A semiparametric model to examine stock–recruitment relationships incorporating environmental data

2001 ◽  
Vol 58 (6) ◽  
pp. 1178-1186 ◽  
Author(s):  
D G Chen ◽  
J R Irvine
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Semiparametric Model ◽  
Clupea Harengus ◽  
Environmental Data ◽  
Sea Surface ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Ricker Model ◽  
Stock Recruitment

A novel semiparametric model that can incorporate environmental and fishery data is developed to analyze stock–recruitment relationships. Unlike traditional stock–recruitment models that assume a log-linear relationship between recruitment and environmental and fishery variables, the new model uses a nonparametric smoothing algorithm, which helps quantify the underlying empirical relationships and enables more accurate parameter estimates. Bootstrap resampling is used to evaluate uncertainties in the model parameters. Distribution plots can be produced for stock–recruitment productivity and capacity parameters. This approach is applied to southeast Alaska pink salmon (Oncorhynchus gorbuscha) with sea surface temperature as the environmental variable and West Coast Vancouver Island herring (Clupea harengus) with sea surface temperature and hake biomass as two environmental variables. Results from diagnostic tests indicate that our model performed better than the traditional Ricker model and a Ricker model that was extended to include environmental effects.

A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships

2000 ◽  
Vol 57 (9) ◽  
pp. 1878-1887 ◽  
Author(s):  
D G Chen ◽  
N B Hargreaves ◽  
D M Ware ◽  
Y Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Logic Model ◽  
Sea Surface ◽  
Oncorhynchus Gorbuscha ◽  
Fish Stock ◽  
Fuzzy Logic Model ◽  
Stock Recruitment

A new fuzzy logic model with a genetic algorithm is developed that overcomes some of the inherent uncertainties in the fish stock-recruitment process. This model is applied to stock-recruitment relationships for the Southeast Alaska pink salmon (Oncorhynchus gorbuscha) and the West Coast Vancouver Island Pacific herring (Clupea pallasi) stocks. In both examples, the annual mean sea surface temperature is used as an environmental intervention in the model. The fuzzy logic model provides the functional relationship between the number of fish spawners and the sea surface temperature that is used to reconstruct the historical fish recruitment time series and also to predict the number of fish that will recruit in the future. Globally optimized genetic learning algorithms are used to find the optimal values of the parameters for the fuzzy logic model. The results from this fuzzy logic model are compared with results from both a traditional Ricker stock-recruitment model and a recent artificial neural network model. These comparisons demonstrate the superior capability of the fuzzy logic model for addressing problems of uncertainty and vagueness in both the data and the stock-recruitment relationship. The fuzzy logic model approach is recommended as a useful addition to the analytical tools currently available for fish stock assessment and management.

Growth and Maturation of Pacific Herring (Clupea harengus pallasi) in the Strait of Georgia

1985 ◽  
Vol 42 (S1) ◽  
pp. s138-s146 ◽  
Author(s):  
V. Haist ◽  
M. Stocker
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Clupea Harengus ◽  
Sea Surface ◽  
Pacific Herring ◽  
Juvenile Growth ◽  
Strait Of Georgia ◽  
Surplus Energy ◽  
Clupea Harengus Pallasi ◽  
Stock Biomass

Juvenile growth rate, adult surplus energy, and the maturation schedule for the Strait of Georgia Pacific herring (Clupea harengus pallasi) stock were investigated over the period 1950–81. The variance in weight at age 2 is largely accounted for by juvenile abundance and sea surface temperature, indicating density-dependent juvenile growth moderated by environmental factors. Density and environmental factors have been equally important in moderating juvenile growth. Yearly variation in maturation of 3-yr-old herring is related to their average length; however, in two of the eight years studied the 3-yr-olds matured at considerably smaller sizes. The variance in adult surplus energy (growth plus gonad production) was largely accounted for by body weight, adult biomass, and sea surface temperature. A dome-shaped relationship between surplus energy and biomass was indicated, suggesting that over a broad range of population size, adult surplus energy is not density dependent. The relationship of sea surface temperature to both juvenile growth and adult surplus energy was quadratic with an optimum value. Recruitment biomass has been a relatively larger component than adult production of total stock growth, particularly during the period of high fishing intensity. This resulted in large fluctuations in stock biomass; in recent years, with lower fishing intensity, adult production has been a larger component of stock growth, and the stock biomass has become more stable.

scholarly journals Incorporation of optimal environmental signals in the prediction of fish recruitment using random forest algorithms

2019 ◽  
Vol 76 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Szymon Smoliński
Keyword(s):  
Random Forest ◽  
Environmental Factors ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Baltic Sea ◽  
Time Window ◽  
Clupea Harengus ◽  
Sea Surface ◽  
Optimal Time ◽  
Fish Recruitment

The drivers of recruitment of selected Baltic sprat (Sprattus sprattus) and herring (Clupea harengus) stocks were investigated. Data on the interaction dynamics among fish species, the biological characteristics of the stocks, the biomass of the main predators, and the hydroclimatic environmental factors (Baltic Sea Index and sea surface temperature) were used in the analysis. The combination of random forest (Boruta algorithm) and “sliding window” approaches was tested on the simulated data and then used for the selection of relevant predictors and the optimal time window for real environmental variables. Sea surface temperature had a significant positive effect on the recruitment processes. Moreover, contrasting effects were observed in the mean Baltic Sea Index from two different time windows. The same environmental variable generated contrasting short-term and long-term effects on fish recruitment. This paper highlights the potential benefits of random forest and data mining applications for the incorporation of environmental factors in the assessment of stocks. The proposed analytical approach may be valuable for the investigations of complex environmental impacts in a broad range of ecological studies.

scholarly journals Modeling Seasonal Distribution of Irrawaddy Dolphins (Orcaella brevirostris) in a Transnational Important Marine Mammal Area

2021 ◽  
Vol 8 ◽  
Author(s):  
Gregory M. Verutes ◽  
Sarah E. Tubbs ◽  
Nick Selmes ◽  
Darren R. Clark ◽  
Peter Walker ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Marine Mammal ◽  
Habitat Suitability ◽  
Statistical Tests ◽  
Monsoon Season ◽  
Habitat Preferences ◽  
Data Depth ◽  
Environmental Data ◽  
Sea Surface

Fishing activities continue to decimate populations of marine mammals, fish, and their habitats in the coastal waters of the Kep Archipelago, a cluster of tropical islands on the Cambodia-Vietnam border. In 2019, the area was recognized as an Important Marine Mammal Area, largely owing to the significant presence of Irrawaddy dolphins (Orcaella brevirostris). Understanding habitat preferences and distribution aids in the identification of areas to target for monitoring and conservation, which is particularly challenging in data-limited nations of Southeast Asia. Here, we test the hypothesis that accurate seasonal habitat models, relying on environmental data and species occurrences alone, can be used to describe the ecological processes governing abundance for the resident dolphin population of the Kep Archipelago, Cambodia. Leveraging two years of species and oceanographic data—depth, slope, distance to shore and rivers, sea surface temperature, and chlorophyll-a concentration—we built temporally stratified models to estimate distribution and infer seasonal habitat importance. Overall, Irrawaddy dolphins of Kep displayed habitat preferences similar to other populations, and were predominately encountered in three situations: (1) water depths ranging from 3.0 to 5.3 m, (2) surface water temperatures of 27–32°C, and (3) in close proximity to offshore islands (< 7.5 km). With respect to seasonality, statistical tests detected significant differences for all environment variables considered except seafloor slope. Four predictor sets, each with a unique combination of variables, were used to map seasonal variation in dolphin habitat suitability. Models with highest variable importance scores were water depth, pre- and during monsoon season (61–62%), and sea surface temperature, post-monsoon (71%), which suggests that greater freshwater flow during the wet season may alter primary productivity and dolphin prey abundance. Importantly, findings show the majority of areas with highest habitat suitability are not currently surveyed for dolphins and located outside Kep’s Marine Fisheries Management Area. This research confirms the need to expand monitoring to new areas where high-impact fisheries and other human activities operate. Baseline knowledge on dolphin distribution can guide regional conservation efforts by taking into account the seasonality of the species and support the design of tailored management strategies that address transboundary threats to an Important Marine Mammal Area.

scholarly journals Incorporating sea surface temperature into the stock-recruitment relationship: Applications to jack mackerel (Trachurus murphyi) off Chile

2016 ◽  
Vol 51 (1) ◽  
pp. 137-145
Author(s):  
Fernando Espíndola ◽  
Juan Carlos Quiroz ◽  
Rodrigo Wiff ◽  
Eleuterio Yáñez
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Jack Mackerel ◽  
Stock Recruitment ◽  
Trachurus Murphyi

Patterns and influences on Dolly Varden migratory timing in the Chignik Lakes, Alaska, and comparison of populations throughout the northeastern Pacific and Arctic oceans

2013 ◽  
Vol 70 (5) ◽  
pp. 655-665 ◽  
Author(s):  
Morgan H. Bond ◽  
Thomas P. Quinn
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Environmental Data ◽  
Sea Surface ◽  
Upstream Migration ◽  
Dolly Varden ◽  
Factors Affecting ◽  
Migration Timing ◽  
The North

Dolly Varden (Salvelinus malma) are a facultatively anadromous salmonid common around much of the North Pacific Rim, but little is known about the environmental factors affecting the timing and diversity of their migration. We combined telemetry of anadromous fish with long-term monitoring of Dolly Varden upstream migration timing and environmental data in the Chignik Lakes watershed in Alaska and then compared the timing data with that of other streams where only count data were available. Telemetry revealed two upstream migration modes: midsummer and late fall at the Chignik Lakes. Weir counts indicated that timing fluctuated markedly over the monitoring period (1996–2011) and was negatively correlated with June sea surface temperature. The relationship between sea surface temperature and migration timing in other watersheds with long-term records was as follows: negative (Buskin River), positive (Auke Creek), or nonexistent (Goodnews and Kanektok rivers). Among 18 streams and rivers throughout the eastern Pacific range of Dolly Varden, median upstream migration date increased with latitude. Overall, Dolly Varden migration timing is more variable, protracted, and more strongly influenced by local sea surface temperatures than is typical of semelparous salmonids. These results are likely indicative of other iteroparous salmonids in Pacific waters that share similar environments and life-history characteristics.

scholarly journals Model of Habitat Characteristics of Sardinella lemuru in the Bali Strait, Indonesia

2021 ◽  
Vol 56 (3) ◽  
pp. 229-240
Author(s):  
Adi Wijaya ◽  
Abu Bakar Sambah ◽  
Daduk Setyohadi ◽  
Umi Zakiyah
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Environmental Conditions ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Sea Surface ◽  
Model Parameters ◽  
Photosynthetically Available Radiation ◽  
Chlorophyll A Concentration

This article describes a new approach to the study of the environmental conditions that relate to the Sardinella lemuru habitat in the Bali Strait, through remote sensing data and fish catch data using the generalized additive model. Data that are acquired daily and then compiled into monthly data for sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and sea surface depth (SSD) were used for the years 2008–2010. The objectives of the study are to describe the variability of the environmental conditions in the Bali Strait, to analyze a combination model of environmental factors in estimating the Sardinella lemuru habitat, and to map potential Sardinella lemuru fishing areas. We illustrate the proposed method by constructing seven generalized additive models with catches of Sardinella lemuru as a variable response and use sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and SSD as covariant models for assessing the environmental characteristics of the abundance of Sardinella lemuru. Predicted values were validated using a linear model. Based on the three model parameters, habitat selection for Sardinella lemuru was significantly (P < 0.0001) influenced by photosynthetically available radiation (50–55 Einstein m-2 d-1), sea surface chlorophyll-a concentration (0.2–2.0 mgm-3), sea surface temperature (28.95–29.64 °C), and SSD (60–150 m). Catch predictions show a consistent trend toward environmental conditions and water depth. Our method allows for improvement with the validation of catch predictions that were observed and collected monthly, and the result was significant (P < 0.001, r2 = 0.816). Photosynthetically available radiation explains the highest deviation in continued generalized additive models; therefore, it was considered to be the best predictor of habitat, followed by sea surface chlorophyll-a concentration, sea surface temperature, and then SSD. New research results supplement several previous studies that relate to the analysis of environmental parameters in estimating the fish habitat and can be used in mapping the distribution of potential Sardinella lemuru fishing areas in spatial and temporal scales.

scholarly journals Improving WRF-Hydro Runoff Predictions of Heavy Floods Through Higher Spatio-Temporal Sea Surface Temperature Products

2021 ◽  
Author(s):  
Berina Kilicarslan ◽  
ismail yucel ◽  
Heves Pilatin ◽  
Eren Duzenli ◽  
Mustafa Yılmaz
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wrf Model ◽  
Sea Surface ◽  
Model Parameters ◽  
Flood Events ◽  
Near Surface ◽  
Daily Streamflow ◽  
Spatio Temporal ◽  
The Impact

In this study, the impact of spatio-temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)-Hydro system to simulate hydrological response during two catastrophic flood events over Eastern Black Sea (EBS) and Mediterranean (MED) regions of Turkey is investigated. Three time-varying and high spatial resolution external SST products (GHRSST, Medspiration, and NCEP-SST) and one coarse-resolution and invariable SST product (ERA5- and GFS-SST for EBS and MED regions, respectively) already embedded in the initial and boundary condition dataset of WRF model are used in deriving near-surface weather variables through WRF. After the proper event-based calibration performed to the WRF-Hydro using hourly and daily streamflow data of small catchments in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST-triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher temporal and spatial correlation revealed significant improvement in flood hydrographs for both regions. The higher spatial and temporal correlations of GHRSST dataset show RMSE reduction up to 20% and increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region. The error reduction with GHRSST reached 35% after the calibration of hydrological model parameters compared to not calibrated model. The use of both GHRSST and Medspiration SST data characterized with high spatiotemporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m3/s by reducing the overestimation seen in not calibrated runs over the MED region.

scholarly journals Embedding sea surface temperature anomalies into the stock recruitment relationship of red mullet (Mullus barbatus L. 1758) in the Strait of Sicily

2003 ◽  
Vol 67 (S1) ◽  
pp. 259-268 ◽  
Author(s):  
D. Levi ◽  
M. G. Andreoli ◽  
A. Bonanno ◽  
F. Fiorentino ◽  
G. Garofalo ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Strait Of Sicily ◽  
Red Mullet ◽  
Mullus Barbatus ◽  
Stock Recruitment ◽  
Sea Surface Temperature Anomalies ◽  
Relationship Of
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