These indices are a generalization of regional processes that should not be used for management purposes. We point to the regional ecosystem status reports to report on the actual data.
Understanding the Gauge plots
The gauge plots that accompany the indicator time series are meant to reflect the current status of that ecosystem component at the regional or national level. The numerical scores are determined as the percentile rank of the average (mean) value of that indicator over the last five years of the time series, relative to the series as a whole. The values typically represent quantitative scores, with more desirable conditions in the darker blue. Thus, some gauges are "right-handed" with the higher values being in darker blue, whereas other gauges are "left-handed" with lower values being in darker blue (indicating that lower values are preferable). In some instances (e.g. climate measures), the scores represented are unitless and are presented as two-way gauges, indicating that either high or low scores are observed, implying neither higher nor lower values are necessarily preferred.
Understanding the Time series plots
Time series plots show the changes in each indicator as a function of time, over the period 1980-present. Each plot also shows horizontal lines that indicate the median (middle) value of that indicator, as well as the 10th and 90th percentiles, each calculated for the entire period of measurement. Time series plots were only developed for datasets with at least 10 years of data. Two symbols located to the right of each plot describe how recent values of an indicator compare against the overall series. A black circle indicates whether the indicator values over the last five years are on average above the series 90th percentile (plus sign), below the 10th percentile (minus sign), or between those two values (solid circle). Beneath that an arrow reflects the trend of the indicator over the last five years; an increase or decrease greater than one standard deviation is reflected in upward or downward arrows respectively, while a change of less than one standard deviation is recorded by a left-right arrow.
El Niño-Southern Oscillation (ENSO)
Positive values of this indicator, greater than +0.5, indicate warm El Niño conditions, while negative values, less than -0.5, indicate cold La Niña conditions.
Values correspond to Index scores
Description of time series:
The Oceanic Niño Index (ONI) is NOAA’s primary index for monitoring the El Niño-Southern Oscillation climate pattern. It is based on Sea Surface Temperature values in a particular part of the central equatorial Pacific, which scientists refer to as the Niño 3.4 region. Positive values of this indicator, greater than +0.5, indicate warm El Niño conditions, while negative values, less than -0.5, indicate cold La Niña conditions. The ONI shows a shift from El Niño to La Niña conditions in 2020, followed by a brief period of ENSO-neutral conditions, and then another dip to La Niña conditions in 2021. This pattern is sometimes called a “double dip” La Niña. In early 2023 there was a shift to ENSO-neutral conditions. Additional information can be found in the Climate Prediction Center’s Official ENSO Briefing and the climate.gov ENSO blog
Description of gauge:
The unitless two-way gauge depicts the most recent seasonal value for the ONI showing how far it is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.
Description of El Niño-Southern Oscillation (ENSO):
El Niño and La Niña are opposite phases of the El Niño-Southern Oscillation (ENSO), a cyclical condition occurring across the Equatorial Pacific Ocean with worldwide effects on weather and climate. During an El Niño, surface waters in the central and eastern equatorial Pacific become warmer than average and the trade winds - blowing from east to west - greatly weaken. During a La Niña, surface waters in the central and eastern equatorial Pacific become much cooler, and the trade winds become much stronger. El Niños and La Niñas generally last about 6 months but can extend up to 2 years. The time between events is irregular, but generally varies between 2-7 years. To monitor ENSO conditions, NOAA operates a network of buoys, which measure temperature, currents, and winds in the equatorial Pacific.
This climate pattern impacts people and ecosystems around the world. Interactions between the ocean and atmosphere alter weather globally and can result in severe storms or mild weather, drought or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences. For example, along the west coast of the U.S., warm El Niño events are known to inhibit the delivery of nutrients from subsurface waters, suppressing local fisheries. El Niño events are typically associated with fewer hurricanes in the Atlantic while La Niña events typically result in greater numbers of Atlantic hurricanes.
Data Background:
ENSO ONI data was accessed from NOAA’s Earth Systems Research Laboratory (https://psl.noaa.gov/data/timeseries/monthly/NINO4/). The data are plotted in degrees Celsius and represent Sea Surface Temperature anomalies averaged across the so-called Niño 3.4 region in the east-central tropical Pacific between 120°-170°W.
Multivariate ENSO Index (MEI)
The MEI indicator has shown mostly La Nina conditions since 2020, but in early 2023 shifted to ENSO-neutral conditions.
Values correspond to Index scores
Description of time series:
Like the Oceanic Niño Index, positive MEI values indicate warm, El Niño conditions and negative MEI values indicate cold, La Niña conditions. The MEI indicator has shown mostly La Nina conditions since 2020, but in early 2023 shifted to ENSO-neutral conditions. Additional information can be found in the Climate Prediction Center’s Official ENSO Briefing and the climate.gov ENSO blog.
Description of gauge:
The unitless two-way gauge depicts the most recent seasonal value for the MEI showing how far it is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.
Description of Multivariate El Niño-Southern Oscillation Index:
The Multivariate El Niño-Southern Oscillation Index (MEI) is a more holistic representation of the atmospheric and oceanic conditions that occur during ENSO events and characterizes their intensity. MEI is determined from five variables from the central and eastern equatorial Pacific (Sea-level pressure, surface wind components, sea surface temperature, surface air temperature, and cloudiness), while ENSO ONI is calculated from only two (sea surface temperature and trade wind strength). This index is calculated twelve times per year for each sliding bi-monthly season i.e. Dec-Jan, Jan-Feb, Feb-Mar, etc. We present data from the Pacific Islands, Alaska, and California Current regions.
This climate condition impacts people and ecosystems across the globe and each of the indicators presented here. Interactions between the ocean and atmosphere alter weather around the world and can result in severe storms or mild weather, drought, or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences.
Data Background:
MEI data was accessed from NOAA’s Earth Systems Research Laboratory (https://psl.noaa.gov/enso/mei/). The data plotted are unitless anomalies.
Pacific Decadal Oscillation (PDO)
During the last five years, the PDO indicator shows a significant downward trend.
Values correspond to Index scores
Description of time series:
Positive PDO values typically mean cool surface water conditions in the interior of the North Pacific Ocean and warm surface waters along the North American Pacific Coast while negative PDO conditions typically mean warm surface water conditions in the interior to the North Pacific Ocean and cool surface waters along the North American Pacific Coast. During the last five years, the PDO indicator shows a significant downward trend.
Description of gauge:
The unitless two-way gauge depicts whether the average of the last 5 years of data for the climate indicator is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.
Description of Pacific Decadal Oscillation (PDO):
The Pacific Decadal Oscillation (PDO) is a long-term pattern of Pacific climate variability. The extreme phases of this climatic condition are classified as warm or cool, based on deviations from average ocean temperature in the northeast and central North Pacific Ocean. When the PDO has a positive value, sea surface temperatures are below average (cool) in the interior North Pacific and warm along the Pacific Coast. When the PDO has a negative value, the climate patterns are reversed, with above average sea surface temperatures in the interior and sea surface temperatures below average along the North American coast. The PDO waxes and wanes; warm and cold phases may persist for decades. Major changes in northeast Pacific marine ecosystems have been correlated with phase changes in the PDO. Warm phases have seen enhanced coastal ocean biological productivity in Alaska and inhibited productivity off the west coast of the United States, while cold PDO phases have seen the opposite, north-south pattern of marine ecosystem productivity. We present data from the Pacific Islands, Alaska, and California Current regions.
Data Background:
Climate indicator data was accessed from the NOAA NCEI (https://www.NCEI.noaa.gov/teleconnections/pdo/data.csv). The data plotted are unitless and based on Sea Surface Temperature anomalies averaged across a given region
East Pacific - North Pacific Index (EP-NP)
During the last five years, the EP-NP indicator shows no significant trend.
Values correspond to Index scores
Description of time series:
Positive EP-NP values mean above-average surface temperatures over the eastern North Pacific, and below-average temperatures over the central North Pacific and eastern North America and the opposite for negative EP-NP values. During the last five years, the EP-NP indicator shows no significant trend.
Description of gauge:
The unitless two-way gauge depicts whether the average of the last 5 years of data for the climate indicator is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.
Description of East Pacific/ North Pacific Teleconnection Pattern Index:
The East Pacific/ North Pacific Teleconnection Pattern Index is a measure of climate variability. Positive EP-NP values mean above-average surface temperatures over the eastern North Pacific, and below-average temperatures over the central North Pacific and eastern North America and the opposite for negative EP-NP values.
This climate condition impacts people and ecosystems across the globe and each of the indicators presented here. Interactions between the ocean and atmosphere alter weather around the world and can result in severe storms or mild weather, drought, or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences. The positive phase of the EP-NP pattern is associated with above-average surface temperatures over the eastern North Pacific, and below-average temperatures over the central North Pacific and eastern North America. The main precipitation anomalies associated with this pattern reflect above-average precipitation in the area north of Hawaii and below-average precipitation over southwestern Canada.
Data Background:
Climate indicator data was accessed from Columbia University (https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.Indices/.NHTI…). The data plotted are unitless anomalies and averaged across a given region.
North Atlantic Oscillation (NAO)
During the last five years, the NAO indicator shows no significant trend but has remained largely in the negative phase.
Values correspond to Index scores
Description of time series:
Positive NAO values mean significantly warmer winters over the upper Midwest and New England and negative NAO values can mean cold winter outbreaks and heavy snowstorms. During the last five years, the NAO indicator shows no significant trend but has remained largely in the negative phase.
Description of gauge:
The unitless two-way gauge depicts whether the average of the last 5 years of data for the climate indicator is above or below the median value of the entire time series. High values in either direction mean extreme variation from the median value of the entire time series.
Description of North Atlantic Oscillation (NAO):
The North Atlantic Oscillation (NAO) Index measures the relative strengths and positions of a permanent low-pressure system over Iceland (the Icelandic Low) and a permanent high-pressure system over the Azores (the Azores High). When the index is positive (NAO+) significantly warmer winters can occur over the upper Midwest and New England. On the East Coast of the United States a NAO+ can also cause increased rainfall, and thus warmer, less saline surface water. This prevents nutrient-rich upwelling, which reduces productivity. When the NAO index is negative, the upper central and northeastern portions of the United States can incur winter cold outbreaks and heavy snowstorms. This climate condition impacts people and ecosystems across the globe and each of the indicators presented here. Interactions between the ocean and atmosphere alter weather around the world and can result in severe storms or mild weather, drought, or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences.
Data:
Climate indicator data was accessed from the NOAA NCEI (https://www.NCEI.noaa.gov/teleconnections/nao/data.csv). The data plotted are unitless anomalies and averaged across a given region
Atlantic Multidecadal Oscillation (AMO)
During the last five years, the AMO indicator shows a positive, nonsignificant trend.
Values correspond to Index scores
Description of time series:
Positive AMO values indicate the warm phase, during which surface waters in the North Atlantic Ocean are warmer than average, and negative AMO values indicate the cold phase, during which surface waters in the North Atlantic Ocean are cooler than average. During the last five years, the AMO indicator shows a positive, insignificant trend.
Description of gauge:
The unitless two-way gauge depicts whether the average of the last 5 years of data for the climate indicator is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.
Description of Atlantic Multidecadal Oscillation (AMO):
The Atlantic Multidecadal Oscillation is a series of long-duration changes in the North Atlantic sea surface temperature, with cool and warm phases that may last for 20-40 years. Most of the Atlantic between the equator and Greenland changes in unison. Some areas of the North Pacific also seem to be affected. This broadscale climate condition affects air temperatures and rainfall over much of the Northern Hemisphere. It is also related to major droughts in the Midwest and the Southwest of the U.S. In the warm phase, these droughts tend to be more frequent and/or severe. Vice-versa for the cold phase. During the warm phases the number of tropical storms that mature into severe hurricanes is much greater than during cool phases. Despite the association of AMO with multiple weather and climate impacts, recent scientific debate has questioned whether this indicator is a natural climate variation, like the other climate indicators presented here, or a response of the climate system to human-caused climate change. Whether natural or a result of human-caused climate change, AMO is a useful feature for tracking large-scale weather and climate events. This climate condition impacts people and ecosystems across the globe and each of the indicators presented here. Interactions between the ocean and atmosphere alter weather around the world and can result in severe storms or mild weather, drought, or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences.
Data Background:
Climate indicator data was accessed from NOAA’s Earth Systems Research Laboratory (https://www.esrl.noaa.gov/psd/data/timeseries/AMO/). The data plotted are unitless anomalies and averaged across a given region
Sea Surface Temperature
Between 2016 and 2021 the mean sea surface temperature for the entire US was higher than 89% of the temperatures between 1985 and 2021
Sea surface temperature is defined as the average temperature of the top few millimeters of the ocean. Sea surface temperature monitoring tells us how the ocean and atmosphere interact, as well as providing fundamental data on the global climate system
Data Interpretation:
Time series: The time series shows the integrated sea surface temperature across the US. During the last five years there has been no notable trend and values were between the 10th and 90th percentile of all observed data in the time series.
Gauge: The gauge value of 89 indicates that between 2016 and 2021 the mean sea surface temperature for the entire US was higher than 89% of the temperatures between 1985 and 2021 .
Description of Sea Surface Temperature:
Sea surface temperature (SST) is defined as the temperature of the top few millimeters of the ocean. This temperature directly or indirectly impacts the rate of all physical, chemical, and most biological processes occurring in the ocean. SST is globally monitored by sensors on satellites, buoys, ships, ocean reference stations, autonomous underwater vehicles (AUVs) and other technologies.
SST monitoring tells us how the ocean and atmosphere interact, as well as providing fundamental data on the global climate system. This information also aids us in weather prediction, i.e. identifying the onset of El Niño and La Niña cycles - multiyear shifts in atmospheric pressure and wind speeds. These shifts affect ocean circulation, global weather patterns, and marine ecosystems. SST anomalies have been linked to shifting marine resources. With warming temperatures, we observe the poleward movements of fish and other species. Temperature extremes—both ocean heatwaves and cold spells—have been linked to coral bleaching as well as fishery and aquaculture mortality. We present the annual average SST in all regions.
Data:
The SST product used for this analysis is the NOAA Coral Reef Watch CoralTemp v3.1 SST composited monthly (https://coralreefwatch.noaa.gov/product/5km/index_5km_sst.php) accessed from CoastWatch (https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v3_1_monthly.g…) . Great Lakes SST data were accessed from (https://coastwatch.glerl.noaa.gov/glsea/glsea.html).
The data are plotted in degrees Celsius.
Data background and limitations:
The NOAA Coral Reef Watch (CRW) daily global 5km Sea Surface Temperature (SST) product, also known as CoralTemp, shows the nighttime ocean temperature measured at the surface. The CoralTemp SST data product was developed from two, related reanalysis (reprocessed) SST products and a near real-time SST product. Monthly composites were used for this analysis
Sea Level
Mean sea level between 2016 and 2021 for the U.S. was higher than for 93% of the entire time-series record.
Sea level varies due to the force of gravity, the Earth’s rotation and irregular features on the ocean floor. Other forces affecting sea levels include temperature, wind, ocean currents, tides, and other similar processes.
Description of time series:
The time series shows the relative sea level across the United States. During the last five years, values are above the 90th percentile but there has been no trend.
Description of gauge:
The gauge value of 93 indicates that the mean sea level between 2016 and 2021 for the U.S. was higher than for 93% of the entire time-series record.
Description of Sea Level:
Sea level varies due to the force of gravity, the Earth’s rotation and irregular features on the ocean floor. Other forces affecting sea levels include temperature, wind, ocean currents, tides, and other similar processes. With 40 percent of Americans living in densely populated coastal areas, having a clear understanding of sea level trends is critical to societal and economic well being.
Measuring and predicting sea levels, tides and storm surge are important for determining coastal boundaries, ensuring safe shipping, emergency preparedness, and other aspects of the well-being of coastal communities.
Indicator and source information:
Global sea level has been rising over the past century, with increasing rates in recent decades driven primarily by warming of the ocean (since water expands as it warms) and by increased melting of land-based ice, such as glaciers and ice sheets. Local and regional sea-level change may be more or less than the global average due to local factors such as land subsidence, changes in regional ocean currents, and whether the land is still rebounding from the compressive weight of Ice Age glaciers.
With 40 percent of Americans living in densely populated coastal areas, having a clear understanding of sea level trends is critical to societal and economic well being. Measuring and predicting sea levels, tides and storm surge are important for determining coastal boundaries, ensuring safe shipping, and emergency preparedness, etc. NOAA monitors sea levels using tide stations and satellite laser altimeters. Tide stations around the globe tell us what is happening at local levels, while satellite measurements provide us with the average height of the entire ocean. Taken together, data from these sources are fed into models that tell us how our ocean sea levels are changing over time. For this site, data from tide stations around the US were combined to create regionally averaged records of sea-level change since 1980. We present data for all regions.
Data background and limitations:
Source: http://www.cmar.csiro.au/sealevel/sl_data_cmar.html These data are measurements of global tide-gauge and satellite data that have been combined statistically to create a picture of global relative sea level (see Church, J. A. and N.J. White (2011), Sea-level rise from the late 19th to the early 21st Century. Surveys in Geophysics, doi:10.1007/s10712-011-9119-1 for methods). Data are in centimeters relative to the year 1880.
Arctic Sea Ice
Mean sea ice extent between 2017 and 2021 for the Alaska-Arctic region was only higher than 12% of the sea ice extent measurements between 1979 and 2021.
Values correspond to millions of square kilometers of sea ice cover
Data Interpretation:
Time series: This time series shows the sea ice extent for the Alaska-Arctic region from 1979 to 2021. During the last five years, there has been no notable trend and values are within the 10th and 90th percentiles, albeit near the lower end of the time series.
Gauge: The gauge value of 12 indicates that mean sea ice extent between 2017 and 2021 for the Alaska-Arctic region was only higher than 12% of the sea ice extent measurements between 1979 and 2021.
Indicator and source information:
Sea ice is ice that forms, expands, and melts in the ocean. Sea ice has an important effect on Earth’s heat balance by reflecting solar energy back to space; it also plays a role in determining ocean salinity and currents, and it provides important habitat for many marine organisms.
The time series shows the Sea Ice extent in September of each year to give a sense of the summertime (i.e., minimum annual) extent through the years of sea ice across the entire Northern Hemisphere, which includes the Arctic Ocean and the Hudson Bay.
Data background and limitations:
Sea ice figures and data was accessed from NOAA NCEIr for the northern hemisphere, https://www.ncei.noaa.gov/access/monitoring/snow-and-ice-extent/ The data are plotted in units of millions of square km.
The time series shows the Sea Ice extent in September of each year to give a sense of the summertime (i.e., minimum annual) extent through the years of sea ice across the entire Northern Hemisphere, which includes the Arctic Ocean and the Hudson Bay.
Data background and limitations:
Sea ice data was accessed from the NOAA National Climatic Data Center for the northern hemisphere, https://www.ncdc.noaa.gov/snow-and-ice/extent/ ; With the data pulled from here: https://www.ncdc.noaa.gov/snow-and-ice/extent/sea-ice/N/0.csv. The data are plotted in units of million square km.
Great Lakes Sea Ice
Mean sea ice extent between 2016 and 2021 for the Great Lakes region was higher than 63% of the sea ice extent measurements between 1979 and 2021.
Values correspond to annual maximum percentage of total lake surface area of lake ice cover
Data Interpretation:
Time series: This time series shows the sea ice extent for the Great Lakes region from 1979 to 2021. During the last five years, there has been no notable trend and values are within the 10th and 90th percentiles.
Gauge: The gauge value of 63 indicates that mean sea ice extent between 2016 and 2021 for the Great Lakes region was higher than 63% of the sea ice extent measurements between 1979 and 2021.
Indicator and source information:
The time series shows the Lake Ice extent for the Great Lakes Region for each winter. The annual maximum extent as percentage of total lake surface area between December and May of each winter season in the Great Lakes region.
Data background and limitations:
Great Lakes ice data was accessed from the NOAA Great Lakes Environmental Research Laboratory, https://www.glerl.noaa.gov/data/ice. Original ice charts from 1973 through 1988 are from the Canadian Ice Service. Beginning in 1989, the source was the U.S. National Ice Center (USNIC). Currently, data from both Canadian and U.S. sources are combined in USNIC's daily products. The data are plotted as a percentage of total lake surface area.
Chlorophyll-a
Between 2016 and 2021 the national concentration levels of chlorophyll a were at the long term median state of all chlorophyll a concentration levels between 1998 and 2021.
Chlorophyll a, a pigment produced by phytoplankton, can be measured to determine the amount of phytoplankton present in water bodies. From a human perspective, high values of chlorophyll a can be good (abundance of nutritious diatoms as food for fish) or bad (Harmful Algal Blooms that may cause respiratory distress for people), based on the associated phytoplankton species.
Data Interpretation:
Time series: This time series shows the average concentration of chlorophyll a across all US LMEs. During the last five years there has been an increasing trend and values were between the 10th and 90th percentiles of all observed data in the time series.
Gauge: The gauge value of 50 indicates that between 2016 and 2021 the national concentration levels of chlorophyll a were at the long term median state of all chlorophyll a concentration levels between 1998 and 2021.
Gauge values
0–10: Chlorophyll a was significantly lower than the long term median state.
10–25: Chlorophyll a was considerably lower than the long term median state.
25–50: Chlorophyll a was slightly lower than the long term median state.
50: Chlorophyll a was at the long term median state.
50–75: Chlorophyll a was slightly higher than the long term median state.
75–90: Chlorophyll a was considerably higher than the long term median state.
90–100: Chlorophyll a was significantly higher than the long term median state.
Description of Chlorophyll a:
Phytoplankton are microscopic plants at the base of most marine food webs and produce much of the Earth’s oxygen. One way we estimate the number of phytoplankton in the ocean is by measuring the amount of chlorophyll a in the water. Chlorophyll a is a green pigment (the same pigment that makes tree leaves appear green) that the phytoplankton use to absorb sunlight. The amount (or concentration) of chlorophyll a in surface waters can be calculated by measuring the color of the water (also referred to as “ocean color”) which can be “seen” by sensors on satellites in space almost like your eyes see the color of the ocean. Environmental and oceanographic factors continuously influence the abundance, species composition, spatial distribution, and productivity of phytoplankton. Tracking the amount of phytoplankton in the ocean conveys how much food is available at the base of the food web for other animals. Changes in the amount of phytoplankton in the ocean are part of the natural seasonal cycle (similar to seasonal changes of plants on land), but can also indicate an ecosystem’s response to a major external disturbance such as a hurricane or typhoon.
Indicator and source information:
Chlorophyll a concentration values for this indicator were obtained using the ESA OC-CCI product, a merged product from the European Spatial Agency (ESA) that is a validated, error-characterized, Essential Climate Variable (ECV) and climate data record (CDR) from satellite observations specifically developed for climate studies. The dataset (v5.0) is created by band shifting and bias-correcting SeaWiFS, MODIS, VIIRS and OLCI data to match MERIS data, merging the datasets and computing per-pixel uncertainty estimates. Source: https://climate.esa.int/en/projects/ocean-colour/news-and-events/news/ocean-colour-version-50-data-release/
https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg
Annual means for each LME for each year were calculated from the average of the LME 12 monthly means in that year on a pixel by pixel basis. Then for each year, the median average was taken spatially to yield one value per year per LME. The overall “National Annual Mean” mean was calculated as the average of all LME annual means. See the Data Background section for more details.
Data background and limitations:
Satellite chlorophyll a data was extracted for each LME from the ESA OC-CCI v5.0 product. These 4 km mapped, monthly composited data were - averaged over each year to produce pixel by pixel annual composites, then the spatial median was calculated for each LME, resulting in one value per year per LME. This technique was used for each LME from North America and Hawaii. The overall “National Annual Mean” was calculated as the average of all the LME annual means. Phytoplankton concentrations are highly variable (spatially and temporally), largely driven by changing oceanographic conditions and seasonal variability.
Zooplankton
Between 2015 and 2019 the average concentration of zooplankton biomass in the five US regions with data was much higher than the median value of all zooplankton biomass concentration levels from 1987 through 2019.
Description of time series:
Between 2015 and 2019 the national average concentration of zooplankton biomass showed no significant trend.
Description of gauge:
The gauge value of 79 indicates that between 2015 and 2019 the average concentration of zooplankton biomass in the five US regions with data (Alaska, the California Current, the Gulf of Mexico, Hawai'i-Pacific Islands and the Northeast) was much higher than the median value of all zooplankton biomass concentration levels from 1987 through 2019.
Gauge values
High values of zooplankton can be good (lots of lipid rich colder water species) or bad (lots of lipid poor warmer water species), depending on the region.
0 - 10: The five-year zooplankton biomass average is very low compared to the median value.
10 - 25: The five-year zooplankton biomass average is much lower than the median value.
25 - 50: The five-year zooplankton biomass average is lower than the median value.
50: The five-year zooplankton biomass average equals the median value.
50 - 75: The five-year zooplankton biomass average is higher than the median value.
75 - 90: The five-year zooplankton biomass average is much higher than the median value.
90 - 100: The five-year zooplankton biomass average is very high compared to the median value.
Description of Zooplankton:
Zooplankton are a diverse group of animals found in oceans, bays, and estuaries. By eating phytoplankton, and each other, zooplankton play a significant role in the transfer of materials and energy up the oceanic food web (e.g., fish, birds, marine mammals, humans.) Like phytoplankton, environmental and oceanographic factors continuously influence the abundance, composition and spatial distribution of zooplankton. These include the abundance and type of phytoplankton present in the water, as well as the water’s temperature, salinity, oxygen, and pH. Zooplankton can rapidly react to changes in their environment. For this reason monitoring the status of zooplankton is essential for detecting changes in, and evaluating the status of ocean ecosystems. We present the annual average total biovolume of zooplankton in the Alaska, California Current, Gulf of Mexico, Hawai'i-Pacific Islands and Northeast regions.
Data Background and Caveats:
Unlike previous years, all value are now standardized to "ml/m3". For example, EcoMon data units went from "ml/100m3" to just "ml/m3", but that did not affect the shape of the trends as it is a linear multiplicative factor. CalCOFI, however, went from "ml/m2" to "ml/m3", and the trend has changed noticeably. It is now noisier and no clear trend. One converts "ml/m2" to "ml/m3" by dividing by the towing depth (m). That is a non-linear muplicative factor, so it can affect each data point and change the data shape.
HI -Note that Hawai’i is Wet Mass (g/m3) , not DV (ml/m3).
Finally, a log10 value frequency histogram of the raw data values showed that 99.9% of the DV data values were less than 15 ml/m3. To reduce the impact of large outliers (i.e., due to a large jellyfish or an algal mat caught in the net), any DV value greater than 15 was capped at a value of just 15. Again, this would only affect < 0.1% of the data. In some extreme cases, original DV values were over 100+ ... which greatly skewed the means and trends if not removed. This is actually standard practice. CalCOFI offers both a "large" and "small" DV value (with the latter having large values removed), for example, and some programs automatically remove any plankter larger than the 5 cm length from the net sample before measuring the DV.
Zooplankton data for each region were obtained from the NOAA Fisheries Coastal & Oceanic Plankton Ecology, Production, & Observations Database, an integrated data set of quality-controlled, globally distributed plankton biomass and abundance data with common biomass units and served in a common electronic format with supporting documentation and access software. Source: https://www.st.nmfs.noaa.gov/copepod/about/about-copepod.html
Overfished Stocks
Between 2017 and 2022 the number of overfished stocks shows an upward trend.
The x-axis represents years. The y-axis represents the number of fish stocks or fish populations that are deemed by NOAA as overfished. Overfished means the population of fish is too low. Therefore the population can not support a large amount of fishing.
Description of time series:
The series shows the number of fish populations that have qualified as overfished since 2000. Between 2017 and 2022 the number of overfished stocks shows an upward trend.
Description of Overfished stocks:
An overfished stock is a population of fish that is too low. Therefore the population can not support a large amount of fishing. A fish population can be “overfished” as the result of many factors, including overfishing, as well as habitat degradation, pollution, climate change, and disease. Stocks are determined to be overfished by NOAA as mandated in the Magnuson-Stevens Act, based on the results of stock assessments
Description of Overfished Stocks:
An overfished stock is a population of fish that is too low. From a technical standpoint, a stock that is overfished is depleted below a minimum level and active rebuilding is required. Stocks that are overfished cannot support a large amount of fishing. A fish stock can be listed as overfished as the result of many factors including overfishing, habitat degradation, pollution, climate change, and disease. The Magnuson-Stevens Act requires the status of overfished stocks be reported annually.
Stock assessments provide information to determine if a stock is overfished or experiencing overfishing (harvest higher than a maximum fishing threshold). This is done by estimating fishing intensity and the abundance of fish stocks and comparing those estimates to management reference points. Stock assessments can provide the science that supports the steps necessary to rebuild overfished stocks to sustainable levels.
It is important to track the status of fish stocks because fish play an important role in marine ecosystems, such as supporting the ecological structure of many marine food webs. Fish also support significant parts of coastal economies including recreational and commercial fisheries, and play an important cultural role in many regions.
This site presents the number of overfished stocks by year in all US Large Marine Ecosystems (LMEs).
Overall Scores mean the following:
High values for overfished stocks are bad, low numbers are good.
- 0 - 10: The five-year overfished stock status average is very low compared to the median value.
- 10 - 25: The five-year overfished stock status average is much lower than the median value.
- 25 - 50: The five-year overfished stock status average is lower than the median value.
- 50: The five-year overfished stock status average equals the median value.
- 50 - 75: The five-year overfished stock status average is higher than the median value.
- 75 - 90: The five-year overfished stock status average is much higher than the median value.
- 90 - 100: The five-year overfished stock status average is very high compared to the median value.
Data Source:
Data were obtained from the NOAA Fisheries Fishery Stock Status website. Stocks that met the criteria for overfished status were summed by year for each region.
Threatened or Endangered Marine Mammals
Endangered Species Act threatened / endangered marine mammals
Description of time series and gauge:
Trend and gauge analysis was not appropriate for Endangered Species Act (ESA) threatened or endangered marine mammals data.
Description of Threatened and Endangered Marine Mammals (ESA):
NOAA Fisheries is responsible for the protection, conservation, and recovery of endangered and threatened marine and anadromous species under the Endangered Species Act (ESA). The ESA aims to conserve these species and the ecosystems they depend on. Under the ESA, a species is considered endangered if it is in danger of extinction throughout all or a significant portion of its range, or threatened if it is likely to become endangered in the foreseeable future throughout all or a significant portion of its range See a species directory of all the threatened and endangered marine species under NOAA Fisheries jurisdiction, including marine mammals.
Under the ESA, a species must be listed if it is threatened or endangered because of any of the following 5 factors:
1) Present or threatened destruction, modification, or curtailment of its habitat or range;
2) Over-utilization of the species for commercial, recreational, scientific, or educational purposes;
3) Disease or predation;
4) Inadequacy of existing regulatory mechanisms; and
5) Other natural or manmade factors affecting its continued existence.
The ESA requires that listing determinations be based solely on the best scientific and commercial information available; economic impacts are not considered in making species listing determinations and are prohibited under the ESA. There are two ways by which a species may come to be listed (or delisted) under the ESA:
- NOAA Fisheries receives a petition from a person or organization requesting that NOAA lists a species as threatened or endangered, reclassify a species, or delist a species.
- NOAA Fisheries voluntarily chooses to examine the status of a species by initiating a status review of a species.
Data:
Summary data tables from the NOAA Fisheries Protected Resources Species Information System were obtained from the database manager. The number of ESA threatened and endangered species were summed for each region by year.
Strategic and Depleted Marine Mammals
Marine Mammal Protection Act
Description of time series:
Trend and gauge analysis are not appropriate for Marine Mammal Protection Act (MMPA) strategic/depleted species data.
Data:
Data methods Summary data tables from the NOAA Fisheries Protected Resources Species Information System were obtained from the database manager. The number of MMPA strategic and depleted stock species were summed for each region by year.
Description of Marine Mammal Strategic and Depleted Stocks (MMPA):
A stock is defined by the Marine Mammal Protection Act (MMPA), as a group of marine mammals of the same species or smaller taxa in a common spatial arrangement, that interbreed when mature. See a list of the marine mammal stocks NOAA protects under the MMPA.
A strategic stock is defined by the MMPA as a marine mammal stock—
- For which the level of direct human-caused mortality exceeds the potential biological removal level or PBR (defined by the MMPA as the maximum number of animals, not including natural mortalities, that may be removed from a marine mammal stock while allowing that stock to reach or maintain its optimum sustainable population);
- Which, based on the best available scientific information, is declining and is likely to be listed as a threatened species under the Endangered Species Act (ESA) within the foreseeable future; or
- Which is listed as a threatened or endangered species under the ESA, or is designated as depleted under the MMPA.
A depleted stock is defined by the MMPA as any case in which—
- The Secretary of Commerce, after consultation with the Marine Mammal Commission and the Committee of Scientific Advisors on Marine Mammals established under MMPA title II, determines that a species or population stock is below its optimum sustainable population;
- A State, to which authority for the conservation and management of a species or population stock is transferred under section 109, determines that such species or stock is below its optimum sustainable population; or
- A species or population stock is listed as an endangered species or a threatened species under the ESA.
Coastal Population
The coastal population between 2014 and 2019 for the US was higher than 94% of the coastal population values between 1970 and 2019.
Values correspond to the total coastal population in a given region.
Time Series
The 2014 – 2019 national average coastal population was substantially above historic levels, although the recent trend is not different from historical trends.
Gauge
The gauge value of 94 indicates that the coastal population between 2014 and 2019 for the US was higher than 94% of the coastal population values between 1970 and 2019.
Description of Coastal Population:
While marine ecosystems are important for people all across the country, they are essential for people living in coastal communities. The population density of coastal counties is over six times greater than inland counties. In the U.S. coastal counties make up less than 10 percent of the total land area (not including Alaska), but account for 39 percent of the total population. From 1970 to 2010, the population of these counties increased by almost 40% and are projected to increase by over 10 million people or 8+% into the 2020s.
The population density of an area is an important factor for economic planning, emergency preparedness, understanding environmental impacts, resource demand, and many other reasons. Thus, this indicator is important to track. We present the number of residents within all regions.
Extreme Gauge values:
A value of zero on the gauge means that the average coastal population over the last 5 years of data was below any annual population level up until that point, while a value of 100 would indicate the average over that same period was above any annual population level up until that point.
Indicator Source Information:
The American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation.
Data Background and Caveats:
The values represented here are coastal county population estimates for states bordering US Large Marine Ecosystems as calculated by the US Census Bureau from the American Community Survey.
Coastal Tourism GDP
Between 2013 and 2018 the average change in coastal county tourism sector GDP was higher than the median change in coastal county tourism sector GDP between 2006 and 2018.
Values correspond to percent change in the GDP of the Tourism Sector of Coastal Counties in US States that border a region
Description of Time Series: Between 2013 and 2018 the average change in coastal county tourism GDP showed no significant trend.
Description of Gauge: The gauge value of 77 indicates that between 2013 and 2018 the average change in coastal county tourism sector GDP was higher than the median change in coastal county tourism sector GDP between 2006 and 2018.
Extreme Gauge values:
A value of zero on the gauge means that the average coastal tourism GDP over the last 5 years of data was below any annual coastal tourism GDP level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism GDP value up until that point.
Description of Coastal Tourism:
U.S. coasts are host to a multitude of travel, tourism, and recreation activities. To manage our coasts, plan for development, and assess impacts as a result of coastal hazards including sea level rise, it is important to have baseline economic information. To accomplish this, we need indicators of the economic value of recreation and tourism. We present the annual total change in billions of dollars of goods and services (GDP), employment and annual wages provided from tourism industries in the Gulf of Mexico, Mid-Atlantic, Northeast, Hawaii-Pacific Islands, Southeast, and California Current regions. This data does not include industries located in U.S. territories.
Indicator Information:
Coastal tourism Gross Domestic Product is the total measure (in billions of 2012 dollars) of goods and services provided from various industries involved in tourism services and products along the coast. Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.
Data Source:
Coastal Tourism GDP, employment, and real wage data was taken from NOAA’s Office of Coastal Management Economics: National Ocean Watch (ENOW) custom report building tool, with contextual data taken from the 2020 NOAA Report on the U.S. Marine Economy: Regional and State Profiles. Growth was estimated by subtracting the previous year’s value from the current year’s value, then dividing by the previous year’s value to present a percentage. All data was deflated to 2012 constant dollars using the Bureau of Economic Analysis’ chained dollar methodology.
Coastal Tourism Employment
Between 2013 and 2018 the average change in coastal county tourism sector employment was higher than the median change in coastal county tourism sector employment between 2006 and 2018.
Values correspond to percent change in the Employment in the Tourism Sector of Coastal Counties in US States that border a region
Description of Time Series: Between 2013 and 2018 the average change in coastal county employment showed a decreasing trend.
Description of Gauge: Between 2013 and 2018 the average change in coastal county tourism sector employment was higher than the median change in coastal county tourism sector employment between 2006 and 2018.
Extreme Gauge values:
A value of zero on the gauge means that the average coastal tourism employment over the last 5 years of data was below any annual coastal tourism employment level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism employment level up until that point.
Description of Coastal Tourism:
U.S. coasts are host to a multitude of travel, tourism, and recreation activities. To manage our coasts, plan for development, and assess impacts as a result of coastal hazards including sea level rise, it is important to have baseline economic information. To accomplish this, we need indicators of the economic value of recreation and tourism. We present the annual total change in billions of dollars of goods and services (GDP), employment and annual wages provided from tourism industries in the Gulf of Mexico, Mid-Atlantic, Northeast, Hawaii-Pacific Islands, Southeast, and California Current regions. This data does not include industries located in U.S. territories.
Indicator Source Information:
Coastal tourism employment is the total measure of jobs in tourism industries along the coast. Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.
Data Source:
Coastal Tourism GDP, employment, and real wage data was taken from NOAA’s Office of Coastal Management Economics: National Ocean Watch (ENOW) custom report building tool, with contextual data taken from the 2020 NOAA Report on the U.S. Marine Economy: Regional and State Profiles. Growth was estimated by subtracting the previous year’s value from the current year’s value, then dividing by the previous year’s value to present a percentage. All data was deflated to 2012 constant dollars using the Bureau of Economic Analysis’ chained dollar methodology.
Coastal Tourism Wage Compensation
Between 2013 and 2018 the average change in coastal county tourism sector real wage compensation was higher than the median change in coastal county tourism sector real wage compensation between 2006 and 2018.
Values correspond to percent change in the Real Wage Compensation the Tourism Sector of Coastal Counties in US States that border a region
Description of Time Series: Between 2013 and 2018 the average change in coastal county real wage compensation showed a decreasing trend.
Description of Gauge: Between 2013 and 2018 the average change in coastal county tourism sector real wage compensation was higher than the median change in coastal county tourism sector real wage compensation between 2006 and 2018.
Extreme Gauge values:
A value of zero on the gauge means that the average coastal tourism wage compensation over the last 5 years of data was below any annual coastal tourism wage compensation level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism wage compensation level up until that point.
Description of Coastal Tourism:
U.S. coasts are host to a multitude of travel, tourism, and recreation activities. To manage our coasts, plan for development, and assess impacts as a result of coastal hazards including sea level rise, it is important to have baseline economic information. To accomplish this, we need indicators of the economic value of recreation and tourism. We present the annual total change in billions of dollars of goods and services (GDP), employment and annual wages provided from tourism industries in the Gulf of Mexico, Mid-Atlantic, Northeast, Hawaii-Pacific Islands, Southeast, and California Current regions. This data does not include industries located in U.S. territories.
Indicator Source Information:
Coastal tourism wage is the measure of wages (nominal) paid to employees in tourism industries along the coast. Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act
Data Source:
Coastal Tourism GDP, employment, and real wage data was taken from NOAA’s Office of Coastal Management Economics: National Ocean Watch (ENOW) custom report building tool, with contextual data taken from the 2020 NOAA Report on the U.S. Marine Economy: Regional and State Profiles. Growth was estimated by subtracting the previous year’s value from the current year’s value, then dividing by the previous year’s value to present a percentage. All data was deflated to 2012 constant dollars using the Bureau of Economic Analysis’ chained dollar methodology.
Total Coastal Employment
Between 2014 and 2019 for the entire US was higher than 89% of all years between 2005 and 2019.
Values correspond to total employment in all industries in a given coastal region
Time Series
Across the nation, average coastal employment between 2014 and 2019 was similar to historical levels, and an increasing trend is apparent over that same period.
Gauge
The gauge value of 89 indicates that coastal employment between 2014 and 2019 for the entire US was higher than 89% of all years between 2005 and 2019.
Extreme Gauge values:
A value of zero on the gauge means that the average coastal employment level over the last 5 years of data was below any annual employment level up until that point, while a value of 100 would indicate the average over that same period was above any annual employment level up until that point.
Description of coastal employment:
The total coastal employment is the number of jobs in coastal communities. Businesses in coastal counties employ tens of millions of people nationally. This includes hundreds of thousands of ocean-dependent businesses that pay over $100 billion in wages annually. Many coastal and ocean amenities attracting visitors are free, generating no direct employment, wages, or gross domestic product. However, these “nonmarket” features are key drivers for many coastal businesses. We present data for all regions.
Data Source:
Coastal employment numbers were downloaded from the NOAA ENOW Explorer Tool, filtered to present only coastal county values using the Census Bureau’s list of coastal counties within each state. ENOW Explorer streamlines the task of obtaining and comparing economic data, both county and state, for the six sectors dependent on the ocean and Great Lakes: living resources, marine construction, marine transportation, offshore mineral resources, ship and boat building, and tourism and recreation. Data are derived from Economics: National Ocean Watch (ENOW), available on NOAA’s Digital Coast. Of note is that these data fail to include self-employed individuals. Coastal county employment numbers were then summed within each region for reporting purposes.
Commercial Fishery Landings - Tonnage
Mean annual commercial landings between 2015 and 2020 for the entire US was higher than 62% of all years between 1950 and 2020.
Values correspond to landings in millions of metric tons
Commercial Landings Time Series
Between 2015 and 2020, commercial landings were similar to historic levels nationally, and there is no recent trend apparent.
Commercial Landings Gauge
The gauge value of 62 indicates that the mean annual commercial landings between 2015 and 2020 for the entire US was higher than 62% of all years between 1950 and 2020.
Extreme Gauge values:
A value of zero on the gauge means that the average revenue or landings over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.
Description of Commercial Fishing (Landings and Revenue):
Commercial landings are the weight of, or revenue from, fish that are caught, brought to shore, processed, and sold for profit. It does not include sport or subsistence (to feed themselves) fishermen or for-hire sector, which earns its revenue from selling recreational fishing trips to saltwater anglers.
Commercial landings make up a major part of coastal economies. U.S. commercial fisheries are among the world’s largest and most sustainable; producing seafood, fish meal, vitamin supplements, and a host of other products for both domestic and international consumers.
The weight (tonnage), and revenue from the sale of commercial landings provides data on the ability of marine ecosystems to continue to supply these important products.
Indicator Source Information:
Landings are reported in pounds of round (live) weight for all species or groups except univalve and bivalve mollusks, such as clams, mussels, oysters and scallops, which are reported as pounds of meats (excludes shell weight). Landings data may sometimes differ from state-reported landings due to our reporting of mollusks in meat weights rather than gallons, shell weight, or bushels. Also, NMFS includes some species such as kelp and oysters that are sometimes reported by state agricultural agencies and may not be included with state fishery agency landings data.
Data Background and Caveats:
All landings summaries will return only non confidential landing statistics. Federal statutes prohibit public disclosure of landings (or other information) that would allow identification of the data contributors and possibly put them at a competitive disadvantage. Most summarized landings are non confidential, but whenever confidential landings occur they have been combined with other landings and usually reported as "Withheld for Confidentiality" Total landings by state include confidential data and will be accurate, but landings reported by individual species may, in some instances, be misleading due to data confidentiality.
Landings data do not indicate the physical location of harvest but the location at which the landings either first crossed the dock or were reported from.
Many fishery products are gutted or otherwise processed while at sea and are landed in a product type other than round (whole) weight. Our data partners have standard conversion factors for the majority of the commonly caught species that convert their landing weights from any product type to whole weight. It is the whole weight that is displayed in our web site landing statistics. Caution should be exercised when using these statistics. An example of a potential problem is when landings statistics are used to monitor fishery quotas. In some situations, specific conversion factors may have been designated in fishery management plans or Federal rule making that differ from those historically used by NOAA Fisheries in reporting landings statistics.
The dollar value of the landings are ex-vessel (as paid to the fisherman at time of first sale) and are reported as nominal (current at the time of reporting) values. Users can use the Consumer Price Index (CPI) or the Producer Price Index (PPI) to convert these nominal landing values into real (deflated) values.
Landings do not include aquaculture products except for clams, mussels and oysters.
Pacific landings summarized by state include an artificial “state” designation of “At-Sea Process, Pac.” This designation was assigned to landings consisting of primarily whiting caught in the EEZ off Washington and Oregon that were processed aboard large vessels while at sea. No Pacific state lists these fish on their trip tickets which are used to report state fishery landing, hence the at-sea processor designation was used to insure that they would be listed as a U.S. landing.
Landing summaries are compiled from data bases that overlap in time and geographic coverage, and come from both within and outside of NOAA Fisheries.
Commercial Fishery Revenue
Mean annual commercial revenue between 2015 and 2020 for the entire US was higher than 39% of all years between 1950 and 2020.
Values correspond to revenue in 2021 US Dollars
Commercial Revenue Time Series
Between 2015 and 2020, national average annual commercial revenue was not different than historical patterns, although there is a decreasing trend in values.
Commercial Revenue Gauge
The gauge value of 39 indicates that the mean annual commercial revenue between 2015 and 2020 for the entire US was higher than 39% of all years between 1950 and 2020.
Extreme Gauge values:
A value of zero on the gauge means that the average revenue or landings over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.
Description of Commercial Fishing (Landings and Revenue):
Commercial landings are the weight of, or revenue from, fish that are caught, brought to shore, processed, and sold for profit. It does not include sport or subsistence (to feed themselves) fishermen or for-hire sector, which earns its revenue from selling recreational fishing trips to saltwater anglers.
Commercial landings make up a major part of coastal economies. U.S. commercial fisheries are among the world’s largest and most sustainable; producing seafood, fish meal, vitamin supplements, and a host of other products for both domestic and international consumers.
The weight (tonnage), and revenue from the sale of commercial landings provides data on the ability of marine ecosystems to continue to supply these important products.
Indicator Source Information:
Landings are reported in pounds of round (live) weight for all species or groups except univalve and bivalve mollusks, such as clams, mussels, oysters and scallops, which are reported as pounds of meats (excludes shell weight). Landings data may sometimes differ from state-reported landings due to our reporting of mollusks in meat weights rather than gallons, shell weight, or bushels. Also, NMFS includes some species such as kelp and oysters that are sometimes reported by state agricultural agencies and may not be included with state fishery agency landings data.
Data Background and Caveats:
All landings summaries will return only non confidential landing statistics. Federal statutes prohibit public disclosure of landings (or other information) that would allow identification of the data contributors and possibly put them at a competitive disadvantage. Most summarized landings are non confidential, but whenever confidential landings occur they have been combined with other landings and usually reported as "Withheld for Confidentiality" Total landings by state include confidential data and will be accurate, but landings reported by individual species may, in some instances, be misleading due to data confidentiality.
Landings data do not indicate the physical location of harvest but the location at which the landings either first crossed the dock or were reported from.
Many fishery products are gutted or otherwise processed while at sea and are landed in a product type other than round (whole) weight. Our data partners have standard conversion factors for the majority of the commonly caught species that convert their landing weights from any product type to whole weight. It is the whole weight that is displayed in our web site landing statistics. Caution should be exercised when using these statistics. An example of a potential problem is when landings statistics are used to monitor fishery quotas. In some situations, specific conversion factors may have been designated in fishery management plans or Federal rule making that differ from those historically used by NOAA Fisheries in reporting landings statistics.
The dollar value of the landings are ex-vessel (as paid to the fisherman at time of first sale) and are reported as nominal (current at the time of reporting) values. Users can use the Consumer Price Index (CPI) or the Producer Price Index (PPI) to convert these nominal landing values into real (deflated) values.
Landings do not include aquaculture products except for clams, mussels and oysters.
Pacific landings summarized by state include an artificial “state” designation of “At-Sea Process, Pac.” This designation was assigned to landings consisting of primarily whiting caught in the EEZ off Washington and Oregon that were processed aboard large vessels while at sea. No Pacific state lists these fish on their trip tickets which are used to report state fishery landing, hence the at-sea processor designation was used to insure that they would be listed as a U.S. landing.
Landing summaries are compiled from data bases that overlap in time and geographic coverage, and come from both within and outside of NOAA Fisheries.
Recreational Fishing Effort
The recreational fishing effort between 2016 and 2020 for the US was higher than 54% of the recreational fishing effort values between 1982 and 2020.
Values correspond to total angler trips
Description of time series:
Between 2016 and 2020, recreational fishing effort in the US is around historic levels and shows no trend.
Description of gauge:
The gauge value of 54 indicates that the recreational fishing effort between 2016 and 2020 for the US was higher than 54% of the recreational fishing effort values between 1982 and 2020.
Extreme Gauge values:
A value of zero on the gauge means that the average effort or harvest over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.
Description of Recreational Fishing (Effort and Harvest):
U.S. saltwater recreational fishing is an important source of seafood, jobs, and recreation for millions of anglers and for-hire recreational businesses. Recreational fishing effort is measured as “Angler Trips”, which is the number of recreational fishing trips people go on. Recreational fishing harvest is the number of fish caught and brought to shore on recreational fishing trips.
Recreational effort and harvest help us understand how recreational opportunities and seafood derived from our marine environment is changing over time. Fisheries managers use this data to set annual catch limits and fishing regulations, including season lengths, size, and daily catch limits. We present the total number of fish harvested and angler trips annually for all marine fish in all regions.
Indicator Source Information
Recreational harvest and effort data pulled from National Summary Query. Units of data are in Effort in Angler Trips and Harvest in numbers of fish. The data from these queries is used by state, regional and federal fisheries scientists and managers to maintain healthy and sustainable fish stocks.
Data Background and Caveats:
To properly interpret this information, it is important to consider the following key points:
- When comparing harvest estimates across an extended time series, note differences in sampling coverage through the years. Some estimates may not be comparable over long time series.
Changes may occur between preliminary and final estimates and year to year, meaning that the data may change when updated. Please review the Limitations and other sections on the Using the Data page from the source for more information.
Recreational fishing harvest
The recreational fishing harvest between 2016 and 2020 for the US was only higher than 33% of the recreational fishing harvest values between 1982 and 2020.
Values correspond to millions of fish caught
Description of time series:
Between 2016 and 2020, recreational fishing harvest in the US is around historic levels and shows a significant downward trend.
Description of gauge:
The gauge value of 54 indicates that the recreational fishing harvest between 2016 and 2020 for the US was only higher than 33% of the recreational fishing harvest values between 1982 and 2020.
Extreme Gauge values:
A value of zero on the gauge means that the average effort or harvest over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.
Description of Recreational Fishing (Effort and Harvest):
U.S. saltwater recreational fishing is an important source of seafood, jobs, and recreation for millions of anglers and for-hire recreational businesses. Recreational fishing effort is measured as “Angler Trips”, which is the number of recreational fishing trips people go on. Recreational fishing harvest is the number of fish caught and brought to shore on recreational fishing trips.
Recreational effort and harvest help us understand how recreational opportunities and seafood derived from our marine environment is changing over time. Fisheries managers use this data to set annual catch limits and fishing regulations, including season lengths, size, and daily catch limits. We present the total number of fish harvested and angler trips annually for all marine fish in all regions.
Indicator Source Information
Recreational harvest and effort data pulled from National Summary Query. Units of data are in Effort in Angler Trips and Harvest in numbers of fish. The data from these queries is used by state, regional and federal fisheries scientists and managers to maintain healthy and sustainable fish stocks.
Data Background and Caveats:
To properly interpret this information, it is important to consider the following key points:
- When comparing harvest estimates across an extended time series, note differences in sampling coverage through the years. Some estimates may not be comparable over long time series.
Changes may occur between preliminary and final estimates and year to year, meaning that the data may change when updated. Please review the Limitations and other sections on the Using the Data page from the source for more information.
Commercial Fishing Engagement
The average annual commercial fishing engagement between 2014 and 2019 for coastal communities across the U.S. was higher than 18% of all years in the time series.
The x-axis on this time series represents years and the y-axis represents the percent of communities that are moderate to highly engaged in commercial fishing across the U.S. Commercial fishing engagement is measured by the number permits, fish dealers, and vessel landings across the U.S.
Description of time series:
This time series shows the percent of communities moderately or highly engaged in commercial fishing in the U.S. from 2009 to 2019. Between 2014 and 2019 the percent of communities moderately or highly engaged in commercial fishing showed a downward trend, with 2019 near a series low level.
Description of gauge:
The gauge value of 18 indicates that the average annual commercial fishing engagement between 2014 and 2019 for coastal communities across the U.S. was higher than 18% of all years in the time series.
Description of U.S. Commercial Fishing Engagement:
Commercial fishing engagement is measured by the presence of fishing activity in coastal communities. The commercial engagement index is measured through permits, fish dealers, and vessel landings. A high rank within these indicates more engagement in fisheries. For details on both data sources and indicator development, please see https://www.fisheries.noaa.gov/national/socioeconomics/social-indicator….
NOAA Monitors commercial fishing engagement to better understand the social and economic impacts of fishing policies and regulations on our nation’s vital fishing communities. This and other social indicators help assess a coastal community’s resilience. NOAA works with state and local partners to monitor these indicators. We present data from the Northeast, Southeast, Gulf of Mexico, California Current, Alaska, and Hawaiian Island regions.
Extreme Gauge values:
A value of zero on the gauge means that the average percentage of communities engaged in commercial or recreational fishing over the last 5 years of data was below any annual engagement level up until that point, while a value of 100 would indicate the average over that same period was above any engagement level up until that point.
Data Source:
Commercial fishing engagement data is from the National Marine Fisheries Service’s social indicator data portal:https://www.st.nmfs.noaa.gov/data-and-tools/social-indicators/ The percentage of all communities in each region classified as medium, medium high, or highly engaged is presented for both recreational and commercial fishing.
Extreme Gauge values:
A value of zero on the gauge means that the average percentage of communities engaged in commercial or recreational fishing over the last 5 years of data was below any annual engagement level up until that point, while a value of 100 would indicate the average over that same period was above any engagement level up until that point.
Data Source:
Commercial fishing engagement data is from the National Marine Fisheries Service’s social indicator data portal:https://www.st.nmfs.noaa.gov/data-and-tools/social-indicators/ The percentage of all communities in each region classified as medium, medium high, or highly engaged is presented for both recreational and commercial fishing.
Recreational Fishing Engagement
The average annual recreational fishing engagement between 2014 and 2019 for the US was only higher than 36% of all years between 2009 and 2019.
The x-axis on this time series represents years and the y-axis represents the percent of communities that are moderately to highly engaged in recreational fishing across the US.
Recreational Engagement Time Series
This time series shows the percent of communities moderately to highly engaged in recreational fishing in the US from 2009 to 2019. Between 2014 and 2019 (highlighted in green) the percent of communities moderately or highly engaged in recreational fishing showed no significant trend.
Recreational Engagement Gauge
The gauge value of 36 indicates that the average annual recreational fishing engagement between 2014 and 2019 for the US was only higher than 36% of all years between 2009 and 2019.
Extreme Gauge values
A value of zero on the gauge means that the average percentage of communities engaged in recreational or recreational fishing over the last 5 years of data was below any annual engagement level up until that point, while a value of 100 would indicate the average over that same period was above any engagement level up until that point
Description of Fishing Engagement:
Recreational and commercial fishing engagement is measured by the presence of fishing activity in coastal communities. The commercial engagement index is measured through permits, fish dealers, and vessel landings. The data for recreational engagement indicators varies by state. A high rank within these indicates more engagement in fisheries. For details on both data sources and indicator development, please see https://www.fisheries.noaa.gov/national/socioeconomics/social-indicators-fishing-communities-0. Please note that the methodology related to these indicators has changed since our previous data update.
NOAA Monitors recreational and commercial fishing engagement to better understand the social and economic impacts of fishing policies and regulations on our nation’s vital fishing communities. This and other social indicators help assess a coastal community’s resilience. NOAA works with state and local partners to monitor these indicators. We present data from the Northeast, Southeast, Gulf of Mexico, California Current, Alaska, and Pacific Island regions.
Extreme Gauge values
A value of zero on the gauge means that the average percentage of communities engaged in recreational or recreational fishing over the last 5 years of data was below any annual engagement level up until that point, while a value of 100 would indicate the average over that same period was above any engagement level up until that point
Data Background and Caveats
The data for recreational engagement indicators varies by state. A high rank within these indicates more engagement in fisheries. For details on both data sources and indicator development, please see https://www.fisheries.noaa.gov/national/socioeconomics/social-indicator….
Billion-Dollar Disasters
The number of disasters over the past 5 years is substantially higher than historical levels of events, and there is an upward trend in the number of events.
Values correspond to the number of events in a given year
Time Series
Across the nation, the number of billion dollar disasters is variable over time, fluctuating between 0 and 52 disasters a year. The number of disasters over the past 5 years is substantially higher than historical levels of events, and there is an upward trend in the number of events. For the full U.S. (including Puerto Rico and the USVIs) the 1980–2021 annual average is 7.7 events (CPI-adjusted). The annual average for the most recent 5 years (2017–2021) is 17.8 events (CPI-adjusted).
Gauge
The gauge value of 93 indicates that the number of billion dollar disasters between 2017 and 2021 for the US was higher than 93% of all years between 1980 and 2021.
Description of billion dollar disasters:
In the United States, the number of weather and climate-related disasters exceeding 1 billion dollars has been increasing since 1980. These events have significant impacts to coastal economies and communities. The Billion Dollar Disaster indicator provides information on the frequency and the total estimated costs of major weather and climate events that occur in the United States. This indicator compiles the annual number of weather and climate-related disasters across seven event types. We Present the total annual number of disaster events for all regions.
Extreme Gauge values
A value of zero on the gauge means that the average number of disasters over the last 5 years of data was below any annual level up until that point, while a value of 100 would indicate the average over that same period was above any annual number of disasters up until that point.
Data Background and Caveats:
Events are included if they are estimated to cause more than one billion U.S. dollars in direct losses. The cost estimates of these events are adjusted for inflation using the Consumer Price Index (CPI) and are based on costs documented in several Federal and private-sector databases.
EPA-Reported Beach Closure Days
Between 2017 and 2021 the national average number of beach closure days was above 91% of all beach closure days between 2000 and 2021.
Beach closures are the number of days when beach water quality is determined to be unsafe.
Data Interpretation:
Time series: This time series shows the average number of beach closure days across the United States from 2000 to 2021. Across the United States during the last five years, there has been substantially more beach closures due to unsafe water quality and it is trending upward.
Gauge: The gauge value of 91 indicates that between 2017 and 2021 the national average number of beach closure days was above 91% of all beach closure days between 2000 and 2021.
Gauge values
0–10: The five-year beach closure days average is very low compared to the median value.
10–25: The five-year beach closure days average is much lower than the median value.
25–50: The five-year beach closure days average is lower than the median value.
50: The five-year beach closure days average equals the median value.
50–75: The five-year beach closure days average is higher than the median value.
75–90: The five-year beach closure days average is much higher than the median value.
90–100: The five-year beach closure days average is very high compared to the median.
* gauge value is the percentile rank of the last five years based on the time series.
Indicator and source information:
Unsafe water quality may have significant impacts on human health, local economies, and the ecosystem. Beach water quality is determined by the concentration of bacteria in the water (either Enterococcus sp. or Escherichia coli).
The US Environmental Protection Agency (EPA) supports coastal states, counties and tribes in monitoring beach water quality, and notifying the public when beaches must be closed. The information presented is from states, counties, and tribes that submit data to the EPA Beach Program reporting database (BEACON). Data obtained from the EPA BEACON 2.0 website have been provided to EPA by the coastal and Great Lakes states, tribes and territories that receive grants under the BEACH Act. Data were refined to closure, by state or territory, by year.
Data background and limitations:
Data compiled by states or territories are combined in regions defined as US Large Marine Ecosystems (LME). Changes in the number of beach closure days may be driven by changes in the number of beaches monitored under the BEACH Act versus by state and local municipalities and not by changes in water and/or air quality. Not all US beach closures are captured in this database, because not all beaches in a state or territory are monitored through the EPA BEACH Act. Data that were not identified to a water body or identified as inland water were not included. Data for beaches monitored by state and local municipalities are not included.
Data from 2020 and beyond may be inflated by the Covid-19 pandemic, as there was no consistent way for states to report pandemic-related closures.
