of reviewed studies

Across the 14 studies examined, offshore aquaculture site-selection research has been dominated by work on low-trophic species, particularly bivalves (oysters, mussels) and seaweed. Most studies focus on suitability mapping, typically using GIS-based MCE frameworks that integrate environmental, technical, and spatial constraints into composite suitability scores (Barillé et al., 2020; Chen et al., 2024; Gimpel et al., 2015; Cosgrove et al., 2023). In contrast, far fewer studies explicitly quantify ECC through mechanistic or biogeochemical models (Bricker et al., 2016; Chen et al., 2024; Ferreira et al., 2012; Maar et al., 2020). This imbalance underscores a broader pattern in the literature, while site-selection mapping tools have matured and diversified, the incorporation of ecosystem feedback and production limits remains comparatively scarce.

Table 3 summarizes key characteristics of the reviewed studies, including their methodological design, focal species, geographic context, and degree of ECC integration. The heterogeneity of approaches reflects both the diversity of the available tools and the varying objectives of site-selection exercises, from basic suitability mapping to fully integrated ecosystem modeling. A broader shift toward hybrid frameworks (e.g., GIS–DEB, GIS–Marxan, or hydrodynamic–biogeochemical–DEB) indicates emerging methodological convergence and provides promising pathways for ECC-aware marine spatial planning.

Table 3. Overview of reviewed studies and the level of ECC integration in offshore aquaculture site selection.

ECC integration levels: L1 = proxy-based suitability; L2 = process-informed; L3 = explicit ECC quantification; L4 = integrated ecosystem approach.

Offshore implementation scale: S1 = conceptual/theoretical; S2 = pilot/experimental; S3 = early commercial development; S4 = established offshore commercial systems.

3.1.1 Geographic distribution of studies

The reviewed studies span a wide geographical range but are heavily concentrated in European offshore waters (n = 8) (Figure 2). France and Germany are the most represented countries, reflecting long-standing investments in offshore bivalve aquaculture and marine spatial planning. Additional European case studies come from Ireland, Italy, Portugal, Denmark, and the United Kingdom. Beyond Europe, offshore aquaculture suitability assessments were conducted in China (n = 3) and the United States (n = 3), demonstrating growing global interest in marine aquaculture expansion. The uneven spatial distribution indicates that while methodological innovation is widespread, applied offshore aquaculture planning is still concentrated in regions with strong regulatory frameworks and established aquaculture sectors.

3.2 Models and criteria used in the selection

3.2.1 GIS-based multi-criteria evaluation

GIS-based Multi-Criteria Evaluation (MCE) remains the most frequently applied approach for offshore aquaculture site selection, used in 8 of the 14 reviewed studies. MCE is a hierarchical decision-support method widely used in spatial planning and commonly applied to aquaculture site selection. These frameworks integrate a combination of environmental variables (e.g., temperature, salinity, currents, Chl-a, turbidity), technical constraints (depth, distance to port), and spatial-use conflicts (navigation zones, MPAs, fishing effort) to generate suitability maps (Barillé et al., 2020; Chen et al., 2024; Gimpel et al., 2015).

The reviewed studies demonstrate the versatility of MCE across regions and species. Barillé et al. (2020) coupled satellite remote-sensing data with a Dynamic Energy Budget (DEB) growth model for Pacific oyster and integrated these outputs into a GIS-based Spatial MCE (SMCE) that also incorporated technical, environmental, and socio-economic factors to identify suitable offshore sites along the French Atlantic coast. Chen et al. (2024) applied an EAA in the Nantong Sea by integrating Marxan-based ecological zoning with a GIS-MCE suitability analysis to map optimal areas for shellfish, laver, and rare seafood cultivation. The approach combined assessments of environmental and ECC with GIS-based species-specific suitability mapping to identify natural farming zones within a comprehensive site-selection framework. Cosgrove et al. (2023) used a Multi-Criteria Analysis Site Selection (MCASS) GIS tool, integrating long-term hindcast datasets (wave height, wave period, currents, bathymetry) reclassified into suitability scores, alongside spatial constraints such as protected areas, shipping lanes, and fishing effort, to produce high-resolution suitability maps for seaweed cultivation along the Irish coast. Gimpel et al. (2015) applied a GIS-based MCE combined with an Ordered Weighted Averaging (OWA) technique to identify suitable co-location zones for aquaculture and offshore wind farms in the German North Sea, incorporating key environmental variables and evaluating 13 aquaculture candidates including seaweeds, and bivalves across different spatial scenarios. The OWA weighting approach enabled exploration of risk-averse and risk-tolerant decision scenarios, providing a flexible means of handling uncertainty in site-suitability analyses.

The Analytic Hierarchy Process (AHP) is frequently integrated into GIS-based MCE to derive transparent, expert-informed weighting of environmental, technical, and socio-economic indicators. Gao et al. (2025) applied an AHP–GIS framework to assess mariculture suitability in Lianyungang, China, evaluating species including portunids, Chinese prawn, nori, oysters, mussels, scallops, and perch. The study integrated weighted natural and socio-economic indicators such as marine primary productivity index, seawater quality index, seawater eutrophication index, Chl-a concentration, depth, temperature, salinity, and distances to coastline, fishing ports, and towns. Each factor was standardized into suitability scores and combined into a Mariculture Suitability Assessment Index (MSAI).

Mizuta and Wikfors (2019) used SMCE to analyze historical oceanographic datasets on temperature and Chl-a across the Northeast of the USA, to identify depth selection for blue mussel cultivation. Using species-specific thresholds for thermal tolerance, byssal attachment performance, and food supply, the study identified optimal submerged longline depths.

SMCE has also been applied to depth-specific site selection. Mizuta and Wikfors (2019) used historical oceanographic data (temperature, Chl-a) from Northeast Shelf of the USA to evaluate depth suitability for blue mussel, using species-specific thresholds for thermal tolerance, byssal attachment, and food availability. The study identified optimal submerged longline depths, and validated predictions through in situ biodeposition measurements, demonstrating the value of combining environmental time series with field validation.

Overall, GIS-based MCE provides a transparent, flexible, and reproducible framework for integrating heterogeneous spatial datasets and evaluating trade-offs, making it a foundational tool in offshore aquaculture site-selection research. However, most GIS-based MCE studies included ecological variables primarily as suitability constraints rather than as indicators of ecosystem limits. For this reason, these approaches were classified as representing proxy-based or partial ECC consideration, as they did not explicitly quantify ecosystem feedback processes or ecological thresholds.

3.2.2 Dynamic energy budget models

DEB theory provides a generic, mechanistic framework for modeling the uptake and flow of energy within organisms, and its subsequent growth and reproduction, in response to environmental conditions. By using variables such as sea surface temperature (SST), Chl-a, and suspended particulate matter (SPM), DEB models can estimate growth rates, time to market size, or flesh condition, producing spatially explicit growth-potential maps (Barillé et al., 2020; Bertolini et al., 2021; Palmer et al., 2020).

Five reviewed studies applied DEB modeling within offshore site-selection contexts. Barillé et al. (2020) calibrated a DEB model for Pacific oyster using intertidal and offshore environmental data along the French Atlantic coast and integrated model outputs into a Spatial MCE to identify suitable offshore farming zones. Bertolini et al. (2021) used remotely sensed SST and Chl-a time series to drive a DEB model in the western Adriatic Sea, mapping Pacific oyster growth indicators and revealing spatial gradients useful for aquaculture zoning. Palmer et al. (2020) applied a DEB model in Bourgneuf Bay (France) to simulate intertidal and offshore Pacific oyster growth, producing industry-relevant indicators that highlighted offshore areas with substantially higher growth potential. Palmer et al. (2021) extended DEB modeling for Pacific oyster by coupling current speed outputs from a marine ecosystem model encompassing the coastal North East Atlantic, the North Sea, and the Mediterranean Sea. Suitable growth areas were identified using threshold-based tolerance masking applied to model variables and bathymetry data. DEB-simulated growth time series were then transformed into industry-relevant indicators and mapped for both early and late-century climate scenarios, revealing climate-robust offshore production zones. Yin et al. (2024) integrated a DEB model with an AHP to develop a Suitable Market-Aquaculture Site-Selection Model (SMASM) for offshore Pacific oyster in Shandong, China, combining market-oriented indicators across management zones to generate suitability maps.

Overall, DEB models provide a powerful mechanistic basis for offshore aquaculture site selection by quantifying species-specific growth potential, incorporating environmental variability, and enabling climate-scenario testing, particularly when combined with spatial analysis or multi-criteria decision frameworks. Because DEB models simulate organism growth responses to environmental conditions but typically do not represent ecosystem-level feedback such as nutrient depletion or benthic impacts, they were classified as providing process-informed but incomplete ECC representation.

3.2.3 Farm aquaculture resource management model

The Farm Aquaculture Resource Management (FARM) model provides a holistic framework for assessing aquaculture production and environmental impacts by coupling growth modeling with nutrient cycling, water quality, and biodeposition processes. Unlike purely physiological models, FARM explicitly quantifies ECC by estimating nutrient removal, oxygen demand, and farm-scale environmental externalities (Bricker et al., 2016; Ferreira et al., 2012).

Bricker et al. (2016) calibrated the FARM model for Eastern oyster using environmental data from nearshore and offshore sites in Connecticut state of the USA, including temperature, salinity, Chl-a, and particulate matter. The model was used to estimate oyster growth from seed to harvestable size and to generate spatial production maps integrated within Connecticut’s Aquaculture Mapping Atlas. By combining environmental, regulatory, and use-conflict layers with modeled growth outputs, the study produced a GIS-based decision-support tool to identify sites with high production potential. Ferreira et al. (2012) used the FARM model within an Integrated Multi-Trophic Aquaculture (IMTA) framework to evaluate the co-cultivation of Gilthead bream and Pacific oyster under both pond and offshore conditions. By simulating physical, chemical, and biological processes, including nutrient cycling and biodeposition. This integrated modeling approach highlights the potential of IMTA systems to improve sustainability and reduce future ecological externalities in aquaculture.

Overall, the FARM model provides a comprehensive tool for offshore aquaculture planning by linking production outcomes with ecological impacts, offering a practical framework for integrating ECC considerations into site selection and supporting the design of sustainable and mitigation-oriented aquaculture systems. FARM-based approaches were classified as demonstrating high ECC integration because they explicitly simulate nutrient cycling, organic deposition, and ecosystem responses to aquaculture activities, enabling a direct evaluation of ecological limits.

3.2.4 Marine spatial planning and trade-off analysis framework

MSP based trade-off frameworks integrate ecological, economic, and social dimensions to evaluate where offshore aquaculture can be developed with minimal conflict and maximum co-benefit. Lester et al. (2018) applied such a framework in the Southern California Bight of the USA, evaluating potential offshore sites for Mediterranean mussel, striped bass, and sugar kelp. Species-specific bioeconomic models incorporated environmental variables including depth, substrate type, temperature, and particulate organic carbon to estimate growth, yield, and profitability. These outputs were integrated with four additional stakeholder sectors (wild halibut fisheries, benthic habitat health, visual impact, and disease risk) within a multi-sector spatial optimization. The framework identified locations capable of maximizing economic returns from aquaculture, while minimizing the ecological impacts and user conflict. This demonstrates the value of explicit trade-off analysis for MSP and for guiding strategic offshore aquaculture expansion in multi-use seascapes. These frameworks were classified as representing proxy-based ECC consideration because ecological impacts were evaluated using indicators of environmental compatibility, rather than through explicit modeling of ecosystem feedback or ecological thresholds.

3.2.5 Biogeochemical–hydrodynamic modeling for mitigation farming

Biogeochemical–hydrodynamic models provide ecosystem-scale assessments of nutrient dynamics, pelagic–benthic coupling, and mitigation potential of extractive species, offering some of the most detailed representations of ECC in offshore aquaculture planning. These models can simulate nutrient loading from finfish farms and evaluate the capacity of shellfish mitigation farms to remove nutrients and improve water quality.

Maar et al. (2020) applied a coupled 3D biogeochemical model (ERGOM) and hydrodynamic advection module to the Samsø Belt in Denmark (a eutrophic, nitrogen-limited coastal system) to evaluate the placement and performance of mussel mitigation farms adjacent to a proposed rainbow trout farm. The model resolved nutrient cycling (N, P, Si), phytoplankton and zooplankton dynamics, detritus, and oxygen processes on a refined unstructured mesh, allowing high-resolution simulation around the farm site. Mussel growth was modeled using a DEB-based approach driven by temperature, salinity, and food availability, with filtration-linked nutrient removal feeding back into pelagic and benthic processes. Ten scenarios tested alternative mussel-farm placements based on current patterns and food flux. Model outputs (nutrient removal, Chl-a concentration, Secchi depth, denitrification, sediment organic content, and oxygen flux), were normalized to enable site comparison. The framework provided a robust decision-support tool for identifying optimal mitigation farm locations, demonstrating how ecosystem-coupled modeling can support ECC-based planning in nutrient-limited offshore environments. These models were identified as providing explicit ECC quantification because they resolve ecosystem interactions, nutrient fluxes, and feedback mechanisms at system scale.

3.3 Evidence for ecological carrying capacity integration, knowledge gaps and suggestions

Although offshore aquaculture site-selection studies increasingly acknowledge the importance of ECC, only a subset of the reviewed literature explicitly incorporates ecosystem feedback into their modeling frameworks. While bivalve and seaweed aquaculture are both classified as low-trophic or extractive aquaculture systems, their ecosystem-engineering functions and associated ECC considerations differ substantially. Bivalve aquaculture primarily influences ecosystems through the filtration and removal of suspended particulate matter, potentially affecting food availability, biodeposition, benthic oxygen demand, and pelagic–benthic coupling. In contrast, seaweed aquaculture mainly interacts with ecosystems through the uptake of dissolved nutrients and carbon, with potential effects related to nutrient depletion, hydrodynamic alteration, and shading. These contrasting ecological interactions highlight the need for species-specific ECC assessment approaches within offshore aquaculture planning (Byron et al., 2024; Callier et al., 2026).

The strongest ECC integration appears in studies using the FARM model (Bricker et al., 2016; Ferreira et al., 2012) or coupled hydrodynamic–biogeochemical systems (Maar et al., 2020), which simulate nutrient removal, biodeposition, oxygen dynamics, and broader ecosystem responses to aquaculture activities. For example, the hydrodynamic biogeochemical model applied in the Samsø Belt (Maar et al., 2020) demonstrates how mussel mitigation farms can reduce nutrient loads and improve water quality in nitrogen-limited systems by resolving pelagic–benthic coupling at high spatial resolution and evaluating multiple farm-placement scenarios. Model results showed feedback in both the water column and benthic environment, including reduced phytoplankton biomass and improved water clarity through mussel filtration, alongside increased sediment organic deposition, oxygen consumption, and enhanced denitrification processes beneath farms. Such ecosystem coupled models represent the most integrative approach to ECC assessment among the reviewed studies because they link organismal performance with system-level nutrient cycling and ecological feedback.

ECC was also incorporated through the EAA, most notably in Chen et al. (2024). Their framework for the Nantong Sea coupled Marxan-based ecological zoning with GIS-MCE species suitability. The study quantified marine environmental and ECC using measured water-quality indicators (eutrophication, heavy metals, petroleum), sediment characteristics (total organic carbon, heavy metals, petroleum), and ecological indicators describing biological community conditions (phytoplankton density, phytoplankton diversity, zooplankton biomass, zooplankton diversity, benthic biomass, benthic biodiversity, and Chl-a). Spatial continuity of these indicators was assessed and classified into five levels aligned with national environmental standards, resulting in a spatially explicit ECC index from which areas exceeding the threshold were retained for mariculture. Species-specific suitability was then mapped through GIS-MCE, and ecological zoning, suitability layers, and sea-use constraints were integrated to generate a comprehensive mariculture site-selection scheme. This hybrid approach demonstrates a structured pathway for embedding ECC into marine spatial planning, while still simplifying ecosystem processes relative to fully coupled biogeochemical models.

DEB models offer partial ECC coverage by simulating physiological growth responses to environmental forcing (e.g., temperature, Chl-a, turbidity). These models produce realistic growth-potential maps and allow scenario testing, including future climate projections (e.g., Palmer et al., 2021). But they do not represent ecosystem-level feedback such as nutrient depletion, particulate waste accumulation, or competition for food unless explicitly coupled to hydrodynamic or biogeochemical models. Thus, DEB models enhance biological realism but cannot independently quantify system-level carrying capacity.

Most GIS-based suitability studies emphasize environmental matching and conflict avoidance without evaluating ecological thresholds or feedback processes. This highlights a persistent methodological gap between identifying feasible sites and ensuring ecologically sustainable production levels. Furthermore, empirical validation, such as in situ growth trials, filtration or biodeposition measurements, and benthic flux observations, was limited across the reviewed literature, constraining the robustness and transferability of ECC thresholds. To facilitate comparison across approaches, Table 4 summarizes the principal advantages and disadvantages of the methods applied in the reviewed studies, highlighting their relative strengths and limitations in addressing ECC within offshore aquaculture site selection.

Table 4. Advantages and limitations of methods used in offshore aquaculture site-selection studies and their relevance for ecological carrying capacity (ECC) integration.

While progress toward ECC aware offshore aquaculture planning is evident, the field remains dominated by static suitability mapping, with relatively few studies incorporating mechanistic ecosystem processes. Future advances will require broader adoption of coupled biogeochemical, hydrodynamic, and organism level models, supported by targeted observational datasets and climate sensitive simulations. Embedding these approaches within MSP frameworks will be critical for transitioning from feasibility based to ECC bounded site selection and ensuring sustainable offshore aquaculture expansion. ECC integration into offshore aquaculture site selection requires richer environmental datasets, improved model forcing, and systematic validation. Emerging data streams offer significant opportunities to enhance both mechanistic and spatial modeling. Satellite remote sensing (Chl-a, and SST) provides consistent, synoptic coverage suitable for tracking large scale variability in food availability, thermal regimes, and turbidity. Real-time current patterns that influence larval transport, food flux, and depositional provide long-term physical–biogeochemical baselines and can be used to drive DEB, FARM, or hydrodynamic–biogeochemical models. In situ observational systems, moorings and autonomous nutrient or oxygen sensors, offer high-frequency ground-truthing for offshore conditions (Ji et al., 2021). Ultimately, ECC assessment demands systematic validation. Specifically, targeted offshore campaigns measuring growth, filtration and biodeposition rates, and benthic fluxes (e.g., oxygen or nutrient turnover) are required to constrain model predictions. Integrating these multiple data streams into hybrid modeling frameworks will help close the gap between ecological theory and operational site selection (Filgueira et al., 2020).

3.4 Policy and governance implications

Effective policy and governance frameworks are essential to ensure that offshore aquaculture expansion aligns with ECC. Incorporating ECC thresholds directly into environmental permits and MSP zoning can prevent overexploitation of offshore ecosystems, particularly in multi-use (MU) regions where aquaculture intersects with fisheries, conservation, and offshore energy development. Large farms or clusters should be required to undergo mechanistic modeling to quantify nutrient loading, depositional footprints, feedback with the natural faunal communities, and ecological risks before approval (Fisher et al., 2023; Gentry et al., 2016).

Governance frameworks can incentivize the adoption of IMTA, especially in settings where extractive species such as bivalve or seaweed can mitigate nutrient emissions from fed aquaculture. Coordination with the offshore renewable energy sector offers opportunities for co-location, reducing space use conflicts and enabling integrated monitoring and data sharing (Elabor, 2025; Hooper et al., 2015). Competition for space is expected to become an increasingly severe constraint in heavily utilized offshore regions, where energy infrastructure, conservation objectives, and commercial activities already occupy large proportions of available sea space. In these contexts, MU planning may be crucial for the long-term development of offshore aquaculture, but its success will depend on transparent governance, clear allocation of responsibilities among sectors, and the application of suitable modeling and planning tools capable of evaluating ecological trade-offs, cumulative impacts, and spatial compatibility. Spatial optimization tools, ecosystem-based modeling approaches, and ECC-informed decision-support frameworks therefore represent key enablers for effective MU implementation (Gentry et al., 2016; Fujita et al., 2022).

Standardizing monitoring protocols, including water quality indicators, benthic condition assessments, and biomass reporting, will improve transparency and support adaptive licensing. Embedding ECC considerations within long term MSP frameworks will ensure that offshore aquaculture contributes to food security, climate resilience, and blue-economy goals without compromising ecosystem integrity.

At the same time, governance challenges extend beyond ecological considerations. The administrative complexity associated with obtaining offshore aquaculture licenses is frequently perceived as a stronger barrier by small and medium-sized enterprises than technical feasibility, site selection, or investment requirements. Streamlining permitting procedures, while maintaining robust environmental safeguards, will therefore be critical for enabling realistic offshore development. The process and modeling approaches demonstrated in the reviewed studies provide transferable examples that can guide policy development in other coastal and offshore regions globally and can be adapted to different species and management contexts (Barillé et al., 2020).

4 Conclusions

Offshore aquaculture of extractive species can contribute to food security while delivering ecosystem services, but only if development is aligned with ECC and broader EAA principles. This review shows that offshore site selection is still largely dominated by GIS-based MCE that emphasize environmental matching, technical feasibility, and conflict avoidance. In contrast, comparatively few studies operationalize ECC through approaches that capture ecosystem feedback (e.g., FARM, coupled hydrodynamic–biogeochemical models, or EAA-based ecological zoning), and empirical validation in offshore settings remains limited.

Progress toward ECC-aware siting will require moving beyond static suitability mapping toward integrated and adaptive frameworks that combine spatial decision tools with mechanistic models (e.g., GIS–DEB, GIS–FARM, or hydrodynamic–biogeochemical–DEB), supported by standardized indicators and targeted monitoring. Improved access to offshore-relevant environmental time series (remote sensing, hindcasts, moorings, and autonomous sensors) and dedicated field campaigns measuring growth, filtration, biodeposition, and benthic fluxes will be essential to constrain uncertainties and establish transferable ecological thresholds.

Finally, translating ECC from concept to practice depends on governance. Embedding ECC thresholds into licensing and MSP, particularly in MU seascapes such as offshore wind farm co-location can help ensure that expansion occurs within ecosystem limits. By integrating ecosystem-coupled modeling, evidence-based monitoring, and adaptive management into siting and permitting, offshore extractive aquaculture can be scaled strategically while maintaining ecosystem function and resilience.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: D. K. Meena, Central Inland Fisheries Research Institute (ICAR), India

Reviewed by: M. Refik Orhun, Southeast Fisheries Science Center (NOAA), United States

Sophie Koch, Ifremer Station de Palavas, France

Rabi Elabor, Florida Agricultural and Mechanical University, United States