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.

Global production of fish and seafood has quadrupled over the past 50 years. Not only has the world population more than doubled over this period, the average person now eats almost twice as much seafood as half a century ago.

This has increased pressure on fish stocks across the world. Globally, the share of fish stocks which are overexploited – meaning we catch them faster than they can reproduce to sustain population levels – has more than doubled since the 1980s and this means that current levels of wild fish catch are unsustainable.

One innovation has helped to alleviate some of the pressure on wild fish catch: aquaculture, the practice of fish and seafood farming. The distinction between farmed fish and wild catch is similar to the difference between raising livestock rather than hunting wild animals. Except that for land-based animals, farming is many thousand years old while it was very uncommon for seafood until just over 50 years ago.

In the visualizations and tables we see: - Captured and farmed (production) levels per year and per country or region - Consumption levels throughout the world for the past 50 years - Levels of sustainable vs overexploited fish - Global fishery types and their production levels - Types of fish produced per country

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

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.

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.

Over 200 species of reef fish around the world form spawning aggregations to reproduce at specific times and locations. The locations of many reef fish spawning aggregations in the Caribbean have been known and fished for decades. Red Hind (Epinephelus guttatus), a species of grouper important in Caribbean fisheries, migrate to form spawning aggregations which have historically experienced intense fishing pressure. The Red Hind Bank Marine Conservation District (MCD) was established in the United States Virgin Islands to protect a known Red Hind spawning aggregation site. The MCD was closed seasonally to fishing in 1990 and then permanently in 1999. Our goal was to evaluate the success of this marine conservation effort by assessing how the Red Hind population at the spawning aggregation responded to changing levels of protection. We documented Red Hind population demographics at the spawning aggregation site in the MCD during peak spawning events from 2018 to 2020. After 30 years of protection, the mean size of Red Hind at the spawning aggregation increased by >35% and the population sex ratio of females to males was less skewed compared to population characteristics at the spawning aggregation prior to protection. To evaluate stock status relative to management benchmarks, we used length-based stock assessment models that included in situ size distribution data spanning 1988 to 2020 to estimate population spawning potential ratio (SPR) over time. We found that the SPR of the Red Hind population at the spawning aggregation prior to protection was 0.32 (95% CI: 0.25, 0.39) and under seasonal protection, The SPR increased slightly to 0.35 (95% CI: 0.28, 0.42). Under permanent protection, The SPR increased to its highest value yet at 0.49 (95% CI: 0.42, 0.56), which is above the benchmark value considered sustainable for many fish species. Our work demonstrates demographic recovery of the protected Red Hind spawning population and highlights the value of using size distribution data to evaluate the response of data-limited reef fish populations to seasonal and permanent protection at spawning aggregation sites.

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.

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

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.

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.

This dataset includes the information on state wise population of male and female fishermen. The information is categorised by type of employment such as full time, part time, occasional, deep sea, and unspecified. NB: Data for 2017 is not available segregated by Inland and Marine.

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.

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.

The statistic displays the distribution of water bodies assessed for fish by the Water Framework Directive (WFD) in the United Kingdom (UK) in 2019, by band classes of fish population. In 2019, it was found that *** percent of assessed water bodies classified with a "bad" fish population. More information on fishing and other water sports in the UK can be found in the report Water sports in the United Kingdom.

About this dataset

Context

The dataset tabulates the Fishing Creek township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Fishing Creek township. The dataset can be utilized to understand the population distribution of Fishing Creek township by age. For example, using this dataset, we can identify the largest age group in Fishing Creek township.

Key observations

The largest age group in Fishing Creek Township, Pennsylvania was for the group of age 65-69 years with a population of 178 (14.26%), according to the 2021 American Community Survey. At the same time, the smallest age group in Fishing Creek Township, Pennsylvania was the 80-84 years with a population of 16 (1.28%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 in consideration
• Population: The population for the specific age group in the Fishing Creek township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Fishing Creek township total population. 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 Age. You can refer the same here

The state of a fish population is assessed according to *** main criteria: fishing pressure, or the share of the population taken by fishing, and breeding biomass, meaning the quantity of adult fish of reproductive age. According to these criteria, it was established that, in 2021, ** percent of the fish landings population was in good condition, while ** percent of the fish landing population was overfished, and *** percent was deemed as collapsed.

During fiscal year 2021, with over *********** fishermen the eastern state of Bihar had the highest fishermen population in India. This was followed by Uttar Pradesh with about ************ fishermen. There were over ********** fishermen across the country in the same period.

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

BFspawner_mean_length_dataThis file contains the time series of mean catch length data of adult Pacific bluefin tuna based on three fisheries: Taiwanese longline (TWL), Japanese coastal longline (JCL), and Japanese purse seine (JPS). The unit of length is cm. FYEAR: fishing year. Description of the sampling methods is available in the Material and Methods of the paper and supplemental material Table S1.

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).