scholarly journals Modelling spatially dependent predation mortality of eastern Bering Sea walleye pollock, and its implications for stock dynamics under future climate scenarios

2016 ◽  
Vol 73 (5) ◽  
pp. 1330-1342 ◽  
Author(s):  
Paul D. Spencer ◽  
Kirstin K. Holsman ◽  
Stephani Zador ◽  
Nicholas A. Bond ◽  
Franz J. Mueter ◽  
...  
Keyword(s):  
Bering Sea ◽  
Population Model ◽  
Walleye Pollock ◽  
Cold Pool ◽  
Relative Distribution ◽  
Eastern Bering Sea ◽  
Predation Mortality ◽  
Wide Range ◽  
Arrowtooth Flounder ◽  
Pool Area

Abstract Arrowtooth flounder (Atheresthes stomias) are an important predator of juvenile walleye pollock (Gadus chalcogramus) in the eastern Bering Sea (EBS) shelf and have increased 3-fold in biomass from 1977 to 2014. Arrowtooth flounder avoid the summer “cold pool” (bottom water ≤2°C) and variability in cold pool size and location has affected their spatial overlap with juvenile walleye pollock. Developing a method to account for the relationship between climate change and pollock mortality can highlight ecosystem dynamics and contribute to better assessments for fisheries management. Consequently, spatially resolved predation mortality rates were estimated within an age-structured walleye pollock stock assessment population model (based on spatial information on diet and abundance from trawl surveys), along with the effect of sea surface temperature (SST) on pollock recruitment. Projections of SST and cold pool area to 2050 were obtained (or statistically downscaled) from nine global climate models and used within an age-structure population model to project pollock abundance given estimated relationships between environmental variables and predator and prey spatial distributions, pollock recruitment, and maximum rate of arrowtooth flounder consumption. The climate projections show a wide range of variability but an overall trend of increasing SST and decreasing cold pool area. Projected pollock biomass decreased largely due to the negative effect of increased SST on pollock recruitment. A sensitivity analysis indicated that the decline in projected pollock biomass would be exacerbated if arrowtooth flounder increased their relative distribution in the EBS northwest middle shelf (an area of relatively high density of juvenile pollock) in warm years.

A probabilistic cellular automata approach for predator–prey interactions of arrowtooth flounder (Atheresthes stomias) and walleye pollock (Theragra chalcogramma) in the eastern Bering Sea

2012 ◽  
Vol 69 (2) ◽  
pp. 259-272
Author(s):  
Kun Chen ◽  
Kung-Sik Chan ◽  
Kevin M. Bailey ◽  
Kerim Aydin ◽  
Lorenzo Ciannelli
Keyword(s):  
Cellular Automata ◽  
Bering Sea ◽  
Environmental Changes ◽  
Spatial Scales ◽  
Walleye Pollock ◽  
Theragra Chalcogramma ◽  
Predator Prey ◽  
Eastern Bering Sea ◽  
Walleye Pollock Theragra Chalcogramma ◽  
Arrowtooth Flounder

We developed a hybrid cellular automata (CA) modelling approach to explore the dynamics of a key predator–prey interaction in a marine system; our study is motivated by the quest for better understanding of the scale and heterogeneity-related effects on the arrowtooth flounder (Atheresthes stomias) and walleye pollock (Theragra chalcogramma) dynamics during the summer feeding season in the eastern Bering Sea (EBS), but can be readily extended to other systems. The spatially explicit and probabilistic CA model incorporates individual behaviours and strategies and local interactions among species, as well as spatial and temporal heterogeneity due to geographical and (or) environmental changes in the physical environment. The model is hybridized, with an individual-based model (IBM) approach for increasing its capacity and continuum and for balancing between computational efficiency and model validity, which makes it suitable for simulating predator–prey dynamics in a large, complex ecological environment. We focus on the functional and aggregative responses of predators to prey density at different spatial scales, the effects of individual behaviours, and the impacts of systematic heterogeneity. Simulations from the model with suitable parameter values share qualitatively similar features found in field observations, e.g., local aggregations around hydrographical features. Spatial heterogeneity is an important aspect of whether local-scale functional and aggregative responses reflect those operating over large, or global, scales.

scholarly journals Comparison of the biophysical and trophic characteristics of the Bering and Barents Seas

2005 ◽  
Vol 62 (7) ◽  
pp. 1245-1255 ◽  
Author(s):  
George L. Hunt ◽  
Bernard A. Megrey
Keyword(s):  
Bering Sea ◽  
Barents Sea ◽  
Single Species ◽  
Atlantic Water ◽  
Walleye Pollock ◽  
Fish Stocks ◽  
Eastern Bering Sea ◽  
The Barents Sea ◽  
Shelf Seas ◽  
Bottom Waters

Abstract The eastern Bering Sea and the Barents Sea share a number of common biophysical characteristics. For example, both are seasonally ice-covered, high-latitude, shelf seas, dependent on advection for heat and for replenishment of nutrients on their shelves, and with ecosystems dominated by a single species of gadoid fish. At the same time, they differ in important respects. In the Barents Sea, advection of Atlantic Water is important for zooplankton vital to the Barents Sea productivity. Advection of zooplankton is not as important for the ecosystems of the southeastern Bering Sea, where high levels of diatom production can support production of small, neritic zooplankton. In the Barents Sea, cod are the dominant gadoid, and juvenile and older fish depend on capelin and other forage fish to repackage the energy available in copepods. In contrast, the dominant fish in the eastern Bering Sea is the walleye pollock, juveniles and adults of which consume zooplankton directly. The southeastern Bering Sea supports considerably larger fish stocks than the Barents. In part, this may reflect the greater depth of much of the Barents Sea compared with the shallow shelf of the southeastern Bering. However, walleye pollock is estimated to occupy a trophic level of 3.3 as compared to 4.3 for Barents Sea cod. This difference alone could have a major impact on the abilities of these seas to support a large biomass of gadoids. In both seas, climate-forced variability in advection and sea-ice cover can potentially have major effects on the productivity of these Subarctic seas. In the Bering Sea, the size and location of pools of cold bottom waters on the shelf may influence the likelihood of predation of juvenile pollock.

scholarly journals Evaluating total uncertainty for biomass- and abundance-at-age estimates from eastern Bering Sea walleye pollock acoustic-trawl surveys

2016 ◽  
Vol 73 (9) ◽  
pp. 2208-2226 ◽  
Author(s):  
Mathieu Woillez ◽  
Paul D. Walline ◽  
James N. Ianelli ◽  
Martin W. Dorn ◽  
Christopher D. Wilson ◽  
...  
Keyword(s):  
Bering Sea ◽  
Walleye Pollock ◽  
Target Strength ◽  
Acoustic Backscatter ◽  
Trawl Survey ◽  
Data Types ◽  
Total Uncertainty ◽  
Functional Relationships ◽  
Eastern Bering Sea ◽  
Trawl Surveys

Abstract A comprehensive evaluation of the uncertainty of acoustic-trawl survey estimates is needed to appropriately include them in stock assessments. However, this evaluation is not straightforward because various data types (acoustic backscatter, length, weight, and age composition) are combined to produce estimates of abundance- and biomass-at-age. Uncertainties associated with each data type and those from functional relationships among variables need to be evaluated and combined. Uncertainty due to spatial sampling is evaluated using geostatistical conditional (co-) simulations. Multiple realizations of acoustic backscatter were produced using transformed Gaussian simulations with a Gibbs sampler to handle zeros. Multiple realizations of length frequency distributions were produced using transformed multivariate Gaussian co-simulations derived from quantiles of the empirical length distributions. Uncertainty due to errors in functional relationships was evaluated using bootstrap for the target strength-at-length and the weight-at-length relationships and for age–length keys. The contribution of each of these major sources of uncertainty was assessed for acoustic-trawl surveys of walleye pollock in the eastern Bering Sea in 2006–2010. This simulation framework allows a general computation for estimating abundance- and biomass-at-age variance–covariance matrices. Such estimates suggest that the covariance structure assumed in fitting stock assessment models differs substantially from what careful analysis of survey data actually indicate.

scholarly journals Geostatistical simulations of eastern Bering Sea walleye pollock spatial distributions, to estimate sampling precision

2007 ◽  
Vol 64 (3) ◽  
pp. 559-569 ◽  
Author(s):  
Paul D. Walline
Keyword(s):  
Bering Sea ◽  
Walleye Pollock ◽  
Geostatistical Simulation ◽  
Normal Space ◽  
Total Biomass ◽  
Spatial Distributions ◽  
Acoustic Data ◽  
Eastern Bering Sea ◽  
Abundance Estimates ◽  
Length Frequency Data

Abstract Walline, P. D. 2007. Geostatistical simulations of eastern Bering Sea walleye pollock spatial distributions, to estimate sampling precision. – ICES Journal of Marine Science, 64: 559–569. Sequential Gaussian and sequential indicator geostatistical simulation methods were used to estimate confidence intervals (CIs) for biomass estimates from six echo-integration trawl surveys of eastern Bering Sea walleye pollock (Theragra chalcogramma) biomass. Uncertainty in the acoustic and the length frequency data was combined in the calculation of CIs. Sampling in 2002 provided evidence for isotropy in the spatial distribution. Variogram models were characterized by long ranges (75–122 nautical miles for non-zero acoustic data, for example) compared with the smallest dimension of the survey area (∼100 nautical miles) and small nugget effects (∼20% of the semi-variance in transformed normal space for acoustic data). The 95% CIs obtained for the abundance estimates did not vary greatly between years and were similar to those from a one-dimensional transitive geostatistical analysis, i.e. ± 5–9% of estimated total biomass.

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