ICES Journal of Marine Science

Bottom trawls have been used for centuries, yet studies of their impact on marine ecosystems have largely been restricted to recent decades. Here, we reconstruct fishing effort for the international trawler fleets in the North Sea from the age of sail to early steam trawling, by synthesising historical data describing vessel numbers and specifications, gear dimensions, fishing grounds, and operational characteristics. The trawler fleet increased from ca 800 sailing vessels in the 1820s to ca 3500 at the peak in sail trawling in the 1880s. Subsequently, steam trawling fleets emerged, increasing to almost 2000 vessels in the 1910s, while sailing fleets declined. Trawling grounds, covering ca 7% of the North Sea in 1820s, expanded from coastal to offshore grounds, reaching ca 25% in the 1880s, and 46% in the 1910s after the transition to steam trawling. Using a hydro- and aerodynamic approach to model the wind conditions required for sail trawling, we show that about 55% to 80% of the time at sea was suitable for trawling, providing a new quantitative basis for estimating historical fishing effort. The surface area swept by the trawl per year increased from 35,000 km2 in 1820s to 225,000 km2 in 1880s and 500,000 km2 in 1910s, corresponding to ca 60% of present levels. The trawling intensity (swept area ratio) varied between 0.9-1.7 year-1 in the era of sail, increasing to ca 2.0 in the 1910s. The trawling footprint (unique area trawled) increased to 160,000 km2, about half the present level.

As anthropogenic threats have intensified over the past 500 years, we find ourselves in the midst of a sixth mass extinction, with continued losses of biodiversity threatening ecosystem stability. This biodiversity loss has caused species extinctions across taxa, and placed several others at high risk of functional extinction. These disturbance-driven impacts represent one of the most acute biodiversity crises facing global marine systems. Species exhibiting slow life histories characteristically have low resilience to disturbance. Here, we assess the risk of functional extinction and identify policy pathways for population recovery of the slow-living, Critically Endangered elasmobranch, the spinetail devil ray (Mobula mobular). We develop a stochastic, state-structured Integral Projection Model (IPM) parameterised with demographic data collected from fishery landings data in India, the world's largest mobulid fishery, and supplemented with data on vital rates from published literature. Using the IPM, we estimate that the population is declining at approximately 12% annually, experiencing substantial limiting pressure from fisheries overexploitation and failing to approach its biological maximum growth potential. Our results indicate that populations of M. mobular will be at high risk of functional extinction if 'business as usual' harvest scenario persists for another decade. We further show that long-term population recovery is only possible if survival increases significantly across all size classes, especially among large reproductive females, alongside a concurrent increase in fecundity. We conclude that no single policy measure is sufficient to recover population of M. mobular along the southeastern coast of India. Instead, combined protection through maximum bycatch mitigation and protection of nursery areas in no-take zones will be required for population recovery. This research demonstrates that recovery of overexploited populations often requires integrated resource management across life stages, and that the Critically Endangered M. mobular warrants urgent conservation action to avoid functional extinction.

Offshore wind farms are rapidly expanding to meet growing demands for renewable energy, with development expected to extend further offshore into deeper waters. This expansion requires a robust understanding of the long-term ecological consequences of offshore wind farms (OWFs) and how these may interact with ongoing climate change. We used the coupled hydrodynamic-ecosystem-biogeochemical water-column model (GOTM-ERSEM-BFM) to investigate ecosystem-wide responses to environmental changes associated with OWFs and climate warming. Specifically, we examined OWF-related scenarios of reduced benthic suspension-feeding activity, representing potential effects of contaminant emissions from OWFs, and reduced wind forcing, together with increased sea surface temperature. The scenarios were simulated individually and in combination to explore potential interactive effects. These scenarios were simulated at two contrasting locations in the North Sea, representing a well-mixed coastal site and a seasonally stratified offshore site. The coastal site exhibited comparatively modest ecosystem responses across the scenarios, whereas responses were generally stronger at the deeper offshore site. At the offshore site, changes in stratification altered vertical nutrient dynamics and contributed to pronounced differences in ecosystem responses between the surface and bottom layers. Our results demonstrate that ecosystem responses to OWF-related and climate-driven environmental changes are strongly dependent on local environmental conditions, suggesting that ecological consequences may differ substantially as wind farm development expands into deeper offshore environments.

Despite increasing calls for sustainability and ecosystem objectives to manage fishing gear interactions with bottom habitats there are few quantitative approaches for assessing risks from bottom contact fishing. Risk assessments for bottom longline fisheries are particularly challenging due to a lack of information for estimating bottom contact areas from longline gear. In this paper, we demonstrate how data sensors and video cameras deployed on fishing gear can be used to quantify the bottom contact area for longline trap and hook fishing gear from the British Columbia Sablefish fishery. Our bottom contact estimates indicate that Sablefish fishing risks to bottom habitat are low in the majority of fishing areas, since 91.8% of the area fished is expected to have had zero bottom contact over the last 17 years. For the other 8.2% of Sablefish fishing areas that experience some contact from fishing gear, the majority are only contacted once. This indicates that most habitats contacted by Sablefish gear can be expected to have a minimum of 17 years to recover between subsequent bottom contact events. We demonstrate an approach for estimating fisheries bottom contact that can be widely implemented across longline fisheries. Our findings address key data gaps in bottom impacts research for longline gear fisheries, allowing fishing risks to be quantified over fine spatial scales. Such quantitative approaches for habitat risk assessment can provide essential information for management decisions aimed at determining acceptable trade-offs between habitat preservation and fishery benefits.

In the Northeast Atlantic, advice for many fish stocks follows the ICES MSY approach, where a zero catch will be recommended if the stock is below its limit reference point, Blim, and cannot rebuild in the short-term. How-ever, zero catch advice are rarely implemented by managers. This study used medium-term stochastic forecasts with harvest control rules (HCRs) to investigate the consequences of allowing reduced fishing below Blim. We applied the method to western Baltic herring and North Sea cod, two contrasting species currently estimated below Blim. We show that the minimum rebuilding probability of 95% required by the MSY approach could be impossible to reach in the short-to medium-term. When this is the case, a lower probability may need to be considered instead in the short-term. Recruitment is the largest source of uncertainty in stock response to management, and can exceed differences between HCRs. Reference points should be estimated in accordance with current recruitment levels if they are to be used for short-term advice or as realistic rebuilding targets. For both stocks, it is possible to keep fishing at reduced levels for similar cumulative catch, SSB and risk on the stock in the medium-term compared to no catch below Blim. Medium-term trade-offs between stock conservation and fisheries considerations may be needed when fishery closure cannot be implemented in practice.

Development of offshore wind energy resources has led to growing concerns for marine wildlife. However, significant uncertainty remains regarding the technologys potential to impact species of interest that may occupy planned development sites. This is further compounded by the difficulty of monitoring highly migratory or data-poor species in marine waters, making practical assessment of site- or species-specific threats that could require additional management intervention particularly problematic. Here, I identify a highly generalizable framework to inform species interactions in marine habitats allocated for offshore resource exploitation, using telemetry-derived artificial intelligence species distribution models. Results from a case study of the federally protected Atlantic Sturgeon (Acipenser oxyrinchus) demonstrate excellent discriminatory capacity (i.e., AUC [≥] 0.9) at a relatively fine scale (raster resolution = 1 km2), while providing critical information on predicted occurrence over a broad swath of unmonitored marine habitats (i.e., the Atlantic OCS region of the US; area > 620,000 km2). Furthermore, ensemble map products developed from these models are readily scalable to ongoing management needs and, when overlaid with offshore wind energy lease areas, can feed directly into management strategies to inform best practices for potential habitat influences on Atlantic Sturgeon, as well as other species of commercial or conservation interest.

Effective cetacean conservation depends on robust monitoring, yet traditional visual and passive-acoustic surveys have constraints. We evaluated environmental DNA (eDNA) metabarcoding coupled with species distribution modeling (SDM) as a tool to study habitat use of cetaceans along the Washington State coast, USA. Seawater was collected at the surface and at a 50 m depth from 43 sites (86 samples) during the 2019 U.S.-Canada Integrated Ecosystem & Acoustic-Trawl Survey. A partial section of the mitochondrial control region was amplified with cetacean - specific primers, sequenced on an Illumina MiSeq, and taxonomically assigned with a curated reference database. Nine species were detected; we modelled the three most frequent: Pacific white-sided dolphin (Lagenorhynchus obliquidens) (10 detections), humpback whale (Megaptera novaeangliae) (8 detections), and Rissos dolphin (Grampus griseus) (6 detections). Binomial generalized additive models related presence-absence to bathymetry, distance to shore, longitude, slope, and sea-surface temperature; model performance was assessed with stratified five-fold cross-validation. SDMs explained 17-51% of null deviance and presented high specificity ([≥] 0.80). The Pacific white-sided dolphin showed the highest eDNA presence probabilities offshore, beyond the shelf break. Humpback whale eDNA presence probabilities showed hotspots along the shelf break with secondary high-probability patches in near-shore waters. Rissos dolphin eDNA presence probabilities were elevated in offshore zones characterized by steep bathymetric gradients, particularly northwest of the sampled transect. These spatial patterns are consistent with historical visual-acoustic records, suggesting that eDNA-informed SDMs can capture cetacean habitat use. This proof of concept indicates that combining eDNA detections with flexible SDMs could provide a cost-effective, non-invasive complement to conventional surveys and may offer a scalable pathway for marine-mammal monitoring and spatial planning.

Ship strikes are a significant and growing threat to marine megafauna, yet few mitigation measures are implemented at scale due to perceived economic costs to shipping. Here, we present a proof of concept for integrating biodiversity considerations into commercial voyage optimisation, using priority aggregation sites for the endangered whale shark (Rhincodon typus) as a case study. We simulated eight port-to-port voyages for two vessel classes--a crude oil tanker and a container ship--under three routing scenarios: baseline optimisation, speed reduction to 10 kts within core habitats, and complete avoidance of these areas. Across routes, fuel-use changes ranged from -0.13% to 9.65%, with minimal impacts (<1%) for most long-distance voyages. Results indicate that speed reduction is the more efficient mitigation for short voyages, while area avoidance is preferable for longer voyages, with impacts varying by vessel type and operational constraints. Incorporating dynamic, species-specific habitat layers into voyage planning could enable targeted ship-strike mitigation with negligible disruption to global trade. Adoption of such measures - supported by improved data pipelines, real-time forecasting, and integration into regulatory and incentive frameworks - offers a scalable pathway to align biodiversity conservation with decarbonisation goals in the maritime sector.

The development of Artificial Intelligence (AI) presents novel opportunities for tackling complex marine resource management challenges. Among AI models, neural networks are a powerful class of tools capable of learning nonlocal and lagged patterns from fisheries data as well as approximating nonlinear relationships among multiple latent variables using estimation methods that automatically implement statistical shrinkage. This gives them potential to effectively handle data obtained from fisheries populations subject to dynamic environments. We highlight two flexible subclasses and one application of neural networks: Long Short-Term Memory (LSTM) and Convolutional Neural networks (CNNs) and policy discovery via Reinforcement Learning. LSTMs are designed to handle sequential data by allowing prediction from past values at both short and long time-lags. CNNs are not explicitly designed to handle temporal information, but can interpolate a spatial latent variable based on its value within a geographic neighborhood, and can be combined with LSTM models for this purpose. This "Food for Thought" paper introduces and applies these neural network approaches, both alone and in combination, to demonstrate their potential application for several foundational topics in fisheries science: 1) the forecasting of population weight-at-age, 2) the standardization of spatio-temporal indices of relative abundance, and 3) the discovery of harvest policies to optimize catches and maintain spawning biomass. Each section provides a simple, simulated example and describes the tradeoffs - particularly the lack of inferential capability - presented by using neural networks over traditional approaches for each topic. We then outline medium-term research questions that may clarify, facilitate or qualify the applicability of these tools to fisheries management science. Finally, we discuss how future combinations of these approaches could result in simplified ways to estimate and forecast stock biomass and provide harvest advice.

The management of Atlantic bluefin tuna (Thunnus thynnus) is currently defined by a long-standing life-history paradox: a stark divergence in maturation schedules between the Eastern and Western stocks. While the Mediterranean contingent matures at approximately 104 cm straight fork length (SFL; age 3-5), the western stock has long been characterized as a late-maturing, at [~]190 cm SFL (age 8+), and as late as age 15.8, based on size-at-catch data in the presumed exclusive spawning areas of the Gulf of Mexico. This discrepancy defies maturity states confirmed by endocrine hormones as well as established life-history principles that link metabolic energetics to reproductive biology. To resolve this critical issue in stock assessment, we present a sensitivity analysis based on the first histological evidence of spawning- capable individuals from 42 bluefin females (97-244 cm curved fork length or [~]95- 236 cm SFL) sampled in June 2025 within the Slope Sea, Northwest Atlantic. This historically overlooked, temperate spawning ground must be recognized and past estimates of stock productivity reevaluated. Our data provide a conservative estimate of length-at-50% maturity, L50 for females at 123.3 cm SFL, effectively reconciling the reproductive parameters of the two stocks based on histology. Our findings align established bluefin tuna metabolic and evolutionary symmetries in life history, irrespective of management boundaries. Incorporating this previously unrecognized spawning group (i.e., smaller, younger fish) calls for a substantial upward revision of Western spawning stock biomass (SSB). This biomass represents an intrinsic biological buffer that could contribute to the Atlantic bluefins adaptation and resilience. Appreciating demographic diversity is critical to accurately diagnosing stock vulnerability, ensuring that management frameworks protect long-term population stability in the face of climate-driven oceanographic shifts and ongoing exploitation pressure.

Antarctic krill (Euphasia superba) are a key component in the Southern Ocean ecosystem, especially in the Atlantic sector, where the majority of the population is concentrated. The Norwegian commercial krill fishery exclusively targets three subareas in the Antarctic: the western Antarctic Peninsula, and the northern shelves of both the South Orkney Islands and South Georgia. Given its reliance on oceanic transport from other regions and the potential impact of rising sea temperatures on the northern habitat, the South Georgian krill population is particularly sensitive to altering environmental conditions. The relative distance from the peninsular regions to South Georgia means that choosing to trawl in this region implies a higher risk, which is why it is exclusively targeted in winter when extensive sea-ice makes peninsular regions unsafe and inaccessible to commercial fishery operations. In this article, we show that relative to operations at South Orkney and the Antarctic Pensinsula, average catches at South Georgia have been lower with higher variability over the past 15 years. Using a Lagrangian modelling approach, we illustrate that variability in advection from source regions in the Antarctic Peninsula are correlated with proceeding catch values at South Georgia. This was not the case for source release sites at the South Orkney Islands. The dominant transport pathways for krill were strongly determined by position of regional fronts and the source sites of recruits to South Georgia were related to the position of fronts at both the Antarctic Peninsula and South Orkney Islands. This study highlights the importance of advective patterns on the variability in krill fishing activity and supports the hypothesis that South Georgia is a sink region for krill in the Southern Ocean while the western Antarctic Peninsula is a central source site.

The precautionary approach to fisheries management advocates for risk-averse management strategies that include biological reference points as well as decision rules and account for scientific uncertainty. In this regard, two approaches have been recommended: (i) harvest control rules (HCRs) with threshold reference points to safeguard against low stock biomass, and (ii) the P* method, a probability-based HCR that reduces the catch limit as a function of scientific uncertainty (i.e. process, model, and observation uncertainty). This study compares the effectiveness of these precautionary approaches in recovering over-exploited fish stocks with various life-history traits and under a wide range of levels of scientific uncertainty. We use management strategy evaluation based on a stochastic, age-based operating model with quarterly time steps and a stochastic surplus production model. The results show that the most effective HCR includes both a biomass threshold as well as the P* method, and leads to high and stable long-term yield with a decreased risk of low stock biomass. For highly dynamics stocks, management strategies that aim for higher biomass targets than the traditionally used BMSY result in higher long-term yield. This study makes the case for probability-based HCRs by demonstrating their benefit over deterministic HCRs and provides a list of recommendations regarding their definition and implementation.

Small-scale, multispecific fisheries in the Gulf of California face significant challenges including limited species-specific catch data, uncertainty about climate change impacts, and insufficient biological information needed for traditional deterministic models. These knowledge gaps hamper efforts to forecast future conditions and develop appropriate management strategies accurately. The complexity of these multi-species fisheries, combined with data scarcity for many target species, creates substantial barriers to quantifying and addressing climate vulnerability. Deep learning approaches offer a promising alternative by leveraging available data to identify patterns and project trends despite these limitations, providing valuable insights for fisheries management in data-poor contexts. Here, we apply a Mixture of Expert, a deep learning forecasting models for small-scale, multi-specific fisheries in the Gulf of California under future climate change scenarios. Results show varied responses across marine habitats, with reef and benthic fish projected to experience substantial declines (-12.46% and -9.37%) during the 2050s-2060s, followed by recovery in the 2070s-2080s. Economic implications are significant, with reef fish facing projected losses of $1.2 million by the 2050s before recovering by the 2080s. Shapley Additive Explanations (SHAP) analysis was applied to evaluate the importance of features for each predictive model, the analysis revealed the effects of the temperature in different depths for each fishery, and the sensitive analysis pointed to the magnitude of the effect. Our findings suggest that climate impacts will not be uniform across the Gulf, necessitating region-specific management approaches and highlighting the value of maintaining diverse fishing portfolios to enhance resilience against climate-driven changes.

Warming temperatures elicit shifts in habitat use and geographic distributions of fishes, with uneven effects across life stages. Spawners and embryos are particularly sensitive to environmental conditions, with direct impacts of temperature on spawning habitat, as well as indirect connections between their population dynamics and fisheries effort, productivity, and management. Here, we ask how changing environmental conditions and thermal sensitivities of developing embryos confer spatiotemporal variability of thermally-suitable spawning habitat for Pacific cod in the eastern Bering Sea. Specifically, we use bottom temperature values from regionally downscaled global climate models coupled with an experimentally-derived relationship between hatch success and temperature to predict how the extent, mean latitude, and consistency of suitable spawning habitat has changed in the past and may change into the future. We then validate our predictions of suitable spawning habitat with distributions of adults and larvae and examine whether thermal habitat availability relates to recruitment success into the adult cod into the population. We find that the extent and mean latitude of suitable spawning habitat increase over time, particularly if no climate change mitigation occurs in the future. Hotspots of suitable spawning habitat are consistent across shorter time periods but do shift across the Bering Sea shelf by the end of the century. Finally, we find no correlation between the availability of suitable spawning habitat and annual estimates of recruitment. Collectively, our results suggest that as temperatures warm, the availability of suitable spawning habitat will increase and expand spatially and, thus, is not likely to limit recruitment. This work highlights the importance of coupling experimental data with climate models to identify the complex and mechanistic dynamics among temperature, life histories, and ecology, and offers a pathway for examining life stage-specific changes in habitat use and distribution with continued climate change.

Projections of future conditions from Earth systems models (ESMs) are necessary to understand and predict effects of changing environmental conditions on biological systems. Such projections suffer from biases, or mismatches between model output and observations. While adjusting or bias-correcting model output is common, many methods exist with little understanding of their effects on forecasts of biological change. Here, we explore the bias-correction process and its effects on downstream predictive biological models. As an example, we use the Bering 10K, a downscaled ESM for a productive and economically important subarctic ecosystem. We first characterize existing biases for three categories of variables exhibiting different scales and challenges: bottom temperature, sea ice, and net primary production. We then apply eight bias-correction approaches to six indices generated from the three categories and quantify sources of uncertainty in the trajectories of these ecosystem variables. Finally, we demonstrate how different bias-correction approaches affect downstream biological models using three case studies: 1) fish thermal spawning habitat suitability, (2) predicted zooplankton abundance, and (3) match-mismatch of phytoplankton and zooplankton bloom timing. We find that biases manifest in absolute values over time and in the timing of seasonal events. Time series of all six indices differed depending on bias-correction method, differences that were propagated to downstream biological models. For a given year and simulation, depending on method, thermal spawning habitat suitability and zooplankton abundance differed up to 149% and 151%, and match-mismatch increased or did not change. Our work highlights that bias correction reduces mismatches between observations and model output but choosing an approach requires careful consideration as to not amplify and propagate bias in downstream biological models. To that end, we identify best practices for bias correcting global or regional ESMs, including a decision tree to help improve forecasts of the effects of climate change on biological systems.

The recovery of marine mammals from historical over-exploitation in the 1970s represents one of the largest changes in trophic structure in the northeast Pacific Ocean over the last century, for which the impacts on key forage species such as Pacific Herring (Clupea pallasii) are poorly understood. This has prompted hypotheses that increasing marine mammal populations are the primary cause for productivity declines for some fish stocks and their lack of recovery to historical abundance levels. In this study, we evaluate such a hypothesis for Pacific Herring by quantifying historical predation rates by key predators including cetaceans (Pacific Humpbacks, Grey Whales), pinnipeds (Stellar Sea Lions, Harbour Seals), and piscivorous fish (Pacific Hake). Predation mortality is quantified via a novel approach that integrates a single-species catch-at-age model with estimates of predator consumption derived from bioenergetic models. We found that predator consumption, largely driven by Humpback Whales, explained increasing Pacific Herring natural mortality rates in recent years and could be used to forecast future mortality. Incorporating higher future natural mortality rates produced higher estimates of current stock status (1.09-1.2B0) based on lower estimates of equilibrium unfished biomass (17.5-20.3 kt). Conversely, models that assumed mortality was more like the historical average had lower stock status (0.63B0) and higher estimates of unfished biomass (32.4 kt). We demonstrate a practical approach for ecosystem modelling that can be used to develop operating model scenarios for management strategy evaluation, improving scientific defensibility by removing an element of analyst choice for future mortality scenarios. We discuss how simpler modifications to single-species model assumptions can be more pragmatic for providing fisheries management advice, while more complex multi-species or ecosystem models might provide more nuanced insights for exploring research questions related to multi-species ecosystems and fisheries interactions.

Area and time restrictions are widely used in fisheries management for their simplicity and conservation benefits. Static closures (STCs) often fail to protect migratory fish; therefore, real-time closures (RTCs) are increasingly being adopted. However, RTCs require extensive and rapid data collection and analysis. The vessel monitoring system and daily landing reports provide near real-time data on fishing activities and fish abundance. We conducted a management strategy evaluation of RTCs and STCs in the Gulf of Thailand short mackerel fishery to clarify the efficacy of using RTCs to minimise fishing mortality, while considering their appropriate use with surveillance data for a migratory fish. The results support RTCs as more flexible and requiring a smaller closure area compared with STCs to achieve management objectives. We recommend gathering CPUE data on a monthly basis and using the highest CPUE threshold level to define a closure unit; no unit should be shut down until all units achieve the threshold level. Our results validate the efficacy of the RTC strategy for curtailing fishing mortality of a mobile species and demonstrate the effective use of RTCs to mitigate uncertainty in the migratory patterns of the species in an otherwise unpredictable, fluctuating environment.

We assembled estimated biomass (B) time series from stock assessments for 24 Pacific Canadian groundfish stocks and modelled average and stock status through 2020 based on biomass relative to each stocks (1) Limit Reference Point (B/LRP), (2) Upper Stock Reference (B/USR), and (3) biomass at maximum sustainable yield (B/BMSY). The overall mean B/LRP in 2020 was 3.2 (95% credible interval [CI]: 2.6-3.9). The overall mean B/USR and B/BMSY in 2020 was 1.5 (95% CI: 1.3-1.9) and 1.4 (95% CI: 1.1-1.7), respectively. Average stock status declined from 1950 to around 2000 and has remained relatively stable since then. The change around 2000 followed the implementation of ITQs (individual transferable quotas) for the trawl fleet and the commencement of the synoptic trawl surveys. As of their last assessment, four stocks (Strait of Georgia Lingcod [Area 4B], coastwide Bocaccio, and inside and outside Quillback Rockfish) had a greater than 5% probability of being below their LRP (i.e., in the " critical zone"); Pacific Cod in Area 3CD had a 4.6% probability. Roughly one-third of stocks had a greater than 1 in 4 chance of being below their USR (i.e., in the " cautious zone"). Conversely, two-thirds of assessed groundfish stocks had a high (>75%) probability of being above the USR (i.e., in the " healthy zone").

Sustainable management of marine ecosystems has to take into account the conservation of many species that are not themselves targets of fishing, in addition to those that are commercially exploited. Dynamic size-spectrum models suggest that fishing which leads species to have similar ratios of yield to production (i.e. similar exploitation ratios) is not sufficient to protect those that are rare: these species need to experience lower exploitation ratios. Here, the status of the demersal fish assemblage in the Celtic Seas is examined from the perspective of yield and production, using survey data collected over the period 2012 to 2016, and incorporating both common and rare species. The results give no evidence that rarer species have lower exploitation ratios than common ones. This suggests that current management methods are not not operating in a way that conserves the fish assemblage as a whole.

We developed an agent-based model (ABM) to assess how area-based controls on fishing methods can reduce fishing mortality and population declines. The model incorporates the behavior and distributions of dolphins and fishing vessels, and realistic displacement of fishing effort when protection is extended. Our case study is New Zealand dolphin - Hectors and Maui dolphins. The model was designed and calibrated using pattern-oriented modeling. Our results show that mortality due to entanglement in fishing gears has been reduced thanks to a gradual increase in dolphin protection. However, current protection is not as effective as previously thought, and scarce populations are negatively affected by Allee effects. Neither national nor international goals for reducing bycatch are met by current dolphin protection. The IUCN has recommended banning gillnet and trawl fisheries in New Zealand waters < 100m deep. For most New Zealand dolphin populations, this would be effective in achieving national and international goals for reducing bycatch. Only two populations would require additional protection. This modelling approach is also suitable for assessing impacts of bycatch and ship strikes for other marine species, making it suitable for informing management decisions in many regions.