- 31.1 Methods
-
@@ -939,7 +940,7 @@
- 11.1.4 Data processing -
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/commercial_div.html b/commercial_div.html
index 7a1621ea..5de416c7 100644
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+++ b/commercial_div.html
@@ -23,7 +23,7 @@
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+
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+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/conceptual-models.html b/conceptual-models.html
index 7f5848ad..2107e90e 100644
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@@ -23,7 +23,7 @@
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+
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- 11.1.4 Data processing
- - 31.1 Methods
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- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
@@ -1588,12 +1589,12 @@
- 11.1.4 Data processing
16.1.3.5 Communication tools16.1.3.5 Communication tools= 2.10) ## NeedsCompilation: no -## Packaged: 2024-03-14 15:45:35 UTC; runner +## Packaged: 2024-04-05 21:21:37 UTC; runner ## Author: Brandon Beltz [cre, aut], Kimberly Bastille [aut], Sean ## Hardison [aut] ## Maintainer: Brandon Beltz
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/energy_density.html b/energy_density.html
index 3091be85..188d12a2 100644
--- a/energy_density.html
+++ b/energy_density.html
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+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/engagement.html b/engagement.html
index 3d4b866a..bd4726bc 100644
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@@ -23,7 +23,7 @@
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- 11.1.4 Data processing
- - 31.1 Methods
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- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/epu.html b/epu.html
index 59ebcd8b..afd41229 100644
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@@ -23,7 +23,7 @@
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- 11.1.4 Data processing
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/erddap.html b/erddap.html
index 392fae3d..0995afee 100644
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/exp_n.html b/exp_n.html
index f5ac571e..13f8cf34 100644
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/forage_index.html b/forage_index.html
index b2e13d01..98b7b26f 100644
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/glossary.html b/glossary.html
index 15724d1f..88988afa 100644
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@@ -23,7 +23,7 @@
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/gsi.html b/gsi.html
index 7275972b..ea10a5d3 100644
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/hab-occu.html b/hab-occu.html
index 96e129a8..72a5e6b2 100644
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/habitat-vulnerability.html b/habitat-vulnerability.html
index 6314bcb2..4ed899df 100644
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- 11.1.4 Data processing
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/habitat_diversity.html b/habitat_diversity.html
index f745aedc..715297c6 100644
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- 11.1.4 Data processing
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/habs.html b/habs.html
index 3e8edf30..8f2a69ad 100644
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@@ -23,7 +23,7 @@
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+
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+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
@@ -939,7 +940,7 @@
32.1.2 Data Extraction
32.1.3 Data Analysis
-The spatial distribution and abundance of cyst cells from the annual survey are used to drive an ecosystem forecast model for the Gulf of Maine (Anderson et al. (2005), Y. Li et al. (2009), Y. Li et al. (2020), McGillicuddy et al. (2011)). The model also includes many other inputs of dynamic oceanographic data such as currents, temperature, and nutrients. Operational Harmful Algal Bloom forecast is served online at https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/.
+The spatial distribution and abundance of cyst cells from the annual survey are used to drive an ecosystem forecast model for the Gulf of Maine (Anderson et al. (2005), Li et al. (2009), Li et al. (2020), McGillicuddy et al. (2011)). The model also includes many other inputs of dynamic oceanographic data such as currents, temperature, and nutrients. Operational Harmful Algal Bloom forecast is served online at https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/.
- 31.1 Methods
- 11.1.4 Data processing -
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
@@ -919,11 +920,11 @@
- 11.1.4 Data processing
-diff --git a/harmful-algal-bloom-indicator---mid-atlantic.html b/harmful-algal-bloom-indicator---mid-atlantic.html index 06a6e715..4a8fa4b7 100644 --- a/harmful-algal-bloom-indicator---mid-atlantic.html +++ b/harmful-algal-bloom-indicator---mid-atlantic.html @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@31 Harbor Porpoise and Gray Seal Bycatch
-Description: Harbor Porpoise and Gray Seal Indicator
+31 Harbor Porpoise Bycatch
+Description: Harbor Porpoise Bycatch Indicator
Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2018+), State of the Ecosystem - Mid-Atlantic (2018+)
Indicator category: Synthesis of published information; Published methods
-Contributor(s): Christopher D. Orphandies, Debra Palka
+Contributor(s): Kristin Precoda, Christopher D. Orphanides, Debra Palka
Data steward: Debra Palka debra.palka@noaa.gov
Point of contact: Debra Palka debra.palka@noaa.gov
Public availability statement: Source data are available in public stock assessment reports.
@@ -931,7 +932,7 @@31 Harbor Porpoise and Gray Seal
31.1 Methods
31.1.1 Data sources
-Reported harbor porpoise bycatch estimates and potential biological removal levels can be found in publicly available documents; detailed in Marine Mammal Protection Stock Assessments. More detailed documentation as to the methods employed can be found in NOAA Fisheries Northeast Fisheries Science Center (NEFSC) Center Reference Documents (CRDs) found on the NEFSC publications page.
+Reported harbor porpoise bycatch estimates and potential biological removal levels can be found in publicly available documents; detailed in Marine Mammal Protection Stock Assessments. More detailed documentation as to the methods employed can be found in NOAA Fisheries Northeast Fisheries Science Center (NEFSC) Center Reference Documents (CRDs) found on the NEFSC publications page.
31.1.2 Data extraction
@@ -940,10 +941,9 @@31.1.2 Data extraction
31.1.3 Data analysis
Bycatch estimates as found in stock assessment reports were plotted along with confidence intervals. The confidence intervals were calculated from published CVs assuming a normal distribution (\(\sigma = \mu CV\); \(CI = \bar{x} \pm \sigma * 1.96\)).
-Data were analyzed and formatted for inclusion in the
+ecodataR package using the R code found here, Harbor Porpoise and Gray Seal.Data were analyzed and formatted for inclusion in the
ecodataR package using the R code found here, Harbor Porpoise.catalog link -https://noaa-edab.github.io/catalog/harborporpoise.html -https://noaa-edab.github.io/catalog/grayseal.html
+https://noaa-edab.github.io/catalog/harborporpoise.html
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/harmful-algal-bloom-indicator---new-england.html b/harmful-algal-bloom-indicator---new-england.html
index 601cf4de..0a29c4f4 100644
--- a/harmful-algal-bloom-indicator---new-england.html
+++ b/harmful-algal-bloom-indicator---new-england.html
@@ -23,7 +23,7 @@
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+
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+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/harmful-algal-blooms---paralytic-shellfish-poisoning-indicator.html b/harmful-algal-blooms---paralytic-shellfish-poisoning-indicator.html
index 704454b3..86b7de7b 100644
--- a/harmful-algal-blooms---paralytic-shellfish-poisoning-indicator.html
+++ b/harmful-algal-blooms---paralytic-shellfish-poisoning-indicator.html
@@ -23,7 +23,7 @@
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+
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- 11.1.4 Data processing
- - 31.1 Methods
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- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/heatwave.html b/heatwave.html
index a2424da8..aee970ba 100644
--- a/heatwave.html
+++ b/heatwave.html
@@ -23,7 +23,7 @@
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- 11.1.4 Data processing
- - 31.1 Methods
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/hms_cpue.html b/hms_cpue.html
index 3d042d89..b38ea39b 100644
--- a/hms_cpue.html
+++ b/hms_cpue.html
@@ -23,7 +23,7 @@
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
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- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/hms_landings.html b/hms_landings.html
index a41b924d..c1b38cf1 100644
--- a/hms_landings.html
+++ b/hms_landings.html
@@ -23,7 +23,7 @@
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+
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+
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+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
@@ -924,30 +925,27 @@
- 11.1.4 Data processing
36 Highly Migratory Species Landi
Found in: State of the Ecosystem - Gulf of Maine & Georges Bank (2020(Different Methods), 2021+), State of the Ecosystem - Mid-Atlantic (2020(Different Methods), 2021+)
Indicator category: Database pull with analysis
Contributor(s): Heather Baertlein, Jackie Wilson, George Silva, Jennifer Cudney
-Data steward: Kimberly Bastille
+Data steward: Jennifer Cudney jennifer.cudney@noaa.gov
Point of contact: Jennifer Cudney jennifer.cudney@noaa.gov
Public availability statement: Source data are NOT publicly available.
diff --git a/hms_stock_status.html b/hms_stock_status.html index 10ed2604..c257295d 100644 --- a/hms_stock_status.html +++ b/hms_stock_status.html @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@36.1 Methods
36.1.1 Data Sources
-Data from eDealer database (https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-dealer-reporting) and Bluefin Tuna Dealer reports on SAFIS (https://www.accsp.org/what-we-do/safis/). The eDealer data were supplemented with ACCSP records, GulfFIN records, and vessel logbook catches for which no dealer reports were submitted.
+Data from eDealer database (https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-dealer-reporting) and Bluefin Tuna Dealer reports on SAFIS (https://www.accsp.org/what-we-do/safis/). The eDealer data were supplemented with ACCSP records, GulfFIN records, and pelagic and coastal fisheries vessel logbook catches reported to SEFSC for which no dealer reports were submitted.
36.1.2 Data Analysis
-Data were processed for Fisheries of the United States and then aggregated by regions to avoid confidentiality issues. Data of Atlantic shark, swordfish, bigeye tuna, albacore tuna, yellowfin tuna and skipjack tuna were initially extracted from our eDealer database. Additional landings of these HMS not in eDealer were found in ACCSP, GulfFIN, and the SEFSC Atlantic HMS vessel logbook records. Bluefin tuna landings data from the Bluefin Tuna Dealer reports in SAFIS were also extracted and combined with the eDealer data for other HMS .
-Procedures of quality assurance were conducted. Duplicate records were removed from the data. This may occur from multiple submissions of reports by the same dealer. It may also occur when two or more dealers report the same landings in “Packing” situations. While most vessels immediately sell their catch to the dealer at their port of landing, some vessels sell their catch to a dealer(s) in another location. Transport to alternate locations requires processing of the fish to preserve quality. This processing activity is done by the dealer at the port of landing and is referred to as “Packing”. Differences in federal and state definitions of who is considered the “dealer” of the product, and thus ultimately responsible for submitting the landings report, often results in multiple reports being created for the same landings. These duplicate reports need to be accounted for when summarizing the data to reflect accurate landings. Therefore, searches for duplicate reports of the same landing were conducted and eliminated prior to summarizing the data for the Fisheries of the United States.
-All reported landings were converted to live weights using conversion ratios appropriate for the species/species group and reported grade of the product. Shark fins were not reported to live weight as these weights are included in the converted whole weight of the reported shark landing.
-States, where the landings occurred, were grouped into ‘ecological production units’ (EPUs), as defined by GARFO staff. “New England”, or NE, includes Maine, New Hampshire and Massachusetts, as well as landings from Canada. The “Mid-Atlantic Bight”, or MAB, includes states from Rhode Island to North -Carolina.
+Data, from 2015-2022, were processed for Fisheries of the United States and then aggregated by regions to avoid confidentiality issues. Data of Atlantic shark, swordfish, bigeye tuna, albacore tuna, yellowfin tuna and skipjack tuna were initially extracted from our eDealer database and the Caribbean Commercial Vessel logbook database. Additional landings of these HMS not in this dataset were collected from ACCSP, GulfFIN, and the SEFSC Atlantic HMS vessel logbook databases. Bluefin tuna landings data from the Bluefin Tuna Dealer reports in SAFIS were also extracted and combined with the eDealer data for other HMS.
+Procedures of quality assurance were conducted. Duplicate records were removed from the data. This may occur from multiple submissions of reports by the same dealer. It may also occur when two or more dealers report the same landings in “Packing” situations. While most vessels immediately sell their catch to the dealer at their port of landing, some vessels sell their catch to a dealer(s) in another location. Transport to alternate locations requires processing of the fish to preserve quality. This processing activity is done by the dealer at the port of landing and is referred to as “Packing”. Differences in federal and state reporting requirements and definitions of who is considered the “dealer” of the product, and thus ultimately responsible for submitting the landings report, may result in multiple reports being created for the same landings. These duplicate reports need to be accounted for when summarizing the data to reflect accurate landings prior to summarizing the data for analyses, including the Fisheries of the United States.
+All reported landings were converted to live weights using conversion ratios appropriate for the species/species group and reported grade of the product. Shark fins, shark heads, shark tails, and shark bellies were not reported to live weight as these weights are included in the converted whole weight of the reported shark landing.
+States, where the landings occurred, were grouped into ‘ecological production units’ (EPUs), as defined by GARFO staff. “New England”, or NE, includes Maine, New Hampshire and Massachusetts, as well as landings from Canada. The “Mid-Atlantic Bight”, or MAB, includes states from Rhode Island to North Carolina. Landings in states outside of these EPUs were excluded from further summaries.
Seven HMS Management Groups represent 26 highly migratory species in the dataset. HMS Management Groups may include a single species or a group of species. HMS groups include “Bluefin Tuna”, “BAYS”, “Swordfish”, “Large Coastal Sharks”, “Small Coastal Sharks”, “Pelagic Sharks”, “Smoothhound Sharks”. “BAYS” includes bigeye, albacore, yellowfin and skipjack tunas. “Large Coastal Sharks” includes blacktip, bull, great hammerhead, scalloped hammerhead, smooth hammerhead, lemon, nurse, sandbar, silky, spinner, and tiger sharks. “Small Coastal Sharks” includes Atlantic sharpnose, blacknose, bonnethead, finetooth sharks. “Pelagic Sharks” includes blue, porbeagle, shortfin mako, and thresher sharks. “Smoothhound Sharks” includes smooth dogfish shark.
-Price per pound was used to determine the ex-vessel value. For landings with prices per pound reported as “N/A”, 0, $0.01 or left blank, average prices were calculated for each species and state. Those averages replaced the missing values to determine landings revenue. Revenue from sales to the aquarium trade were also excluded to avoid extreme values associated with shipping live specimens.
-High migratory landings include 26 species of tunas, sharks and swordfish.
-Data were processed and analyzed using SAS and Microsoft Excel pivot tables. -The count of dealers and vessels in each regional species/management group sum was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential. Additionally, ratios of landings reported by dealers/fishermen were compared in each regional species/managment group sum to determine if any one entity contributed more than 2/3 of the total which would require it being marked as confidential.
+Price per pound was used to determine the ex-vessel value. For landings with a reported disposition of “Food” and prices per pound reported as “N/A”, 0, $0.01 or left blank, average prices were calculated for each species and state. Those average prices replaced the missing values to determine landings revenue. Revenue from sales to the aquarium trade were also excluded to avoid extreme values associated with shipping live specimens.
36.1.3 Data Processing
+Highly migratory species landings include 26 species of tunas, sharks and swordfish. Data were processed and analyzed using SAS and Microsoft Excel pivot tables. The count of entities represented was used to determine if a sufficient number of records were available to make the data public or if it needed to be marked as confidential; generally, data was marked confidential if it did not meet the “rule of three” (i.e., at least three unique entities represented).
HMS landings data were formatted for inclusion in the
ecodataR package using the R code found here.
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
diff --git a/hudson-river-flow.html b/hudson-river-flow.html
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+
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+
-
+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
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- 11.1.4 Data processing -
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
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+
-
+
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- 11.1.4 Data processing
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
@@ -923,16 +924,16 @@
52 Right Whale Abundancechris.orphanides@noaa.gov -
Point of contact: Richard Pace, richard.pace@noaa.gov
+Contributor(s): Debra Palka
+Data steward: Debra Palka, debra.palka@noaa.gov
+Point of contact: Daniel Linden, daniel.linden@noaa.gov
Public availability statement: Source data are available from the New England Aquarium upon request. Derived data are available here.
52.1 Methods
52.1.1 Data sources
-The North Atlantic right whale abundance estimates were taken from a published document (see Pace, Corkeron, and Kraus 2017), except for the most recent 2016 and 2017 estimates. Abundance estimates from 2016 and 2017 were taken from the 2016 National Oceanographic and Atmospheric Administration marine mammal stock assessment (Hayes et al. 2017) and an unpublished 2017 stock assessment.
-Calves birth estimates are taken from a published report (Pettis, Pace, and Hamilton 2019) put out yearly by the North American Right Whale Consortium.
+The North Atlantic right whale abundance estimates were taken from a published document (see Linden_pop_2023?).
+Calves birth estimates are available in Pace, Corkeron, and Kraus (2017), with more recent years shared here.
52.1.2 Data extraction
@@ -940,28 +941,20 @@52.1.2 Data extraction
52.1.3 Data analysis
-Analysis for right whale abundance estimates is provided by Pace, Corkeron, and Kraus (2017), and code can be found in the supplemental materials.
+Analysis for right whale abundance estimates is based on methods by Pace, Corkeron, and Kraus (2017), as documented most recently by (Linden_pop_2023?). Data and code can be found in the following Github repository: NEFSC/PSD-NARW_popsize.
52.1.4 Data processing
Time series of right whale and calf abundance estimates were formatted for inclusion in the
-ecodataR package using this R code.catalog link -https://noaa-edab.github.io/catalog/narw.html
- 31.1 Methods
- 11.1.4 Data processing
Technical Documentation, State of the Ecosystem Report
-22 March 2024
+5 April 2024
Introduction
diff --git a/libs/datatables-binding-0.32/datatables.js b/libs/datatables-binding-0.33/datatables.js similarity index 99% rename from libs/datatables-binding-0.32/datatables.js rename to libs/datatables-binding-0.33/datatables.js index 6a3c3d5c..765b53cb 100644 --- a/libs/datatables-binding-0.32/datatables.js +++ b/libs/datatables-binding-0.33/datatables.js @@ -1032,6 +1032,9 @@ HTMLWidgets.widget({ updateColsSelected(); updateCellsSelected(); }) + updateRowsSelected(); + updateColsSelected(); + updateCellsSelected(); } var selMode = data.selection.mode, selTarget = data.selection.target; diff --git a/libs/htmltools-fill-0.5.8.1/fill.css b/libs/htmltools-fill-0.5.8.1/fill.css new file mode 100644 index 00000000..841ea9d5 --- /dev/null +++ b/libs/htmltools-fill-0.5.8.1/fill.css @@ -0,0 +1,21 @@ +@layer htmltools { + .html-fill-container { + display: flex; + flex-direction: column; + /* Prevent the container from expanding vertically or horizontally beyond its + parent's constraints. */ + min-height: 0; + min-width: 0; + } + .html-fill-container > .html-fill-item { + /* Fill items can grow and shrink freely within + available vertical space in fillable container */ + flex: 1 1 auto; + min-height: 0; + min-width: 0; + } + .html-fill-container > :not(.html-fill-item) { + /* Prevent shrinking or growing of non-fill items */ + flex: 0 0 auto; + } +} diff --git a/long_term_sst.html b/long_term_sst.html index 3c2d6769..cf53a25e 100644 --- a/long_term_sst.html +++ b/long_term_sst.html @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@References
-diff --git a/ocean_acidification.html b/ocean_acidification.html index de618cb2..e21582f0 100644 --- a/ocean_acidification.html +++ b/ocean_acidification.html @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@-Hayes, Sean Arthur, Elizabeth Josephson, Katherine Maze-Foley, Patricia E Rosel, Barbie L Byrd, Timothy VN Cole, Laura Engleby, et al. 2017. “US Atlantic and Gulf of Mexico Marine Mammal Stock Assessments - 2016.” NOAA Technical Memorandum NMFS-NE-241, no. June: 274. http://nova.nefsc.noaa.gov/palit/Waring et al.{\_}2013{\_}NOAA Tech Memo NMFS-NE-223{\_}U . S . Atlantic and Gulf of Mexico Marine Mammal Stock Assessments - 2012.pdf. -Pace, Richard M., Peter J. Corkeron, and Scott D. Kraus. 2017. “State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales.” Ecology and Evolution. https://doi.org/10.1002/ece3.3406.--Pettis, H. M, R. M. Pace, and P. K. Hamilton. 2019. “North American Right Whale Consortium 2019 Annual Report Card.” https://www.narwc.org/uploads/1/1/6/6/116623219/2019reportfinal.pdf. -
- - 31.1 Methods
- 12 Chesapeake Bay Seasonal SST Anomalies +
- 12 Chesapeake Bay Seasonal SST Anomalies
- 13 Chesapeake Bay Water Quality Standards Attainment @@ -475,7 +476,7 @@
- 30.1.4 Plotting -
- 31 Harbor Porpoise and Gray Seal Bycatch +
- 31 Harbor Porpoise Bycatch
- 31.1 Methods
-
@@ -923,105 +924,60 @@
- A plot of bottom \(\Omega_{Arag}\) in summer over the entire U.S. Northeast Shelf (all available data from 2007-present and includes both glider-based measurements and vessel-based discrete samples) +
- Maps of four 2023 glider deployments on the coastal New Jersey shelf and the resulting vertical profiles of oceanographic parameters characterizing the evolution of temperature (in °C), chlorophyll, dissolved oxygen, pH, and aragonite saturation state from Spring through Early Fall. See Figure 1 in the Ocean Acidification and Other Stressors catalog page and available here. +
- Mission tracks of three gliders deployed off the coast of New Jersey in August and September and locations of hypoxic levels of dissolved oxygen (< 3 mg/liter) and low aragonite saturation state (< 1) measured along the glider mission tracks and locations of reported fish, lobster, and/or crab mortalities. See Figure 2 in the Ocean Acidification and Other Stressors catalog page. +
- Complete cross-sections of dissolved oxygen concentrations, pH, and aragonite saturation state measured along the associated mission tracks during the deployments of the three gliders during August and September 2023. See Figure 3 in the Ocean Acidification and Other Stressors catalog page. +
- Animated map of summer-time bottom pH on the U.S. Northeast Shelf (2007-2023): access here. Includes all available data from 2007-2023 and includes both glider-based measurements and vessel-based discrete samples. +
- Animated map of summer-time bottom aragonite saturation state (omega) on the U.S. Northeast Shelf (2007-2023): access here. Includes all available data from 2007-2023 and includes both glider-based measurements and vessel-based discrete samples. +
- Individual maps (by year, 2007-2023) used to make the animated map for summer-time bottom pH on the U.S. Northeast Shelf: access here. Includes all available data from 2007-2023 and includes both glider-based measurements and vessel-based discrete samples. +
- Individual maps (by year, 2007-2023) used to make the animated map for summer-time bottom aragonite saturation state (omega) on the U.S. Northeast Shelf: access here. Includes all available data from 2007-2023 and includes both glider-based measurements and vessel-based discrete samples. +
- Maps of locations where species sensitivity levels for aragonite saturation state (omega) were reached in bottom water (all summer-time data 2007-2023, or by individual summers between 2007-2023): access here.
- This map is included in both MAFMC and NEFMC reports. -
- Bottom values were defined as the median of the measurements (or calculated -\(\Omega_{Arag}\) values) within the deepest 1m of a glider profile or, for vessel-based measurements, the deepest measurement of a vertical CTD/Rosette cast where water samples were collected, for profiles deeper than 10m. In order to validate whether the deepest depth was at or near the bottom, the sampling depth was compared to water column depth (when provided) or water depths extracted from a GEBCO bathymetry grid based on the sample collection coordinates. Any glider profiles/vessel-based casts with the deepest measurement shallower than the bottom 20% of total water column depth were removed. This allowed for a sliding scale instead of providing a strict cut off (e.g., 1 m above the bottom). -
- Maps depicting locations where summer bottom \(\Omega_{Arag}\) reached lab-derived sensitivity levels of designated target species + +
- Includes all available data from 2007-2023 and includes both glider-based measurements and vessel-based discrete samples. +
- Sensitivity levels of ΩArag were defined for each species as values of ΩArag where negative responses by an organism were observed during an experimental laboratory study. Typically, these laboratory experiments measure organism responses under ocean acidification conditions (lower pH, lower ΩArag) against a control under ambient conditions (higher pH, higher ΩArag). Most laboratory experiments have used a range of ΩArag between 0.5 to 2.0, which does not encompass the full range of ΩArag observed in situ. The metrics measured (e.g., survival, growth, calcification) can be different between experiments, but negative responses could include decreased survival, reduced growth, reduced calcification rate, reduced hatching success, and malformation. Because laboratory perturbation experiments testing the responses of organisms to ocean acidification conditions are a relatively new approach and logistically quite challenging, there are currently few published studies for individual species. Recent studies have also started incorporating additional stressors, which makes defining an OA-focused sensitivity level difficult. Therefore, with additional future studies, the ΩArag sensitivity levels defined here for these species are subject to change. +
- For the Mid-Atlantic region, designated target species included Atlantic sea scallop (Placopecten magellanicus) and Longfin squid (Doryteuthis pealeii). The sensitivity value used for Atlantic sea scallop was ΩArag ≤ 1.1 at 9 °C, based on reduced adult calcification rate observed at this level in Cameron et al. (2022). The sensitivity value used for longfin squid was ΩArag ≤ 0.96, based on embryo and paralarvae malformation, increased time to hatching and decreased hatching success, and changes to mantle length and statolith morphology observed at this level in Zafroff et al. (2019) and Zafroff & Mooney (2020). Habitat depth ranges used for plotting the observed ΩArag values ≤ sensitivity ΩArag values for Atlantic sea scallop and longfin squid were limited to 25-200 meters (NEFSC 2014) and 0-400 meters (Jacobson et al. 2005), respectively. Bottom water data collected during 2023 were incorporated to update this product for the Mid-Atlantic species, Atlantic sea scallop and longfin squid (available here). +
- For the New England region, designated target species included Atlantic cod (Gadus morhua) and American lobster (Homarus americanus). The sensitivity value used for Atlantic cod was ΩArag ≤ 1.31 at 10 °C, based on decreased larval survival observed at this level in Stiasny et al. (2016). The sensitivity value used for American lobster was ΩArag ≤ 1.09, based on decreased stage V and VI juvenile survival observed at this level in Noisette et al. (2021). Habitat depth ranges used for plotting the observed ΩArag values ≤ sensitivity ΩArag values for Atlantic cod and American lobster were limited to 10-200 meters (Gregory et al. 2004; DeCelles et al. 2017) and 10-700 meters (Mercaldo-Allen et al. 1994), respectively. Because there were no additional 2023 bottom water aragonite saturation state data available in the Gulf of Maine to update this same product from the previous year’s report, maps for Atlantic cod and American lobster are not included in this year’s catalog page. However, the maps for the individual years between 2007-2022 and the combined map for this same time period are available for these species here.
- Sensitivity levels of \(\Omega_{Arag}\) were defined for each species as values of \(\Omega_{Arag}\) where negative responses by an organism were observed during an experimental laboratory study. Typically, these laboratory experiments measure organism responses under ocean acidification conditions (lower pH, lower \(\Omega_{Arag}\)) against a control under ambient conditions (higher pH, higher \(\Omega_{Arag}\)). Most laboratory experiments have used a range of \(\Omega_{Arag}\) between 0.5 to 2.0, which does not encompass the full range of \(\Omega_{Arag}\) observed in situ. The metrics measured (e.g., survival, growth, calcification) can be different between experiments, but negative responses could include decreased survival, reduced growth, reduced calcification rate, reduced hatching success, and malformation. Because laboratory perturbation experiments testing the responses of organisms to ocean acidification conditions are a relatively new approach and logistically quite challenging, there are currently few published studies for individual species. Recent studies have also started incorporating additional stressors, which makes defining an OA-focused sensitivity level difficult. Therefore, with additional future studies, the \(\Omega_{Arag}\) sensitivity levels defined here for these species are subject to change. -
- For the MAFMC report, designated target species included Atlantic sea scallop (Placopecten magellanicus) and Longfin squid (Doryteuthis pealeii). The sensitivity value used for Atlantic sea scallop was \(\Omega_{Arag}\) ≤ 1.1 at 9 C, based on reduced adult calcification rate observed at this level in Cameron, Grabowski, and Ries (2022). The sensitivity value used for longfin squid was \(\Omega_{Arag}\) ≤ 0.96, based on embryo and paralarvae malformation, increased time to hatching and decreased hatching -success, and changes to mantle length and statolith morphology observed at this level in T. A. B. Zakroff Casey; Mooney (2019) and C. J. Zakroff and Mooney (2020) Habitat depth ranges used for plotting the observed \(\Omega_{Arag}\) values ≤ sensitivity \(\Omega_{Arag}\) values for Atlantic sea scallop and longfin squid were limited to 25-200 meters (NEFSC 2014) and 0-400 meters (Jacobson (2005)), respectively. -
- For the NEFMC report, designated target species included Atlantic cod (Gadus morhua) and American lobster (Homarus americanus). The sensitivity value used for Atlantic cod was \(\Omega_{Arag}\) ≤ 1.31 at 10 C, based on decreased larval survival observed at this level in Stiasny (2016). The sensitivity value used for American lobster was \(\Omega_{Arag}\) ≤ 1.09, based on decreased stage V and VI juvenile survival observed at this level in Noisette et al. (2021). Habitat depth ranges used for plotting the observed \(\Omega_{Arag}\) values ≤ sensitivity\(\Omega_{Arag}\) values for Atlantic cod and American lobster were limited to 10-200 meters (Lough (2004); DeCelles et al. (2017)) and 10-700 meters (Mercaldo-Allen (1994)), respectively. -
44 Ocean Acidificationsaba@marine.rutgers.edu -
Point of contact: Grace Saba saba@marine.rutgers.edu
+Contributor(s): Grace Saba, Lori Garzio
+Data steward: Grace Saba saba@marine.rutgers.edu, Lori Garzio lgarzio@marine.rutgers.edu
+Point of contact: Grace Saba saba@marine.rutgers.edu, Lori Garzio lgarzio@marine.rutgers.edu
Public availability statement: Source data is available to the public (see Data Sources).
-44.1 Methods
The New England Fishery Management Council (NEFMC) and Mid-Atlantic Fishery Management Council (MAFMC) have recently requested regional Ocean Acidification (OA) information in the State of the Ecosystem reports. The work included in the State of the Ecosystem 2021 report, seasonal dynamics of pH in shelf waters in the Mid-Atlantic, was synthesized from Wright-Fairbanks et al. (2020). The maps included in the State of the Ecosystem 2022 reports include a plot of bottom pH in summer over the entire U.S. Northeast Shelf (2007-2021), and glider-based pH profiles during summer 2021 in both the Mid-Atlantic Bight (MAB) and the Gulf of Maine. The plots in the State of the Ecosystem 2023 reports included maps of bottom summer aragonite saturation (ΩArag, or omega) over the entire U.S. Northeast Shelf (2007-2022) and locations where summer bottom ΩArag reached lab-derived sensitivity levels of designated target species.
The products developed for the State of the Ecosystem 2024 reports include: plots of the seasonal progression (Spring-Fall 2023) of oceanographic properties (including temperature, chlorophyll, dissolved oxygen, pH, and aragonite saturation state) on the New Jersey coastal shelf; plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023; static and animated maps of summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf (2007-2023); and maps of locations where species sensitivity levels for aragonite saturation state were reached in bottom water during the summer (2007-2023).
-Products from all State of the Ecosystem reports to date were developed using openly accessible, quality-controlled data from vessel-based discrete samples and glider-based measurements (see Data Sources), and data from published laboratory-based experimental studies (see Plotting).
+Products from all State of the Ecosystem reports to date were developed using openly accessible, quality-controlled data from vessel-based discrete samples and glider-based measurements (see Data Sources), and data from published laboratory-based experimental studies (see Plotting)
44.1.1 Data sources
Glider-based observations of pH (and other variables including temperature, salinity, chlorophyll-a, and dissolved oxygen) began in the southern MAB region in May 2018 (Saba et al. 2019), and seasonal glider pH missions thereafter began in February 2019 (Wright-Fairbanks et al. 2020; although no deployments occurred in 2020 as a result of the COVID pandemic). Simultaneous measurements from the glider’s pH, temperature, and salinity sensors enable the derivation of total alkalinity and calculation of other carbonate system parameters including aragonite saturation state (ΩArag). The glider pH observation program expanded spatially and temporally, with additional deployments in the northern MAB (New York Bight) and the Gulf of Maine, starting in February 2021. A typical glider mission runs for about 4 weeks, covers 500 km, and collects data though the full water column.
-For the 2023 glider data cross-section plots (2023 seasonal progression of oceanographic properties on the New Jersey coastal shelf and plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023): -Full-resolution delayed-mode glider datasets from six deployments (Spring-Fall 2023) were downloaded from the RUCOOL Glider ERDDAP server. These datasets were QC’d and pH was calculated from raw variables (where applicable) using python code, and the final processed NetCDF datasets containing all relevant metadata can be found here.
-For the summer 2007-2023 bottom-water pH and aragonite saturation state data synthesis (static and animated maps of summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf [2007-2023]; maps of summer-time bottom locations where species sensitivity levels for aragonite saturation state were reached [2007-2023]): -Full-resolution delayed-mode glider datasets from seven deployments were downloaded from the RUCOOL Glider ERDDAP server and the IOOS Glider DAC ERDDAP server (SBU01 2022-2023 deployments). These datasets were QC’d and pH was calculated from raw variables (where applicable) using python code, and the final processed NetCDF datasets containing all relevant metadata for each glider deployment used in the synthesis can be found here. Resulting data files containing combined summer-time bottom pH and aragonite saturation state data from glider-based (and vessel-based, see below) measurements can be found here. -Vessel-based data were mined from the Coastal Ocean Data Analysis Product in North America, version v2021 (Jiang et al. 2021). This data product synthesizes two decades of quality-controlled inorganic carbon system parameters (including pH, total alkalinity, dissolved inorganic carbon) along with other physical and chemical parameters (temperature, salinity, dissolved oxygen, nutrients) collected from the North American continental shelves. Additionally, four recent vessel-based datasets that were not included in CODAP-NA (Jiang et al. 2021) were included in this synthesis. These datasets were collected during more recent NOAA NEFSC Ecosystem Monitoring (EcoMon) surveys: June 2019 (Cruise ID HB1902), August 2019 (Cruise ID GU1902), August 2021 (Cruise ID PC2104), June 2022 (Cruise ID HB2204). These datasets were downloaded via the NCEI Ocean Carbon and Acidification Data Portal. Resulting data files containing combined summer-time bottom pH and aragonite saturation state data from vessel-based (and glider-based, see above) measurements can be found here.
+For the 2023 glider data cross-section plots (2023 seasonal progression of oceanographic properties on the New Jersey coastal shelf and plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023): Full-resolution delayed-mode glider datasets from six deployments (Spring-Fall 2023) were downloaded from the RUCOOL Glider ERDDAP server. These datasets were QC’d and pH was calculated from raw variables (where applicable) using python code, and the final processed NetCDF datasets containing all relevant metadata can be found here.
+For the summer 2007-2023 bottom-water pH and aragonite saturation state data synthesis (static and animated maps of summer-time bottom pH and aragonite saturation state on the U.S. Northeast Shelf [2007-2023]; maps of summer-time bottom locations where species sensitivity levels for aragonite saturation state were reached [2007-2023]): Full-resolution delayed-mode glider datasets from seven deployments were downloaded from the RUCOOL Glider ERDDAP server and the IOOS Glider DAC ERDDAP server (SBU01 2022-2023 deployments). These datasets were QC’d and pH was calculated from raw variables (where applicable) using python code, and the final processed NetCDF datasets containing all relevant metadata for each glider deployment used in the synthesis can be found here. Resulting data files containing combined summer-time bottom pH and aragonite saturation state data from glider-based (and vessel-based, see below) measurements can be found here. Vessel-based data were mined from the Coastal Ocean Data Analysis Product in North America, version v2021 (Jiang et al. 2021). This data product synthesizes two decades of quality-controlled inorganic carbon system parameters (including pH, total alkalinity, dissolved inorganic carbon) along with other physical and chemical parameters (temperature, salinity, dissolved oxygen, nutrients) collected from the North American continental shelves. Additionally, four recent vessel-based datasets that were not included in CODAP-NA (Jiang et al. 2021) were included in this synthesis. These datasets were collected during more recent NOAA NEFSC Ecosystem Monitoring (EcoMon) surveys: June 2019 (Cruise ID HB1902), August 2019 (Cruise ID GU1902), August 2021 (Cruise ID PC2104), June 2022 (Cruise ID HB2204). These datasets were downloaded via the NCEI Ocean Carbon and Acidification Data Portal. Resulting data files containing combined summer-time bottom pH and aragonite saturation state data from vessel-based (and glider-based, see above) measurements can be found here.
44.1.2 Data extraction
Glider data were processed and quality-controlled by software technician Lori Garzio at Rutgers University.
-CODAP-NA data were accessed and downloaded on October 14, 2021.
+CODAP-NA, Version 2021 data were accessed and downloaded on October 14, 2021.
EcoMon datasets were accessed and downloaded on October 13, 2022 (Cruise IDs HB1902, GU1902, PC2104) and November 17, 2023 (Cruise ID HB2204).
44.1.3 Data processing
-For processing and quality-control procedures of glider-based data, see Wright-Fairbanks et al. (2020). For the 2023 glider data cross-section plots (2023 seasonal progression of oceanographic properties on the New Jersey coastal shelf and plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023): datasets were QC’d and pH was calculated from raw variables (where applicable) using python code, and the final processed NetCDF datasets containing all relevant metadata can be found here.
+For processing and quality-control procedures of glider-based data, see Wright-Fairbanks et al. (2020). For the 2023 glider data cross-section plots (2023 seasonal progression of oceanographic properties on the New Jersey coastal shelf and plots summarizing a multi-stressor event in the Mid-Atlantic during summer 2023): datasets were QC’d and pH was calculated from raw variables (where applicable) using python code, and the final processed NetCDF datasets containing all relevant metadata can be found here.
Data from CODAP-NA were filtered temporally to include only those collected during summer months (June-August) and were spatially limited to the U.S. Northeast Shelf. The resulting datasets included those from major vessel-based campaigns (East Coast Ocean Acidification, ECOA I and II cruises 2015 and 2018; The Gulf of Mexico and East Coast Carbon cruises, GOMECC 2007 and 2012; EcoMon 2012-2013, 2015-2019).
For vessel-based datasets, when ΩArag was unavailable it was calculated using PyCO2SYS (Humphreys et al. 2020) with inputs of pressure, temperature, salinity, total alkalinity, and pH.
For MAB glider datasets, total alkalinity was calculated from salinity using a linear relationship determined from in situ water sampling data taken during glider deployment and recovery in addition to ship-based water samples (Wright-Fairbanks et al. 2020). For the Gulf of Maine glider dataset, total alkalinity was calculated from temperature and salinity using Table 3 Equation IV in McGarry et al. (2021). Calculations for ΩArag were then conducted using PyCO2SYS (Humphreys et al. 2020) with inputs of pressure, temperature, salinity, total alkalinity, and pH.
-Bottom water values were defined as the median of the measurements (or calculated ΩArag values) within the deepest 1m of a glider profile or, for vessel-based measurements, the deepest measurement of a vertical CTD/Rosette cast where water samples were collected, for profiles deeper than 10m. In order to validate whether the deepest depth was at or near the bottom, the sampling depth was compared to water column depth (when provided) or water depths extracted from a GEBCO bathymetry grid based on the sample collection coordinates. Any glider profiles/vessel-based casts with the deepest measurement shallower than the bottom 20% of total water column depth were removed. This allowed for a sliding scale instead of providing a strict cut off (e.g., 1 m above the bottom). Resulting data files containing combined summer-time bottom pH and aragonite saturation state data from glider- and vessel-based measurements can be found here.
+Bottom water values were defined as the median of the measurements (or calculated ΩArag values) within the deepest 1m of a glider profile or, for vessel-based measurements, the deepest measurement of a vertical CTD/Rosette cast where water samples were collected, for profiles deeper than 10m. In order to validate whether the deepest depth was at or near the bottom, the sampling depth was compared to water column depth (when provided) or water depths extracted from a GEBCO bathymetry grid based on the sample collection coordinates. Any glider profiles/vessel-based casts with the deepest measurement shallower than the bottom 20% of total water column depth were removed. This allowed for a sliding scale instead of providing a strict cut off (e.g., 1 m above the bottom). Resulting data files containing combined summer-time bottom pH and aragonite saturation state data from glider- and vessel-based measurements can be found here.
44.1.4 Plotting
+A set of plots was constructed for the 2024 State of the Ecosystem reports:
-
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+
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-
Code for data manipulation and plotting can be found here: https://github.com/lgarzio/cinar-soe.
---
--Figure 44.1: Left panel: Bottom aragonite saturation state (\(\Omega_{Arag}\); summer only: June-August) on the U.S. Northeast Shelf based on quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Locations where summer bottom \(\Omega_{Arag}\) were at or below the laboratory-derived sensitivity level for Atlantic sea scallop (top panel) and longfin squid (bottom). Gray circles indicate locations where carbonate chemistry samples were collected, but bottom \(\Omega_{Arag}\) values were higher than sensitivity values determined for that species. -
---
--Figure 44.2: Left panel: Bottom aragonite saturation state (\(\Omega_{Arag}\); summer only: June-August) on the U.S. Northeast Shelf based on quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Locations where summer bottom \(\Omega_{Arag}\) were at or below the laboratory-derived sensitivity level for Atlantic cod (top panel) and American lobster (bottom). The Atlantic cod sensitivity value of \(\Omega_{Arag}\) ≤ 1.31 is based on decreased larval survival observed at this level in Stiasny et al. (2016). The American lobster sensitivity value of \(\Omega_{Arag}\) ≤ 1.09 is based on decreased stage V and VI juvenile survival observed at this level in Noisette et al. (2021). Gray circles indicate locations where carbonate chemistry samples were collected, but bottom \(\Omega_{Arag}\) values were higher than sensitivity values determined for that species. -
-catalog link -https://noaa-edab.github.io/catalog/ocean_acidification.html
+Data processing code can be found on Github here, and all data files use in these analyses and syntheses can be found here.
+catalog link https://noaa-edab.github.io/catalog/ocean_acidification.html
- 31.1 Methods
- 11.1.4 Data processing
14.1.1 Data Sources14.1.2 Data Analysis
14.1.2.1 Cold Pool Domain
-The first step was to define the Cold Pool domain, which is typically located within the MAB and the southern flank of Georges Bank (Chen et al. (2018); Robert W. Houghton et al. (1982); Lentz (2017a)). Here, we delineated a spatial domain covering the management area of the SNEMA yellowtail flounder (since this method was initially developed to study the Cold Pool impact on yellowtail flounder recruitment) comprising the MAB and in the SNE shelf between the 20 and 200 m isobaths (Chen et al. (2018); Chen and Curchitser (2020)). We restricted the time period from June (to match the start of the settlement period; SULLIVAN, COWEN, and STEVES (2005)) to September (which is the average end date of the Cold Pool (calendar day 269) estimated by Chen and Curchitser (2020). The Cold Pool domain was defined as the area, wherein average bottom temperature was cooler than 10°C between June and September from 1959 to 2022. We then developed the three Cold Pool indices using bottom temperature from ocean models.
+The first step was to define the Cold Pool domain, which is typically located within the MAB and the southern flank of Georges Bank (Chen et al. (2018b); Robert W. Houghton et al. (1982); Lentz (2017a)). Here, we delineated a spatial domain covering the management area of the SNEMA yellowtail flounder (since this method was initially developed to study the Cold Pool impact on yellowtail flounder recruitment) comprising the MAB and in the SNE shelf between the 20 and 200 m isobaths (Chen et al. (2018b); Chen and Curchitser (2020)). We restricted the time period from June (to match the start of the settlement period; SULLIVAN, COWEN, and STEVES (2005)) to September (which is the average end date of the Cold Pool (calendar day 269) estimated by Chen and Curchitser (2020). The Cold Pool domain was defined as the area, wherein average bottom temperature was cooler than 10°C between June and September from 1959 to 2022. We then developed the three Cold Pool indices using bottom temperature from ocean models.
@@ -976,7 +977,7 @@14.1.2.2 Cold Pool Index (Model_CPI)
@@ -957,7 +958,7 @@14.1.2.4 Spatial Extent Index (Mo
The spatial component of the Cold Pool and the habitat provided by the cold pool was calculated using the Spatial Extent Index (Model_SEI). The Model_SEI is estimated by the number of cells where bottom temperature remains below 10C for at least 2 months between June and September.
The Bottom temperature data is the average ROMS-NWA bottom temperature over the decade \[d\] in the grid cell \[i\]. All above methods duPontavice et al. (2022).
Bottom temperature from Glorys reanalysis and Global Ocean Physics Analysis were not being processed.
-Bottom temperature from ROMS-NWA (used for the period 1959-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5C during the stratified seasons over the period of 1958-2007 (Chen et al. (2018); Chen and Curchitser (2020)). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov, Baranova, Johnson, et al. (2016), Seidov, Baranova, Boyer, et al. (2016)) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994) in the MAB and in the SNE shelf:
+Bottom temperature from ROMS-NWA (used for the period 1959-1992) were bias-corrected. Previous studies that focused on the ROMS-NWA-based Cold Pool highlighted strong and consistent warm bias in bottom temperature of about 1.5C during the stratified seasons over the period of 1958-2007 (Chen et al. (2018b); Chen and Curchitser (2020)). In order to bias-correct bottom temperature from ROMS-NWA, we used the monthly climatologies of observed bottom temperature from the Northwest Atlantic Ocean regional climatology (NWARC) over decadal periods from 1955 to 1994. The NWARC provides high resolution (1/10° grids) of quality-controlled in situ ocean temperature based on a large volume of observed temperature data (Seidov, Baranova, Johnson, et al. (2016), Seidov, Baranova, Boyer, et al. (2016)) (https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology). The first step was to re-grid the ROMS-NWA to obtain bottom temperature over the same 1/10° grid as the NWARC. Then, a monthly bias was calculated in each grid cell and for each decade (1955–1964, 1965–1974, 1975–1984, 1985–1994) in the MAB and in the SNE shelf:
\[{BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}\]
where \[T_{i,d}^{Climatology}\] is the NWARC bottom temperature in the grid cell i for the decade d and \[{\bar{T}}_{i,\ d}^{ROMS-NWA}\] is the average ROMS-NWA bottom temperature over the decade d in the grid cell i. All above methods duPontavice et al. (2022).
14.2.1 Data Sources
14.2.2 Data Analysis
-Depth-averaged spatial temperature is calculated based on the daily Cold Pool dataset, which is quantified following Chen et al. (2018).
+Depth-averaged spatial temperature is calculated based on the daily Cold Pool dataset, which is quantified following Chen et al. (2018b).
14.2.3 Data processing
@@ -1012,7 +1013,7 @@References“Interannual Variability of the Mid-Atlantic Bight Cold Pool.” Journal of Geophysical Research: Oceans 125 (8): e2020JC016445. https://doi.org/https://doi.org/10.1029/2020JC016445.
-Chen, Zhuomin, Enrique Curchitser, Robert Chant, and Dujuan Kang. 2018. “Seasonal Variability of the Cold Pool over the Mid-Atlantic Bight Continental Shelf.” Journal of Geophysical Research: Oceans 123 (11): 8203–26. https://doi.org/https://doi.org/10.1029/2018JC014148. +Chen, Zhuomin, Enrique Curchitser, Robert Chant, and Dujuan Kang. 2018b. “Seasonal Variability of the Cold Pool over the Mid-Atlantic Bight Continental Shelf.” Journal of Geophysical Research: Oceans 123 (11): 8203–26. https://doi.org/https://doi.org/10.1029/2018JC014148.duPontavice, Hubert, Timothy J Miller, Brian C Stock, Zhuomin Chen, and Vincent S Saba. 2022. “Ocean model-based covariates improve a marine fish stock assessment when observations are limited.” ICES Journal of Marine Science 79 (4): 1259–73. https://doi.org/10.1093/icesjms/fsac050. diff --git a/comdat.html b/comdat.html index ac99a937..c0ed3afe 100644 --- a/comdat.html +++ b/comdat.html @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@-32.1.4 Data Processing
diff --git a/harborporpoise.html b/harborporpoise.html index 04c1c150..c16f54b1 100644 --- a/harborporpoise.html +++ b/harborporpoise.html @@ -4,18 +4,18 @@ -31 Harbor Porpoise and Gray Seal Bycatch | Technical Documentation, State of the Ecosystem Report +31 Harbor Porpoise Bycatch | Technical Documentation, State of the Ecosystem Report - + - + @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@References
--diff --git a/persistent_hotspots.html b/persistent_hotspots.html index fdcc6bed..48b9e32f 100644 --- a/persistent_hotspots.html +++ b/persistent_hotspots.html @@ -23,7 +23,7 @@ - + @@ -54,9 +54,10 @@ + - + @@ -278,13 +279,13 @@-Cameron, Louise P., Jonathan H. Grabowski, and Justin B. Ries. 2022. “Effects of Elevated pCO2 and Temperature on the Calcification Rate, Survival, Extrapallial Fluid Chemistry, and Respiration of the Atlantic Sea Scallop Placopecten Magellanicus.” Limnology and Oceanography 67 (8): 1670–86. https://doi.org/https://doi.org/10.1002/lno.12153. ---DeCelles, Gregory R., David Martins, Douglas R. Zemeckis, and Steven X. Cadrin. 2017. “Using Fishermen’s Ecological Knowledge to Map Atlantic Cod Spawning Grounds on Georges Bank.” ICES Journal of Marine Science 74 (6): 1587–1601. https://doi.org/10.1093/icesjms/fsx031. ---Jacobson, Lawrence D. 2005. “Essential Fish Habitat Source Document. Longfin Inshore Squid, Loligo Pealeii, Life History and Habitat Characteristics.” Northeast Fisheries Science Center (U.S.), no. NOAA technical memorandum NMFS-NE ; 193. https://repository.library.noaa.gov/view/noaa/4035. ---Lough, R. Gregory. 2004. “Essential Fish Habitat Source Document. Atlantic Cod, Gadus Morhua, Life History and Habitat Characteristics.” Northeast Fisheries Science Center (U.S.), no. NOAA technical memorandum NMFS-NE ; 190. https://repository.library.noaa.gov/view/noaa/4032. ---Mercaldo-Allen, Catherine A., Renee;Kuropat. 1994. “Review of American Lobster (Homarus Americanus) Habitat Requirements and Responses to Contaminant Exposures.” Northeast Fisheries Science Center (U.S.), no. NOAA technical memorandum NMFS-NE ; 105. https://repository.library.noaa.gov/view/noaa/6222. ---Noisette, Fanny, Piero Calosi, Diana Madeira, Mathilde Chemel, Kayla Menu-Courey, Sarah Piedalue, Helen Gurney-Smith, Dounia Daoud, and Kumiko Azetsu-Scott. 2021. “Tolerant Larvae and Sensitive Juveniles: Integrating Metabolomics and Whole-Organism Responses to Define Life-Stage Specific Sensitivity to Ocean Acidification in the American Lobster.” Metabolites 11 (9): 584. https://doi.org/10.3390/metabo11090584. ---Stiasny, Felix H. AND Sswat, Martina H. AND Mittermayer. 2016. “Ocean Acidification Effects on Atlantic Cod Larval Survival and Recruitment to the Fished Population.” PLOS ONE 11 (8): 1–11. https://doi.org/10.1371/journal.pone.0155448. ---Zakroff, Casey J., and T. Aran Mooney. 2020. “Antagonistic Interactions and Clutch-Dependent Sensitivity Induce Variable Responses to Ocean Acidification and Warming in Squid (*Doryteuthis Pealeii*) Embryos and Paralarvae.” Frontiers in Physiology 11. https://doi.org/10.3389/fphys.2020.00501. ---Zakroff, T. Aran; Berumen, Casey; Mooney. 2019. “Dose-Dependence and Small-Scale Variability in Responses to Ocean Acidification During Squid, Doryteuthis Pealeii, Development.” Marine Biology 166. https://doi.org/10.1007/s00227-019-3510-8. -
