The Environment Agency undertakes fisheries monitoring work on rivers, lakes and transitional and coastal waters (TraC).

This dataset contains site and survey information, the numbers and species of fish caught, fish lengths, weights and ages (where available), for all the freshwater fish surveys carried out across England from 1975 onwards.

Notes: - These survey data are stored in an archive more commonly known as the NFPD (National Fish Populations Database). - This dataset contains Freshwater fish surveys only. - Third party data held on the NFPD are excluded from the dataset. - Some historic surveys (particularly in Anglian Central) have incorrect survey lengths and survey widths. These can be identified by a survey length of 1 and a survey width that is equal to the area. The survey areas are correct. This is due to the migration of old historic data from previous databases into the NFPD. - Approved for Access under AfA347.

Please see the Dataset Documentation for further detail.

Data included in this dataset include: 1) population estimate data; 2) microhabitat use data; and 3) microhabitat availability data for the Santa Ana Sucker (Catostomus santaanae) and the Arroyo Chub (Gila orcutti) in the Santa Ana River.

This dataset includes 2020 reach fish data and reach habitat data collected to support development of the upper Santa Ana River Habitat Conservation Plan for the Santa Ana Sucker (Catostomus santaanae) and the Arroyo Chub (Gila orcutti) in the Santa Ana River, California.

The effective management of fish populations requires understanding of both the biology of the species being managed and the behavior of the humans who harvest those species. For many marine fisheries, recreational harvests represent a significant portion of the total fishing mortality. For such fisheries, therefore, a model that captures the dynamics of angler choices and the fish population would be a valuable tool for fisheries management. In this study, we provide such a model, focusing on red drum and spotted seatrout, which are the two of the main recreational fishing targets in the Gulf of Mexico. The biological models are in the form of vector autoregressive models. The anglers’ decision model takes the discrete choice approach, in which anglers first decide whether to go fishing and then determine the location to fish based on the distance and expected catch of two species of fish if they decide to go fishing. The coupled model predicts that, under the level of fluctuation in the abundance of the two species experienced in the past 35 years, the number of trips that might be taken by anglers fluctuates moderately. This fluctuation is magnified as the cost of travel decreases because the anglers can travel long distance to seek better fishing conditions. On the other hand, as the cost of travel increases, their preference to fish in nearby areas increases regardless of the expected catch in other locations and variation in the trips taken declines. The model demonstrates the importance of incorporating anglers’ decision processes in understanding the changes in a fishing effort level. Although the model in this study still has a room for further improvement, it can be used for more effective management of fish and potentially other populations.

This dataset includes: 1) microhabitat use data for fish 100 mm or larger; 2) reach fish data; and 3) reach habitat data for the Santa Ana Sucker (Catostomus santaanae) and the Arroyo Chub (Gila orcutti) in the Santa Ana River, California. Habitat availability data was not collected in 2017.

Here are a few use cases for this project:

1. "Fish Identification App for Anglers and Biologists": Create a mobile application to assist anglers, marine biologists, and environmentalists in identifying fish species in real-time using their smartphone camera. People can take a picture of a fish and instantly know the species, its fishing restrictions (if applicable), habitat, and other relevant information.

2. "Automated Fish Monitoring for Aquaculture and Fisheries Management": Implement the "Fish" model in underwater cameras and drones to automate fish identification, monitoring fish populations, and tracking species distribution in aquaculture farms and natural habitats. This can help in effective management and conservation efforts to ensure sustainable fishery practices.

3. "Education and Research Tools for Marine Science": Incorporate the "Fish" model into interactive educational resources (such as Virtual Reality or Augmented Reality experiences in educational institutions) to teach students about fish species and their habitats, promoting interest and understanding of marine science and biodiversity.

4. "Fish Sorting and Distribution in the Seafood Industry": Develop an automated fish sorting system for fish markets and processing plants that uses the "Fish" model to identify fish species swiftly and accurately. This would help businesses in improving efficiency, reducing waste, and complying with legal requirements.

5. "Citizen Science Initiatives for Fish Conservation": Create a platform where people can contribute their fish photos and location data to help researchers study fish populations and distribution, allowing them to input images into the "Fish" model to verify the species. This collaborative environment allows people to participate in the conservation efforts and increases environmental awareness.

Here are a few use cases for this project:

1. Fisheries Management: The Fish Detection model could be employed in fisheries to automate the identification and sorting of different fish species. This would not only streamline operations but also help control the overfishing of certain species.

2. Aquaculture Monitoring System: The model can be used in aquafarm surveillance, which tracks and monitors the fish species in the pens. The system can raise alerts when unusual or unintended species are detected.

3. Environmental Studies and Marine Research: The Fish Detection model can be deployed in marine ecosystems by researchers or environmental conservationists to study fish population dynamics, which includes species segmentation, count, and migrations.

4. Commercial Fishing: The model can be integrated into commercial fishing operations to facilitate real-time detection and classification of fish during a trip. This could help fishermen to comply with regional fishing quotas and avoid catching non-target species.

5. Food Industry Quality Control: In seafood restaurants, fish markets, or food processing industries, the model can be used to ensure that the correct species of fish is being marketed or prepared, thus enhancing consumer trust and quality assurance.

A distribution with high spatial variability may impair the bet-hedging capacity of a population, threatening population sustainability. Although the association between aggregation and life history traits of a species (e.g. body size) has been documented, the relationship between aggregation and size within a population has rarely been explored. As selective over-fishing may induce size truncation in the targeted stocks,it is critical to understand if such a truncation also undermines the distribution patterns of the population. In this study, we examined if and how the ‘aggregation tendency’ varies among different size classes of a population. Aggregation tendency was quantified as the exponent b of Taylor’s power law (V = a × Mb), which measures the change in spatial variance (V) with the mean abundance (M) of a population. We estimated b by size class for each of the nine commercially important fish species in the North Sea, using ICES survey data from 1991 to 2015. Our study found that the relationship between b and body size within a population is hump-shaped, with a peak slightly larger than the 50% mature length of the species. This result indicates larger adults in a population tend to distribute less heterogeneously when abundance increases, suggesting that larger size classes play a critical role in reducing the variability of population distribution. Our findings highlight the importance of considering the combined effects of fishing-induced size truncation and changes in aggregation patterns in fishery management. That is, maintaining the size and spatial structure for the target stocks of selective fisheries is critical for the sustainability of the populations.

Methods The raw cpue data, maturation stage, and fishing mortality data were downloaded from the DATRAS (https://datras.ices.dk/Data_products/Download/Download_Data_public.aspx). The temperature data was download from the ICES website (https://ocean.ices.dk/HydChem/HydChem.aspx?plot=yes). The life history traits data was extracted from Thorp et al.'s (2015) study.

Using R code (https://github.com/ruo-yu-Pan/Hump-shaped-relationship_Aggregation-tendency_vs_bodysize), we processed the raw data, calculated the size-based Taylor's exponents, and investigated the effect of body size and temperature on the size-based Taylor's exponents within the population.

Lake Erie Biological Station (LEBS), located in Sandusky, Ohio, is a field station of the USGS Great Lakes Science Center (GLSC). LEBS is the primary federal agency for applied fisheries science excellence in Lake Erie. Since 2004, LEBS has participated in a collaborative, multiagency effort to assess forage fish populations in the western basin of Lake Erie. Assessing the distribution and abundance of both predator and prey (forage) fish species is a cornerstone of ecosystem-based based fishery management, and supports decision making that considers food-web interactions. The objectives of this survey were to provide estimates of densities of key forage and predator species in the western basin of Lake Erie, to assess seasonal and spatial distributions of fishes, and to assess year class strength. In 2012, the original vessel used since 2004, the R/V Musky II, was retired and replaced with the R/V Muskie. The change in vessel necessitated changing the gear used to capture fish. Previous surveys used a different catch processing protocol that did not include measurements of biomass or lengths of all species; thus, those historical data are not compatible with the current data format. Under the new protocol, 41 stations were sampled during June (Spring) and September (Autumn). The 2013 western basin survey season marked the first year in which the grid sampling design was employed in both spring and autumn. Thus, we present data starting from 2013. The data sets will automatically update with new data as surveys are completed in future years.

Censuses of the native prey fish populations during lionfish surveys in Eleuthera, Bahamas from July to August in 2012 access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=This was an observational field study conducted from June - August 2012 to determine whether lionfish behavior and movements change at different local lionfish and prey fish densities. \u00a0The study was conducted on sixteen reefs in Rock Sound, Elethera, The Bahamas.\u00a0 All reefs were at least 300 m from any reef on which lionfish removals had occurred, and were selected to encompass a range of natural lionfish densities and reef sizes. \u00a0

A pair of divers visited each reef at three times of day: within 2 hours of sunrise (\u2018dawn\u2019), greater than 3 hours from sunrise or sunset (\u2018midday\u2019), and within 2 hours of sunset (\u2018dusk\u2019).\u00a0 Upon arriving at a reef, observers counted the number of lionfish present by conducting lionfish-focused searches.\u00a0 For each lionfish, observers recorded the size (total length, visually estimated to the nearest cm), behavior, and location the moment it was sighted.\u00a0 Behaviors were categorized as resting (sitting on the substrate, not moving), hovering (in the water column oriented parallel to the bottom, but not moving), swimming (actively moving), or hunting (oriented head down with pectoral fins flared).\u00a0 Location was categorized as the microhabitat on which lionfish were observed (e.g. under a ledge, on top of the reef, in the surrounding seagrass) and later divided into two major categories: sheltering (hidden under structure) or exposed (on top of reef or in surrounding area).\u00a0 Then, 10-minute focal observations were conducted on two randomly-selected lionfish or a single lionfish when there was only one individual present per reef.\u00a0 During focal observations, a trained observer recorded the behavior of lionfish at 30-second intervals for 10 minutes using the same categories as above.\u00a0 The observers also noted any strikes at prey, successful kills, and obviously aggressive interactions (chases, posturing) between lionfish or between lionfish and other species.\u00a0 Throughout the entire visit to each reef, divers noted the time when any lionfish departed from or arrived at the reef and its behavior.\u00a0 A lionfish was defined as departing from the reef if it traveled at least 10 m from the reef.\u00a0 A lionfish was considered arriving at a reef if it swam in from the surrounding areas and had not been previously observed at that reef during that observation period.\u00a0 At the conclusion of the focal observations, the divers re-counted the number of lionfish present while conducting a survey of resident native fishes.\u00a0 Divers recorded the abundance and body size (TL) of all fish 1 - 15 cm TL, native mesopredators that are ecologically similar to lionfish (e.g. Cephalopholis cruentata [graysby grouper]), and top predators (e.g. Epinephelus striatus [Nassau grouper]) on and within 1 m of the reef. awards_0_award_nid=561016 awards_0_award_number=OCE-1233027 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1233027 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Behavior and Movement - Native Fish Survey Lead PI: Mark Hixon Sub-Project Lead: Casey Benkwitt Version 10 August 2016 Species codes are first two letters of genus and species; See species key. Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.653816.1 infoUrl=https://www.bco-dmo.org/dataset/653816 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/653816 param_mapping={'653816': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/653816/parameters people_0_affiliation=University of Hawaii people_0_person_name=Mark Hixon people_0_person_nid=51647 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Oregon State University people_1_affiliation_acronym=OSU people_1_person_name=Cassandra E. Benkwitt people_1_person_nid=51706 people_1_role=Contact people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Hannah Ake people_2_person_nid=650173 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=BiodiversityLossEffects_lionfish projects_0_acronym=BiodiversityLossEffects_lionfish projects_0_description=The Pacific red lionfish (Pterois volitans), a popular aquarium fish, was introduced to the Atlantic Ocean in the vicinity of Florida in the late 20th century. Voraciously consuming small native coral-reef fishes, including the juveniles of fisheries and ecologically important species, the invader has undergone a population explosion that now ranges from the U.S. southeastern seaboard to the Gulf of Mexico and across the greater Caribbean region. The PI's past research determined that invasive lionfish (1) have escaped their natural enemies in the Pacific (lionfish are much less abundant in their native range); (2) are not yet controlled by Atlantic predators, competitors, or parasites; (3) have strong negative effects on populations of native Atlantic fishes; and (4) locally reduce the diversity (number of species) of native fishes. The lionfish invasion has been recognized as one of the major conservation threats worldwide. The Bahamas support the highest abundances of invasive lionfish globally. This system thus provides an unprecedented opportunity to understand the direct and indirect effects of a major invader on a diverse community, as well as the underlying causative mechanisms. The PI will focus on five related questions: (1) How does long-term predation by lionfish alter the structure of native reef-fish communities? (2) How does lionfish predation destabilize native prey population dynamics, possibly causing local extinctions? (3) Is there a lionfish-herbivore-seaweed trophic cascade on invaded reefs? (4) How do lionfish modify cleaning mutualisms on invaded reefs? (5) Are lionfish reaching densities where natural population limits are evident? projects_0_end_date=2016-07 projects_0_geolocation=Three Bahamian sites: 24.8318, -076.3299; 23.8562, -076.2250; 23.7727, -076.1071; Caribbean Netherlands: 12.1599, -068.2820 projects_0_name=Mechanisms and Consequences of Fish Biodiversity Loss on Atlantic Coral Reefs Caused by Invasive Pacific Lionfish projects_0_project_nid=561017 projects_0_project_website=http://hixon.science.oregonstate.edu/content/highlight-lionfish-invasion projects_0_start_date=2012-08 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=year,length_max_45,length_max_50,length_max_100,length_max_150 version=1 xml_source=osprey2erddap.update_xml() v1.3

The Florida Fish and Wildlife Conservation Commission (FWC) collected annual trawl data at 27 open-water sites from 1987 to 1991 (Bull et al. 1995). Nearly 37,000 fish were recorded in 438 10-minute open-water trawls (Bull et al. 1995). Seven species accounted for 98% of the total number and total fish biomass. Clustering of sites based on mean catch of the primary species expressed as number and weight produced four distinct groups. The groups were labeled as the northeast shore, northwest shore, south-southwest shore and open water area. Areal fish distribution patterns also were compared using analysis of variance (ANOVA) and Tukey’s HSD post hoc test. Within the four groups there were significant differences in the distribution of certain fish species. In addition to the open-water trawl sites, the FWC has utilized electrofishing techniques to collect annual largemouth bass (Micropterous salmodies) (LMB) data from 22 near-shore and interior marsh locations since 1999 (Havens et al. 2004). Although the trawl and electrofishing data provide some baseline information, still there is limited data regarding temporal changes in the community structure, density and condition of the primary sport fish LMB, black crappie (Pomoxis nigromaculatus), bluegill (Lepomis macrochirus) and redear (Lepomis microlophus) sunfish) and other fish species in Lake Okeechobee. During this study, fish species will be collected from 49 historic sampling locations. Fish assemblages in the 27 open water regions of the lake will be sampled with an Otter Trawl net. The 22 near-shore and interior marsh sites will be sampled utilizing electrofishing gear. Ancillary data, including water temperature, dissolved oxygen, pH, turbidity, conductivity, and sediment/aquatic plant type will be recorded at the 49 sampling locations.
The two historic sets of non-MAP data will be used to help establish baseline conditions for the near-shore, interior marsh and open-water fishery. It is appropriate to include the non-MAP data in our analysis as current sampling will occur at the historical locations and sampling methods will be similar. We anticipate significant spatial differences in fish abundance and biomass will exist at the near-shore, interior marsh and open water sites. Therefore, similar statistical tests including cluster analysis and analysis of variance should be used to evaluate temporal changes in the near-shore and open water fishery. Detailed statistical analysis should be conducted at a minimum of every three years to evaluate long-term trends and establish relationships between fish distribution, condition, and community structure and environmental conditions including habitat and water depth.
The objectives of this project are to evaluate temporal changes in Lake Okeechobee’s fishery by determining annual changes in the areal distribution, condition, density and community structure (year classes) of all major fish species found in the near-shore, interior marsh and open-water regions of the lake. Ancillary data including water temperature, dissolved oxygen, pH, turbidity, conductivity, and sediment type also will be recorded.

The Biodiversity Science Database is a compilation of fish community data from DFO Science Surveys. Data includes: sampling site, date, fish counts, fish species, and associated habitat information. This database was established to support fish species at risk research through the DFO Species at Risk Program and is used primarily for updating the current status of fish species at risk populations across Southern Ontario. The dataset has been constrained to sampling site characteristics for placement on Great Lakes DataStream; the original is available via the Government of Canada Open Data portal, see Data Source URL.

Fish population dynamics are represented by age-structured models that account for reproduction, natural mortality and fishing mortality. These models are calibrated for the major species groups and …Show full descriptionFish population dynamics are represented by age-structured models that account for reproduction, natural mortality and fishing mortality. These models are calibrated for the major species groups and are detailed in Sections 3 and 15 in the companion model specification report (Gray et al. 2006). The selection of the parameters for each species was done by running the historical period of the model over a range of values of these parameters, and comparing the initial biomass of the simulation to a range of species biomass that was thought to occur in nature. Parameters were selected at three levels within this range of biomass to give the optimistic, base-case and pessimistic specifications.

Assessing the distribution and abundance of both predator and prey (forage) fish species is a cornerstone of ecosystem-based fishery management, and supports decision making that considers food-web interactions. In support of binational Great Lakes fishery management the objectives of this survey were to: provide estimates of densities of key forage and predator species in the western basin of Lake Erie, to assess seasonal and spatial distributions of fishes in tandem with water quality information, and to assess year class strength. A systematic grid sampling approach with 41 stations was sampled via bottom trawl during June (Spring) and September (Autumn), starting in 2013. This data release adds 2019 data to the set for a total of seven years observation using the same gear and sampling design.

Animal populations are spatially structured in heterogeneous landscapes, in which local patches with differing vital rates are connected by dispersal of individuals to varying degrees. Although there is evidence that vital rates differ among local populations, much less is understood about how vital rates covary among local patches in spatially heterogeneous landscapes. In this study, we conducted a 9-year annual mark-recapture survey to characterize spatial covariation of survival and growth for two Japanese native salmonids, white-spotted charr (Salvelinus leucomaenis japonicus) and red-spotted masu salmon (Oncorhynchus masou ishikawae), in a headwater stream network composed of distinctly different tributary and mainstem habitats. Spatial structure of survival and growth differed by species and age class, but results provided support for negative covariation between vital rates, where survival was higher in the tributary habitat but growth was higher in the mainstem habitat. Thus, neither habitat was apparently more important than the other, and local habitats with complementary vital rates may make this spatially structured population less vulnerable to environmental change (i.e., portfolio effect). Despite the spatial structure of vital rates and possibilities that fish can exploit spatially distributed resources, movement of fish was limited due partly to a series of low-head dams that prevented upstream movement of fish in the study area. This study shows that spatial structure of vital rates can be complex and depend on species and age class, and this knowledge is likely paramount to elucidating dynamics of spatially structured populations.

Methods Field surveys were conducted annually (the third weekend of October) in 2009–2017. Fish were captured using a backpack electrofishing unit (300–400 V DC, model 12B or LR20, Smith-Root, Inc., Vancouver, WA, USA) and 3-mm mesh dip nets. Two passes of electrofishing were conducted for fish density estimates of each section with a depletion method (Zippen 1958). Captured fish were anaesthetised with phenoxyethanol (ca. 0.5 ml/L water), measured for fork length (FL: nearest 1 mm), and were marked individually with visible implant elastomer tags (Northwest Marine Technology Inc., WA, USA) or their individual code was recorded if recaptured. A unique combination of four elastomer colours were subcutaneously administered to the forehead of each individual. All captured fish with FL > 43 mm were marked. During each year of the study, juveniles (young-of-the-year, YOY) and adults (age 1+ and older) were distinguished based on length-frequency histograms. For individuals which cannot be assigned to an age class due to their intermediate body size, a few scales were taken using a scalpel and the annuli were counted. All fish were returned alive to the capture site (< 20 m for mainstem, < 40 m for tributaries) after recovering from anaesthesia. All captured individuals retained at least two of the four elastomer colours, and were marked again with the lost colour(s). We identified all individuals uniquely based on species, sex, body size, and study section at mark (i.e., asymmetrical movement at dams).

Fish population dynamics are represented by age-structured models that account for reproduction, natural mortality and fishing mortality. These models are calibrated for the major species groups and are detailed in Sections 3 and 15 in the companion model specification report (Gray et al. 2006). The selection of the parameters for each species was done by running the historical period of the model over a range of values of these parameters, and comparing the initial biomass of the simulation to a range of species biomass that was thought to occur in nature. Parameters were selected at three levels within this range of biomass to give the optimistic, base-case and pessimistic specifications.

Drone-Assisted Reef Fish Population Survey Market Outlook

According to our latest research, the global drone-assisted reef fish population survey market size reached USD 412.7 million in 2024, driven by increasing demand for non-invasive, real-time, and accurate marine ecosystem monitoring. The market is exhibiting a robust growth trajectory with a CAGR of 12.6% during the forecast period, and is projected to attain USD 1,205.4 million by 2033. This growth is primarily propelled by technological advancements in drone capabilities, rising environmental concerns, and the need for efficient marine biodiversity management.

The primary growth factor in the drone-assisted reef fish population survey market is the escalating global awareness regarding the critical role of coral reefs and associated fish populations in maintaining marine biodiversity. Coral reefs are under unprecedented threat due to climate change, overfishing, and pollution, making precise and timely data collection essential for conservation efforts. Drones have revolutionized the way researchers and conservationists monitor marine life by enabling high-resolution, large-scale surveys at a fraction of the traditional cost and effort. Their ability to access remote or hazardous areas, combined with advanced imaging and AI-driven analytics, has significantly improved the accuracy and efficiency of population assessments, habitat mapping, and behavioral studies in reef ecosystems.

Another significant growth driver is the rapid technological innovation in drone hardware and software. The integration of advanced imaging systems, such as multispectral and hyperspectral cameras, coupled with AI-based analytics, has enabled the identification of fish species, assessment of population densities, and monitoring of behavioral patterns with unprecedented precision. These technological advancements are not only enhancing data quality but also reducing the need for human divers, thereby minimizing the disturbance to sensitive reef habitats and ensuring the safety of research personnel. The expanding applications of drone technology in marine research, including species identification, habitat mapping, and behavioral studies, are further fueling market growth.

The emergence of Drone-Assisted Microplastic Ocean Survey technologies is adding a new dimension to marine research. These drones are specifically designed to track and analyze the distribution of microplastics across vast oceanic expanses. By utilizing advanced sensors and AI-driven analytics, these drones can detect and quantify microplastic concentrations with remarkable precision. This capability is crucial for understanding the impact of microplastics on marine ecosystems, as they pose a significant threat to marine life, including reef fish populations. The integration of microplastic surveys into existing drone-assisted reef fish population studies could provide a more comprehensive understanding of the environmental challenges facing our oceans.

The increasing involvement of government agencies, research institutes, and environmental organizations in marine conservation initiatives is also a key factor driving market expansion. Governments worldwide are implementing stricter regulations and investing in advanced technologies to monitor and protect marine biodiversity. Collaborative projects involving fisheries, academic institutions, and non-governmental organizations are leveraging drones for large-scale reef monitoring and data collection. These efforts are supported by international funding and policy frameworks aimed at achieving sustainable development goals related to ocean health. The growing emphasis on data-driven decision-making in marine management is expected to sustain the upward momentum of the drone-assisted reef fish population survey market in the coming years.

From a regional perspective, the Asia Pacific region dominates the market, accounting for the largest share in 2024, followed by North America and Europe. This trend is attributed to the extensive coral reef systems in Southeast Asia and Australia, coupled with significant investments in marine research and conservation. The Middle East & Africa and Latin America are also witnessing increasing adoption of drone technologies for reef monitoring, driven by growing environmental awareness and international collaborati

Here are a few use cases for this project:

1. Fish Population Studies: Researchers can use the Fishnet model to analyze footage from underwater cameras in lakes, rivers or sea, helping to identify specific fish species for population dynamics studies, monitoring biodiversity, or inform conservation efforts.

2. Commercial Fishing: fishnet can be used to improve sustainable fishing practices. It will be able to identify the type of fish caught in real-time, allowing the crew to release non-target fish species or undersized fish of target species back into the water, thus increasing the survival chances of those fish.

3. Aquaculture Farming: The Fishnet can help reinforce the detection process in fish farms, enhancing the overall process of farming fish by identifying and separating different species in the rearing tanks.

4. Research and Educational Use: Fishnet can be utilized in academic institutions or research laboratories to assist students and researchers in species identification during fieldwork or experiments.

5. Fish Market Regulation: Authorities can use Fishnet to ensure the correct labeling of fish products on the market, protecting consumers from mislabeling scams and ensuring the proper monitoring of fishing regulations.

SNP data for orange roughy populations across the Atlantic Ocean. Please read the README document for details on the files.