From d7173d6aebf98c8f10f8c291632531cf7b5f18d7 Mon Sep 17 00:00:00 2001 From: kimberly-bastille Date: Mon, 28 Mar 2022 18:47:59 -0400 Subject: [PATCH] forever editting for the PDF build --- bibliography/cold_pool_index.bib | 91 ++++++++- bibliography/habs.bib | 89 +++++++++ bibliography/ocean_acidification.bib | 58 ++++++ bibliography/sandlance.bib | 12 ++ chapters/Annual_SST_cycle_indicator.Rmd | 74 +++---- chapters/Aquaculture_indicators.Rmd | 3 - chapters/Bennet_indicator.Rmd | 2 +- .../Catch_and_Fleet_Diversity_indicators.Rmd | 3 +- chapters/Comm_rel_vuln_indicator.Rmd | 4 +- chapters/Phyto_size_class.Rmd | 4 - chapters/Species_density_estimates.Rmd | 2 +- chapters/Thermal_hab_proj_indicator.Rmd | 20 +- chapters/aggregate_groups.rmd | 4 +- chapters/ches_bay_sst.Rmd | 8 +- chapters/ches_bay_water_quality.Rmd | 2 +- chapters/cold_pool_index.Rmd | 22 +-- chapters/ecosystem_overfishing.Rmd | 5 +- chapters/habs_alexandrium.Rmd | 27 +-- chapters/habs_psp.Rmd | 13 +- chapters/hms_cpue.Rmd | 2 +- chapters/hms_stock_status.Rmd | 5 +- chapters/hudson_river_flow.Rmd | 7 +- chapters/occupancy_indicator.Rmd | 180 +++++++++--------- chapters/ocean_acidification.Rmd | 25 +-- chapters/plankton_diversity.Rmd | 1 + chapters/sandlance.Rmd | 7 +- chapters/survey_data.rmd | 17 +- index.Rmd | 2 +- 28 files changed, 434 insertions(+), 255 deletions(-) create mode 100644 bibliography/habs.bib create mode 100644 bibliography/sandlance.bib diff --git a/bibliography/cold_pool_index.bib b/bibliography/cold_pool_index.bib index c723f9f9..4bf2869b 100644 --- a/bibliography/cold_pool_index.bib +++ b/bibliography/cold_pool_index.bib @@ -23,4 +23,93 @@ @article{chen2018 eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018JC014148}, abstract = {Abstract The Mid-Atlantic Bight (MAB) Cold Pool is a distinctive cold (lower than 10°C) and relatively fresh (lower than 34 practical salinity unit) water mass. It is located over the middle and outer shelf of the MAB, below the seasonal thermocline, and is attached to the bottom. Following this definition, we put forward a method that includes three criteria to capture and quantify Cold Pool characteristics, based on a 50-year (1958–2007) high-resolution regional ocean model hindcast. The seasonal climatology of the Cold Pool and its properties are investigated during its onset-peak-decline cycle. Three stages of the Cold Pool event are defined according to its evolution and characteristics. The Cold Pool cores travel along the 60-m isobath starting south of the New England shelf to the Hudson Shelf Valley at a speed of 2–3cm/s. Furthermore, the northern extent of the Cold Pool retreats about 2.6 times faster than the southern extent during the summer progression. The heat balance of near-bottom waters over the MAB and Georges Bank is computed and it is found that the heat advection, rather than vertical diffusion, dominates the resulting spatial patterns of warming. Possible origins of the Cold Pool are investigated by performing a lead-lag correlation analysis. Results suggest that the Cold Pool originates not only from local remnants of winter water near the Nantucket Shoals, but has an upstream source traveling in the spring time from the southwestern flank of the Georges Bank along the 80-m isobath.}, year = {2018} -} \ No newline at end of file +} + +@article{Chen2018, +title = "Seasonal Variability of the Cold Pool Over the Mid-Atlantic Bight Continental Shelf", +keywords = "coastal dynamics, cold pool, continental shelf, seasonal variability", +author = "Zhuomin Chen and Enrique Curchitser and Robert Chant and Dujuan Kang", +year = "2018", +month = nov, +doi = "https://doi.org/10.1029/2018JC014148", +language = "English (US)", +volume = "123", +pages = "8203--8226", +journal = "Journal of Geophysical Research: Oceans", +issn = "0148-0227", +publisher = "Wiley-Blackwell", +number = "11", +} + +@article{Chen2020, +author = {Chen, Zhuomin and Curchitser, Enrique N.}, +title = {Interannual Variability of the Mid-Atlantic Bight Cold Pool}, +journal = {Journal of Geophysical Research: Oceans}, +volume = {125}, +number = {8}, +pages = {e2020JC016445}, +keywords = {Mid-Atlantic Bight, Cold Pool, continental shelf, temperature balance, interannual variability, near-bottom temperature}, +doi = {https://doi.org/10.1029/2020JC016445}, +url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JC016445}, +eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020JC016445}, +note = {e2020JC016445 2020JC016445}, +year = {2020} +} + + + +@Article{Lellouche2018, +AUTHOR = {Lellouche, J.-M. and Greiner, E. and Le Galloudec, O. and Garric, G. and Regnier, C. and Drevillon, M. and Benkiran, M. and Testut, C.-E. and Bourdalle-Badie, R. and Gasparin, F. and Hernandez, O. and Levier, B. and Drillet, Y. and Remy, E. and Le Traon, P.-Y.}, +TITLE = {Recent updates to the Copernicus Marine Service global ocean monitoring and +forecasting real-time 1/12 degree high-resolution system}, +JOURNAL = {Ocean Science}, +VOLUME = {14}, +YEAR = {2018}, +NUMBER = {5}, +PAGES = {1093--1126}, +URL = {https://os.copernicus.org/articles/14/1093/2018/}, +DOI = {10.5194/os-14-1093-2018} +} + + +@misc{Seidov2016a, + doi = {10.7289/V5RF5S2Q}, + url = {https://www.ncei.noaa.gov/archive/accession/0155889}, + author = {Seidov, Dan and Baranova, Olga K. and Johnson, Daphne R. and Boyer, Tim P. and Mishonov, Alexey V. and Parsons, A. Rost}, + title = {Northwest Atlantic Regional Climatology (NCEI Accession 0155889)}, + publisher = {NOAA National Centers for Environmental Information}, + year = {2016} +} + +@misc{Seidov2016b, + doi = {10.7289/V5/ATLAS-NESDIS-80}, + url = {https://repository.library.noaa.gov/view/noaa/12209}, + author = {Seidov, Dan and Baranova, Olga K. and Boyer, Tim P. and Cross, Scott L. and Mishonov, Alexey V. and Parsons, A. Rost}, + title = {Northwest Atlantic regional ocean climatology}, + publisher = {NOAA National Centers for Environmental Information}, + year = {2016} +} + + +@article{Shchepetkin2005, +title = {The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model}, +journal = {Ocean Modelling}, +volume = {9}, +number = {4}, +pages = {347-404}, +year = {2005}, +issn = {1463-5003}, +doi = {https://doi.org/10.1016/j.ocemod.2004.08.002}, +url = {https://www.sciencedirect.com/science/article/pii/S1463500304000484}, +author = {Alexander F. Shchepetkin and James C. McWilliams} +} + + +@article{Fernandez2018, +title = {Product user manual for the global ocean physical reanalysis product GLORYS12V1}, +journal = {Copernicus Product User Manual}, +volume = {4}, +pages = {1-15}, +year = {2018}, +author = {E. Fernandez, and Lellouche, J. M.} +} diff --git a/bibliography/habs.bib b/bibliography/habs.bib new file mode 100644 index 00000000..33d2d876 --- /dev/null +++ b/bibliography/habs.bib @@ -0,0 +1,89 @@ +@article{Anderson1997, +author = {Anderson, Donald M.}, +title = {Bloom dynamics of toxic Alexandrium species in the northeastern U.S}, +journal = {Limnology and Oceanography}, +volume = {42}, +number = {5part2}, +pages = {1009-1022}, +doi = {https://doi.org/10.4319/lo.1997.42.5\_part\_2.1009}, +url = {https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.4319/lo.1997.42.5_part_2.1009}, +eprint = {https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lo.1997.42.5_part_2.1009}, +year = {1997} +} + +@article{Anderson2005, +title = {Identification and enumeration of Alexandrium spp. from the Gulf of Maine using molecular probes}, +journal = {Deep Sea Research Part II: Topical Studies in Oceanography}, +volume = {52}, +number = {19}, +pages = {2467-2490}, +year = {2005}, +note = {The Ecology and Oceanography of Toxic Alexandrium fundyense Blooms in the Gulf of Maine}, +issn = {0967-0645}, +doi = {https://doi.org/10.1016/j.dsr2.2005.06.015}, +url = {https://www.sciencedirect.com/science/article/pii/S0967064505001621}, +author = {Donald M. Anderson and David M. Kulis and Bruce A. Keafer and Kristin E. Gribble and Roman Marin and Christopher A. Scholin} +} + + + +@article{Li2009, +title = {Investigation of the 2006 Alexandrium fundyense bloom in the Gulf of Maine: In-situ observations and numerical modeling}, +journal = {Continental Shelf Research}, +volume = {29}, +number = {17}, +pages = {2069-2082}, +year = {2009}, +issn = {0278-4343}, +doi = {https://doi.org/10.1016/j.csr.2009.07.012}, +url = {https://www.sciencedirect.com/science/article/pii/S0278434309002301}, +author = {Yizhen Li and Ruoying He and Dennis J. McGillicuddy and Donald M. Anderson and Bruce A. Keafer}, +keywords = {Harmful algal bloom, Coastal circulation, Gulf of Maine, Bio-physical interaction} +} + +@article{Li2020, +title = {Dynamics of an intense Alexandrium catenella red tide in the Gulf of Maine: satellite observations and numerical modeling}, +journal = {Harmful Algae}, +volume = {99}, +pages = {101927}, +year = {2020}, +issn = {1568-9883}, +doi = {https://doi.org/10.1016/j.hal.2020.101927}, +url = {https://www.sciencedirect.com/science/article/pii/S1568988320302067}, +author = {Yizhen Li and Richard P. Stumpf and D.J. McGillicuddy and Ruoying He} +} + + +@article{McGillicuddy2011, +author = {McGillicuddy, D. J., Jr. and Townsend, D. W. and He, R. and Keafer, B. A. and Kleindinst, J. L. and Li, Y. and Manning, J. P. and Mountain, D. G. and Thomas, M. A. and Anderson, D. M.}, +title = {Suppression of the 2010 Alexandrium fundyense bloom by changes in physical, biological, and chemical properties of the Gulf of Maine}, +journal = {Limnology and Oceanography}, +volume = {56}, +number = {6}, +pages = {2411-2426}, +doi = {https://doi.org/10.4319/lo.2011.56.6.2411}, +url = {https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.4319/lo.2011.56.6.2411}, +eprint = {https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2011.56.6.2411}, +year = {2011} +} + + +@inproceedings{Chung2010, + title={Measuring paralytic shellfish toxins in mussels from New Hampshire coastal waters using zwitterionic hydrophilic liquid chromatography/electrospray mass spectrometry}, + author={Lee Lee Chung}, + year={2010}, + url={https://scholars.unh.edu/thesis/539} +} + +@article{Kleindinst2014, + author = {Kleindinst, Judith L. and Anderson, Donald M. and McGillicuddy, Dennis J. and Stumpf, Richard P. and Fisher, Kathleen M. and Couture, Darcie A. and Michael Hickey, J. and Nash, Christopher}, + title = {Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management}, + journal = {Deep Sea Research Part II: Topical Studies in Oceanography}, + year = 2014, + month = may, + volume = {103}, + pages = {277-287}, + doi = {10.1016/j.dsr2.2013.03.027}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2014DSRII.103..277K}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} \ No newline at end of file diff --git a/bibliography/ocean_acidification.bib b/bibliography/ocean_acidification.bib index 2b2191fa..c4f2a383 100644 --- a/bibliography/ocean_acidification.bib +++ b/bibliography/ocean_acidification.bib @@ -12,4 +12,62 @@ @article{Wright2020 note = {e2020JC016505 2020JC016505}, abstract = {Abstract Ocean acidification alters the oceanic carbonate system, increasing potential for ecological, economic, and cultural losses. Historically, productive coastal oceans lack vertically resolved high-resolution carbonate system measurements on time scales relevant to organism ecology and life history. The recent development of a deep ion-sensitive field-effect transistor (ISFET)-based pH sensor system integrated into a Slocum glider has provided a platform for achieving high-resolution carbonate system profiles. From May 2018 to November 2019, seasonal deployments of the pH glider were conducted in the central Mid-Atlantic Bight. Simultaneous measurements from the glider's pH and salinity sensors enabled the derivation of total alkalinity and calculation of other carbonate system parameters including aragonite saturation state. Carbonate system parameters were then mapped against other variables, such as temperature, dissolved oxygen, and chlorophyll, over space and time. The seasonal dynamics of carbonate chemistry presented here provide a baseline to begin identifying drivers of acidification in this vital economic zone.}, year = {2020} +} + + + + +@article{Humphreys2022, +AUTHOR = {Humphreys, M. P. and Lewis, E. R. and Sharp, J. D. and Pierrot, D.}, +TITLE = {PyCO2SYS v1.8: marine carbonate system calculations in Python}, +JOURNAL = {Geoscientific Model Development}, +VOLUME = {15}, +YEAR = {2022}, +NUMBER = {1}, +PAGES = {15--43}, +URL = {https://gmd.copernicus.org/articles/15/15/2022/}, +DOI = {10.5194/gmd-15-15-2022} +} + +@article{Jiang2021, +AUTHOR = {Jiang, L.-Q. and Feely, R. A. and Wanninkhof, R. and Greeley, D. and Barbero, L. and Alin, S. and Carter, B. R. and Pierrot, D. and Featherstone, C. and Hooper, J. and Melrose, C. and Monacci, N. and Sharp, J. D. and Shellito, S. and Xu, Y.-Y. and Kozyr, A. and Byrne, R. H. and Cai, W.-J. and Cross, J. and Johnson, G. C. and Hales, B. and Langdon, C. and Mathis, J. and Salisbury, J. and Townsend, D. W.}, +TITLE = {Coastal Ocean Data Analysis Product in North America (CODAP-NA) +-- an internally consistent data product for discrete inorganic carbon, +oxygen, and nutrients on the North American ocean margins}, +JOURNAL = {Earth System Science Data}, +VOLUME = {13}, +YEAR = {2021}, +NUMBER = {6}, +PAGES = {2777--2799}, +URL = {https://essd.copernicus.org/articles/13/2777/2021/}, +DOI = {10.5194/essd-13-2777-2021} +} + + +@article{Saba2019, +AUTHOR={Saba, Grace K. and Wright-Fairbanks, Elizabeth and Chen, Baoshan and Cai, Wei-Jun and Barnard, Andrew H. and Jones, Clayton P. and Branham, Charles W. and Wang, Kui and Miles, Travis}, +TITLE={The Development and Validation of a Profiling Glider Deep ISFET-Based pH Sensor for High Resolution Observations of Coastal and Ocean Acidification}, +JOURNAL={Frontiers in Marine Science}, +VOLUME={6}, +YEAR={2019}, +URL={https://www.frontiersin.org/article/10.3389/fmars.2019.00664}, +DOI={10.3389/fmars.2019.00664}, +ISSN={2296-7745} +} + + + +@article{Wright2020, +author = {Wright-Fairbanks, Elizabeth K. and Miles, Travis N. and Cai, Wei-Jun and Chen, Baoshan and Saba, Grace K.}, +title = {Autonomous Observation of Seasonal Carbonate Chemistry Dynamics in the Mid-Atlantic Bight}, +journal = {Journal of Geophysical Research: Oceans}, +volume = {125}, +number = {11}, +pages = {e2020JC016505}, +keywords = {glider, ocean acidification, Mid-Atlantic Bight, carbonate system, autonomous underwater vehicle}, +doi = {https://doi.org/10.1029/2020JC016505}, +url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JC016505}, +eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020JC016505}, +note = {e2020JC016505 2020JC016505}, +year = {2020} } \ No newline at end of file diff --git a/bibliography/sandlance.bib b/bibliography/sandlance.bib new file mode 100644 index 00000000..97f9ff8f --- /dev/null +++ b/bibliography/sandlance.bib @@ -0,0 +1,12 @@ +@article{Silva2020, + doi = {10.1111/csp2.274}, + url = {https://doi.org/10.1111%2Fcsp2.274}, + year = 2020, + month = {oct}, + publisher = {Wiley}, + volume = {3}, + number = {2}, + author = {Tammy L. Silva and David N. Wiley and Michael A. Thompson and Peter Hong and Les Kaufman and Justin J. Suca and Joel K. Llopiz and Hannes Baumann and Gavin Fay}, + title = {High collocation of sand lance and protected top predators: Implications for conservation and management}, + journal = {Conservation Science and Practice} +} \ No newline at end of file diff --git a/chapters/Annual_SST_cycle_indicator.Rmd b/chapters/Annual_SST_cycle_indicator.Rmd index 58966032..71845fbb 100644 --- a/chapters/Annual_SST_cycle_indicator.Rmd +++ b/chapters/Annual_SST_cycle_indicator.Rmd @@ -1,38 +1,38 @@ -# Annual SST Cycles - -**Description**: Annual SST Cycles - -**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) - -**Indicator category**: Database pull with analysis - -**Contributor(s)**: Sean Hardison, Vincent Saba - -**Data steward**: Kimberly Bastille, - -**Point of contact**: Kimberly Bastille, - -**Public availability statement**: Source data are available [here](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html). - -## Methods - -### Data sources -Data for annual sea surface tempature (SST) cycles were derived from the NOAA optimum interpolation sea surface temperature (OISST) high resolution dataset ([NOAA OISST V2 dataset](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html)) provided by NOAA's Earth System Research Laboratory's Physical Sciences Devision, Boulder, CO. The data extend from 1981 to present, and provide a 0.25° x 0.25° global grid of SST measurements [@Reynolds2007]. Gridded SST data were masked according to the extent of Ecological Production Units (EPU) in the Northeast Large Marine Ecosystem (NE-LME) (See ["EPU_Extended" shapefiles](https://github.com/NOAA-EDAB/tech-doc/tree/master/gis)). - - -### Data extraction -Daily mean sea surface temperature data for 2017 and for each year during the period of 1981-2012 were downloaded from the NOAA [OI SST V2 site](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html) to derive the long-term climatological mean for the period. The use of a 30-year climatological reference period is a standard procedure for metereological observing [@WMO2017]. These reference periods serve as benchmarks for comparing current or recent observations, and for the development of standard anomaly data sets. The reference period of 1982-2012 was chosen to be consistent with previous versions of the State of the Ecosystem report. - -R code used in extraction and processing can be found [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/annual_sst_cycles_extraction_and_processing.R) - - -### Data analysis -We calculated the long-term mean and standard deviation of SST over the period of 1982-2012 for each EPU, as well as the daily mean for 2017. - -R code used for analysis and plotting can be found [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/annual_sst_cycles_analysis_and_plotting.R) - -```{r , fig.width=5, fig.asp = 0.45, fig.cap = "Long-term mean SSTs for the Mid-Atlantic Bight (A), Georges Bank (B), and Gulf of Maine (C). Orange and cyan shading show where the 2017 daily SST values were above or below the long-term mean respectively; red and dark blue shades indicate days when the 2017 mean exceeded +/- 1 standard deviation from the long-term mean.", echo = F, fig.align="center", eval=T } - -knitr::include_graphics(file.path(image.dir, "annual_SST_cycle_plot.png")) - +# Annual SST Cycles + +**Description**: Annual SST Cycles + +**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) + +**Indicator category**: Database pull with analysis + +**Contributor(s)**: Sean Hardison, Vincent Saba + +**Data steward**: Kimberly Bastille, + +**Point of contact**: Kimberly Bastille, + +**Public availability statement**: Source data are available [here](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html). + +## Methods + +### Data sources +Data for annual sea surface tempature (SST) cycles were derived from the NOAA optimum interpolation sea surface temperature (OISST) high resolution dataset ([NOAA OISST V2 dataset](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html)) provided by NOAA's Earth System Research Laboratory's Physical Sciences Devision, Boulder, CO. The data extend from 1981 to present, and provide a 0.25° x 0.25° global grid of SST measurements [@Reynolds2007]. Gridded SST data were masked according to the extent of Ecological Production Units (EPU) in the Northeast Large Marine Ecosystem (NE-LME) (See ["EPU_Extended" shapefiles](https://github.com/NOAA-EDAB/tech-doc/tree/master/gis)). + + +### Data extraction +Daily mean sea surface temperature data for 2017 and for each year during the period of 1981-2012 were downloaded from the NOAA [OI SST V2 site](https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html) to derive the long-term climatological mean for the period. The use of a 30-year climatological reference period is a standard procedure for metereological observing [@WMO2017]. These reference periods serve as benchmarks for comparing current or recent observations, and for the development of standard anomaly data sets. The reference period of 1982-2012 was chosen to be consistent with previous versions of the State of the Ecosystem report. + +R code used in extraction and processing can be found [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/annual_sst_cycles_extraction_and_processing.R) + + +### Data analysis +We calculated the long-term mean and standard deviation of SST over the period of 1982-2012 for each EPU, as well as the daily mean for 2017. + +R code used for analysis and plotting can be found [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/annual_sst_cycles_analysis_and_plotting.R) + +```{r , out.width="80%", fig.asp = 0.45, fig.cap = "Long-term mean SSTs for the Mid-Atlantic Bight (A), Georges Bank (B), and Gulf of Maine (C). Orange and cyan shading show where the 2017 daily SST values were above or below the long-term mean respectively; red and dark blue shades indicate days when the 2017 mean exceeded +/- 1 standard deviation from the long-term mean.", echo = F, fig.align="center", eval=T } + +knitr::include_graphics(file.path(image.dir, "annual_SST_cycle_plot.png")) + ``` \ No newline at end of file diff --git a/chapters/Aquaculture_indicators.Rmd b/chapters/Aquaculture_indicators.Rmd index 63477bbe..2a353ee6 100644 --- a/chapters/Aquaculture_indicators.Rmd +++ b/chapters/Aquaculture_indicators.Rmd @@ -62,6 +62,3 @@ Data were collected directly from state aquaculture reports. Oyster harvest data ### Data analysis No data analyses occurred for this indicator. - - - diff --git a/chapters/Bennet_indicator.Rmd b/chapters/Bennet_indicator.Rmd index 6a1975bd..0b5a8bc9 100644 --- a/chapters/Bennet_indicator.Rmd +++ b/chapters/Bennet_indicator.Rmd @@ -20,7 +20,7 @@ Data used in the Bennet Indicator were derived from the Comland data set; a processed subset of the Commercial Fisheries Database System (CFDBS). The derived Comland data set is available for download [here](https://comet.nefsc.noaa.gov/erddap/tabledap/group_landings_soe_v1.html). ### Data extraction -For information regarding processing of CFDBS, please see [Comland](#comdat) methods. The Comland dataset containing seafood landings data was subsetted to US landings after 1964 where revenue was $\ge$ 0 for each Ecological Production Unit (i.e. Mid-Atlantic Bight, Georges Bank, and Gulf of Maine). Each EPU was run in an individual R script, and the code specific to Georges Bank is shown [here](. +For information regarding processing of CFDBS, please see [Comland](#comdat) methods. The Comland dataset containing seafood landings data was subsetted to US landings after 1964 where revenue was $\ge$ 0 for each Ecological Production Unit (i.e. Mid-Atlantic Bight, Georges Bank, and Gulf of Maine). Each EPU was run in an individual R script, and the code specific to Georges Bank is shown [here](https://github.com/NOAA-EDAB/tech-doc/blob/master/R/stored_scripts/bennet_extraction.R). ### Data analysis diff --git a/chapters/Catch_and_Fleet_Diversity_indicators.Rmd b/chapters/Catch_and_Fleet_Diversity_indicators.Rmd index e0b8c75a..04214dca 100644 --- a/chapters/Catch_and_Fleet_Diversity_indicators.Rmd +++ b/chapters/Catch_and_Fleet_Diversity_indicators.Rmd @@ -38,7 +38,8 @@ spp <- spp %>% dplyr::select(Group, NESPP3, 'Common Name', 'Scientific Name') knitr::kable(spp, caption="Species grouping", booktabs=T, longtable = T) %>% - kableExtra::kable_styling(latex_options = c("repeat_header"), font_size = 8) %>% + kableExtra::kable_styling( + latex_options = c("repeat_header","scale_down"), font_size = 5) %>% kableExtra::collapse_rows(columns = 1) ``` diff --git a/chapters/Comm_rel_vuln_indicator.Rmd b/chapters/Comm_rel_vuln_indicator.Rmd index 1c17c961..61a138a6 100644 --- a/chapters/Comm_rel_vuln_indicator.Rmd +++ b/chapters/Comm_rel_vuln_indicator.Rmd @@ -45,7 +45,7 @@ Code used to build the community engagement indicator plot below can be found [h ```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-commercial-engagement.R"), eval = T, fig.cap= "Commercial engagement, reliance and environmental justice vulnerability for thetop commercial fishing communities in the Mid-Atlantic. (* Scored high (1.00 and above)) for both commercial engagement and reliance indicators)."} ``` -```{r , fig.cap = "Environmental justice indicators (Poverty index, population composition index, and personal disruption index) for top commercial fishing communities in the Mid-Atlantic.", fig.width=50} +```{r , fig.cap = "Environmental justice indicators (Poverty index, population composition index, and personal disruption index) for top commercial fishing communities in the Mid-Atlantic.", out.width="90%"} knitr::include_graphics(c(file.path(image.dir, "EJ_Commercial_MAB.png"))) @@ -56,7 +56,7 @@ knitr::include_graphics(c(file.path(image.dir, "EJ_Commercial_MAB.png"))) ```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-recreational-engagement.R"), eval = T, fig.cap= "Recreational engagement, reliance and environmental justice vulnerability for the top recreational fishing communities in the Mid-Atlantic. (* Scored high (1.00 and above)) for both recreational engagement and reliance indicators)."} ``` -```{r , fig.cap = "Environmental justice indicators (Poverty index, population composition index, and personal disruption index) for top recreational fishing communities in the Mid-Atlantic.", fig.width=50} +```{r , fig.cap = "Environmental justice indicators (Poverty index, population composition index, and personal disruption index) for top recreational fishing communities in the Mid-Atlantic.", out.width="90%"} knitr::include_graphics(c(file.path(image.dir, "EJ_Recreational_MAB.png"))) diff --git a/chapters/Phyto_size_class.Rmd b/chapters/Phyto_size_class.Rmd index 48d4e8d1..11ed120f 100644 --- a/chapters/Phyto_size_class.Rmd +++ b/chapters/Phyto_size_class.Rmd @@ -41,7 +41,3 @@ Code for plotting Georges Bank and Gulf of Maine bottom temperature time series ```{r, code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_MAB.Rmd-weekly-phyto-size.R"), eval=TRUE, echo = FALSE, fig.cap = "Mid-Atlantic phytoplankton size class."} ``` - -### Resources - - diff --git a/chapters/Species_density_estimates.Rmd b/chapters/Species_density_estimates.Rmd index 242f67ef..47908573 100644 --- a/chapters/Species_density_estimates.Rmd +++ b/chapters/Species_density_estimates.Rmd @@ -33,7 +33,7 @@ Code used for species density analysis can be found [here](https://github.com/NO ### Plotting -```{r kde-fig, fig.cap="Current and historical sea scallop kernel density estimates derived from spring survey data. Current estimates derived from 2016-2018 data.", echo = F, eval = T} +```{r kde-fig, fig.cap="Current and historical sea scallop kernel density estimates derived from spring survey data. Current estimates derived from 2016-2018 data.", echo = F, eval = T, out.width="90%"} knitr::include_graphics(file.path(image.dir, "species_density_estimate.png")) ``` diff --git a/chapters/Thermal_hab_proj_indicator.Rmd b/chapters/Thermal_hab_proj_indicator.Rmd index a59fc9af..70234242 100644 --- a/chapters/Thermal_hab_proj_indicator.Rmd +++ b/chapters/Thermal_hab_proj_indicator.Rmd @@ -1,13 +1,5 @@ # Thermal Habitat Projections - -```{r, echo = F, message=F} - -#Load packages -library(knitr) -library(rmarkdown) - -``` **Description**: Species Thermal Habitat Projections **Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) @@ -24,35 +16,39 @@ library(rmarkdown) ## Methods + This indicator is based on work reported in @Kleisner2017. ### Data sources #### Global Climate Model Projection + We used [National Oceanographic and Atmosheric Administration's Geophysical Fluid Dynamics Laboratory (NOAA GFDL) CM2.6 simulation](https://www.gfdl.noaa.gov/high-resolution-climate-modeling/) consisting of (1) a 1860 pre-industrial control, which brings the climate system into near-equilibrium with 1860 greenhouse gas concentrations, and (2) a transient climate response (2xCO2) simulation where atmospheric CO2 is increased by 1% per year, which results in a doubling of CO2 after 70 years. The climate change response from CM2.6 was based on the difference between these two experimental runs. Refer to @Saba2016 for further details. #### Modeling Changes in Suitable Thermal Habitat -The NOAA Northeast Fisheries Science Center, U.S. Northeast Shelf (NES) bottom trawl survey, which has been conducted for almost 50-years in the spring and fall, provides a rich source of data on historical and current marine species distribution, abundance, and habitat, as well as oceanographic conditions [@Azarovitz1981]. The survey was implemented to meet several objectives: (1) monitor trends in abundance, biomass, and recruitment, (2) monitor the geographic distribution of species, (3) monitor ecosystem changes, (4) monitor changes in life history traits (e.g., trends in growth, longevity, mortality, and maturation, and food habits), and (5) collect baseline oceanographic and environmental data. These data can be leveraged for exploring future changes in the patterns of abundance and distribution of species in the region. - - +The NOAA Northeast Fisheries Science Center, U.S. Northeast Shelf (NES) bottom trawl survey, which has been conducted for almost 50-years in the spring and fall, provides a rich source of data on historical and current marine species distribution, abundance, and habitat, as well as oceanographic conditions [@Azarovitz1981]. The survey was implemented to meet several objectives: (1) monitor trends in abundance, biomass, and recruitment, (2) monitor the geographic distribution of species, (3) monitor ecosystem changes, (4) monitor changes in life history traits (e.g., trends in growth, longevity, mortality, and maturation, and food habits), and (5) collect baseline oceanographic and environmental data. These data can be leveraged for exploring future changes in the patterns of abundance and distribution of species in the region. ### Data analysis + #### Global Climate Model Projection + The CM2.6 80-year projections can be roughly assigned to a time period by using the International Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs), which describe four different 21st century pathways of anthropogenic greenhouse gas emissions, air pollutant emissions, and land use [@IPCC2014]. There are four RCPs, ranging from a stringent mitigation scenario (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and one scenario with very high greenhouse gas emissions (RCP8.5). For RCP8.5, the global average temperature at the surface warms by 2C by approximately 2060-2070 relative to the 1986-2005 climatology (see Figure SPM.7a in [IPCC, 2013](https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_SPM_FINAL.pdf)). For CM2.6, the global average temperature warms by 2C by approximately years 60-80 (see Fig. 1 in @winton_has_2014). Therefore, the last 20 years of the transient climate response simulation roughly corresponds to 2060-2080 of the RCP8.5 scenario. Here, the monthly differences in surface and bottom temperatures ('deltas') for spring (February-April) and fall (September- November) are added to an average annual temperature climatology for spring and fall, respectively, derived from observed surface and bottom temperatures to produce an 80-year time series of future bottom and surface temperatures in both seasons. The observed temperatures come from the NEFSC spring and fall bottom trawl surveys conducted from 1968 to 2013 and represent approximately 30,000 observations over the time series. #### Modeling Changes in Suitable Thermal Habitat + We modeled individual species thermal habitat across the whole U.S. NES and not by sub-region because we did not want to assume that species would necessarily maintain these assemblages in the future. Indeed, the goal here is to determine future patterns of thermal habitat availability for species on the U.S. NES in more broad terms. We fit one generalizaed additive model (GAM) based on both spring and fall data (i.e., an annual model as opposed to separate spring and fall models) and use it to project potential changes in distribution and magnitude of biomass separately for each season for each species. By creating a single annual model based on temperature data from both spring and fall, we ensure that the full thermal envelope of each species is represented. For example, if a species with a wide thermal tolerance has historically been found in cooler waters in the spring, and in warmer waters in the fall, an annual model will ensure that if there are warmer waters in the spring in the future, that species will have the potential to inhabit those areas. Additionally, because the trawl survey data are subject to many zero observations, we use delta-lognormal GAMs [@Wood2011a], which model presence-absence separately from logged positive observations. The response variables in each of the GAMs are presence/absence and logged positive biomass of each assemblage or individual species, respectively. A binomial link function is used in the presence/absence models and a Gaussian link function is used in the models with logged positive biomass. + The predictor variables are surface and bottom temperature and depth (all measured by the survey at each station), fit with penalized regression splines, and survey stratum, which accounts for differences in regional habitat quality across the survey region. Stratum may be considered to account for additional information not explicitly measured by the survey (e.g., bottom rugosity). Predictions of species abundance are calculated as the product of the predictions from the presence-absence model, the exponentiated predictions from the logged positive biomass model, and a correction factor to account for the retransformation bias associated with the log transformation [@Duan1983; and see @Pinsky2013]. We calculated the suitable thermal habitat both in terms of changes in 'suitable thermal abundance', defined as the species density possible given appropriate temperature, depth and bathymetric conditions, and changes in 'suitable thermal area', defined as the size of the physical area potentially occupied by a species given appropriate temperature, depth and bathymetric conditions. Suitable thermal abundance is determined from the predictions from the GAMs (i.e., a prediction of biomass). However, this quantity should not be interpreted directly as a change in future abundance or biomass, but instead as the potential abundance of a species in the future given changes in temperature and holding all else (e.g., fishing effort, species interactions, productivity, etc.) constant. Suitable thermal area is determined as a change in the suitable area that a species distribution occupies in the future and is derived from the area of the kernel density of the distribution. To ensure that the estimates are conservative, we select all points with values greater than one standard deviation above the mean. We then compute the area of these kernels using the `gArea` function from the `rgeos` package in R [@Bivand2011]. ### Plotting -```{r th-maps,fig.cap="Current thermal habitat estimate (A), and 20-40 year thermal habitat projection (B) for summer flounder on the Northeast Continental Shelf." , fig.width = 8,fig.height = 5.5, fig.show='hold',fig.pos='H', warning=F, message=F} +```{r th-maps,fig.cap="Current thermal habitat estimate (A), and 20-40 year thermal habitat projection (B) for summer flounder on the Northeast Continental Shelf." , out.width="90%", warning=F, message=F} knitr::include_graphics(file.path(image.dir, "thermal_habitat.png")) diff --git a/chapters/aggregate_groups.rmd b/chapters/aggregate_groups.rmd index 1036f1a3..8630f168 100644 --- a/chapters/aggregate_groups.rmd +++ b/chapters/aggregate_groups.rmd @@ -36,7 +36,7 @@ soe.17.class <- data.frame('Feeding Guild' = c('Apex Predator', 'Piscivore', 'Things not classified above')) kable(soe.17.class, booktabs = TRUE, - caption = "Aggregate groups use in 2017 SOE. Classifications are based on @garrison2000dietary . \\label{}") + caption = "Aggregate groups use in 2017 SOE. Classifications are based on Garrison and Link (2000). \\label{}") ``` ```{r soe2018class, eval = T, echo = F} @@ -49,7 +49,7 @@ soe.18.class <- data.frame('Feeding Guild' = c('Apex Predator', 'Piscivore', 'Things not classified above')) kable(soe.18.class, booktabs = TRUE, - caption = "Aggregate groups use since 2018 SOE. Classifications are based on @link2006EMAX.") + caption = "Aggregate groups use since 2018 SOE. Classifications are based on Link et al. (2006).") ``` ### Data sources diff --git a/chapters/ches_bay_sst.Rmd b/chapters/ches_bay_sst.Rmd index 6dba1012..e783af4a 100644 --- a/chapters/ches_bay_sst.Rmd +++ b/chapters/ches_bay_sst.Rmd @@ -20,7 +20,7 @@ ### Data sources -Data for Chesapeake Bay seasonal sea surface temperature (SST) anomalies were derived from the NOAA Multi-satellite AVHRR SST data set, available from NOAA CoastWatch [East Coast Regional Node](https://eastcoast.coastwatch.noaa.gov). The data set is a composite of overpasses from all operational satellites currently flying the Advanced Very High Resolution Radiometer (AVHRR) instrument. SST is derived using the Operational Non-linear Multichannel SST Algorithm (Li, et al, 2001). Both daytime and nighttime overpasses are composited into daily and then seasonal SST products. The data extend from 2008 to present, and provide a 1.25 km x 1.25 km grid of SST measurements. +Data for Chesapeake Bay seasonal sea surface temperature (SST) anomalies were derived from the NOAA Multi-satellite AVHRR SST data set, available from NOAA CoastWatch [East Coast Regional Node](https://eastcoast.coastwatch.noaa.gov). The data set is a composite of overpasses from all operational satellites currently flying the Advanced Very High Resolution Radiometer (AVHRR) instrument. SST is derived using the Operational Non-linear Multichannel SST Algorithm (@Li2001a, @Li2001b). Both daytime and nighttime overpasses are composited into daily and then seasonal SST products. The data extend from 2008 to present, and provide a 1.25 km x 1.25 km grid of SST measurements. ### Data analysis Anomaly maps of SST are generated by creating long-term ‘climatological’ seasonal average SST for the years from 2008 to the year immediately prior to the current year. The reference period serves as a benchmark for comparing current observations. The current-year seasonal SST is then subtracted from the long-term seasonal average. Seasons for Chesapeake Bay are Dec-Feb (winter), Mar-May (spring), Jun-Aug (summer), and Sep-Nov (fall). @@ -39,9 +39,3 @@ Code used to create the figure below can be [here](https://raw.githubusercontent ```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_MAB.Rmd-ches-bay-sst.R"), echo = F, eval = T, fig.cap="Maps of seasonal SST anomalies are generated for an SST range of +5.0 - -5.0 C. Positive values show current-season temperatures above the long-term seasonal average, whereas negative values show current-season temperatures below the long-term seasonal average."} ``` - -### Resources -Li, X., W. Pichel, P. Clemente-Colón, V. Krasnopolsky, and J. Sapper. 2001. “Validation of Coastal Sea and Lake Surface Temperature Measurements Derived from NOAA/AVHRR Data.” International Journal of Remote Sensing 22 (7): 1285–1303. https://doi.org/10.1080/01431160151144350. - -Li, X., W. Pichel, E. Maturi, P. Clemente-Colón, and J. Sapper. 2001. “Deriving the Operational Nonlinear Multichannel Sea Surface Temperature Algorithm Coefficients for NOAA-15 AVHRR/3.” International Journal of Remote Sensing 22 (4): 699–704. https://doi.org/10.1080/01431160010013793. - diff --git a/chapters/ches_bay_water_quality.Rmd b/chapters/ches_bay_water_quality.Rmd index a87e0d6e..2295e75a 100644 --- a/chapters/ches_bay_water_quality.Rmd +++ b/chapters/ches_bay_water_quality.Rmd @@ -44,7 +44,7 @@ Patterns of attainment of individual DUs are variable (Figure 2). Changes in OW- -```{r,fig.width = 5, fig.asp = 0.45, fig.cap = "Time series of the estimated attainment of water quality standards (i.e., DO: dissolved oxygen; CHLA: chlorophyll-a; Clarity/SAV: water clarity/submerged aquatic vegetation) for five Chesapeake Bay designated uses (MSN: migratory spawning and nursery; OW: open water; DW: deep water; DC: deep channel; SW: shallow water) for each 3-year assessment period between 1985-1987 and 2015-2017."} +```{r,out.width="80%", fig.asp = 0.45, fig.cap = "Time series of the estimated attainment of water quality standards (i.e., DO: dissolved oxygen; CHLA: chlorophyll-a; Clarity/SAV: water clarity/submerged aquatic vegetation) for five Chesapeake Bay designated uses (MSN: migratory spawning and nursery; OW: open water; DW: deep water; DC: deep channel; SW: shallow water) for each 3-year assessment period between 1985-1987 and 2015-2017."} knitr::include_graphics(file.path(image.dir, "cb_water_quality_attainment_2022.png")) diff --git a/chapters/cold_pool_index.Rmd b/chapters/cold_pool_index.Rmd index 03aca3f2..d8a7a908 100644 --- a/chapters/cold_pool_index.Rmd +++ b/chapters/cold_pool_index.Rmd @@ -23,25 +23,28 @@ The cold pool is an area of relatively cold bottom water that forms on the US no ### Data Sources Three bottom temperature products are used to get bottom temperature from 1958 to 2021: + 1. Numerical simulation of the NWA Ocean was performed with the Regional Ocean Modelling System (ROMS-NWA) PERIOD: 1958-1992 INITIAL RESOLUTION: ~7km REGRID: 1/10° -(Shchepetkin and McWilliams, 2005) +(@Shchepetkin2005) https://www.sciencedirect.com/science/article/pii/S1463500304000484 + 2. Global Ocean Physics Reanalysis (Glorys reanalysis) PERIOD: 1993-2019 INITIAL RESOLUTION: 1/12° REGRID: 1/10° https://resources.marine.copernicus.eu/product-detail/GLOBAL_REANALYSIS_PHY_001_030/INFORMATION + 3. Global Ocean Physics Analysis and Forecast updated Daily PERIOD: 2020-2021 INITIAL RESOLUTION: 1/12° REGRID: 1/10° -(Fernandez and Lellouche, 2018; Lellouche et al., 2018) +(@Fernandez2018; @Lellouche2018) https://resources.marine.copernicus.eu/product-detail/GLOBAL_ANALYSIS_FORECAST_PHY_001_024/INFORMATION -Furthermore, we use bottom temperature from the Northwest Atlantic Regional Climatology to estimate a monthly decadal bias (Seidov et al., 2016a, 2016b). +Furthermore, we use bottom temperature from the Northwest Atlantic Regional Climatology to estimate a monthly decadal bias (@Seidov2016a, @Seidov2016b). INITIAL RESOLUTION: 1/10° https://www.ncei.noaa.gov/products/northwest-atlantic-regional-climatology @@ -77,7 +80,7 @@ The Bottom temperature data are from ROMS-NWA between 1958 and 1992, from Glorys Bottom temperature from Glorys reanalysis and Global Ocean Physics Analysis were not being processed. -Bottom temperature from ROMS-NWA (used for the period 1958-1992) 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 et al., 2016a, 2016b) (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 1958-1992) 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 (@Chen2018; @Chen2020). 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 (@Seidov2016a, @Seidov2016b) (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: $${BIAS}_{i,\ d}=\ T_{i,d}^{Climatology}\ -\ {\bar{T}}_{i,\ d}^{ROMS-NWA}$$ @@ -107,7 +110,7 @@ The plot below was built using the code found The three-dimensional temperature of the Northeast US shelf is downloaded from the CMEMS (https://marine.copernicus.eu/). Source data is available [at this link](https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_030). ### 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 @Chen2018. ### Data processing @@ -138,19 +141,10 @@ This data represents the annual mean bottom temperature residual for Sept-Oct in ### Data analysis Methods published @miller2016, [original MATLAB source code](https://github.com/NOAA-EDAB/tech-doc/tree/master/R/stored_scripts/cold_pool_analysis.txt) used in that paper was provided by Jon Hare and used in this analysis. -### Resources -Chen, Z., Curchitser, E., Chant, R., and Kang, D. 2018. Seasonal Variability of the Cold Pool Over the Mid-Atlantic Bight Continental Shelf. Journal of Geophysical Research: Oceans, 123: 8203–8226. -Chen, Z., and Curchitser, E. N. 2020. Interannual Variability of the Mid‐Atlantic Bight Cold Pool. Journal of Geophysical Research: Oceans, 125. https://onlinelibrary.wiley.com/doi/10.1029/2020JC016445 (Accessed 13 January 2021). -Fernandez, E., and Lellouche, J. M. 2018. Product user manual for the global ocean physical reanalysis product GLORYS12V1. Copernicus Product User Manual, 4: 1–15. -Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., et al. 2018. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 112° high-resolution system. Ocean Science, 14: 1093–1126. -Miller, T. J., Hare, J. A., and Alade, L. A. 2016. A state-space approach to incorporating environmental effects on recruitment in an age-structured assessment model with an application to southern New England yellowtail flounder. Canadian Journal of Fisheries and Aquatic Sciences, 73: 1261–1270. -Seidov, D., Baranova, O. K., Johnson, D. R., Boyer, T. P., Mishonov, A. V., and Parsons, A. R. 2016a. Northwest Atlantic Regional Climatology (NCEI Accession 0155889). NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/archive/accession/0155889 (Accessed 25 March 2021). -Seidov, D., Baranova, O. K., Boyer, T., Cross, S. L., Mishonov, A. V., and Parsons, A. R. 2016b. Northwest Atlantic regional ocean climatology.: 3.2 MB. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Centers for Environmental Information. -Shchepetkin, A. F., and McWilliams, J. C. 2005. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9: 347–404. diff --git a/chapters/ecosystem_overfishing.Rmd b/chapters/ecosystem_overfishing.Rmd index 52aafe1d..e5ac5e7b 100644 --- a/chapters/ecosystem_overfishing.Rmd +++ b/chapters/ecosystem_overfishing.Rmd @@ -95,9 +95,10 @@ See the [workedExample vignette](https://NOAA-EDAB.github.io/eofindices/articles Figures for Mid-Atlantic Bight are presented in this document. For Georges Bank and the Gulf of Maine, please visit [here](https://noaa-edab.github.io/eofindices/articles/currentIndices.html) -##### Mid-Atlantic Bight (MAB) +#### Mid-Atlantic Bight (MAB) -```{r, pprmab, echo=FALSE, out.width='50%'} + +```{r, pprmab, echo=FALSE, out.width='50%', fig.cap="Ecosystem overfishing indicators."} knitr::include_graphics(paste0(imagePath,"/PPR-MAB-0_80.png")) knitr::include_graphics(paste0(imagePath,"/MTL-MAB-0_80.png")) knitr::include_graphics(paste0(imagePath,"/PP-MAB.png")) diff --git a/chapters/habs_alexandrium.Rmd b/chapters/habs_alexandrium.Rmd index 145014c6..6d372aca 100644 --- a/chapters/habs_alexandrium.Rmd +++ b/chapters/habs_alexandrium.Rmd @@ -18,7 +18,7 @@ ## Methods ### Data Sources -*Alexandrium* cysts in sediments of the Gulf of Maine have been monitored through a cooperative effort of NOAA, WHOI, and other partners for over twenty years. Sampling methods are described in Anderson et al. 2005. In the annual survey cruises, samples are obtained with a Craib corer, and *Alexandrium* cysts are counted from the top 1- cm of sediment layer. Results are extrapolated to estimate overall cyst abundance in the eastern, western, and entire Gulf of Maine.Results are reported as estimated total numbers of cells (10 to the 16th power) in Eastern Gulf of Maine (east of Penobscot Bay), Western Gulf of Maine (west of Penobscot Bay), Bay of Fundy (2003-2013 only), and entire Gulf of Maine. +*Alexandrium* cysts in sediments of the Gulf of Maine have been monitored through a cooperative effort of NOAA, WHOI, and other partners for over twenty years. Sampling methods are described in @Anderson2005. In the annual survey cruises, samples are obtained with a Craib corer, and *Alexandrium* cysts are counted from the top 1- cm of sediment layer. Results are extrapolated to estimate overall cyst abundance in the eastern, western, and entire Gulf of Maine.Results are reported as estimated total numbers of cells (10 to the 16th power) in Eastern Gulf of Maine (east of Penobscot Bay), Western Gulf of Maine (west of Penobscot Bay), Bay of Fundy (2003-2013 only), and entire Gulf of Maine. @@ -27,7 +27,7 @@ Tabular data provided by Yizhen Li, NOAA/NOS NCCOS Stressor Detection and Impact ### 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, Li et al. 2009 & 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 (@Anderson2005, @Li2009, @Li2020, @McGillicuddy2011). 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/ ### Data Processing @@ -37,36 +37,19 @@ Code for processing *Alexandrium* cyst data can be found [here](https://github.c The script used to develop the figure in the SOE Indicator Catalog can be found [here](https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-alex.R). -```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-alex.R"), echo = F, fig.align="center", eval = T, fig.cap="Gulf of Maine Alexandrium Cyst abundance."} +```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-alex.R"), echo = F, eval = T, fig.cap="Gulf of Maine Alexandrium Cyst abundance."} ``` -```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-alex-distibution.R"), echo = F, fig.align="center", eval = T, fig.cap="Gulf of Maine Alexandrium Cyst distribution."} +```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-alex-distibution.R"), echo = F, fig.align="center", eval = T,out.width = "80%", fig.cap="Gulf of Maine Alexandrium Cyst distribution."} ``` -```{r , echo = F, fig.align="center", eval = T} +```{r , echo = F, fig.align="center", eval = T, out.width = "80%"} knitr::include_graphics(file.path(image.dir, "HAB_dashboard_GMaine_July8_2021 - David Nelson - NOAA Federal.png")) ``` -## References -Anderson, D.M. 1997. Bloom dynamics of toxic Alexandrium species in the northeastern U.S. Limnology and Oceanography 42(5): 1009-1022. - -Anderson, D.M., D.M. Kulis, B.A. Keafer, K.E. Gribble, R. Marin, and C.A. Scholin. 2005. Identification and enumeration of Alexandrium spp. from the Gulf of Maine using molecular probes. Deep-sea Research II 52:2467-2490. - -Jin, D., and P. Hoagland. 2008. The value of harmful algal bloom predictions to the nearshore commercial shellfish fishery in the Gulf of Maine. WHOI Marine Policy Center, Woods Hole MA. May 1, 2008. 29 pp. - -Li, Yizhen. Pers. Comm. NOAA/NOS NCCOS Stressor Detection and Impacts Division, Silver Spring MD. - -Li, Y., R. He, D.J. McGillicuddy, D.M. Anderson, and B.A. Keafer. 2009. Investigation of the 2006 Alexandrium fundyense bloom in the Gulf of Maine: In-situ observations and numerical modeling. Continental Shelf Research 29:2069-2082. - -Li, Y., R.P. Stumpf, D.J. McGillicuddy, and R. He. 2020. Dynamics of an intense Alexandrium catenella red tide in the Gulf of Maine: satellite observations and numerical modeling. Harmful Algae 99:101927. - -McGillicuddy, D.J., D.W. Townsend, R. He, B.A. Keafer, J.L. Kleindinst, Y. Li, J.P. Manning, D.G. Mountain, M.A. Thomas, and D.M. Anderson. 2011. Suppression of the 2010 Alexandrium fundyense bloom by changes in physical, biological, and chemical properties of the Gulf of Maine. Limnology and Oceanography 56(6):2411–2426. - -NOAA/NCCOS. 2021. Gulf of Maine Alexandrium catenella Predictive Models. Interactive website with operational Nowcast/Forecast simulation. https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/ - diff --git a/chapters/habs_psp.Rmd b/chapters/habs_psp.Rmd index 4e0f3e0a..ecc49372 100644 --- a/chapters/habs_psp.Rmd +++ b/chapters/habs_psp.Rmd @@ -32,7 +32,7 @@ Original data were collected by the State of Maine, Department of Marine Resourc ### Data Analysis -Blue mussels (*Mytilis edulis*) were sampled at designated sites each year from March through October, and tissues were analyzed for presence and quantity of PSP toxins. Saxitoxin (STX) is a well-known PSP toxin, but a bloom can generate a range of related PSP toxins. Therefore, in many monitoring programs, toxins are reported as ""ug STX equivalents per 100 grams of shellfish tissue", where the quantity of each toxin present is normalized by it's toxicity compared to STX (Chung 2010). +Blue mussels (*Mytilis edulis*) were sampled at designated sites each year from March through October, and tissues were analyzed for presence and quantity of PSP toxins. Saxitoxin (STX) is a well-known PSP toxin, but a bloom can generate a range of related PSP toxins. Therefore, in many monitoring programs, toxins are reported as ""ug STX equivalents per 100 grams of shellfish tissue", where the quantity of each toxin present is normalized by it's toxicity compared to STX (@Chung2010). Data include total number of samples at multiple locations collected in each calendar year (2005-2019), numbers of samples above and below the PSP threshold of 44 ug/100g, and percentage of samples above the threshold. Simple bar and line graphs are used to plot the values for each variable as time series, 2005-2019. @@ -46,23 +46,16 @@ Code for processing salinity data can be found [here](https://github.com/NOAA-ED #Plotting The script used to develop the figure in the SOE Indicator Catalog can be found [here](https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-psp.R). -```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-psp.R"), echo = F, fig.align="center", eval = T, fig.cap=""} +```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-psp.R"), echo = F, fig.align="center", eval = T, fig.cap="Percent of state samples that are found to have PSP"} ``` -```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-psp-closures.R"), echo = F, fig.align="center", eval = T, fig.cap=""} +```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_NE.Rmd-habs-psp-closures.R"), echo = F, out.width = "80%", fig.align="center", eval = T, fig.cap="Massachusetts Fishery closures due to PSP. "} ``` -## References - -Chung, L.L. 2010. Measuring paralytic shellfish toxins in mussels from New Hampshire coastal waters using zwitterionic hydrophilic liquid chromatography/electrospray mass spectrometry. Master's Theses and Capstones. 539. https://scholars.unh.edu/thesis/539 - -Kleindinst, J.L., D.M. Anderson, D.J. McGillicuddy Gr., R.P. Stumpf, K.M. Fisher, D.A. Couture, J.M. Hickey, and C. Nash. 2014. Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management. Deep-Sea Research II 103:277-287. - -NOAA/NCCOS. 2021. Gulf of Maine Alexandrium catenella Predictive Models. Interactive website with operational Nowcast/Forecast simulation. https://coastalscience.noaa.gov/research/stressor-impacts-mitigation/hab-forecasts/gulf-of-maine-alexandrium-catenella-predictive-models/" diff --git a/chapters/hms_cpue.Rmd b/chapters/hms_cpue.Rmd index 7dc68bc5..b4e7f10a 100644 --- a/chapters/hms_cpue.Rmd +++ b/chapters/hms_cpue.Rmd @@ -31,7 +31,7 @@ Code used to process this data can be found on github - [NOAA-EDAB/ecodata](http Code used to build the figure below can be found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/chunk-scripts/macrofauna_MAB.Rmd-hms-cpue-sharks.R). -```{r shark-cpue, echo = F, fig.align="center", fig.cap="Species groupings based on list from Debbie Duarte - missing Boonethead, Atlantic Angel shark, Sixgill shark, sevengill shark, nurse shark, white shark, basking shark, lemon shark.."} +```{r shark-cpue, echo = F, fig.align="center", fig.cap="Species groupings based on list from Debbie Duarte - missing Boonethead, Atlantic Angel shark, Sixgill shark, sevengill shark, nurse shark, white shark, basking shark, lemon shark.", out.width="80%"} knitr::include_graphics(c(file.path(image.dir, "hms_pop_cpue.PNG"))) diff --git a/chapters/hms_stock_status.Rmd b/chapters/hms_stock_status.Rmd index 7c2e5ce4..6c862734 100644 --- a/chapters/hms_stock_status.Rmd +++ b/chapters/hms_stock_status.Rmd @@ -43,11 +43,10 @@ Code for processing Atlantic HMS Stock status data can be found [here](https://g ### Plotting Code used to create the figure below can be [here](https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-hms-stock-status.R). + + ```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/human_dimensions_MAB.Rmd-hms-stock-status.R"), fig.cap="Summary of single species status for HMS stocks; key to species names above."} ``` -### References -NOAA. 2021. 2020 Stock Assessment and Fishery Evaluation Report: Atlantic Highly Migratory Species. Atlantic Highly Migratory Species Management Division, Silver Spring, MD. https://www.fisheries.noaa.gov/atlantic-highly-migratory-species/atlantic-highly-migratory-species-stock-assessment-and-fisheries-evaluation-reports - diff --git a/chapters/hudson_river_flow.Rmd b/chapters/hudson_river_flow.Rmd index d5904fe4..5c48b5cd 100644 --- a/chapters/hudson_river_flow.Rmd +++ b/chapters/hudson_river_flow.Rmd @@ -33,9 +33,4 @@ Code for processing salinity data can be found [here](https://github.com/NOAA-ED ```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/LTL_MAB.Rmd-hudson-river-flow.R"), echo = F, fig.align="center", eval = T, fig.cap="Mean Annual flow of the Hudson River at USGS gauge 01358000 at Green Island, New York."} -``` - -## References - - - +``` \ No newline at end of file diff --git a/chapters/occupancy_indicator.Rmd b/chapters/occupancy_indicator.Rmd index ca5bce2c..1d0bc984 100644 --- a/chapters/occupancy_indicator.Rmd +++ b/chapters/occupancy_indicator.Rmd @@ -1,91 +1,91 @@ -# Habitat Occupancy Models {#hab-occu} - -**Description**: Habitat Occupancy - -**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) - -**Indicator category**: Database pull with analysis; Extensive analysis; not yet published; Published methods - -**Contributor(s)**: Kevin Friedland - -**Data steward**: Kevin Friedland, - -**Point of contact**: Kevin Friedland, - -**Public availability statement**: Source data are available upon request (see [Survdat](#survdat), [CHL/PP](#chl-pp), and Data Sources below for more information). Model-derived time series are available [here](https://comet.nefsc.noaa.gov/erddap/tabledap/SOE_habitat_soe_v1.html). - - -## Methods -Habitat area with a probability of occupancy greater than 0.5 was modeled for many [species throughout the Northeast Large Marine Ecosystem (NE-LME)](https://www.nefsc.noaa.gov/ecosys/current-conditions/occupancy-change.html) using random forest decision tree models. - -### Data sources -Models were parameterized using a suite of static and dynamic predictor variables, with occurrence and catch per unit effort (CPUE) of species from spring and fall Northeast Fisheries Science Center (NEFSC) bottom trawl surveys (BTS) serving as response variables. Sources of variables used in the analyses are described below. - -#### Station depth -The NEFSC BTS data included depth observations made concurrently with trawls at each station. Station depth was a static variable for these analyses. - -#### Ocean temperature and salinity -Sea surface and bottom water temperature and salinity measurements were included as dynamic predictor variables in the model, and were collected using Conductivity/Temperature/Depth (CTD) instruments. Ocean temperature and salinity measurements had the highest temporal coverage during the spring (February-April) and fall (September-November) months. Station salinity data were available between 1992-2016. - -#### Habitat descriptors -A variety of benthic habitat descriptors were incorporated as predictor variables in occupancy models (Table \@ref(tab:habitatdesc)). The majority of these parameters are based on depth (e.g. *BPI*, *VRM*, *Prcury*, *rugosity*, *seabedforms*, *slp*, and *slpslp*). The vorticity variable is based on current estimates, and the variable *soft_sed* based on sediment grain size. - - -```{r habitatdesc, echo = F, results='asis', message=F, warning=F} - -tab <- ' -|Variables|Notes|References| -|:-----------------------|:-----------------------|:-----------------------| -|Complexity - Terrain Ruggedness Index|The difference in elevation values from a center cell and the eight cells immediately surrounding it. Each of the difference values are squared to make them all positive and averaged. The index is the square root of this average.|@Riley1999| -|Namera bpi|BPI is a second order derivative of the surface depth using the TNC Northwest Atlantic Marine Ecoregional Assessment ("NAMERA") data with an inner radius=5 and outer radius=50.|@Lundblad2006| -|Namera_vrm|Vector Ruggedness Measure (VRM) measures terrain ruggedness as the variation in three-dimensional orientation of grid cells within a neighborhood based on The Nature Conservancy Northwest Atlantic Marine Ecoregional Assessment ("NAMERA") data.|@Hobson1972; @Sappington2007| -|Prcurv (2 km, 10 km, and 20 km)|Benthic profile curvature at 2km, 10km and 20 km spatial scales was derived from depth data.|@Winship2018| -|Rugosity|A measure of small-scale variations of amplitude in the height of a surface, the ratio of the real to the geometric surface area.|@Friedman2012| -|seabedforms|Seabed topography as measured by a combination of seabed position and slope.|[http://www.northeastoceandata.org/](http://www.northeastoceandata.org/)| -|Slp (2 km, 10 km, and 20 km)|Benthic slope at 2km, 10km and 20km spatial scales.|@Winship2018| -|Slpslp (2 km, 10 km, and 20 km)|Benthic slope of slope at 2km, 10km and 20km spatial scales|@Winship2018| -|soft_sed|Soft-sediments is based on grain size distribution from the USGS usSeabed: Atlantic coast offshore surficial sediment data.|[http://www.northeastoceandata.org/](http://www.northeastoceandata.org/)| -|Vort (fall - fa; spring - sp; summer - su; winter - wi)|Benthic current vorticity at a 1/6 degree (approx. 19 km) spatial scale.|@Kinlan2016| -' - -df<-readr::read_delim(tab, delim="|") -df<-df[-c(1,2,3) ,c("Variables","Notes","References")] -knitr::kable( - df, booktabs = TRUE, - caption = 'Habitat descriptors used in model parameterization.') %>% - kableExtra::kable_styling(font_size = 8) %>% - landscape() -``` - -#### Zooplankton -Zooplankton data are acquired through the NEFSC Ecosystem Monitoring Program ("EcoMon"). For more information regarding the collection process for these data, see @Kane2007, @Kane2011, and @Morse2017. The bio-volume of the 18 most abundant zooplankton taxa were considered as potential predictor variables. - -#### Remote sensing data -Both chlorophyll concentration and sea surface temperature (SST) from remote sensing sources were incorporated as static predictor variables in the model. During the period of 1997-2016, chlorophyll concentrations were derived from observations made by the Sea-viewing Wide Field of View Sensor (SeaWIFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), Medium Resolution Imaging Spectrometer (MERIS), and Visible and Infrared Imaging/Radiometer Suite (VIIRS). - -### Data processing - -#### Zooplankton -Missing values in the EcoMon time series were addressed by summing data over five-year time steps for each seasonal time frame and interpolating a complete field using ordinary kriging. Missing values necessitated interpolation for spring data in 1989, 1990, 1991, and 1994. The same was true of the fall data for 1989, 1990, and 1992. - -#### Remote sensing data -An overlapping time series of observations from the four sensors listed above was created using a bio-optical model inversion algorithm [@Maritorena2010]. Monthly SST data were derived from MODIS-Terra sensor data (available [here](https://oceancolor.gsfc.nasa.gov/data/terra/)). - -#### Ocean temperature and salinity -Date of collection corrections for ocean temperature data were developed using linear regressions for the spring and fall time frames; standardizing to collection dates of April 3 and October 11 for spring and fall. No correction was performed for salinity data. Annual data for ocean temperature and salinity were combined with climatology by season through an optimal interpolation approach. Specifically, mean bottom temperature or salinity was calculated by year and season on a 0.5° grid across the ecosystem, and data from grid cells with >80% temporal coverage were used to calculate a final seasonal mean. Annual seasonal means were then used to calculate combined anomalies for seasonal surface and bottom climatologies. - -An annual field was then estimated using raw data observations for a year, season, and depth using universal kriging [@automap], with depth included as a covariate (on a standard 0.1° grid). This field was then combined with the climatology anomaly field and adjusted by the annual mean using the variance field from kriging as the basis for a weighted mean between the two. The variance field was divided into quartiles with the lowest quartile assigned a weighting of 4:1 between the annual and climatology values. The optimally interpolated field at these locations was therefore skewed towards the annual data, reflecting their proximity to actual data locations and associated low kriging variance. The highest kriging variance quartile (1:1) reflected less information from the annual field and more from the climatology. - -### Data analysis - -#### Occupancy models -Prior to fitting the occupancy models, predictor variables were tested for multi-collinearity and removed if found to be correlated. The final model variables were then chosen utilizing a model selection process as shown by @Murphy2010 and implemented with the R package `rfUtilities` [@rfUtilities-package]. Occupancy models were then fit as two-factor classification models (absence as 0 and presence as 1) using the `randomForest` R package [@randomForest]. - -#### Selection criteria and variable importance -The `irr` R package [@irr] was used to calculate Area Under the ROC Curve (AUC) and Cohen's Kappa for assessing accuracy of occupancy habitat models. Variable importance was assessed by plotting the occurrence of a variable as a root variable versus the mean minimum node depth for the variable [@randomForestExplainer], as well as by plotting the Gini index decrease versus accuracy decrease. - -### Plotting - -```{r occupancy-MAB, fig.cap="Summer flounder spring (A) and fall (B) occupancy habitat area in the Northeast Large Marine Ecosystem. ", echo = F, fig.show='hold', warning = F, message = F,fig.pos='H'} -knitr::include_graphics(file.path(image.dir, "habitat_occupancy_plot.png")) +# Habitat Occupancy Models {#hab-occu} + +**Description**: Habitat Occupancy + +**Found in**: State of the Ecosystem - Gulf of Maine & Georges Bank (2018), State of the Ecosystem - Mid-Atlantic (2018) + +**Indicator category**: Database pull with analysis; Extensive analysis; not yet published; Published methods + +**Contributor(s)**: Kevin Friedland + +**Data steward**: Kevin Friedland, + +**Point of contact**: Kevin Friedland, + +**Public availability statement**: Source data are available upon request (see [Survdat](#survdat), [CHL/PP](#chl-pp), and Data Sources below for more information). Model-derived time series are available [here](https://comet.nefsc.noaa.gov/erddap/tabledap/SOE_habitat_soe_v1.html). + + +## Methods +Habitat area with a probability of occupancy greater than 0.5 was modeled for many [species throughout the Northeast Large Marine Ecosystem (NE-LME)](https://www.nefsc.noaa.gov/ecosys/current-conditions/occupancy-change.html) using random forest decision tree models. + +### Data sources +Models were parameterized using a suite of static and dynamic predictor variables, with occurrence and catch per unit effort (CPUE) of species from spring and fall Northeast Fisheries Science Center (NEFSC) bottom trawl surveys (BTS) serving as response variables. Sources of variables used in the analyses are described below. + +#### Station depth +The NEFSC BTS data included depth observations made concurrently with trawls at each station. Station depth was a static variable for these analyses. + +#### Ocean temperature and salinity +Sea surface and bottom water temperature and salinity measurements were included as dynamic predictor variables in the model, and were collected using Conductivity/Temperature/Depth (CTD) instruments. Ocean temperature and salinity measurements had the highest temporal coverage during the spring (February-April) and fall (September-November) months. Station salinity data were available between 1992-2016. + +#### Habitat descriptors +A variety of benthic habitat descriptors were incorporated as predictor variables in occupancy models (Table \@ref(tab:habitatdesc)). The majority of these parameters are based on depth (e.g. *BPI*, *VRM*, *Prcury*, *rugosity*, *seabedforms*, *slp*, and *slpslp*). The vorticity variable is based on current estimates, and the variable *soft_sed* based on sediment grain size. + + +```{r habitatdesc, echo = F, results='asis', message=F, warning=F} + +tab <- ' +|Variables|Notes|References| +|:-----------------------|:-----------------------|:-----------------------| +|Complexity - Terrain Ruggedness Index|The difference in elevation values from a center cell and the eight cells immediately surrounding it. Each of the difference values are squared to make them all positive and averaged. The index is the square root of this average.|@Riley1999| +|Namera bpi|BPI is a second order derivative of the surface depth using the TNC Northwest Atlantic Marine Ecoregional Assessment ("NAMERA") data with an inner radius=5 and outer radius=50.|@Lundblad2006| +|Namera_vrm|Vector Ruggedness Measure (VRM) measures terrain ruggedness as the variation in three-dimensional orientation of grid cells within a neighborhood based on The Nature Conservancy Northwest Atlantic Marine Ecoregional Assessment ("NAMERA") data.|@Hobson1972; @Sappington2007| +|Prcurv (2 km, 10 km, and 20 km)|Benthic profile curvature at 2km, 10km and 20 km spatial scales was derived from depth data.|@Winship2018| +|Rugosity|A measure of small-scale variations of amplitude in the height of a surface, the ratio of the real to the geometric surface area.|@Friedman2012| +|seabedforms|Seabed topography as measured by a combination of seabed position and slope.|[http://www.northeastoceandata.org/](http://www.northeastoceandata.org/)| +|Slp (2 km, 10 km, and 20 km)|Benthic slope at 2km, 10km and 20km spatial scales.|@Winship2018| +|Slpslp (2 km, 10 km, and 20 km)|Benthic slope of slope at 2km, 10km and 20km spatial scales|@Winship2018| +|soft_sed|Soft-sediments is based on grain size distribution from the USGS usSeabed: Atlantic coast offshore surficial sediment data.|[http://www.northeastoceandata.org/](http://www.northeastoceandata.org/)| +|Vort (fall - fa; spring - sp; summer - su; winter - wi)|Benthic current vorticity at a 1/6 degree (approx. 19 km) spatial scale.|@Kinlan2016| +' + +df<-readr::read_delim(tab, delim="|") +df<-df[-c(1,2,3) ,c("Variables","Notes","References")] +knitr::kable( + df, booktabs = TRUE, + caption = 'Habitat descriptors used in model parameterization.') %>% + kableExtra::kable_styling(font_size = 6) %>% + landscape() +``` + +#### Zooplankton +Zooplankton data are acquired through the NEFSC Ecosystem Monitoring Program ("EcoMon"). For more information regarding the collection process for these data, see @Kane2007, @Kane2011, and @Morse2017. The bio-volume of the 18 most abundant zooplankton taxa were considered as potential predictor variables. + +#### Remote sensing data +Both chlorophyll concentration and sea surface temperature (SST) from remote sensing sources were incorporated as static predictor variables in the model. During the period of 1997-2016, chlorophyll concentrations were derived from observations made by the Sea-viewing Wide Field of View Sensor (SeaWIFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), Medium Resolution Imaging Spectrometer (MERIS), and Visible and Infrared Imaging/Radiometer Suite (VIIRS). + +### Data processing + +#### Zooplankton +Missing values in the EcoMon time series were addressed by summing data over five-year time steps for each seasonal time frame and interpolating a complete field using ordinary kriging. Missing values necessitated interpolation for spring data in 1989, 1990, 1991, and 1994. The same was true of the fall data for 1989, 1990, and 1992. + +#### Remote sensing data +An overlapping time series of observations from the four sensors listed above was created using a bio-optical model inversion algorithm [@Maritorena2010]. Monthly SST data were derived from MODIS-Terra sensor data (available [here](https://oceancolor.gsfc.nasa.gov/data/terra/)). + +#### Ocean temperature and salinity +Date of collection corrections for ocean temperature data were developed using linear regressions for the spring and fall time frames; standardizing to collection dates of April 3 and October 11 for spring and fall. No correction was performed for salinity data. Annual data for ocean temperature and salinity were combined with climatology by season through an optimal interpolation approach. Specifically, mean bottom temperature or salinity was calculated by year and season on a 0.5° grid across the ecosystem, and data from grid cells with >80% temporal coverage were used to calculate a final seasonal mean. Annual seasonal means were then used to calculate combined anomalies for seasonal surface and bottom climatologies. + +An annual field was then estimated using raw data observations for a year, season, and depth using universal kriging [@automap], with depth included as a covariate (on a standard 0.1° grid). This field was then combined with the climatology anomaly field and adjusted by the annual mean using the variance field from kriging as the basis for a weighted mean between the two. The variance field was divided into quartiles with the lowest quartile assigned a weighting of 4:1 between the annual and climatology values. The optimally interpolated field at these locations was therefore skewed towards the annual data, reflecting their proximity to actual data locations and associated low kriging variance. The highest kriging variance quartile (1:1) reflected less information from the annual field and more from the climatology. + +### Data analysis + +#### Occupancy models +Prior to fitting the occupancy models, predictor variables were tested for multi-collinearity and removed if found to be correlated. The final model variables were then chosen utilizing a model selection process as shown by @Murphy2010 and implemented with the R package `rfUtilities` [@rfUtilities-package]. Occupancy models were then fit as two-factor classification models (absence as 0 and presence as 1) using the `randomForest` R package [@randomForest]. + +#### Selection criteria and variable importance +The `irr` R package [@irr] was used to calculate Area Under the ROC Curve (AUC) and Cohen's Kappa for assessing accuracy of occupancy habitat models. Variable importance was assessed by plotting the occurrence of a variable as a root variable versus the mean minimum node depth for the variable [@randomForestExplainer], as well as by plotting the Gini index decrease versus accuracy decrease. + +### Plotting + +```{r occupancy-MAB, fig.cap="Summer flounder spring (A) and fall (B) occupancy habitat area in the Northeast Large Marine Ecosystem. ", echo = F, fig.show='hold', warning = F, message = F,fig.pos='H'} +knitr::include_graphics(file.path(image.dir, "habitat_occupancy_plot.png")) ``` \ No newline at end of file diff --git a/chapters/ocean_acidification.Rmd b/chapters/ocean_acidification.Rmd index 847c4417..06355f07 100644 --- a/chapters/ocean_acidification.Rmd +++ b/chapters/ocean_acidification.Rmd @@ -17,17 +17,17 @@ ## 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](https://noaa-edab.github.io/tech-doc/ocean-acidification.html#ref-Wright2020), 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-present), and glider-based pH profiles during summer 2021 in both the Mid-Atlantic and the Gulf of Maine. These plots were developed using openly accessible, quality-controlled data from vessel-based discrete samples and glider-based measurements of pH (see Data Sources). +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](https://noaa-edab.github.io/tech-doc/ocean-acidification.html#ref-Wright2020), was synthesized from @Wright2020. 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-present), and glider-based pH profiles during summer 2021 in both the Mid-Atlantic and the Gulf of Maine. These plots were developed using openly accessible, quality-controlled data from vessel-based discrete samples and glider-based measurements of pH (see Data Sources). ### Data sources -Glider-based pH observations began in the southern Mid-Atlantic Bight 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). The glider pH observation program expanded spatially, 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. Full-resolution delayed-mode glider datasets containing raw pH voltages can be found on [RUCOOL's Glider ERDDAP Server](http://slocum-data.marine.rutgers.edu/erddap/index.html). Fully-processed and time-shifted pH glider datasets can be found [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/glider_data/). +Glider-based pH observations began in the southern Mid-Atlantic Bight region in May 2018 (@Saba2019), and seasonal glider pH missions thereafter began in February 2019 (@Wright2020; although no deployments occurred in 2020 as a result of the COVID pandemic). The glider pH observation program expanded spatially, 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. Full-resolution delayed-mode glider datasets containing raw pH voltages can be found on [RUCOOL's Glider ERDDAP Server](http://slocum-data.marine.rutgers.edu/erddap/index.html). Fully-processed and time-shifted pH glider datasets can be found [here](https://marine.rutgers.edu/~lgarzio/cinar_soe/glider_data/). -Vessel-based discrete pH data were mined from the Coastal Ocean Data Analysis Product in North America, version v2021 ([CODAP-NA](https://essd.copernicus.org/articles/13/2777/2021/); 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. +Vessel-based discrete pH data were mined from the Coastal Ocean Data Analysis Product in North America, version v2021 ([CODAP-NA](https://essd.copernicus.org/articles/13/2777/2021/); @Jiang2021). 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, two 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) and include quality-controlled spectrophotometric-based pH measurements on discrete water samples. Data can be requested from Chris Melrose (chris.melrose@noaa.gov). +Additionally, two recent vessel-based datasets that were not included in CODAP-NA (@Jiang2021) 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) and include quality-controlled spectrophotometric-based pH measurements on discrete water samples. Data can be requested from Chris Melrose (chris.melrose@noaa.gov). ### Data extraction @@ -38,7 +38,7 @@ Glider data were processed and quality-controlled by software technician Lori Ga ### Data processing -For processing and quality-control procedures of glider-based data, see Wright-Fairbanks et al. (2020). Glider data used in this synthesis were limited to summer only (June - August). +For processing and quality-control procedures of glider-based data, see @Wright2020. Glider data used in this synthesis were limited to summer only (June - August). 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). @@ -52,29 +52,18 @@ For the plot of U.S. Northeast Shelf, bottom pH was defined as the median of the Code for data manipulation and plotting can be found here: https://github.com/lgarzio/cinar-soe. -```{r mab-oa, fig.cap = "Bottom pH (summer only: June-August) on the U.S. Northeast Shelf plotted from available quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Summer 2021 glider-based pH observations on the Mid-Atlantic Bight shelf. North track glider mission (data provider: Charles Flagg, Stony Brook University) ran from 07/20/2021 to 08/20/2021. South track glider mission (data provider: Grace Saba, Rutgers University) ran from 07/16/2021 to 08/20/2021."} +```{r mab-oa, fig.cap = "Bottom pH (summer only: June-August) on the U.S. Northeast Shelf plotted from available quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Summer 2021 glider-based pH observations on the Mid-Atlantic Bight shelf. North track glider mission (data provider: Charles Flagg, Stony Brook University) ran from 07/20/2021 to 08/20/2021. South track glider mission (data provider: Grace Saba, Rutgers University) ran from 07/16/2021 to 08/20/2021.", out.width="90%"} knitr::include_graphics(file.path(image.dir, "Saba_Fig_SOE_MAFMC - Grace Saba.png")) ``` -```{r ne-oa, fig.cap = "Bottom pH (summer only: June-August) on the U.S. Northeast Shelf plotted from available quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Summer 2021 glider-based pH observations in the Gulf of Maine (data provider: Neal Pettigrew, University of Maine). Glider mission ran from 06/30/2021 to 07/21/2021. "} +```{r ne-oa, fig.cap = "Bottom pH (summer only: June-August) on the U.S. Northeast Shelf plotted from available quality-controlled vessel- and glider-based datasets from 2007-present. Right panel: Summer 2021 glider-based pH observations in the Gulf of Maine (data provider: Neal Pettigrew, University of Maine). Glider mission ran from 06/30/2021 to 07/21/2021. ", out.width="90%"} knitr::include_graphics(file.path(image.dir, "Saba_Fig_SOE_NEFMC - Grace Saba.png")) ``` -### Resources - -Humphreys, M. P., Gregor, L., Pierrot, D., van Heuven, S. M. A. C., Lewis, E. R., and Wallace, D. W. R. (2020). PyCO2SYS: marine carbonate system calculations in Python. Zenodo. doi:10.5281/zenodo.3744275. - -Jiang, L.-Q., Feely, R. A., Wanninkhof, R., Greeley, D., Barbero, L., Alin, S., Carter, B. R., Pierrot, D., Featherstone, C., Hooper, J., Melrose, C., Monacci, N., Sharp, J. D., Shellito, S., Xu, Y.-Y., Kozyr, A., Byrne, R. H., Cai, W.-J., Cross, J., Johnson, G. C., Hales, B., Langdon, C., Mathis, J., Salisbury, J., and Townsend, D. W.: Coastal Ocean Data Analysis Product in North America (CODAP-NA) – an internally consistent data product for discrete inorganic carbon, oxygen, and nutrients on the North American ocean margins, Earth Syst. Sci. Data, 13, 2777–2799, https://doi.org/10.5194/essd-13-2777-2021, 2021. - -Lewis, E. and Wallace, D. W. R. (1998) Program Developed for CO2 System Calculations, ORNL/CDIAC-105, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., Oak Ridge, Tenn., 38 pp., https://salish-sea.pnnl.gov/media/ORNL-CDIAC-105.pdf. - -Saba, G.K., Wright-Fairbanks, E., Chen, B., Cai, W.-J., Barnard, A.H., Jones, C.P., Branham, C.W., Wang, K., Miles, T. 2019. The development and validation of a profiling glider Deep ISFET pH sensor for high resolution coastal ocean acidification monitoring. Frontiers in Marine Science 6: 664, https://doi.org/10.3389/fmars.2019.00664. - -Wright-Fairbanks, Elizabeth K., Travis N. Miles, Wei-Jun Cai, Baoshan Chen, and Grace K. Saba. 2020. “Autonomous Observation of Seasonal Carbonate Chemistry Dynamics in the Mid-Atlantic Bight.” Journal of Geophysical Research: Oceans 125 (11): e2020JC016505. https://doi.org/https://doi.org/10.1029/2020JC016505. diff --git a/chapters/plankton_diversity.Rmd b/chapters/plankton_diversity.Rmd index 10d326d0..c7220913 100644 --- a/chapters/plankton_diversity.Rmd +++ b/chapters/plankton_diversity.Rmd @@ -26,6 +26,7 @@ Processing of most samples was conducted at the Morski Instytut Rybacki (MIR) in ### Data extraction Data retrieved from NOAA NEFSC Oceans and Climate branch [public dataset](ftp://ftp.nefsc.noaa.gov/pub/hydro/zooplankton_data/) + (Filename: "EcoMon_Plankton_Data_v3_0.xlsx", File Date: 10/20/2016). ### Data analysis diff --git a/chapters/sandlance.Rmd b/chapters/sandlance.Rmd index 1745f8dc..29ad2660 100644 --- a/chapters/sandlance.Rmd +++ b/chapters/sandlance.Rmd @@ -21,7 +21,7 @@ This data set is taken directly from Table 1, Silva et al. 2020. See full citat ### Data Analysis -Data processing and analysis methods are described in Silva et al. 2020. The catch counts of sand lance and observational counts of humpback whales and great shearwater were used to derive spatial metrics (center of gravity, and inertia) for each species. Equations for these spatial metrics are provided in Table 2 of Silva et al. 2020. The spatial metrics (center of gravity, inertia) were used to calculate the global index of collocation (GIC) to quantify spatial overlap between pairs of species for each cruise. GICs for species pairs are reported in Table 3 of Silva et al. 2020, but data were not sufficient to calculate GICs for each pair of species in each cruise. +Data processing and analysis methods are described in @Silva2020. The catch counts of sand lance and observational counts of humpback whales and great shearwater were used to derive spatial metrics (center of gravity, and inertia) for each species. Equations for these spatial metrics are provided in Table 2 of @Silva2020. The spatial metrics (center of gravity, inertia) were used to calculate the global index of collocation (GIC) to quantify spatial overlap between pairs of species for each cruise. GICs for species pairs are reported in Table 3 of @Silva2020, but data were not sufficient to calculate GICs for each pair of species in each cruise. @@ -48,8 +48,3 @@ image.dir <- here::here("images") knitr::include_graphics(file.path(image.dir, "Stellwagen_sand_lance_sampling_map_1.jpg")) ``` - - -## References - -Silva, T.L., D.N. Wiley, M.A. Thompson, P. Hong, L. Kaufman, J.J. Suca, J.K. Llopiz, H. Baumann, and G. Fay. 2020. High collocation of sand lance and protected top predators: Implications for conservation and management. Conservation Science and Practice 2020;e274. https://doi.org/10.1111/csp2.274 \ No newline at end of file diff --git a/chapters/survey_data.rmd b/chapters/survey_data.rmd index cceb380d..5a674d28 100644 --- a/chapters/survey_data.rmd +++ b/chapters/survey_data.rmd @@ -74,34 +74,31 @@ indicators. For the purposes of the aggregate biomass indicators, fall and spri survey data are treated separately. Additionally, all length data is dropped and species seperated by sex at the catch level are merged back together. -Since 2020, survey strata where characterized as being within an [Ecological Production Unit](#epu) -based on where at least 50% of the area of the strata was located (Figure \@ref(fig:epustrata). While this does not +Since 2020, survey strata where characterized as being within an [Ecological Production Unit](#epu) based on where at least 50% of the area of the strata was located (Figure \@ref(fig:epustrata). While this does not create a perfect match for the EPU boundaries it allows us to calculate the variance associated with the index as the survey was designed. -```{r epustrata, fig.cap="Map of the Northeast Shelf broken into the four Ecological Production Units by strata. Strata were assigned to an EPU based on which one contained at least 50% of the area of the strata.", fig.align='center', echo = F} + +```{r epustrata, fig.cap="Map of the Northeast Shelf broken into the four Ecological Production Units by strata.Strata were assigned to an EPU based on which one contained at least 50% of the area of the strata.", out.width="90%",echo = F} knitr::include_graphics(file.path(image.dir,"EPU_Designations_Map.jpg")) ``` -Prior to 2020, Survdat was first post stratified into EPUs by labeling stations by the EPU they fell within -using the `over` function from the `rgdal` R package [@rgdal]. Next, the total number -of stations within each EPU per year is counted using unique station records. Biomass -is summed by species per year per EPU. Those sums are divided by the appropriate -station count to get the EPU mean. Finally, the mean biomasses are summed by [aggregate groups](#aggroups). These steps are encompassed in the [processing code](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_agg_bio.R), which also includes steps taken to format the data set for inclusion in the `ecodata` R package. + +Prior to 2020, Survdat was first post stratified into EPUs by labeling stations by the EPU they fell within using the `over` function from the `rgdal` R package [@rgdal]. Next, the total number of stations within each EPU per year is counted using unique station records. Biomass is summed by species per year per EPU. Those sums are divided by the appropriate station count to get the EPU mean. Finally, the mean biomasses are summed by [aggregate groups](#aggroups). These steps are encompassed in the [processing code](https://github.com/NOAA-EDAB/ecodata/blob/master/data-raw/get_agg_bio.R), which also includes steps taken to format the data set for inclusion in the `ecodata` R package. ### Plotting Code used to create the figure below can be found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/chunk-scripts/macrofauna.Rmd-agg-bio.R). -```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/macrofauna_MAB.Rmd-aggregate-biomass.R"), fig.cap="Spring (left) and fall (right) surveyed biomass in the Mid-Atlantic Bight. Data from the NEFSC Bottom Trawl Survey are shown in black, with NEAMAP shown in red.", echo = F, fig.align='default',warning = F} +```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/macrofauna_MAB.Rmd-aggregate-biomass.R"), fig.cap="Spring (left) and fall (right) surveyed biomass in the Mid-Atlantic Bight. Data from the NEFSC Bottom Trawl Survey are shown in black, with NEAMAP shown in red.", echo = F,warning = F} ``` #### Survey Diversity Code used to create the figure below can be found [here](https://github.com/NOAA-EDAB/ecodata/blob/master/chunk-scripts/macrofauna_MAB.Rmd-survey-shannon.R). -```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/macrofauna_MAB.Rmd-survey-shannon.R"), fig.cap="Survey diversity measure for the Mid-Atlantic Bight.", echo = F, fig.align='default',warning = F} +```{r , code = readLines("https://raw.githubusercontent.com/NOAA-EDAB/ecodata/master/chunk-scripts/macrofauna_MAB.Rmd-survey-shannon.R"), fig.cap="Survey diversity measure for the Mid-Atlantic Bight.", echo = F,warning = F} ``` diff --git a/index.Rmd b/index.Rmd index 534d501a..fa800fe8 100644 --- a/index.Rmd +++ b/index.Rmd @@ -7,7 +7,7 @@ knit: "bookdown::render_book" output: bookdown::gitbook always_allow_html: true documentclass: book -bibliography: ["bibliography/introduction.bib","bibliography/aggregate_groups.bib","bibliography/seasonal_sst_anomaly_maps.bib","bibliography/Aquaculture.bib","bibliography/Bennet_indicator.bib","bibliography/bottom_temperature.bib","bibliography/Revenue_Diversity.bib","bibliography/ches_bay_water_quality.bib","bibliography/CHL_PPD.bib","bibliography/ecosystem_overfishing.bib","bibliography/comm_eng.bib","bibliography/calanus_stage.bib","bibliography/ches_bay_temp.bib","bibliography/conceptmods.bib","bibliography/Condition.bib","bibliography/EPU.bib","bibliography/Expected_Number.bib","bibliography/phyto_size_class.bib", "bibliography/gulf_stream_index.bib","bibliography/habitat_vulnerability.bib","bibliography/Ich_div.bib","bibliography/long_term_sst.bib","bibliography/MAB_HAB.bib","bibliography/NE_HAB.bib","bibliography/occupancy.bib","bibliography/productivity_tech_memo.bib","bibliography/RW.bib","bibliography/seabird_ne.bib","bibliography/slopewater_proportions.bib","bibliography/Species_dist.bib","bibliography/survey_data.bib","bibliography/thermal_hab_proj.bib","bibliography/trend_analysis.bib","bibliography/zooplankton.bib","bibliography/cold_pool_index.bib","bibliography/forage_energy_density.bib","bibliography/marine_heatwave.bib","bibliography/protected_species_hotspots.bib","bibliography/ocean_acidification.bib", "bibliography/wind_habitat_occupancy.bib","bibliography/warm_core_rings.bib", "bibliography/glossary.bib","packages.bib"] +bibliography: ["bibliography/introduction.bib","bibliography/aggregate_groups.bib","bibliography/seasonal_sst_anomaly_maps.bib","bibliography/Aquaculture.bib","bibliography/Bennet_indicator.bib","bibliography/bottom_temperature.bib","bibliography/Revenue_Diversity.bib","bibliography/ches_bay_water_quality.bib","bibliography/CHL_PPD.bib","bibliography/ecosystem_overfishing.bib","bibliography/comm_eng.bib","bibliography/calanus_stage.bib","bibliography/ches_bay_temp.bib","bibliography/conceptmods.bib","bibliography/Condition.bib","bibliography/EPU.bib","bibliography/Expected_Number.bib","bibliography/phyto_size_class.bib", "bibliography/cold_pool_index.bib", "bibliography/sandlance.bib", "bibliography/gulf_stream_index.bib","bibliography/habitat_vulnerability.bib","bibliography/Ich_div.bib","bibliography/long_term_sst.bib","bibliography/MAB_HAB.bib","bibliography/NE_HAB.bib", "bibliography/habs.bib","bibliography/occupancy.bib","bibliography/productivity_tech_memo.bib","bibliography/RW.bib","bibliography/seabird_ne.bib","bibliography/slopewater_proportions.bib","bibliography/Species_dist.bib","bibliography/survey_data.bib","bibliography/thermal_hab_proj.bib","bibliography/trend_analysis.bib","bibliography/zooplankton.bib","bibliography/cold_pool_index.bib","bibliography/forage_energy_density.bib","bibliography/marine_heatwave.bib","bibliography/protected_species_hotspots.bib","bibliography/ocean_acidification.bib", "bibliography/wind_habitat_occupancy.bib","bibliography/warm_core_rings.bib", "bibliography/glossary.bib","packages.bib"] geometry: "left=1.0in, right=1.0in, top=1.0in, bottom=1.0in, includefoot" biblio-style: apalike link-citations: true