scholarly journals The fishing and natural mortality of large, piscivorous Bull Trout and Rainbow Trout in Kootenay Lake, British Columbia (2008-2013)

2016 ◽  
Author(s):  
Joseph L. Thorley ◽  
Greg Andrusak
Keyword(s):  
Rainbow Trout ◽  
Survival Probability ◽  
Survival Model ◽  
Natural Mortality ◽  
Bull Trout ◽  
Large Lake ◽  
Recreational Fisheries ◽  
Indicator Variable ◽  
High Reward ◽  
Kootenay Lake

ABSTRACTBackgroundEstimates of fishing and natural mortality are important for understanding, and ultimately managing, commercial and recreational fisheries. High reward tags with fixed station acoustic telemetry provides a promising approach to monitoring mortality rates in large lake recreational fisheries. Kootenay Lake is a large lake which supports an important recreational fishery for large Bull Trout and Rainbow Trout.MethodsBetween 2008 and 2013, 88 large (≥ 500 mm) Bull Trout and 149 large (≥ 500 mm) Rainbow Trout were marked with an acoustic transmitter and/or high reward ($100) anchor tags in Kootenay Lake. The subsequent detections and angler recaptures were analysed using a Bayesian individual state-space Cormack-Jolly-Seber (CJS) survival model with indicator variable selection.ResultsThe final CJS survival model estimated that the annual interval probability of being recaptured by an angler was 0.17 (95% CRI 0.11 - 0.23) for Bull Trout and 0.14 (95% CRI 0.09 - 0.19) for Rainbow Trout. The annual interval survival probability for Bull Trout was estimated to have declined from 0.91 (95% CRI 0.77 - 0.97) in 2009 to just 0.45 (95% CRI 0.24 - 0.73) in 2013. Rainbow Trout survival was most strongly affected by spawning. The annual interval survival probability was 0.77 (95% CRI 0.68 - 0.85) for a non-spawning Rainbow Trout compared to 0.42 (95% CRI 0.31 - 0.54) for a spawner. The probability of spawning increased with the fork length for both species and decreased over the course of the study for Rainbow Trout.DiscussionFishing mortality was relatively low and constant while natural mortality was relatively high and variable. The results are consistent with Kokanee abundance as opposed to angler effort as the primary driver of short-term population fluctations in Rainbow Trout abundance. Multi-species stock assessment models need to account for the fact that large Bull Trout are more abundant than large Rainbow Trout in Kootenay Lake.

scholarly journals The fishing and natural mortality of large, piscivorous Bull Trout and Rainbow Trout in Kootenay Lake, British Columbia (2008–2013)

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2874 ◽  
Author(s):  
Joseph L. Thorley ◽  
Greg F. Andrusak
Keyword(s):  
Rainbow Trout ◽  
Survival Probability ◽  
Survival Model ◽  
Natural Mortality ◽  
Bull Trout ◽  
Large Lake ◽  
Recreational Fisheries ◽  
Indicator Variable ◽  
High Reward ◽  
Kootenay Lake

BackgroundEstimates of fishing and natural mortality are important for understanding, and ultimately managing, commercial and recreational fisheries. High reward tags with fixed station acoustic telemetry provides a promising approach to monitoring mortality rates in large lake recreational fisheries. Kootenay Lake is a large lake which supports an important recreational fishery for large Bull Trout and Rainbow Trout.MethodsBetween 2008 and 2013, 88 large (≥500 mm) Bull Trout and 149 large (≥500 mm) Rainbow Trout were marked with an acoustic transmitter and/or high reward ($100) anchor tags in Kootenay Lake. The subsequent detections and angler recaptures were analysed using a Bayesian individual state-space Cormack–Jolly–Seber (CJS) survival model with indicator variable selection.ResultsThe final CJS survival model estimated that the annual interval probability of being recaptured by an angler was 0.17 (95% CRI [0.11–0.23]) for Bull Trout and 0.14 (95% CRI [0.09–0.19]) for Rainbow Trout. The annual interval survival probability for Bull Trout was estimated to have declined from 0.91 (95% CRI [0.76–0.97]) in 2009 to just 0.46 (95% CRI [0.24–0.76]) in 2013. Rainbow Trout survival was most strongly affected by spawning. The annual interval survival probability was 0.77 (95% CRI [0.68–0.85]) for a non-spawning Rainbow Trout compared to 0.41 (95% CRI [0.30–0.53]) for a spawner. The probability of spawning increased with the fork length for both species and decreased over the course of the study for Rainbow Trout.DiscussionFishing mortality was relatively low and constant while natural mortality was relatively high and variable. The results indicate that angler effort is not the primary driver of short-term population fluctations in the Rainbow Trout abundance. Variation in the probability of Rainbow Trout spawning suggests that the spring escapement at the outflow of Trout Lake may be a less reliable index of abundance than previously assumed. Multi-species stock assessment models need to account for the fact that large Bull Trout are more abundant than large Rainbow Trout in Kootenay Lake.

scholarly journals An integrated system for modeling tree and stand survival

2017 ◽  
Vol 47 (10) ◽  
pp. 1405-1409 ◽  
Author(s):  
Quang V. Cao
Keyword(s):  
Survival Probability ◽  
Mathematical Structure ◽  
Survival Model ◽  
Integrated System ◽  
Survival Models ◽  
Tree Level ◽  
Individual Tree ◽  
Tree Survival ◽  
Different Levels ◽  
Model Approach

Traditionally, separate models have been used to predict the number of trees per unit area (stand-level survival) and the survival probability of an individual tree (tree-level survival) at a certain age. This study investigated the development of integrated systems in which survival models at different levels of resolution are related in a mathematical structure. Two approaches for modeling tree and stand survival were considered: deriving a stand-level survival model from a tree-level survival model (approach 1) and deriving a tree survival model from a stand survival model (approach 2). Both approaches rely on finding a tree diameter that yields a tree survival probability equal to the stand-level survival probability. The tree and stand survival models from either approach are conceptually compatible with each other but not numerically compatible. Parameters of these models can be estimated either sequentially or simultaneously. Results indicated that approach 2, with parameters estimated sequentially (first from the stand survival model and then from the derived tree survival model), performed best in predicting both tree- and stand-level survival. Although disaggregation did not help improve prediction of tree-level survival, this method can be used when numerical consistency between stand and tree survival is desired.

scholarly journals An integrated tagging model to estimate mortality rates of Albemarle Sound – Roanoke River striped bass

2017 ◽  
Vol 74 (7) ◽  
pp. 1061-1076 ◽  
Author(s):  
Julianne E. Harris ◽  
Joseph E. Hightower
Keyword(s):  
Striped Bass ◽  
Stock Assessment ◽  
Mortality Rates ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Albemarle Sound ◽  
High Reward ◽  
Accuracy And Precision ◽  
Summer Mortality ◽  
Roanoke River

We developed an integrated tagging model to estimate mortality rates and run sizes of Albemarle Sound – Roanoke River striped bass (Morone saxatilis), including (i) a multistate component for telemetered fish with a high reward external tag; (ii) tag return components for fish with a low reward external or PIT tag; and (iii) catch-at-age data. Total annual instantaneous mortality was 1.08 for resident (458–899 mm total length, TL) and 0.45 for anadromous (≥900 mm TL) individuals. Annual instantaneous natural mortality was higher for resident (0.70) than for anadromous (0.21) fish due to high summer mortality in Albemarle Sound. Natural mortality for residents was substantially higher than currently assumed for stock assessment. Monthly fishing mortality from multiple sectors (including catch-and-release) corresponded to seasonal periods of legal harvest. Run size estimates were 499 000–715 000. Results and simulation suggest increasing sample size for the multistate component increases accuracy and precision of annual estimates and low reward tags are valuable for estimating monthly fishing mortality rates among sectors. Our results suggest that integrated tagging models can produce seasonal and annual mortality estimates needed for stock assessment and management.

scholarly journals Numerical Model of Fish Exploitation – structure and application

2019 ◽  
Vol 27 (2) ◽  
pp. 86-101
Author(s):  
Paweł Buras ◽  
Wiesław Wiśniewolski
Keyword(s):  
Numerical Model ◽  
Simulation Models ◽  
Fishing Effort ◽  
Natural Mortality ◽  
Recreational Fisheries ◽  
Fisheries Resources ◽  
Fish Exploitation ◽  
The Common ◽  
Common Bream ◽  
The Impact

Abstract Fisheries simulation models are tools used for forecasting the effects of exploitation and determining the directions of managing fisheries resources. The Numerical Model of Fish Exploitation (NMFE) and its capabilities were tested on a population of common bream, Abramis brama (L.) in a dam reservoir that is exploited by commercial and recreational fisheries. Based on the designated population parameters of N0, Fij, Mi, and ei and the size and structure of the common bream population in the reservoir, the model was used to examine hypothetical simulation variants of changes in fishing intensity E1 with nets and rods, changes in fishing intensity based on actual fishing effort with nets, changes in natural mortality, changes in the size of fish caught, and the impact of this on the size of the resources. Initial catches with nets and rods were calculated. Increasing fishing effort did not translate proportionally to increased catches, and the function was curvilinear. The results of simulations that reduced the intensity of fishing with nets and decreased catch sizes concurred with data from actual catches. Simulations of changes in natural mortality had various effects on the size of catches. Reducing parameter M did not impact the level of catches, while increasing parameter M reduced the size of catches significantly.

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