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.

During financial year 2021, the total population of fishermen was ***** million in India. The majority of both male and female fishermen were engaged in inland fishery, accounting for over ** million fishermen. In comparison, about **** million fishermen were involved in marine fishery.

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.

About this dataset

Context

The dataset tabulates the Fishing Creek township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Fishing Creek township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Fishing Creek township was 1,550, a 0.78% increase year-by-year from 2022. Previously, in 2022, Fishing Creek township population was 1,538, an increase of 0.26% compared to a population of 1,534 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Fishing Creek township increased by 184. In this period, the peak population was 1,550 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Fishing Creek township is shown in this column.
• Year on Year Change: This column displays the change in Fishing Creek township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Fishing Creek township Population by Year. You can refer the same here

Fish Population

## Overview

Fish Population is a dataset for object detection tasks - it contains Fish annotations for 216 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

About this dataset

Context

The dataset tabulates the Fishing Creek township population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Fishing Creek township. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 761 (57.87% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Fishing Creek township population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Fishing Creek township is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Fishing Creek township is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Fishing Creek township Population by Age. You can refer the same here

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 2024 data to the set for a total of twelve years observation using the same gear and sampling design.

This statistic shows the world's leading fishing nations in 2021, based on capture production. China caught about 13.14 million metric tons of fish in that year. Worldwide fishingThe act of fishing dates back to thousands of years before the Common Era. Fishing was well-suited to the lifestyle of the hunter-gatherer, who would engage himself in the act when presented with a body of water. Ancient fishing tools could consist of a spear, a rod, a net, or merely a pair of human hands. Since then, fishing techniques have developed extensively and one can see many different techniques both traditional and modern at work around the globe today. Worldwide fish production is increasing yearly, presumably meeting the demands of a growing world population. Fresh fish is in highest demand, but there are a myriad of routes to consumption, including frozen, canned, and smoked, as well as certain non-food purposes, such as for fish meal, fish oil, or fertilizer. With such a high demand, overfishing is fast becoming a problem. Overfishing occurs when fish populations are unacceptably reduced due to human fishing activities. This can prove disastrous for fish species as well as fishing-dependent coastal communities, not to mention the effect on the marine ecosystem. While some criticize the taste and life quality of farm-raised fish, this may be a better option for fish-eating consumers concerned about the environment.

Data contains information on demographics, fishing practices and vessel gear characteristics of USVI commercial fishermen

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.

Data from snorkel and bank counts of fishes in each of the 500 m reaches described in the Habitat Assessment Database. Each count is an entry in the database, including date, location, surface temperature, weather, start and end times, GPS coordinates, number of fish of each species (and in some cases, size classes), and total number of fishes seen.

These files contain field data from 3 coral reef surveys, 2 in Puerto Rico and one in Flower Garden Banks. Calculations are made to explore correlations between stony coral physical (size and shape) and biological (diversity and condition) with fish density, biomass and diversity. This dataset is associated with the following publication: Fisher, W. Relating fish populations to coral colony size and complexity. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 148: 110117, (2023).

Elucidating fishing effects on fish population dynamics is a critical step toward sustainable fisheries management. Despite previous studies that have suggested age or size truncation in exploited fish populations, other aspects of fishing effects on population demography, e.g., via altering life histories and density, have received less attention. Here, we investigated the fishing effects altering adult demography via shifting reproductive trade-offs in the iconic, overexploited, Pacific bluefin tuna Thunnus orientalis. We found that, contrary to our expectation, mean lengths of catch increased over time in longline fisheries. On the other hand, mean catch lengths for purse seine fisheries did not show such increasing trends. We hypothesized that the size-dependent energetic cost of the spawning migration and elevated fishing mortality on the spawning grounds potentially drive size-dependent skipped spawning for adult tuna, mediating the observed changes in the catch lengths. Using eco-genetic individual-based modeling, we demonstrated that fishing-induced evolution of skipped spawning and size truncation interacted to shape the observed temporal changes in mean catch lengths for tuna. Skipped spawning of the small adults led to increased mean catch lengths for the longline fisheries, while truncation of small adults by the purse seines could offset such a pattern. Our results highlight the eco-evolutionary dynamics of fishing effects on population demography and caution against using demographic traits as a basis for fisheries management of the Pacific bluefin tuna as well as other migratory species.

This dataset includes 2022 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.

About this dataset

Context

The dataset tabulates the data for the Fishing Creek Township, Pennsylvania population pyramid, which represents the Fishing Creek township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Fishing Creek Township, Pennsylvania, is 19.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Fishing Creek Township, Pennsylvania, is 41.5.
• Total dependency ratio for Fishing Creek Township, Pennsylvania is 61.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Fishing Creek Township, Pennsylvania is 2.4.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Fishing Creek township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Fishing Creek township for the selected age group is shown in the following column.
• Population (Female): The female population in the Fishing Creek township for the selected age group is shown in the following column.
• Total Population: The total population of the Fishing Creek township for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Fishing Creek township Population by Age. You can refer the same here

Illegal harvest is recognized as a widespread problem in natural resource management. The use of multiple methods for quantifying illegal harvest has been widely recommended yet infrequently applied. We used a mixed-method approach to evaluate the extent, character, and motivations of illegal gillnet fishing in Lake Hovsgol National Park, Mongolia and its impact on the lake’s fish populations, especially that of the endangered endemic Hovsgol grayling (Thymallus nigrescens). Surveys for derelict fishing gear indicate that gillnet fishing is widespread and increasing and that fishers generally use 3–4 cm mesh gillnet. Interviews with resident herders and park rangers suggest that many residents fish for subsistence during the spring grayling spawning migration and that some residents fish commercially year-round. Interviewed herders and rangers generally agree that fish population sizes are decreasing but are divided on the causes and solutions. Biological monitoring indicates that the gillnet mesh sizes used by fishers efficiently target Hovsgol grayling. Of the five species sampled in the monitoring program, only burbot (Lota lota) showed a significant decrease in population abundance from 2009–2013. However, grayling, burbot, and roach (Rutilus rutilus) all showed significant declines in average body size, suggesting a negative fishing impact. Data-poor stock assessment methods suggest that the fishing effort equivalent to each resident family fishing 50-m of gillnet 11–15 nights per year would be sufficient to overexploit the grayling population. Results from the derelict fishing gear survey and interviews suggest that this level of effort is not implausible. Overall, we demonstrate the ability for a mixed-method approach to effectively describe an illegal fishery and suggest that these methods be used to assess illegal fishing and its impacts in other protected areas.

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).

This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894.

Fishing Nets Market Outlook

The global fishing nets market size was valued at approximately USD 5.6 billion in 2023 and is anticipated to reach around USD 8.4 billion by 2032, reflecting a CAGR of 4.5% over the forecast period. The significant growth factor underpinning this market expansion is the increasing demand for seafood and aquaculture products, driven by global population growth and rising consumer awareness of healthy eating habits.

One of the primary growth factors for the fishing nets market is the burgeoning global population, which is driving increased demand for seafood. As populations grow, so does the need for food, particularly protein sources. With fish and other seafood being highly nutritious, they are increasingly sought after as healthier dietary options compared to red meat. This surge in demand for seafood is propelling the need for efficient and effective fishing nets, particularly in commercial fishing and aquaculture sectors.

Technological advancements in fishing net materials and design are also contributing to market growth. Modern fishing nets are now more durable, lightweight, and efficient, thanks to advancements in materials such as high-density polyethylene and nylon. These materials not only enhance the longevity of the nets but also improve their efficiency in capturing fish. Additionally, innovations in net design, such as the incorporation of sensors and tracking devices, are allowing for more sustainable fishing practices by reducing bycatch and minimizing ecological impact.

The growth of aquaculture as a sustainable means to meet seafood demand is another major factor driving the market. With wild fish stocks depleting due to overfishing, aquaculture presents a viable alternative to traditional fishing methods. It allows for the controlled breeding, rearing, and harvesting of fish, which helps meet the growing demand while alleviating pressure on wild fish populations. As a result, there is an increasing need for specialized fishing nets designed for use in aquaculture environments.

In the context of evolving technological landscapes, the integration of HetNets, or Heterogeneous Networks, is becoming increasingly relevant in the fishing industry. HetNets refer to the use of different types of network technologies and access points to create a seamless and efficient communication system. In fishing operations, this can translate into improved data collection and management, enabling more precise tracking of fish populations and environmental conditions. By leveraging HetNets, fishing enterprises can optimize their operations, enhance sustainability, and reduce their ecological footprint. This technological advancement aligns with the industry's growing emphasis on sustainable practices and efficient resource management.

Regionally, the Asia Pacific market holds the largest share and is expected to continue its dominance over the forecast period. This region is a major hub for both commercial fishing and aquaculture, supported by favorable government policies, extensive coastlines, and a large consumer base. North America and Europe are also significant markets due to the presence of well-established fishing industries and rising popularity of recreational fishing activities. Additionally, Latin America and the Middle East & Africa regions are experiencing growth due to increasing investments in their respective fishing sectors.

Product Type Analysis

Fishing nets can be broadly classified into several product types, including gill nets, cast nets, drift nets, seine nets, trawl nets, and others. Gill nets are among the most commonly used types in commercial fishing due to their high efficiency in capturing fish by their gills. These nets are typically set in a fixed position and can cover large areas, making them particularly effective in capturing schooling fish. The demand for gill nets is expected to remain strong, driven by their effectiveness and widespread use in various types of fishing.

Cast nets are another popular type, especially in recreational fishing. These nets are thrown by hand and form a circular shape as they sink, trapping fish underneath. Their ease of use and effectiveness in shallow waters make cast nets a favorite among recreational fishers. As recreational fishing continues to grow in popularity, the demand for cast nets is expected to rise correspondingly.

Drift nets, which float freely with the current,