diff --git a/.gitignore b/.gitignore index bef8eda..94707e1 100644 --- a/.gitignore +++ b/.gitignore @@ -42,6 +42,5 @@ Meta /archive/ *.csv 0_update_condition_data.html -/*.bib /*.csl /*.docx/ \ No newline at end of file diff --git a/CopyOfEBS_references.bib b/CopyOfEBS_references.bib new file mode 100644 index 0000000..930820d --- /dev/null +++ b/CopyOfEBS_references.bib @@ -0,0 +1,237 @@ +@article{Barbeaux2020, +abstract = {In 2014–2016 an unprecedented warming event in the North Pacific Ocean triggered changes in ecosystem of the Gulf of Alaska (GOA) impacting fisheries management. The marine heatwave was noteworthy in its geographical extent, depth range, and persistence, with evidence of shifts in species distribution and reduced productivity. In 2017 a groundfish survey indicated that GOA Pacific cod (Gadus macrocephalus) had experienced a 71% decline in abundance from the previous 2015 survey. The GOA Pacific cod fishery supports a $103 million fishery which is 29% of the groundfish harvest value in the GOA. In this paper, we demonstrate that an increase in metabolic demand during this extended marine heatwave as well as a reduced prey supply can explain the decline in GOA Pacific cod biomass. Although increased mortality likely led to the decline in the Pacific cod population, historically low recruitment concurrent with the heatwave portends a slow recovery for the stock and gives a preview of impacts facing this region due to climate change. We evaluate the intersection of climate change with ecosystem-based fisheries management in the context of GOA Pacific cod with a description of the sensitivities of the ecosystem, how the changes in the ecosystem affected the Pacific cod stock, and a description of how the management system in the North Pacific handled this shock. We also provide suggestions on how fisheries management systems could be improved to better contend with the impacts of climate change such as the effects of heatwaves like that experienced in 2014–2016.}, +author = {Barbeaux, Steven J. and Holsman, Kirstin and Zador, Stephani}, +doi = {10.3389/fmars.2020.00703}, +file = {:C\:/Users/sean.rohan/Downloads/fmars-07-00703.pdf:pdf}, +issn = {22967745}, +journal = {Frontiers in Marine Science}, +keywords = {Gulf of Alaska,Pacific cod,climate change,ecosystem-based fisheries management,fisheries,marine heatwave}, +pages = {1--21}, +title = {{Marine heatwave stress test of ecosystem-based fisheries management in the Gulf of Alaska Pacific cod fishery}}, +volume = {7}, +year = {2020} +} + +@article{Blackwell2000, +abstract = {Condition assessment is commonly practiced by fisheries personnel as one tool for evaluating fish populations and communities. Several noninvasive condition measures are available for use, including Fulton's condition factor (K), relative condition factor (Kn), and relative weight (Wr). The use of Wr as a condition measure has increased within several peer-reviewed journals. In 1995 to 1996, survey responses from agency personnel in 48 states indicated that 22 states used Wr as a standard technique, 18 states identified Wr use as occasional, whereas only eight states indicated no Wr use. The regression-line-percentile technique is recommended for developing standard weight (Ws) equations. There are currently Ws equations available for 52 species and three purposeful hybrids. Length-related trends in condition need to be evaluated prior to calculating a population mean Wr. Relative weight target ranges should be adjusted to meet specific management objectives. Relative weight values are influenced by seasonal dynamics. The uses of Wr may go beyond just a measure of fish "plumpness." Relative weight can serve as a surrogate for estimating fish body composition, as a measure of fish health, and to assess prey abundance, fish stockings, and management actions.}, +author = {Blackwell, Brian G. and Brown, Michael L. and Willis, David W.}, +doi = {10.1080/10641260091129161}, +issn = {10641262}, +journal = {Reviews in Fisheries Science}, +keywords = {Condition assessment,Condition measures,Relative weight,Standard weight}, +number = {1}, +pages = {1--44}, +title = {{Relative weight (Wr) status and current use in fisheries assessment and management}}, +volume = {8}, +year = {2000} +} + +@article{Boldt2004, +abstract = {—Declining wild stocks and reduced body size of pink salmon Oncorhynchus gorbuscha in Prince William Sound have led to the hypothesis that the carrying capacity of the sound, and possibly that of the Gulf of Alaska, for salmon has been reached. We compared the length, weight, and energetic content of juvenile pink salmon (1) among different stations, (2) between one hatchery group and wild pink salmon from several stations, and (3) among several hatchery release groups and wild pink salmon from two stations in the sound using analysis of variance. There were significant differences in the lengths of fish among stations but no geographic pattern. Pink salmon at two nearby stations in southwestern Prince William Sound were the smallest (71.5 mm in Whale Bay) and largest (92.4 mm in Bainbridge Passage) of the six stations sampled. There were also differences in size among the hatchery and wild groups. Wild fish were consistently larger than the most abundant hatchery group (Cannery Creek). Within central Prince William Sound, Cannery Creek Hatchery pink salmon were significantly shorter than other hatchery release groups and wild salmon. Size variation was probably dependent on size upon entry and time of entry into marine waters. The energy content of juvenile pink salmon did not differ significantly between wild and hatchery fish but did vary among stations, albeit with no geographic pattern. Hatchery fish west of Naked Island had significantly higher energy content than those east of Naked Island. The consistencies in energy content among groups of fish from the same geographic area suggest that processes occurring on local scales (e.g., the effect of stratification on secondary production and local depletion by planktivores) are important in determining the condition of juvenile pink salmon. Prince William Sound is a large, complex, fjord-type estuary that supports large runs of hatchery and wild pink salmon Oncorhynchus gorbuscha (Niebauer et al. 1994). Salmon enhancement in the sound increased from the mid-1970s to 1989, and in 2001 hatcheries were releasing about 600 mil-lion pink salmon fry annually (McNair 1997; Far-rington 2003). Total pink salmon returns to the Gulf of Alaska and Prince William Sound in-creased in the 1980s and 1990s; however, wild salmon returns in the sound peaked in 1983 and declined through 1995 (Cooney and Brodeur 1998). The body size of commercially caught pink salmon has also decreased since the mid-1970s (Peterman 1987; Bigler et al. 1996). Recent sci-entific debate has therefore focused on potential negative effects of hatchery production on wild salmon stocks and the carrying capacity of the sound (Hilborn and Eggers 2000, 2001; Werthei-mer et al. 2001). Concern over declining wild}, +author = {Boldt, Jennifer L. and Haldorson, Lewis J.}, +doi = {10.1577/t02-138}, +issn = {0002-8487}, +journal = {Transactions of the American Fisheries Society}, +number = {1}, +pages = {173--184}, +title = {{Size and condition of wild and hatchery pink salmon juveniles in Prince William Sound, Alaska}}, +volume = {133}, +year = {2004} +} + +@article{Bolin2021, +abstract = {Marine ecosystem forecasting is an important innovation in fisheries science with considerable value for industry and management, providing new data-driven means of predicting the distribution and availability of commercially exploited fish stocks over a range of timescales, including near-real-time and seasonal. Marine ecosystem forecasting is rapidly advancing as a field, yet tools produced for fisheries to date focus primarily on predicting species distributions. The next generation of marine ecosystem forecasting products could be enhanced by also incorporating predictions of biological characteristics of fish caught, such as body condition and epidemiological status, thereby expanding the utility of these methods beyond predicting distribution alone. Improving the biological dimensions of marine ecosystem forecasting could allow for optimization of efficiencies in wild-capture fisheries by minimizing discarding and waste and maximizing the value of landed fish. These advancements are of direct benefit to industry and management, address several of the United Nations Sustainable Development Goals pertaining to fisheries sustainability and have the potential to support the maintenance of global food and micronutrient security under rapidly changing environmental conditions. Here, we describe the current state of the art in marine ecosystem forecasting; review the physical-biological linkages that underlie variability in the body condition of commercially valuable fish and shellfish with particular reference to marine climate change; and outline key considerations for the next generation of marine ecosystem forecasting tools for wild-capture fisheries.}, +author = {Bolin, Jessica A. and Schoeman, David S. and Evans, Karen J. and Cummins, Scott F. and Scales, Kylie L.}, +doi = {10.1111/faf.12569}, +file = {:C\:/Users/sean.rohan/Downloads/Fish and Fisheries - 2021 - Bolin - Achieving sustainable and climate‐resilient fisheries requires marine ecosystem.pdf:pdf}, +issn = {1467-2960}, +journal = {Fish and Fisheries}, +keywords = {body condition,climate change,commercial fisheries,dynamic ocean management,fish parasites,seasonal forecast}, +month = {sep}, +number = {5}, +pages = {1067--1084}, +title = {{Achieving sustainable and climate‐resilient fisheries requires marine ecosystem forecasts to include fish condition}}, +url = {https://onlinelibrary.wiley.com/doi/10.1111/faf.12569}, +volume = {22}, +year = {2021} +} + + +@article{Bond2015, +abstract = {Strongly positive temperature anomalies developed in the NE Pacific Ocean during the boreal winter of 2013-2014. Based on a mixed layer temperature budget, these anomalies were caused by lower than normal rates of the loss of heat from the ocean to the atmosphere and of relatively weak cold advection in the upper ocean. Both of these mechanisms can be attributed to an unusually strong and persistent weather pattern featuring much higher than normal sea level pressure over the waters of interest. This anomaly was the greatest observed in this region since at least the 1980s. The region of warm sea surface temperature anomalies subsequently expanded and reached coastal waters in spring and summer 2014. Impacts on fisheries and regional weather are discussed. It is found that sea surface temperature anomalies in this region affect air temperatures downwind in Washington state. Key Points Anomalous atmospheric forcing in the NE Pacific in winter 2013-2014 Weak seasonal cooling due to reduced heat fluxes and anomalous advection SST anomalies have impacts on the ecosystem and air temperatures}, +author = {Bond, Nicholas A. and Cronin, Meghan F. and Freeland, Howard and Mantua, Nathan}, +doi = {10.1002/2015GL063306}, +issn = {19448007}, +journal = {Geophysical Research Letters}, +keywords = {SLP anomalies,northeast Pacific Ocean,regional and downwind impacts,seasonal heating mechanisms,warm SST}, +number = {9}, +pages = {3414--3420}, +title = {{Causes and impacts of the 2014 warm anomaly in the NE Pacific}}, +volume = {42}, +year = {2015} +} + +@article{Brodeur2004, +abstract = {Information is summarized on juvenile salmonid distribution, size, condition, growth, stock origin, and species and environmental associations from June and August 2000 GLOBEC cruises with particular emphasis on differences related to the regions north and south of Cape Blanco off Southern Oregon. Juvenile salmon were more abundant during the August cruise as compared to the June cruise and were mainly distributed northward from Cape Blanco. There were distinct differences in distribution patterns between salmon species: chinook salmon were found close inshore in cooler water all along the coast and coho salmon were rarely found south of Cape Blanco. Distance offshore and temperature were the dominant explanatory variables related to coho and chinook salmon distribution. The nekton assemblages differed significantly between cruises. The June cruise was dominated by juvenile rockfishes, rex sole, and sablefish, which were almost completely absent in August. The forage fish community during June comprised Pacific herring and whitebait smelt north of Cape Blanco and surf smelt south of Cape Blanco. The fish community in August was dominated by Pacific sardines and highly migratory pelagic species. Estimated growth rates of juvenile coho salmon were higher in the GLOBEC study area than in areas farther north. An unusually high percentage of coho salmon in the study area were precocious males. Significant differences in growth and condition of juvenile coho salmon indicated different oceanographic environments north and south of Cape Blanco. The condition index was higher in juvenile coho salmon to the north but no significant differences were found for yearling chinook salmon. Genetic mixed stock analysis indicated that during June, most of the chinook salmon in our sample originated from rivers along the central coast of Oregon. In August, chinook salmon sampled south of Cape Blanco were largely from southern Oregon and northern California; whereas most chinook salmon north of Cape Blanco were from the Central Valley in California.}, +author = {Brodeur, Rick D. and Fisher, Joseph P. and Teel, David J. and Emmett, Robert L. and Casillas, Edmundo and Miller, Todd W.}, +issn = {00900656}, +journal = {Fishery Bulletin}, +number = {1}, +pages = {25--46}, +title = {{Juvenile salmonid distribution, growth, condition, origin, and environmental and species associations in the Northern California Current}}, +volume = {102}, +year = {2004} +} + + +@article{Froese2006, +abstract = {This study presents a historical review, a meta-analysis, and recommendations for users about weight-length relationships, condition factors and relative weight equations. The historical review traces the developments of the respective concepts. The meta-analysis explores 3929 weight-length relationships of the type W = aLb for 1773 species of fishes. It shows that 82% of the variance in a plot of log a over b can be explained by allometric versus isometric growth patterns and by di.erent body shapes of the respective species. Across species median b = 3.03 is significantly larger than 3.0, thus indicating a tendency towards slightly positive-allometric growth (increase in relative body thickness or lumpness) in most fishes. The expected range of 2.5 < b < 3.5 is confirmed. Mean estimates of b outside this range are often based on only one or two weight-length relationships per species. However, true cases of strong allometric growth do exist and three examples are given. Within species, a plot of log a vs b can be used to detect outliers in weight-length relationships. An equation to calculate mean condition factors from weight-length relationships is given as Kmean = 100 aLb-3. Relative weight Wrm = 100W/(amLbm) can be used for comparing the condition of individuals across populations, where am is the geometric mean of a and bm is the mean of b across all available weight-length relationships for a given species. Twelve recommendations for proper use and presentation of weight-length relationships, condition factors and relative weight are given. {\textcopyright} 2006 The Author Journal compilation {\textcopyright} 2006 Blackwell Verlag, Berlin.}, +author = {Froese, Rainer}, +doi = {10.1111/j.1439-0426.2006.00805.x}, +issn = {01758659}, +journal = {Journal of Applied Ichthyology}, +number = {4}, +pages = {241--253}, +title = {{Cube law, condition factor and weight-length relationships: History, meta-analysis and recommendations}}, +volume = {22}, +year = {2006} +} + +@article{Gruss2020a, +author = {Gr{\"{u}}ss, A and Gao, J and Thorson, JT and Rooper, CN and Thompson, G and Boldt, JL and Lauth, R}, +doi = {10.3354/meps13213}, +file = {:C\:/Users/sean.rohan/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gr{\"{u}}ss et al. - 2020 - Estimating synchronous changes in condition and density in eastern Bering Sea fishes.pdf:pdf}, +issn = {0171-8630}, +journal = {Marine Ecology Progress Series}, +keywords = {bottom temperature effects,condition,density-dependence,eastern bering sea,groundfishes,le cren,of the publisher,permitted without written consent,resale or republication not,s relative condition index,spatio-temporal models}, +pages = {169--185}, +title = {{Estimating synchronous changes in condition and density in eastern Bering Sea fishes}}, +volume = {635}, +year = {2020} +} + +@article{Gruss2021, +author = {Gr{\"{u}}ss, Arnaud and Thorson, James T and Stawitz, Christine C and Reum, Jonathan C.P. and Rohan, Sean K. and Barnes, Cheryl L.}, +doi = {10.1016/j.pocean.2021.102569}, +issn = {00796611}, +journal = {Progress in Oceanography}, +keywords = {Cold-pool extent,Eastern Bering Sea,Empirical orthogonal functions,Spatio-temporal models,Walleye pollock}, +month = {jun}, +pages = {102569}, +publisher = {Elsevier Ltd}, +title = {{Synthesis of interannual variability in spatial demographic processes supports the strong influence of cold-pool extent on eastern Bering Sea walleye pollock (Gadus chalcogrammus)}}, +url = {https://doi.org/10.1016/j.pocean.2021.102569 https://linkinghub.elsevier.com/retrieve/pii/S0079661121000562}, +volume = {194}, +year = {2021} +} + +@article{Haberle2023, +abstract = {Individual performance defines population dynamics. Condition index – a ratio of weight and some function of length – has been louded as an indicator of individual performance and recommended as a tool in fisheries management and conservation. However, insufficient understanding of the correlation between individual‐level processes and population‐level responses hinders its adoption. To this end, we use composite modelling to link individual's condition, expressed through the condition index, to population‐level status. We start by modelling ontogeny of European pilchard ( Sardina pilchardus , Clupeidae) as a function of food and constant temperature using Dynamic Energy Budget theory. We then provide a framework to simultaneously track the individual‐ and population‐level statistics by incorporating the dynamic energy budget model into an individual‐based model. Lastly, we explore the effects of fishing pressure on the statistics in two constant and food‐limited environmental carrying capacity scenarios. Results show that, regardless of the species' environmental carrying capacity, individual condition index will increase with fishing mortality, that is, with reduction of stock size. Same patterns are observed for gilthead seabream ( Sparus aurata , Sparidae), a significantly different species. Condition index can, therefore, in food‐limited populations, be used to (i) estimate population size relative to carrying capacity and (ii) distinguish overfished from underfished populations. Our findings promote a practical way to operationally incorporate the condition index into fisheries management and marine conservation, thus providing additional use for the commonly collected biometric data. Some real‐world applications, however, may require additional research to account for other variables such as fluctuating environmental conditions and individual variability.}, +author = {Haberle, Ines and Bav{\v{c}}evi{\'{c}}, Lav and Klanjscek, Tin}, +doi = {10.1111/faf.12744}, +issn = {1467-2960}, +journal = {Fish and Fisheries}, +keywords = {dynamic energy budget,fisheries management,fishing mortality,individual-}, +month = {jul}, +number = {4}, +pages = {567--581}, +title = {{Fish condition as an indicator of stock status: Insights from condition index in a food‐limiting environment}}, +url = {https://onlinelibrary.wiley.com/doi/10.1111/faf.12744}, +volume = {24}, +year = {2023} +} + +@techreport{Hurst2021, +author = {Hurst, Thomas P. and O'Leary, Cecilia A. and Rohan, Sean K. and Siddon, Elizabeth C. and Thorson, James T. and Vollenweider, Johanna J.}, +doi = {10.25923/p1yd-0793}, +title = {{Inventory, management uses, and recommendations for fish and crab condition information from the 2021 AFSC Condition Congress. AFSC Processed Rep. 2021-04, 39 p. Alaska Fish. Sci. Cent., NOAA, Nat. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle, WA 981}}, +year = {2021} +} + +@article{Oke2022, +abstract = {The temperature-size rule predicts that climate warming will lead to faster growth rates for juvenile fishes but lower adult body size. Testing this prediction is central to understanding the effects of climate change on population dynamics. We use fisheries-independent data (1999-2019) to test predictions of age-specific climate effects on body size in eastern Bering Sea walleye pollock (Gadus chalcogrammus). This stock supports one of the largest food fisheries in the world but is experiencing exceptionally rapid warming. Our results support the predictions that weight-at-age increases with temperature for young age classes (ages 1, 3-4) but decreases with temperature for old age classes (ages 7-15). Simultaneous demonstrations of larger juveniles and smaller adults with warming have thus far been rare, but pollock provide a striking example in a fish of exceptional ecological and commercial importance. The age-specific response to temperature was large enough (0.5 – 1 SD change in log weight-at-age) to have important implications for pollock management, which must estimate current and future weight-at-age to calculate allowable catch, and for the Bering Sea pollock fishery.}, +author = {Oke, Krista B. and Mueter, Franz J. and Litzow, Michael A.}, +doi = {10.1139/cjfas-2021-0315}, +file = {:C\:/Users/sean.rohan/Downloads/cjfas-2021-0315.pdf:pdf}, +issn = {0706-652X}, +journal = {Canadian Journal of Fisheries and Aquatic Sciences}, +month = {may}, +title = {{Warming leads to opposite patterns in weight-at-age for young versus old age classes of Bering Sea walleye pollock}}, +url = {https://cdnsciencepub.com/doi/10.1139/cjfas-2021-0315}, +year = {2022} +} + +@article{Paul1999, +abstract = {Age-0 Pacific herring were surveyed in October of 4 years in a large northern Gulf of Alaska estuary, to determine the range of variations in length, weight and whole body energy content (WBEC). These parameters reflect their preparedness for surviving their first winter's fast. During the surveys there were distinct regional and interannual variations in all three parameters for individual groups of herring in Prince William Sound. Likewise, with each collection there was typically a large range of size and WBEC values. The average standard length was (± S.D.) 80 ± 13 mm (range=40-118), the mean whole body wet weight was 5.7 ± 3.0 g (range=0.7-29.2) and the average WBEC of all age-0 herring captured, regardless of year or site (n=1471), was 5.4 ± 1.0 kJ g-1 wet weight (range=2.4-9.4). The large range of WBEC and size indicates that age-0 herring at different capture sites were not all equally prepared for surviving their first winter.}, +author = {Paul, A. J. and Paul, J. M.}, +doi = {10.1006/jfbi.1999.0927}, +file = {:C\:/Users/sean.rohan/Downloads/j.1095-8649.1999.tb00852.x (1).pdf:pdf}, +issn = {00221112}, +journal = {Journal of Fish Biology}, +keywords = {Energetics,Pacific herring,Size}, +number = {5}, +pages = {996--1001}, +title = {{Interannual and regional variations in body length, weight and energy content of age-0 Pacific herring from Prince William Sound, Alaska}}, +volume = {54}, +year = {1999} +} + +@article{Rodgveller2019, +abstract = {The objectives of this study were to determine if relative body condition and relative liver size (hepatosomatic index, HSI) could be utilized to predict maturity 6–8 months prior to spawning, when samples are readily available, and if these condition measures were related to fecundity. Female sablefish were sampled on four survey legs during a summer longline survey in July and August 2015 and during a winter survey in December 2015, which is 1–3 months prior to the spawning season in the Gulf of Alaska. The relative body condition and HSI of fish increased throughout the summer survey, reaching measurements similar to those observed during the winter. There were significant differences between immature and mature fish HSI and relative body condition and these differences increased throughout the summer, making these factors useful for predicting maturity on the last legs of the survey. On these later legs, models that utilized relative body condition and HSI, as well as length and age, to predict whether a fish was immature or would spawn produced maturity curves that best matched models based on histological maturity classifications. However, models without HSI may be the best choice for future work because liver weight is not regularly collected on annual surveys and on the last leg of the survey the addition of HSI to predicitive models did not improve maturity-at-age curves. Utilizing the winter data set, which is the time period when fecundity could be enumerated, fecundity was significantly related to relative condition and HSI. Increasing or decreasing these measures of condition by one standard deviation in a model of fecundity, which also included length, resulted in an estimated decrease in fecundity of 32% or an increase of 47% for an average size fish (78 cm). These results show the importance of incorporating fish condition into measures of population productivity.}, +author = {Rodgveller, Cara J.}, +doi = {10.1016/j.fishres.2019.03.013}, +issn = {01657836}, +journal = {Fisheries Research}, +keywords = {Age at maturity,Anoplopoma fimbria,Egg production,Fish maturation,Hepatosomatic index,Skip spawning}, +number = {October 2018}, +pages = {18--28}, +publisher = {Elsevier}, +title = {{The utility of length, age, liver condition, and body condition for predicting maturity and fecundity of female sablefish}}, +url = {https://doi.org/10.1016/j.fishres.2019.03.013}, +volume = {216}, +year = {2019} +} + +@article{Stabeno2019a, +abstract = {The lowest winter-maximum areal sea-ice coverage on record (1980–2019) in the Bering Sea occurred in the winter of 2017/2018. Sea ice arrived late due to warm southerly winds in November. More typical northerly winds (albeit warm) in December and January advanced the ice, but strong, warm southerlies in February and March forced the ice to retreat. The cold pool (shelf region with bottom water < 2 °C) was the smallest on record, because of two related mechanisms: (1) lack of direct cooling in winter by melting sea ice and (2) weaker vertical stratification (no ice melt reduced the vertical salinity gradient) allowing surface heating to penetrate into the near bottom water during summer. February 2019 exhibited another outbreak of warm southerly winds forcing ice to retreat. The number of >31-day outbreaks of southerly winds in winter has increased since 2016.}, +author = {Stabeno, Phyllis J. and Bell, Shaun W.}, +doi = {10.1029/2019GL083816}, +issn = {19448007}, +journal = {Geophysical Research Letters}, +keywords = {Bering Sea,cold pool,sea ice,stratification,winds}, +number = {15}, +pages = {8952--8959}, +title = {{Extreme conditions in the Bering Sea (2017–2018): record-breaking low sea-ice extent}}, +volume = {46}, +year = {2019} +} + +@article{Thorson2019, +abstract = {Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support interdisplinary fisheries management in the US and worldwide. These assessment activities have evolved different models, using different review standards, and are communicated using different vocabulary. Recent research shows that spatio-temporal models can estimate population density for multiple locations, times, and species, and that this is a “common currency” for addressing core goals in stock, ecosystem, habitat, and climate assessments. I therefore review the history and “design principles” for one spatio-temporal modelling package, the Vector Autoregressive Spatio-Temporal (VAST) package. I then provide guidance on fifteen major decisions that must be made by users of VAST, including: whether to use a univariate or multivariate model; when to include spatial and/or spatio-temporal variation; how many factors to use within a multivariate model; whether to include density or catchability covariates; and when to include a temporal correlation on model components. I finally demonstrate these decisions using three case studies. The first develops indices of abundance, distribution shift, and range expansion for arrowtooth flounder (Atheresthes stomias) in the Eastern Bering Sea, showing the range expansion for this species. The second involves “species ordination” of eight groundfishes in the Gulf of Alaska bottom trawl survey, which highlights the different spatial distribution of flathead sole (Hippoglossoides elassodon) relative to sablefish (Anoplopoma fimbria) and dover sole (Microstomus pacificus). The third involves a short-term forecast of the proportion of coastwide abundance for five groundfishes within three spatial strata in the US West Coast groundfish bottom trawl survey, and predicts large interannual variability (and high uncertainty) in the distribution of lingcod (Ophiodon elongatus). I conclude by recommending further research exploring the benefits and limitations of a “common currency” approach to stock, ecosystem, habitat, and climate assessments, and discuss extending this approach to optimal survey design and economic assessments.}, +author = {Thorson, James T.}, +doi = {10.1016/j.fishres.2018.10.013}, +file = {:C\:/Users/sean.rohan/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Thorson - 2019 - Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and.pdf:pdf}, +issn = {01657836}, +journal = {Fisheries Research}, +keywords = {Climate vulnerability analysis,Distribution shift,Habitat assessment,Index standardization,Integrated ecosystem assessment,Spatio-temporal model,Stock assessment,VAST}, +pages = {143--161}, +publisher = {Elsevier}, +title = {{Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments}}, +url = {https://doi.org/10.1016/j.fishres.2018.10.013}, +volume = {210}, +year = {2019} +} + +@article{Wuenschel2019, +abstract = {Measuring fish condition should link ecosystem drivers with population dynamics, if the underlying physiological basis for variations in condition indices are understood. We evaluated traditional (K, Kn, hepatosomatic index, gonadosomatic index, energy density, and percent dry weight of muscle (%DWM) and liver (%DWL)) and newer (bioelectrical impedance analysis (BIA) and scaled mass index (SMI)) condition indices to track seasonal cycles in three flatfishes — winter founder (Pseudopleuronectes americanus; three stocks), yellowtail flounder (Limanda ferruginea; three stocks), and summer flounder (Paralichthys dentatus; one stock) — with contrasting life histories in habitat, feeding, and reproduction. The %DWM and %DWL were good proxies for energy density (r2 > 0.96) and more strongly related to K, Kn, and SMI than to BIA metrics. Principal component analysis indicated many metrics performed similarly across species; some were confounded by size, sex, and maturity along PC1, while others effectively characterized condition along PC2. Stock differences were along PC1 in winter flounder, reflecting different sizes across stocks, whereas in yellowtail flounder differences occurred along PC2 related to condition. These comparisons, within and across species, highlight the broad applicability of some metrics and limitations in others.}, +author = {Wuenschel, Mark J. and McElroy, W. David and Oliveira, Kenneth and McBride, Richard S.}, +doi = {10.1139/cjfas-2018-0076}, +issn = {12057533}, +journal = {Canadian Journal of Fisheries and Aquatic Sciences}, +number = {6}, +pages = {886--903}, +title = {{Measuring fish condition: An evaluation of new and old metrics for three species with contrasting life histories}}, +volume = {76}, +year = {2019} +} + diff --git a/EBS_GroundfishCondition_2023.Rmd b/EBS_GroundfishCondition_2023.Rmd index e09aae1..79b3ea4 100644 --- a/EBS_GroundfishCondition_2023.Rmd +++ b/EBS_GroundfishCondition_2023.Rmd @@ -7,7 +7,7 @@ author: name: Sean Rohan output: word_document fontsize: 12pt -bibliography: EBS_references.bib +bibliography: CopyOfEBS_references.bib csl: fish-and-fisheries.csl addr: l1: 7600 Sand Point Way NE @@ -87,42 +87,42 @@ if(make_map){ legend.position = c(0.2,0.1), legend.background = element_blank()) - png(filename = here::here("plots", region, "EBS_NBS_survey_area.png"), width = 5, height = 5, units = "in", res = 600) + ragg::agg_png(filename = here::here("plots", region, "EBS_NBS_survey_area.png"), width = 5, height = 5, units = "in", res = 600) print(plot_ebs_nbs_survey_stations) dev.off() } ``` -Contributed by Bianca Prohaska^1^ and Sean Rohan^1^, +Contributed by Bianca Prohaska^1^ and Sean Rohan^1^ + ^1^Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA **Contact**: sean.rohan@noaa.gov -**Last updated**: October 2022 - -**Description of Indicator**: Morphometric condition indicators based on length-weight relationships characterize variation in somatic growth and can be considered indicators of prey availability, growth, general health, and habitat condition [@Blackwell2000; @Froese2006]. This contribution presents two morphometric condition indicators based on length-weight relationships: a new relative condition indicator that is estimated using a spatiotemporal model and the historical indicator based on residuals of the length-weight relationship. - -The new model-based relative condition indicator (VAST relative condition) is the ratio of fish weight-at-length relative to the time series mean based on annual allometric intercepts, _a~year~_, in the length-weight equation (_W_ = _aL_^_b_^; _W_ is mass (g), _L_ is fork length (cm)), i.e., $condition = a_{year}/\overline{a}$. Relative condition greater than one indicates better condition (i.e., heavier per unit length) and relative condition less than one indicates poorer condition (i.e., lighter per unit length). - -The historical length-weight indicator based on residuals of the length-weight relationship represents how heavy a fish is per unit body length compared to the time series mean [@Brodeur2004]. Positive length-weight residuals indicate better condition (i.e., heavier per unit length) and negative residuals indicate poorer condition (i.e., lighter per unit length) [@Froese2006]. Fish condition calculated in this way reflects realized outcomes of intrinsic and extrinsic processes that affect fish growth, which can have implications for biological productivity through direct effects on growth and indirect effects on demographic processes such as, reproduction, and mortality (e.g., (@Rodgveller2019); (@Barbeaux2020)). +**Last updated**: October 2023 -The model-based relative condition indicator was estimated using a spatiotemporal model with spatial random effects, implemented in the software VAST v3.8.2 [@Thorson2019; @Gruss2020a]. Allometric intercepts, _a~year~_, are estimated as fixed effects using a multivariate generalized linear mixed model that jointly estimates spatial and temporal variation in _a_ and catch per unit effort (numbers of fish per area). Density-weighted average _a~year~_ is a product of population density, local _a_, and area. Spatial variation in _a~year~_ was represented using a Gaussian Markov random field. The model approximates _a~year~_ using a log-link function and linear predictors [@Gruss2020a]. Parameters are estimated by identifying the values that maximize the marginal log-likelihood. +**Description of Indicator**: Length-weight residuals represent how heavy a fish is per unit body length and are an indicator of somatic growth variability [@Brodeur2004]. Therefore, length-weight residuals can be considered indicators of prey availability, growth, general health, and habitat condition [@Blackwell2000; @Froese2006]. Positive length-weight residuals indicate better condition (i.e., heavier per unit length) and negative residuals indicate poorer condition (i.e., lighter per unit length) [@Froese2006]. Fish condition calculated in this way reflects realized outcomes of intrinsic and extrinsic processes that affect fish growth which can have implications for biological productivity through direct effects on growth and indirect effects on demographic processes such as, reproduction and mortality (e.g., [@Rodgveller2019; @Barbeaux2020]). ```{r map, include=TRUE,out.width="200%",fig.cap="\\label{fig:figs}Figure 1. AFSC/RACE GAP summer bottom trawl survey strata (10-90) and station locations (x) in the eastern Bering Sea and northern Bering Sea. ", echo=FALSE} knitr::include_graphics(path = here::here("plots", region, "ebs_nbs_survey_area.png")) ``` -The historical indicator was estimated from residuals of linear regression models based on a log-transformation of the exponential growth relationship from 1999 to 2022 (EBS: 1999–2022, NBS: 2010 & 2017–2019, 2022). A unique slope (_b_) was estimated for each survey stratum (Fig. 1) to account for spatial-temporal variation in growth and bottom trawl survey sampling. Survey strata 31 and 32 were combined as stratum 30; strata 41, 42, and 43 were combined as stratum 40; and strata 61 and 62 were combined as stratum 60. Northwest survey strata 82 and 90 were excluded from these analyses due to sample size considerations. Length-weight relationships for juvenile length walleye pollock (100–250 mm fork, corresponding with ages 1–2 years) were calculated separately from adult walleye pollock (> 250 mm). Residuals for individual fish were obtained by subtracting observed weights from bias-corrected weights-at-length that were estimated from regression models. +The groundfish morphometric condition indicator is calculated from paired fork lengths (mm) and weights (g) of individual fishes that were collected during bottom trawl surveys of the eastern Bering Sea (EBS) shelf and northern Bering Sea (NBS), which were conducted by the Alaska Fisheries Science Center’s Resource Assessment and Conservation Engineering (AFSC/RACE) Groundfish Assessment Program (GAP). Fish condition analyses were applied to walleye pollock (_Gadus chalcogrammus_), Pacific cod (_Gadus macrocephalus_), arrowtooth flounder (_Atheresthes stomias_), yellowfin sole (_Limanda aspera_), flathead sole (_Hippoglossoides elassodon_), northern rock sole (_Lepidopsetta polyxystra_), and Alaska plaice (_Pleuronectes quadrituberculatus_) collected in bottom trawls at standard survey stations (Figure 1). For these analyses and results, survey strata 31 and 32 were combined as stratum 30; strata 41, 42, and 43 were combined as stratum 40; and strata 61 and 62 were combined as stratum 60. Northwest survey strata 82 and 90 were excluded from these analyses. -For the EBS shelf, individual length-weight residuals were averaged for each stratum and weighted in proportion to total biomass in each stratum from area-swept expansion of bottom-trawl survey catch per unit effort (CPUE; i.e., design-based stratum biomass estimates). Analysis for the NBS was conducted separately from the EBS because of the shorter time series and the NBS was treated as a single stratum without biomass weighting. To minimize the influence of unrepresentative samples on indicator calculations, combinations of species, stratum, and year with sample size <10 were used to fit length-weight regressions but were excluded from calculating length-weight residuals in the EBS. +To calculate indicators, length-weight relationships were estimated from linear regression models based on a log-transformation of the exponential growth relationship, _W_ = _aL_^_b_^, where _W_ is weight (g) and _L_ is fork length (mm) for all areas for the period 1997–2023 (EBS: 1997–2023, NBS: 2010, 2017, 2019, 2021-2023). Unique intercepts (*a*) and slopes (*b*) were estimated for each survey stratum, sex, and interaction between stratum and sex to account for sexual dimorphism and spatial-temporal variation in growth and bottom trawl survey sampling. Length-weight relationships for 100–250 mm fork length walleye pollock (corresponding with ages 1–2 years) were calculated separately from adult walleye pollock (> 250 mm). Residuals for individual fish were obtained by subtracting observed weights from bias-corrected weights-at-length that were estimated from regression models. Length-weight residuals from each stratum were aggregated and weighted proportionally to total biomass in each stratum from area-swept expansion of mean bottom-trawl survey catch per unit effort (CPUE; i.e., design-based stratum biomass estimates). Variation in fish condition was evaluated by comparing average length-weight residuals among years. To minimize the influence of unrepresentative samples on indicator calculations, combinations of species, stratum, and year with a sample size <10 were used to fit length-weight regressions, but were excluded from calculating length-weight residuals for both the EBS and NBS. Morphometric condition indicator time series, code for calculating the indicators, and figures showing results for individual species are available through the *akfishcondition* R package and GitHub repository (https://github.com/afsc-gap-products/akfishcondition). -Both condition indicators were calculated from paired fork lengths and weights of individual fishes that were collected during bottom trawl surveys of the eastern Bering Sea (EBS) shelf and northern Bering Sea (NBS) which were conducted by the Alaska Fisheries Science Center’s Resource Assessment and Conservation Engineering (AFSC/RACE) Groundfish Assessment Program (GAP). Fish condition analyses were applied to walleye pollock (_Gadus chalcogrammus_), Pacific cod (_Gadus macrocephalus_), arrowtooth flounder (_Atheresthes stomias_), yellowfin sole (_Limanda aspera_), flathead sole (_Hippoglossoides elassodon_), northern rock sole (_Lepidopsetta polyxystra_), and Alaska plaice (_Pleuronectes quadrituberculatus_) collected in bottom trawls at standard survey stations (Fig. 1). +**Methodological Changes**: In Groundfish Morphometric Condition Indicator contributions to the 2022 Eastern Bering Sea and Aleutians Islands Ecosystem Status Reports, historical stratum-biomass weighted residuals condition indicators were presented alongside condition indicators that were calculated using the R package VAST following methods that were presented for select GOA species during the Spring Preview of Ecological and Economic Conditions (PEEC) in May 2020. The authors noted there were strong correlations between VAST and stratum-biomass weighted condition indicators for most EBS and NBS species (r = 0.79–0.98). The authors received the following feedback about the change from the BSAI Groundfish Plan Team meeting during their November 2022 meeting: +*"The Team discussed the revised condition indices that now use a different, VAST-based condition index, but felt additional methodology regarding this transition was needed. The Team recommended a short presentation next September to the Team to review the methods and tradeoffs in approaches. The Team encouraged collaboration with the NMFS longline survey team to develop analogous VAST indices."* -**Methodological Changes**: The historical length-weight residual indicator (used in 2020 and 2021) and new VAST relative condition indicator [@Gruss2020a] are both presented this year to allow comparison between methods. Overall, trends were similar between historical and new indicators based on the strong correlation (_r_ > 0.85) between indicators for most species (Figs. 3,4,6). An exception was large walleye pollock (> 250 mm) in the NBS (_r_ = 0.33), which may be due to the small sample size (_n_ = 4) collected exclusively from the southern end of the NBS survey area in 2010. Mean estimates and confidence intervals for the new condition indicator are likely more reliable than the historical indicator because the new indicator affords more precise expansion of individual samples to the population. This indicator also better accounts for spatially and temporally unbalanced sampling that is characteristic of historical bottom trawl survey data due to changes in sampling protocols (e.g., transition from sex-and-length stratified to random sampling). +Based on feedback from the Plan Team, staff limitations, and the lack of a clear path to transition condition indicators for longline survey species to VAST, analyses supporting the transition to VAST were not conducted during 2023. Therefore, the 2023 condition indicator was calculated from statum-biomass weighted residuals of length-weight regressions. -**Status and Trends**: Fish condition, indicated by the model-based condition indicator (VAST relative condition), has varied over time for all species examined (Figs. 2 & 5). In 2019 in the EBS, an upward trend in VAST relative condition was observed for most species relative to 2017–2018; however, in 2021 VAST relative condition had a downward trend in most species examined. In 2022 in the EBS, VAST relative conditions were near the historical mean, or positive for all species examined except for arrowtooth flounder, and large walleye pollock (>250 mm), and while their VAST relative conditions were negative, the mean for both groups fell within one standard deviation of the historical mean (Fig. 2). +Stratum-biomass weighted residuals for NBS strata are presented for the first time in 2023. NBS length-weight samples were previously pooled across strata to calculate region-wide length-weight residuals because of the lack of samples from regular survey sampling prior to 2017. The authors have opted to present stratum-biomass weighted residuals for the NBS in 2023 because of the accumulation of regular length-weight samples in recent years. -```{r figure 2 grid, include=TRUE, echo=FALSE, fig.height=14,fig.width=12,fig.cap="\\label{fig:figs}Figure 2. Morphometric condition of groundfish species collected during AFSC/RACE GAP standard summer bottom trawl surveys of the eastern Bering Sea shelf (1999 to 2023) based on residuals of length-weight regressions. The dash in the blue boxes denote the mean for that year, the box denotes one standard error, and the lines on the boxes denote two standard error. Lines on each plot represent the historical mean, dashed lines denote one standard deviation, and dotted lines denote two standard deviations.", message=FALSE, warning=FALSE} +**Status and Trends**: Fish condition, indicated by length-weight residuals, has varied over time for all species examined in the EBS (Figure 2 & 3). In 2023 a downward trend in condition from 2022 was observed for all species in the EBS, with large walleye pollock (>250 mm), and arrowtooth flounder decreasing since 2019; however, all species were still within one standard deviation of the mean except for large walleye pollock (>250 mm) which was negative but within two standard deviations of the mean (Figure 2). Large walleye pollock (>250 mm) exhibited the second worst condition observed over the full time series with the lowest observed condition occurring in 1999 (Figure 2). In 2019, an upward trend in condition was observed for most species relative to 2017–2018 with positive weighted length-weight residuals relative to historical averages for large walleye pollock (>250 mm), northern rock sole, yellowfin sole, arrowtooth flounder, and Alaska plaice; however, in 2021 condition had a downward trend in most species examined. In 2022 in the EBS, conditions were near the historical mean, or positive for all species examined except for arrowtooth flounder and large walleye pollock (>250 mm). While their conditions were below average, the mean for both groups fell within one standard deviation of the historical mean (Figure 2). + +In the EBS in 2023, condition was negative for large walleye pollock (>250 mm), arrowtooth flounder, and flathead sole across most strata (Figure 3). In 2023, there was a divergence in small walleye pollock (100-250 mm) condition among strata with more positive condition observed on the inner shelf (Stratum 10), and more negative condition observed on the middle shelf (Stratum 30). + +```{r figure 2 grid, include=TRUE, echo=FALSE, fig.height=14,fig.width=12,fig.cap="\\label{fig:figs}Figure 2. Morphometric condition of groundfish species collected during AFSC/RACE GAP standard summer bottom trawl surveys of the eastern Bering Sea shelf (1999 to 2023) based on residuals of length-weight regressions. The dash in the blue boxes denote the mean for that year, the box denotes one standard error, and the lines on the boxes denote two standard errors. Lines on each plot represent the historical mean, dashed lines denote one standard deviation, and dotted lines denote two standard deviations.", message=FALSE, warning=FALSE} # Set factor levels for plotting order fig2 <- plot_anomaly_timeseries(x = EBS_INDICATOR$FULL_REGION, region = "BS", @@ -153,43 +153,45 @@ print(fig2_png + theme_blue_strip()) dev.off() ``` -```{r figure 5 grid, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE} -fig5 <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, - region = "BS", - fill_color = "#54ADDB", - var_y_name = "mean_wt_resid", - var_y_se_name = "se_wt_resid", - var_x_name = "year", - y_title = "Length-weight residual (ln(g))", - plot_type = "box", - format_for = "rmd") - -fig5_png <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, - region = "BS", - fill_color = "#54ADDB", - var_y_name = "mean_wt_resid", - var_y_se_name = "se_wt_resid", - var_x_name = "year", - y_title = "Length-weight residual (ln(g))", - plot_type = "box", - format_for = "png") - - +```{r figure 3 grid, include=TRUE, echo=FALSE, fig.height=14,fig.width=12,fig.cap="\\label{fig:figs}Figure 3. Length-weight residuals by survey stratum (10-60) for seven eastern Bering Sea shelf groundfish species and age 1–2 walleye pollock (100–250 mm) sampled in the AFSC/RACE GAP standard summer bottom trawl survey, 1999-2023. Length-weight residuals are not weighted by stratum biomass.", message=FALSE, warning=FALSE} +# EBS stratum-biomass weighted residual stratum stacked bar plots +fig3 <- akfishcondition::plot_stratum_stacked_bar(x = EBS_INDICATOR$STRATUM, + region = region, + var_x_name = "year", + var_y_name = "stratum_resid_mean", + y_title = "Length-weight residual (ln(g))", + var_group_name = "stratum", + fill_title = "Stratum", + fill_palette = "BrBG") + +fig3_png <- akfishcondition::plot_stratum_stacked_bar(x = EBS_INDICATOR$STRATUM, + region = region, + var_x_name = "year", + var_y_name = "stratum_resid_mean", + y_title = "Length-weight residual (ln(g))", + var_group_name = "stratum", + fill_title = "Stratum", + fill_palette = "BrBG") + +print(fig3 + theme_condition_index()+ theme(strip.text = element_text(size = 20))) ``` -```{r figure 5 grid png, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE} -png(here::here("plots", region, "NBS_condition.png"), width=6, height=7, units="in", res=600) -print(fig5_png + theme_blue_strip()) +```{r figure 3 grid png, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE} +png(here::here("plots", region, "EBS_SBW_stratum_stacked_bar.png"), width = 6, height = 7, units = "in", res = 600) +print(fig3_png + theme_blue_strip() + theme(legend.position = "right", legend.title = element_text(size = 9))) dev.off() ``` -In the NBS in 2022, VAST relative condition of all species examined, including large (>250 mm) and small (100–250 mm) walleye pollock, were negative; however, despite being below the historical average, the VAST relative condition of all species were within one standard deviation of the time series mean (Fig. 5). +In the NBS in 2023, positive condition was observed for large walleye pollock (>250 mm), which has been increasing since 2021. The remaining species exhibited near-average condition in the NBS in 2023, except for yellowfin sole which exhibited negative condition, and has been declining since 2019 (Figure 4). -```{r figure 6 set up, include=FALSE, fig.height=4, fig.width=4, message=FALSE, warning=FALSE} -fig6 <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, - region = "BS", - fill_color = "#54ADDB", +In 2023 large walleye pollock (>250 mm) condition was positive in all NBS strata, whereas condition was previously negative in all strata from 2021-2022 (Figure 5). Pacific cod, small walleye pollock (100-250 mm), Alaska plaice, and yellowfin sole condition have been consistently negative across all strata since 2021, with a notable exception in 2023 of positive condition for Pacific cod in the inner southern NBS shelf, and Alaska plaice in the northern inner NBS shelf and Norton Sound (Figure 5). + +```{r figure 4 grid, include=TRUE, echo=FALSE, fig.height=14,fig.width=12,fig.cap="\\label{fig:figs}Figure 4. Morphometric condition of groundfish species collected during AFSC/RACE GAP standard summer bottom trawl surveys of the northern Bering Sea shelf (2010, 2017, 2019 and 2021-2023) based on residuals of length-weight regressions. The dash in the blue boxes denote the mean for that year, the box denotes one standard error, and the lines on the boxes denote two standard errors. Lines on each plot represent the historical mean, dashed lines denote one standard deviation, and dotted lines denote two standard deviations.", message=FALSE, warning=FALSE} +# Set factor levels for plotting order +fig4 <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, + region = "BS", + fill_color = "#54ADDB", var_y_name = "mean_wt_resid", var_y_se_name = "se_wt_resid", var_x_name = "year", @@ -197,90 +199,38 @@ fig6 <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, plot_type = "box", format_for = "rmd") -fig6_png <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, - region = "BS", - fill_color = "#54ADDB", +fig4_png <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, + region = "BS", + fill_color = "#54ADDB", var_y_name = "mean_wt_resid", var_y_se_name = "se_wt_resid", var_x_name = "year", y_title = "Length-weight residual (ln(g))", plot_type = "box", format_for = "png") -``` -```{r figure 6 grid, include=TRUE, echo=FALSE, fig.height=14, fig.width=12, fig.cap="\\label{fig:figs}Figure 5. VAST relative condition for groundfish species collected during AFSC/RACE GAP summer bottom trawl surveys of the northern Bering Sea, 2010 and 2017 to 2023. The dash in the blue boxes denote the mean for that year, the box denotes one standard error, and the lines on the boxes denote two standard error. Lines on each plot represent the historical mean, dashed lines denote one standard deviation, and dotted lines denote two standard deviations.",message=FALSE, warning=FALSE} -print(fig6 + - theme_condition_index()) -``` +print(fig4 + theme_condition_index()) -```{r figure 6 grid png, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE} -png(here::here("plots", region, "NBS_condition.png"), width = 6, height = 7, units = "in", res = 600) -print(fig6_png + theme_blue_strip()) -dev.off() ``` - -```{r sbw_legacy_plots, include=FALSE, message=FALSE, warning=FALSE} -# EBS stratum-biomass weighted residual time series anomaly -ebs_sbw_timeseries <- plot_anomaly_timeseries(x = EBS_INDICATOR$FULL_REGION, - region = region, - fill_color = "#0085CA", - var_y_name = "mean_wt_resid", - var_y_se_name = "se_wt_resid", - var_x_name = "year", - y_title = "Length-weight residual (ln(g))", - plot_type = "box", - format_for = "png", - set_intercept = 0) - -png(here::here("plots", region, "EBS_SBW_timeseries.png"), width = 6, height = 7, units = "in", res = 600) -print(ebs_sbw_timeseries + akfishcondition::theme_blue_strip()) -dev.off() - -# EBS stratum-biomass weighted residual stratum stacked bar plots -ebs_stacked_bar <- akfishcondition::plot_stratum_stacked_bar(x = EBS_INDICATOR$STRATUM, - region = region, - var_x_name = "year", - var_y_name = "stratum_resid_mean", - y_title = "Length-weight residual (ln(g))", - var_group_name = "stratum", - fill_title = "Stratum", - fill_palette = "BrBG") - -png(here::here("plots", region, "EBS_SBW_stratum_stacked_bar.png"), width = 6, height = 7, units = "in", res = 600) -print(ebs_stacked_bar + theme_blue_strip() + theme(legend.position = "right", legend.title = element_text(size = 9))) -dev.off() - -# EBS single species stratum plots -akfishcondition::plot_species_stratum_bar(x = EBS_INDICATOR$STRATUM, - region = "EBS", - var_x_name = "year", - var_y_name = "stratum_resid_mean", - var_y_se_name = "stratum_resid_se", - y_title = "Length-weight residual (ln(g))", - var_group_name = "stratum", - fill_title = "Stratum", - fill_palette = "BrBG", - write_plot = TRUE) - -# NBS stratum-biomass weighted residual time series anomaly -nbs_sbw_timeseries <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, - region = region, - fill_color = "#0085CA", - var_y_name = "mean_wt_resid", - var_y_se_name = "se_wt_resid", - var_x_name = "year", - y_title = "Length-weight residual (ln(g))", - plot_type = "box", - format_for = "png", - set_intercept = 0) - -png(here::here("plots", region, "NBS_SBW_timeseries.png"), width = 6, height = 7, units = "in", res = 600) -print(nbs_sbw_timeseries + akfishcondition::theme_blue_strip()) +```{r figure 4 grid png, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE} +png(here::here("plots", region, "NBS_condition.png"), width=6, height=7, units="in", res=600) +print(fig4_png + theme_blue_strip()) dev.off() +``` +```{r figure 5 grid, include=TRUE, echo=FALSE, fig.height=14,fig.width=12,fig.cap="\\label{fig:figs}Figure 5. Length-weight residuals by survey stratum (70, 71 and 81) for four northern Bering Sea shelf groundfish species and age 1–2 walleye pollock (100–250 mm) sampled in the AFSC/RACE GAP standard summer bottom trawl survey during 2010, 2017, 2019 and 2021-2023. Length-weight residuals are not weighted by stratum biomass.", message=FALSE, warning=FALSE} # NBS stratum-biomass weighted residual stratum stacked bar plots -nbs_stacked_bar <- akfishcondition::plot_stratum_stacked_bar(x = NBS_INDICATOR$STRATUM, +fig5 <- akfishcondition::plot_stratum_stacked_bar(x = NBS_INDICATOR$STRATUM, + region = region, + var_x_name = "year", + var_y_name = "stratum_resid_mean", + y_title = "Length-weight residual (ln(g))", + var_group_name = "stratum", + fill_title = "Stratum", + fill_palette = "PuBu") + +fig5_png <- akfishcondition::plot_stratum_stacked_bar(x = NBS_INDICATOR$STRATUM, region = region, var_x_name = "year", var_y_name = "stratum_resid_mean", @@ -289,41 +239,122 @@ nbs_stacked_bar <- akfishcondition::plot_stratum_stacked_bar(x = NBS_INDICATOR$S fill_title = "Stratum", fill_palette = "PuBu") +print(fig5 + theme_condition_index()+ theme(strip.text = element_text(size = 20))) + +``` + +```{r figure 5 grid png, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE} png(here::here("plots", region, "NBS_SBW_stratum_stacked_bar.png"), width = 6, height = 7, units = "in", res = 600) -print(nbs_stacked_bar + theme_blue_strip() + theme(legend.position = "right", legend.title = element_text(size = 9))) +print(fig5_png + theme_blue_strip() + theme(legend.position = "right", legend.title = element_text(size = 9))) dev.off() +``` + -# NBS single species stratum plots -akfishcondition::plot_species_stratum_bar(x = NBS_INDICATOR$STRATUM, - region = "NBS", - var_x_name = "year", - var_y_name = "stratum_resid_mean", - var_y_se_name = "stratum_resid_se", - y_title = "Length-weight residual (ln(g))", - var_group_name = "stratum", - fill_title = "Stratum", - fill_palette = "PuBu", - write_plot = TRUE) +```{r sbw_legacy_plots, include=FALSE, message=FALSE, warning=FALSE} +# # EBS stratum-biomass weighted residual time series anomaly +# ebs_sbw_timeseries <- plot_anomaly_timeseries(x = EBS_INDICATOR$FULL_REGION, +# region = region, +# fill_color = "#0085CA", +# var_y_name = "mean_wt_resid", +# var_y_se_name = "se_wt_resid", +# var_x_name = "year", +# y_title = "Length-weight residual (ln(g))", +# plot_type = "box", +# format_for = "png", +# set_intercept = 0) +# +# png(here::here("plots", region, "EBS_SBW_timeseries.png"), width = 6, height = 7, units = "in", res = 600) +# print(ebs_sbw_timeseries + akfishcondition::theme_blue_strip()) +# dev.off() +# +# # EBS stratum-biomass weighted residual stratum stacked bar plots +# ebs_stacked_bar <- akfishcondition::plot_stratum_stacked_bar(x = EBS_INDICATOR$STRATUM, +# region = region, +# var_x_name = "year", +# var_y_name = "stratum_resid_mean", +# y_title = "Length-weight residual (ln(g))", +# var_group_name = "stratum", +# fill_title = "Stratum", +# fill_palette = "BrBG") +# +# png(here::here("plots", region, "EBS_SBW_stratum_stacked_bar.png"), width = 6, height = 7, units = "in", res = 600) +# print(ebs_stacked_bar + theme_blue_strip() + theme(legend.position = "right", legend.title = element_text(size = 9))) +# dev.off() +# +# # EBS single species stratum plots +# akfishcondition::plot_species_stratum_bar(x = EBS_INDICATOR$STRATUM, +# region = "EBS", +# var_x_name = "year", +# var_y_name = "stratum_resid_mean", +# var_y_se_name = "stratum_resid_se", +# y_title = "Length-weight residual (ln(g))", +# var_group_name = "stratum", +# fill_title = "Stratum", +# fill_palette = "BrBG", +# write_plot = TRUE) +# +# # NBS stratum-biomass weighted residual time series anomaly +# nbs_sbw_timeseries <- plot_anomaly_timeseries(x = NBS_INDICATOR$FULL_REGION, +# region = region, +# fill_color = "#0085CA", +# var_y_name = "mean_wt_resid", +# var_y_se_name = "se_wt_resid", +# var_x_name = "year", +# y_title = "Length-weight residual (ln(g))", +# plot_type = "box", +# format_for = "png", +# set_intercept = 0) +# +# png(here::here("plots", region, "NBS_SBW_timeseries.png"), width = 6, height = 7, units = "in", res = 600) +# print(nbs_sbw_timeseries + akfishcondition::theme_blue_strip()) +# dev.off() +# +# # NBS stratum-biomass weighted residual stratum stacked bar plots +# nbs_stacked_bar <- akfishcondition::plot_stratum_stacked_bar(x = NBS_INDICATOR$STRATUM, +# region = region, +# var_x_name = "year", +# var_y_name = "stratum_resid_mean", +# y_title = "Length-weight residual (ln(g))", +# var_group_name = "stratum", +# fill_title = "Stratum", +# fill_palette = "PuBu") +# +# png(here::here("plots", region, "NBS_SBW_stratum_stacked_bar.png"), width = 6, height = 7, units = "in", res = 600) +# print(nbs_stacked_bar + theme_blue_strip() + theme(legend.position = "right", legend.title = element_text(size = 9))) +# dev.off() +# +# +# # NBS single species stratum plots +# akfishcondition::plot_species_stratum_bar(x = NBS_INDICATOR$STRATUM, +# region = "NBS", +# var_x_name = "year", +# var_y_name = "stratum_resid_mean", +# var_y_se_name = "stratum_resid_se", +# y_title = "Length-weight residual (ln(g))", +# var_group_name = "stratum", +# fill_title = "Stratum", +# fill_palette = "PuBu", +# write_plot = TRUE) ``` -**Factors influencing observed trends**: Temperature appears to influence morphological condition of several species in the EBS and NBS, so near-average cold pool extent and water temperatures in 2022 likely played a role in the near-average condition (within 1 S.D. of the mean) for most species. Historically, particularly cold years tend to correspond with negative condition, while particularly warm years tend to correspond to positive condition. For example, water temperatures were particularly cold during the 1999 Bering Sea survey, a year in which negative condition was observed for all species that data were available. In addition, spatiotemporal factor analyses suggest the morphometric condition of age-7 walleye pollock is strongly correlated with cold pool extent in the EBS [@Gruss2021]. In recent years, warm temperatures across the Bering Sea shelf, since the record low seasonal sea ice extent in 2017–2018 and historical cold pool area minimum in 2018 [@Stabeno2019a], may have influenced the positive trend in the condition of several species from 2016 to 2019. +**Factors influencing observed trends**: Temperature appears to influence morphological condition of several species in the EBS and NBS, so near-average cold pool extent and water temperatures in 2023 likely played a role in the near-average condition (within 1 S.D. of the mean) for most species in the EBS and NBS. Historically, particularly cold years tend to correspond with negative condition, while particularly warm years tend to correspond to positive condition. For example, water temperatures were particularly cold during the 1999 Bering Sea survey, a year in which negative condition was observed for all species that data were available. In addition, spatiotemporal factor analyses suggest the morphometric condition of age-7 walleye pollock is strongly correlated with cold pool extent in the EBS [@Gruss2021]. In recent years, warm temperatures across the Bering Sea shelf, since the record low seasonal sea ice extent in 2017–2018 and historical cold pool area minimum in 2018 [@Stabeno2019a], may have influenced the positive trend in the condition of several species from 2016 to 2019. However, despite near-average temperature in 2023 large walleye pollock (>250 mm) condition in the EBS was the second lowest recorded over the time series. Although warmer temperatures may increase growth rates if there is adequate prey to offset temperature-dependent increases in metabolic demand, growth rates may also decline if prey resources are insufficient to offset temperature-dependent increases in metabolic demand. The influence of temperature on growth rates depends on the physiology of predator species, prey availability, and the adaptive capacity of predators to respond to environmental change through migration, changes in behavior, and acclimatization. For example, elevated temperatures during the 2014–2016 marine heatwave in the Gulf of Alaska led to lower growth rates of Pacific cod and lower condition because available prey resources did not make up for increased metabolic demand [@Barbeaux2020]. Other factors that could affect morphological condition include survey timing, stomach fullness, fish movement patterns, sex, and environmental conditions [@Froese2006]. The starting date of annual length-weight data collections has varied from late May to early June and ended in late July-early August in the EBS, and mid-August in the NBS. Although we account for some of this variation by using spatially-varying coefficients in the length-weight relationship, variation in condition could relate to variation in the timing of sample collection within survey strata. Survey timing can be further compounded by seasonal fluctuations in reproductive condition with the buildup and depletion of energy stores [@Wuenschel2019]. Another consideration is that fish weights sampled at sea include gut content weights, so variation in gut fullness may influence weight measurements. Since feeding conditions vary over space and time, prey consumption rates and the proportion of total body weight attributable to gut contents may be an important factor influencing the length-weight residuals. -Finally, although the condition indicators characterize temporal variation in morphometric condition for important fish species in the EBS and NBS they do not inform the mechanisms or processes behind the observed patterns. +Finally, although the condition indicators characterize temporal variation in morphometric condition for important fish species in the EBS and NBS, they do not inform the mechanisms or processes behind the observed patterns. -**Implications**: Fish morphometric condition can be considered an indicator of ecosystem productivity with implications for fish survival, maturity, and reproduction. For example, in Prince William Sound, the pre-winter condition of herring may determine their overwinter survival [@Paul1999], differences in feeding conditions have been linked to differences in morphometric condition of pink salmon in Prince William Sound [@Boldt2004], variation in morphometric condition has been linked to variation in maturity of sablefish [@Rodgveller2019], and lower morphometric condition of Pacific cod was associated with higher mortality and lower growth rates during the 2014–2016 marine heat wave in the Gulf of Alaska [@Barbeaux2020]. Thus, the condition of EBS and NBS groundfishes may provide insight into ecosystem productivity as well as fish survival, demographic status, and population health. However, survivorship is likely affected by many factors not examined here. +**Implications**: Fish morphometric condition can be considered an indicator of ecosystem productivity with implications for fish survival, maturity, and reproduction. For example, in Prince William Sound, the pre-winter condition of herring may determine their overwinter survival [@Paul1999], differences in feeding conditions have been linked to differences in morphometric condition of pink salmon in Prince William Sound [@Boldt2004], variation in morphometric condition has been linked to variation in maturity of sablefish [@Rodgveller2019], and lower morphometric condition of Pacific cod was associated with higher mortality and lower growth rates during the 2014–2016 marine heat wave in the Gulf of Alaska [@Barbeaux2020]. Condition can also be an indicator of stock status relative to carrying capacity because morphometric condition is expected to be high when the stock is at low abundance and low when the stock is at high abundance because of the effects of density-dependent competition [@Haberle2023]. Thus, the condition of EBS and NBS groundfishes may provide insight into ecosystem productivity as well as fish survival, demographic status, and population health. However, survivorship is likely affected by many factors not examined here. -Another important considerations is that fish condition was computed for all sizes of fishes combined, except in the case of walleye pollock. Examining condition of early juvenile stage fishes not yet recruited to the fishery, or the condition of adult fishes separately, could provide greater insight into the value of length-weight residuals as an indicator of individual health or survivorship [@Froese2006], particularly since juvenile and adult walleye pollock exhibited opposite trends in condition in the EBS this year. +Another important consideration is that fish condition was computed for all sizes of fishes combined, except in the case of walleye pollock. Examining condition of early juvenile stage fishes not yet recruited to the fishery, or the condition of adult fishes separately, could provide greater insight into the value of length-weight residuals as an indicator of individual health or survivorship [@Froese2006], particularly since juvenile and adult walleye pollock exhibited opposite trends in condition in the EBS this year. -The near-average condition for most species in 2022 may be related to the near historical average temperatures observed. However, trends in recent years such as prolonged warmer water temperatures following the marine heat wave of 2014-16 [@Bond2015] and reduced sea ice and cold pool areal extent in the eastern Bering Sea [@Stabeno2019a] may affect fish condition in ways that have yet to be determined. As we continue to add years of length-weight data and expand our knowledge of relationships between condition, growth, production, survival, and the ecosystem, these data may increase our understanding of the health of fish populations in the EBS and NBS. +The near-average condition for most species in 2023 may be related to the near historical average temperatures observed. However, trends in recent years such as prolonged warmer water temperatures following the marine heat wave of 2014-16 [@Bond2015] and reduced sea ice and cold pool areal extent in the eastern Bering Sea [@Stabeno2019a] may affect fish condition in ways that have not yet been determined. Additionally, periods of high fishing mortality that reduce population biomass are likely to increase body condition because of the compensatory alleviation of density-dependent competition [@Haberle2023]. As we continue to add years of length-weight data and expand our knowledge of relationships between condition, growth, production, survival, and the ecosystem, these data may increase our understanding of the health of fish populations in the EBS and NBS. -**Research priorities**: The new model-based condition indicator (VAST relative condition) will be further explored for biases and sensitivities to data, model structure, and parameterization. Research is also being planned and implemented across multiple AFSC programs to explore standardization of statistical methods for calculating condition indicators, and to examine relationships among putatively similar indicators of fish condition (i.e., morphometric, bioenergetic, physiological). Finally, we plan to explore variation in condition indices between life history stages alongside density dependence and climate change effects [@Bolin2021; @Oke2022]. +**Research priorities**: Research is being planned and implemented across multiple AFSC programs to explore standardization of statistical methods for calculating condition indicators, and to examine relationships among putatively similar indicators of fish condition (i.e., morphometric, bioenergetic, physiological). Research is underway to evaluate connections between morphometric condition indices, temperature, and density-dependent competition. ## References \ No newline at end of file diff --git a/EBS_references.bib b/EBS_references.bib new file mode 100644 index 0000000..930820d --- /dev/null +++ b/EBS_references.bib @@ -0,0 +1,237 @@ +@article{Barbeaux2020, +abstract = {In 2014–2016 an unprecedented warming event in the North Pacific Ocean triggered changes in ecosystem of the Gulf of Alaska (GOA) impacting fisheries management. The marine heatwave was noteworthy in its geographical extent, depth range, and persistence, with evidence of shifts in species distribution and reduced productivity. In 2017 a groundfish survey indicated that GOA Pacific cod (Gadus macrocephalus) had experienced a 71% decline in abundance from the previous 2015 survey. The GOA Pacific cod fishery supports a $103 million fishery which is 29% of the groundfish harvest value in the GOA. In this paper, we demonstrate that an increase in metabolic demand during this extended marine heatwave as well as a reduced prey supply can explain the decline in GOA Pacific cod biomass. Although increased mortality likely led to the decline in the Pacific cod population, historically low recruitment concurrent with the heatwave portends a slow recovery for the stock and gives a preview of impacts facing this region due to climate change. We evaluate the intersection of climate change with ecosystem-based fisheries management in the context of GOA Pacific cod with a description of the sensitivities of the ecosystem, how the changes in the ecosystem affected the Pacific cod stock, and a description of how the management system in the North Pacific handled this shock. We also provide suggestions on how fisheries management systems could be improved to better contend with the impacts of climate change such as the effects of heatwaves like that experienced in 2014–2016.}, +author = {Barbeaux, Steven J. and Holsman, Kirstin and Zador, Stephani}, +doi = {10.3389/fmars.2020.00703}, +file = {:C\:/Users/sean.rohan/Downloads/fmars-07-00703.pdf:pdf}, +issn = {22967745}, +journal = {Frontiers in Marine Science}, +keywords = {Gulf of Alaska,Pacific cod,climate change,ecosystem-based fisheries management,fisheries,marine heatwave}, +pages = {1--21}, +title = {{Marine heatwave stress test of ecosystem-based fisheries management in the Gulf of Alaska Pacific cod fishery}}, +volume = {7}, +year = {2020} +} + +@article{Blackwell2000, +abstract = {Condition assessment is commonly practiced by fisheries personnel as one tool for evaluating fish populations and communities. Several noninvasive condition measures are available for use, including Fulton's condition factor (K), relative condition factor (Kn), and relative weight (Wr). The use of Wr as a condition measure has increased within several peer-reviewed journals. In 1995 to 1996, survey responses from agency personnel in 48 states indicated that 22 states used Wr as a standard technique, 18 states identified Wr use as occasional, whereas only eight states indicated no Wr use. The regression-line-percentile technique is recommended for developing standard weight (Ws) equations. There are currently Ws equations available for 52 species and three purposeful hybrids. Length-related trends in condition need to be evaluated prior to calculating a population mean Wr. Relative weight target ranges should be adjusted to meet specific management objectives. Relative weight values are influenced by seasonal dynamics. The uses of Wr may go beyond just a measure of fish "plumpness." Relative weight can serve as a surrogate for estimating fish body composition, as a measure of fish health, and to assess prey abundance, fish stockings, and management actions.}, +author = {Blackwell, Brian G. and Brown, Michael L. and Willis, David W.}, +doi = {10.1080/10641260091129161}, +issn = {10641262}, +journal = {Reviews in Fisheries Science}, +keywords = {Condition assessment,Condition measures,Relative weight,Standard weight}, +number = {1}, +pages = {1--44}, +title = {{Relative weight (Wr) status and current use in fisheries assessment and management}}, +volume = {8}, +year = {2000} +} + +@article{Boldt2004, +abstract = {—Declining wild stocks and reduced body size of pink salmon Oncorhynchus gorbuscha in Prince William Sound have led to the hypothesis that the carrying capacity of the sound, and possibly that of the Gulf of Alaska, for salmon has been reached. We compared the length, weight, and energetic content of juvenile pink salmon (1) among different stations, (2) between one hatchery group and wild pink salmon from several stations, and (3) among several hatchery release groups and wild pink salmon from two stations in the sound using analysis of variance. There were significant differences in the lengths of fish among stations but no geographic pattern. Pink salmon at two nearby stations in southwestern Prince William Sound were the smallest (71.5 mm in Whale Bay) and largest (92.4 mm in Bainbridge Passage) of the six stations sampled. There were also differences in size among the hatchery and wild groups. Wild fish were consistently larger than the most abundant hatchery group (Cannery Creek). Within central Prince William Sound, Cannery Creek Hatchery pink salmon were significantly shorter than other hatchery release groups and wild salmon. Size variation was probably dependent on size upon entry and time of entry into marine waters. The energy content of juvenile pink salmon did not differ significantly between wild and hatchery fish but did vary among stations, albeit with no geographic pattern. Hatchery fish west of Naked Island had significantly higher energy content than those east of Naked Island. The consistencies in energy content among groups of fish from the same geographic area suggest that processes occurring on local scales (e.g., the effect of stratification on secondary production and local depletion by planktivores) are important in determining the condition of juvenile pink salmon. Prince William Sound is a large, complex, fjord-type estuary that supports large runs of hatchery and wild pink salmon Oncorhynchus gorbuscha (Niebauer et al. 1994). Salmon enhancement in the sound increased from the mid-1970s to 1989, and in 2001 hatcheries were releasing about 600 mil-lion pink salmon fry annually (McNair 1997; Far-rington 2003). Total pink salmon returns to the Gulf of Alaska and Prince William Sound in-creased in the 1980s and 1990s; however, wild salmon returns in the sound peaked in 1983 and declined through 1995 (Cooney and Brodeur 1998). The body size of commercially caught pink salmon has also decreased since the mid-1970s (Peterman 1987; Bigler et al. 1996). Recent sci-entific debate has therefore focused on potential negative effects of hatchery production on wild salmon stocks and the carrying capacity of the sound (Hilborn and Eggers 2000, 2001; Werthei-mer et al. 2001). Concern over declining wild}, +author = {Boldt, Jennifer L. and Haldorson, Lewis J.}, +doi = {10.1577/t02-138}, +issn = {0002-8487}, +journal = {Transactions of the American Fisheries Society}, +number = {1}, +pages = {173--184}, +title = {{Size and condition of wild and hatchery pink salmon juveniles in Prince William Sound, Alaska}}, +volume = {133}, +year = {2004} +} + +@article{Bolin2021, +abstract = {Marine ecosystem forecasting is an important innovation in fisheries science with considerable value for industry and management, providing new data-driven means of predicting the distribution and availability of commercially exploited fish stocks over a range of timescales, including near-real-time and seasonal. Marine ecosystem forecasting is rapidly advancing as a field, yet tools produced for fisheries to date focus primarily on predicting species distributions. The next generation of marine ecosystem forecasting products could be enhanced by also incorporating predictions of biological characteristics of fish caught, such as body condition and epidemiological status, thereby expanding the utility of these methods beyond predicting distribution alone. Improving the biological dimensions of marine ecosystem forecasting could allow for optimization of efficiencies in wild-capture fisheries by minimizing discarding and waste and maximizing the value of landed fish. These advancements are of direct benefit to industry and management, address several of the United Nations Sustainable Development Goals pertaining to fisheries sustainability and have the potential to support the maintenance of global food and micronutrient security under rapidly changing environmental conditions. Here, we describe the current state of the art in marine ecosystem forecasting; review the physical-biological linkages that underlie variability in the body condition of commercially valuable fish and shellfish with particular reference to marine climate change; and outline key considerations for the next generation of marine ecosystem forecasting tools for wild-capture fisheries.}, +author = {Bolin, Jessica A. and Schoeman, David S. and Evans, Karen J. and Cummins, Scott F. and Scales, Kylie L.}, +doi = {10.1111/faf.12569}, +file = {:C\:/Users/sean.rohan/Downloads/Fish and Fisheries - 2021 - Bolin - Achieving sustainable and climate‐resilient fisheries requires marine ecosystem.pdf:pdf}, +issn = {1467-2960}, +journal = {Fish and Fisheries}, +keywords = {body condition,climate change,commercial fisheries,dynamic ocean management,fish parasites,seasonal forecast}, +month = {sep}, +number = {5}, +pages = {1067--1084}, +title = {{Achieving sustainable and climate‐resilient fisheries requires marine ecosystem forecasts to include fish condition}}, +url = {https://onlinelibrary.wiley.com/doi/10.1111/faf.12569}, +volume = {22}, +year = {2021} +} + + +@article{Bond2015, +abstract = {Strongly positive temperature anomalies developed in the NE Pacific Ocean during the boreal winter of 2013-2014. Based on a mixed layer temperature budget, these anomalies were caused by lower than normal rates of the loss of heat from the ocean to the atmosphere and of relatively weak cold advection in the upper ocean. Both of these mechanisms can be attributed to an unusually strong and persistent weather pattern featuring much higher than normal sea level pressure over the waters of interest. This anomaly was the greatest observed in this region since at least the 1980s. The region of warm sea surface temperature anomalies subsequently expanded and reached coastal waters in spring and summer 2014. Impacts on fisheries and regional weather are discussed. It is found that sea surface temperature anomalies in this region affect air temperatures downwind in Washington state. Key Points Anomalous atmospheric forcing in the NE Pacific in winter 2013-2014 Weak seasonal cooling due to reduced heat fluxes and anomalous advection SST anomalies have impacts on the ecosystem and air temperatures}, +author = {Bond, Nicholas A. and Cronin, Meghan F. and Freeland, Howard and Mantua, Nathan}, +doi = {10.1002/2015GL063306}, +issn = {19448007}, +journal = {Geophysical Research Letters}, +keywords = {SLP anomalies,northeast Pacific Ocean,regional and downwind impacts,seasonal heating mechanisms,warm SST}, +number = {9}, +pages = {3414--3420}, +title = {{Causes and impacts of the 2014 warm anomaly in the NE Pacific}}, +volume = {42}, +year = {2015} +} + +@article{Brodeur2004, +abstract = {Information is summarized on juvenile salmonid distribution, size, condition, growth, stock origin, and species and environmental associations from June and August 2000 GLOBEC cruises with particular emphasis on differences related to the regions north and south of Cape Blanco off Southern Oregon. Juvenile salmon were more abundant during the August cruise as compared to the June cruise and were mainly distributed northward from Cape Blanco. There were distinct differences in distribution patterns between salmon species: chinook salmon were found close inshore in cooler water all along the coast and coho salmon were rarely found south of Cape Blanco. Distance offshore and temperature were the dominant explanatory variables related to coho and chinook salmon distribution. The nekton assemblages differed significantly between cruises. The June cruise was dominated by juvenile rockfishes, rex sole, and sablefish, which were almost completely absent in August. The forage fish community during June comprised Pacific herring and whitebait smelt north of Cape Blanco and surf smelt south of Cape Blanco. The fish community in August was dominated by Pacific sardines and highly migratory pelagic species. Estimated growth rates of juvenile coho salmon were higher in the GLOBEC study area than in areas farther north. An unusually high percentage of coho salmon in the study area were precocious males. Significant differences in growth and condition of juvenile coho salmon indicated different oceanographic environments north and south of Cape Blanco. The condition index was higher in juvenile coho salmon to the north but no significant differences were found for yearling chinook salmon. Genetic mixed stock analysis indicated that during June, most of the chinook salmon in our sample originated from rivers along the central coast of Oregon. In August, chinook salmon sampled south of Cape Blanco were largely from southern Oregon and northern California; whereas most chinook salmon north of Cape Blanco were from the Central Valley in California.}, +author = {Brodeur, Rick D. and Fisher, Joseph P. and Teel, David J. and Emmett, Robert L. and Casillas, Edmundo and Miller, Todd W.}, +issn = {00900656}, +journal = {Fishery Bulletin}, +number = {1}, +pages = {25--46}, +title = {{Juvenile salmonid distribution, growth, condition, origin, and environmental and species associations in the Northern California Current}}, +volume = {102}, +year = {2004} +} + + +@article{Froese2006, +abstract = {This study presents a historical review, a meta-analysis, and recommendations for users about weight-length relationships, condition factors and relative weight equations. The historical review traces the developments of the respective concepts. The meta-analysis explores 3929 weight-length relationships of the type W = aLb for 1773 species of fishes. It shows that 82% of the variance in a plot of log a over b can be explained by allometric versus isometric growth patterns and by di.erent body shapes of the respective species. Across species median b = 3.03 is significantly larger than 3.0, thus indicating a tendency towards slightly positive-allometric growth (increase in relative body thickness or lumpness) in most fishes. The expected range of 2.5 < b < 3.5 is confirmed. Mean estimates of b outside this range are often based on only one or two weight-length relationships per species. However, true cases of strong allometric growth do exist and three examples are given. Within species, a plot of log a vs b can be used to detect outliers in weight-length relationships. An equation to calculate mean condition factors from weight-length relationships is given as Kmean = 100 aLb-3. Relative weight Wrm = 100W/(amLbm) can be used for comparing the condition of individuals across populations, where am is the geometric mean of a and bm is the mean of b across all available weight-length relationships for a given species. Twelve recommendations for proper use and presentation of weight-length relationships, condition factors and relative weight are given. {\textcopyright} 2006 The Author Journal compilation {\textcopyright} 2006 Blackwell Verlag, Berlin.}, +author = {Froese, Rainer}, +doi = {10.1111/j.1439-0426.2006.00805.x}, +issn = {01758659}, +journal = {Journal of Applied Ichthyology}, +number = {4}, +pages = {241--253}, +title = {{Cube law, condition factor and weight-length relationships: History, meta-analysis and recommendations}}, +volume = {22}, +year = {2006} +} + +@article{Gruss2020a, +author = {Gr{\"{u}}ss, A and Gao, J and Thorson, JT and Rooper, CN and Thompson, G and Boldt, JL and Lauth, R}, +doi = {10.3354/meps13213}, +file = {:C\:/Users/sean.rohan/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gr{\"{u}}ss et al. - 2020 - Estimating synchronous changes in condition and density in eastern Bering Sea fishes.pdf:pdf}, +issn = {0171-8630}, +journal = {Marine Ecology Progress Series}, +keywords = {bottom temperature effects,condition,density-dependence,eastern bering sea,groundfishes,le cren,of the publisher,permitted without written consent,resale or republication not,s relative condition index,spatio-temporal models}, +pages = {169--185}, +title = {{Estimating synchronous changes in condition and density in eastern Bering Sea fishes}}, +volume = {635}, +year = {2020} +} + +@article{Gruss2021, +author = {Gr{\"{u}}ss, Arnaud and Thorson, James T and Stawitz, Christine C and Reum, Jonathan C.P. and Rohan, Sean K. and Barnes, Cheryl L.}, +doi = {10.1016/j.pocean.2021.102569}, +issn = {00796611}, +journal = {Progress in Oceanography}, +keywords = {Cold-pool extent,Eastern Bering Sea,Empirical orthogonal functions,Spatio-temporal models,Walleye pollock}, +month = {jun}, +pages = {102569}, +publisher = {Elsevier Ltd}, +title = {{Synthesis of interannual variability in spatial demographic processes supports the strong influence of cold-pool extent on eastern Bering Sea walleye pollock (Gadus chalcogrammus)}}, +url = {https://doi.org/10.1016/j.pocean.2021.102569 https://linkinghub.elsevier.com/retrieve/pii/S0079661121000562}, +volume = {194}, +year = {2021} +} + +@article{Haberle2023, +abstract = {Individual performance defines population dynamics. Condition index – a ratio of weight and some function of length – has been louded as an indicator of individual performance and recommended as a tool in fisheries management and conservation. However, insufficient understanding of the correlation between individual‐level processes and population‐level responses hinders its adoption. To this end, we use composite modelling to link individual's condition, expressed through the condition index, to population‐level status. We start by modelling ontogeny of European pilchard ( Sardina pilchardus , Clupeidae) as a function of food and constant temperature using Dynamic Energy Budget theory. We then provide a framework to simultaneously track the individual‐ and population‐level statistics by incorporating the dynamic energy budget model into an individual‐based model. Lastly, we explore the effects of fishing pressure on the statistics in two constant and food‐limited environmental carrying capacity scenarios. Results show that, regardless of the species' environmental carrying capacity, individual condition index will increase with fishing mortality, that is, with reduction of stock size. Same patterns are observed for gilthead seabream ( Sparus aurata , Sparidae), a significantly different species. Condition index can, therefore, in food‐limited populations, be used to (i) estimate population size relative to carrying capacity and (ii) distinguish overfished from underfished populations. Our findings promote a practical way to operationally incorporate the condition index into fisheries management and marine conservation, thus providing additional use for the commonly collected biometric data. Some real‐world applications, however, may require additional research to account for other variables such as fluctuating environmental conditions and individual variability.}, +author = {Haberle, Ines and Bav{\v{c}}evi{\'{c}}, Lav and Klanjscek, Tin}, +doi = {10.1111/faf.12744}, +issn = {1467-2960}, +journal = {Fish and Fisheries}, +keywords = {dynamic energy budget,fisheries management,fishing mortality,individual-}, +month = {jul}, +number = {4}, +pages = {567--581}, +title = {{Fish condition as an indicator of stock status: Insights from condition index in a food‐limiting environment}}, +url = {https://onlinelibrary.wiley.com/doi/10.1111/faf.12744}, +volume = {24}, +year = {2023} +} + +@techreport{Hurst2021, +author = {Hurst, Thomas P. and O'Leary, Cecilia A. and Rohan, Sean K. and Siddon, Elizabeth C. and Thorson, James T. and Vollenweider, Johanna J.}, +doi = {10.25923/p1yd-0793}, +title = {{Inventory, management uses, and recommendations for fish and crab condition information from the 2021 AFSC Condition Congress. AFSC Processed Rep. 2021-04, 39 p. Alaska Fish. Sci. Cent., NOAA, Nat. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle, WA 981}}, +year = {2021} +} + +@article{Oke2022, +abstract = {The temperature-size rule predicts that climate warming will lead to faster growth rates for juvenile fishes but lower adult body size. Testing this prediction is central to understanding the effects of climate change on population dynamics. We use fisheries-independent data (1999-2019) to test predictions of age-specific climate effects on body size in eastern Bering Sea walleye pollock (Gadus chalcogrammus). This stock supports one of the largest food fisheries in the world but is experiencing exceptionally rapid warming. Our results support the predictions that weight-at-age increases with temperature for young age classes (ages 1, 3-4) but decreases with temperature for old age classes (ages 7-15). Simultaneous demonstrations of larger juveniles and smaller adults with warming have thus far been rare, but pollock provide a striking example in a fish of exceptional ecological and commercial importance. The age-specific response to temperature was large enough (0.5 – 1 SD change in log weight-at-age) to have important implications for pollock management, which must estimate current and future weight-at-age to calculate allowable catch, and for the Bering Sea pollock fishery.}, +author = {Oke, Krista B. and Mueter, Franz J. and Litzow, Michael A.}, +doi = {10.1139/cjfas-2021-0315}, +file = {:C\:/Users/sean.rohan/Downloads/cjfas-2021-0315.pdf:pdf}, +issn = {0706-652X}, +journal = {Canadian Journal of Fisheries and Aquatic Sciences}, +month = {may}, +title = {{Warming leads to opposite patterns in weight-at-age for young versus old age classes of Bering Sea walleye pollock}}, +url = {https://cdnsciencepub.com/doi/10.1139/cjfas-2021-0315}, +year = {2022} +} + +@article{Paul1999, +abstract = {Age-0 Pacific herring were surveyed in October of 4 years in a large northern Gulf of Alaska estuary, to determine the range of variations in length, weight and whole body energy content (WBEC). These parameters reflect their preparedness for surviving their first winter's fast. During the surveys there were distinct regional and interannual variations in all three parameters for individual groups of herring in Prince William Sound. Likewise, with each collection there was typically a large range of size and WBEC values. The average standard length was (± S.D.) 80 ± 13 mm (range=40-118), the mean whole body wet weight was 5.7 ± 3.0 g (range=0.7-29.2) and the average WBEC of all age-0 herring captured, regardless of year or site (n=1471), was 5.4 ± 1.0 kJ g-1 wet weight (range=2.4-9.4). The large range of WBEC and size indicates that age-0 herring at different capture sites were not all equally prepared for surviving their first winter.}, +author = {Paul, A. J. and Paul, J. M.}, +doi = {10.1006/jfbi.1999.0927}, +file = {:C\:/Users/sean.rohan/Downloads/j.1095-8649.1999.tb00852.x (1).pdf:pdf}, +issn = {00221112}, +journal = {Journal of Fish Biology}, +keywords = {Energetics,Pacific herring,Size}, +number = {5}, +pages = {996--1001}, +title = {{Interannual and regional variations in body length, weight and energy content of age-0 Pacific herring from Prince William Sound, Alaska}}, +volume = {54}, +year = {1999} +} + +@article{Rodgveller2019, +abstract = {The objectives of this study were to determine if relative body condition and relative liver size (hepatosomatic index, HSI) could be utilized to predict maturity 6–8 months prior to spawning, when samples are readily available, and if these condition measures were related to fecundity. Female sablefish were sampled on four survey legs during a summer longline survey in July and August 2015 and during a winter survey in December 2015, which is 1–3 months prior to the spawning season in the Gulf of Alaska. The relative body condition and HSI of fish increased throughout the summer survey, reaching measurements similar to those observed during the winter. There were significant differences between immature and mature fish HSI and relative body condition and these differences increased throughout the summer, making these factors useful for predicting maturity on the last legs of the survey. On these later legs, models that utilized relative body condition and HSI, as well as length and age, to predict whether a fish was immature or would spawn produced maturity curves that best matched models based on histological maturity classifications. However, models without HSI may be the best choice for future work because liver weight is not regularly collected on annual surveys and on the last leg of the survey the addition of HSI to predicitive models did not improve maturity-at-age curves. Utilizing the winter data set, which is the time period when fecundity could be enumerated, fecundity was significantly related to relative condition and HSI. Increasing or decreasing these measures of condition by one standard deviation in a model of fecundity, which also included length, resulted in an estimated decrease in fecundity of 32% or an increase of 47% for an average size fish (78 cm). These results show the importance of incorporating fish condition into measures of population productivity.}, +author = {Rodgveller, Cara J.}, +doi = {10.1016/j.fishres.2019.03.013}, +issn = {01657836}, +journal = {Fisheries Research}, +keywords = {Age at maturity,Anoplopoma fimbria,Egg production,Fish maturation,Hepatosomatic index,Skip spawning}, +number = {October 2018}, +pages = {18--28}, +publisher = {Elsevier}, +title = {{The utility of length, age, liver condition, and body condition for predicting maturity and fecundity of female sablefish}}, +url = {https://doi.org/10.1016/j.fishres.2019.03.013}, +volume = {216}, +year = {2019} +} + +@article{Stabeno2019a, +abstract = {The lowest winter-maximum areal sea-ice coverage on record (1980–2019) in the Bering Sea occurred in the winter of 2017/2018. Sea ice arrived late due to warm southerly winds in November. More typical northerly winds (albeit warm) in December and January advanced the ice, but strong, warm southerlies in February and March forced the ice to retreat. The cold pool (shelf region with bottom water < 2 °C) was the smallest on record, because of two related mechanisms: (1) lack of direct cooling in winter by melting sea ice and (2) weaker vertical stratification (no ice melt reduced the vertical salinity gradient) allowing surface heating to penetrate into the near bottom water during summer. February 2019 exhibited another outbreak of warm southerly winds forcing ice to retreat. The number of >31-day outbreaks of southerly winds in winter has increased since 2016.}, +author = {Stabeno, Phyllis J. and Bell, Shaun W.}, +doi = {10.1029/2019GL083816}, +issn = {19448007}, +journal = {Geophysical Research Letters}, +keywords = {Bering Sea,cold pool,sea ice,stratification,winds}, +number = {15}, +pages = {8952--8959}, +title = {{Extreme conditions in the Bering Sea (2017–2018): record-breaking low sea-ice extent}}, +volume = {46}, +year = {2019} +} + +@article{Thorson2019, +abstract = {Fisheries scientists provide stock, ecosystem, habitat, and climate assessments to support interdisplinary fisheries management in the US and worldwide. These assessment activities have evolved different models, using different review standards, and are communicated using different vocabulary. Recent research shows that spatio-temporal models can estimate population density for multiple locations, times, and species, and that this is a “common currency” for addressing core goals in stock, ecosystem, habitat, and climate assessments. I therefore review the history and “design principles” for one spatio-temporal modelling package, the Vector Autoregressive Spatio-Temporal (VAST) package. I then provide guidance on fifteen major decisions that must be made by users of VAST, including: whether to use a univariate or multivariate model; when to include spatial and/or spatio-temporal variation; how many factors to use within a multivariate model; whether to include density or catchability covariates; and when to include a temporal correlation on model components. I finally demonstrate these decisions using three case studies. The first develops indices of abundance, distribution shift, and range expansion for arrowtooth flounder (Atheresthes stomias) in the Eastern Bering Sea, showing the range expansion for this species. The second involves “species ordination” of eight groundfishes in the Gulf of Alaska bottom trawl survey, which highlights the different spatial distribution of flathead sole (Hippoglossoides elassodon) relative to sablefish (Anoplopoma fimbria) and dover sole (Microstomus pacificus). The third involves a short-term forecast of the proportion of coastwide abundance for five groundfishes within three spatial strata in the US West Coast groundfish bottom trawl survey, and predicts large interannual variability (and high uncertainty) in the distribution of lingcod (Ophiodon elongatus). I conclude by recommending further research exploring the benefits and limitations of a “common currency” approach to stock, ecosystem, habitat, and climate assessments, and discuss extending this approach to optimal survey design and economic assessments.}, +author = {Thorson, James T.}, +doi = {10.1016/j.fishres.2018.10.013}, +file = {:C\:/Users/sean.rohan/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Thorson - 2019 - Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and.pdf:pdf}, +issn = {01657836}, +journal = {Fisheries Research}, +keywords = {Climate vulnerability analysis,Distribution shift,Habitat assessment,Index standardization,Integrated ecosystem assessment,Spatio-temporal model,Stock assessment,VAST}, +pages = {143--161}, +publisher = {Elsevier}, +title = {{Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments}}, +url = {https://doi.org/10.1016/j.fishres.2018.10.013}, +volume = {210}, +year = {2019} +} + +@article{Wuenschel2019, +abstract = {Measuring fish condition should link ecosystem drivers with population dynamics, if the underlying physiological basis for variations in condition indices are understood. We evaluated traditional (K, Kn, hepatosomatic index, gonadosomatic index, energy density, and percent dry weight of muscle (%DWM) and liver (%DWL)) and newer (bioelectrical impedance analysis (BIA) and scaled mass index (SMI)) condition indices to track seasonal cycles in three flatfishes — winter founder (Pseudopleuronectes americanus; three stocks), yellowtail flounder (Limanda ferruginea; three stocks), and summer flounder (Paralichthys dentatus; one stock) — with contrasting life histories in habitat, feeding, and reproduction. The %DWM and %DWL were good proxies for energy density (r2 > 0.96) and more strongly related to K, Kn, and SMI than to BIA metrics. Principal component analysis indicated many metrics performed similarly across species; some were confounded by size, sex, and maturity along PC1, while others effectively characterized condition along PC2. Stock differences were along PC1 in winter flounder, reflecting different sizes across stocks, whereas in yellowtail flounder differences occurred along PC2 related to condition. These comparisons, within and across species, highlight the broad applicability of some metrics and limitations in others.}, +author = {Wuenschel, Mark J. and McElroy, W. David and Oliveira, Kenneth and McBride, Richard S.}, +doi = {10.1139/cjfas-2018-0076}, +issn = {12057533}, +journal = {Canadian Journal of Fisheries and Aquatic Sciences}, +number = {6}, +pages = {886--903}, +title = {{Measuring fish condition: An evaluation of new and old metrics for three species with contrasting life histories}}, +volume = {76}, +year = {2019} +} + diff --git a/R/sysdata.rda b/R/sysdata.rda index 1aeb98b..018d17c 100644 Binary files a/R/sysdata.rda and b/R/sysdata.rda differ diff --git a/data/AI_INDICATOR.rda b/data/AI_INDICATOR.rda index 8afdf55..ef14bd7 100644 Binary files a/data/AI_INDICATOR.rda and b/data/AI_INDICATOR.rda differ diff --git a/data/EBS_INDICATOR.rda b/data/EBS_INDICATOR.rda index 4af1650..3a38de0 100644 Binary files a/data/EBS_INDICATOR.rda and b/data/EBS_INDICATOR.rda differ diff --git a/data/GOA_INDICATOR.rda b/data/GOA_INDICATOR.rda index 7d5df2d..270a86d 100644 Binary files a/data/GOA_INDICATOR.rda and b/data/GOA_INDICATOR.rda differ diff 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