Variable Natural Mortality Rates Inflate Variance of Recruitments Estimated from Virtual Population Analysis (VPA)

1992 ◽  
Vol 49 (10) ◽  
pp. 2020-2027 ◽  
Author(s):  
Michael F. Lapointe ◽  
Randall M. Peterman ◽  
Brian J. Rothschild
Keyword(s):  
Mortality Rate ◽  
Environmental Factor ◽  
Statistical Power ◽  
Population Analysis ◽  
Sensitivity Analyses ◽  
Natural Mortality ◽  
Environmental Correlates ◽  
Virtual Population Analysis ◽  
Virtual Population ◽  
Over Time

We used a simulation model to determine whether estimates offish recruitment obtained from virtual population analysis (VPA) (1) have the correct interannual variability and (2) yield high statistical power (>0.8) when correlated with an environmental factor, given that the "true" instantaneous adult natural mortality rate (M) likely varies over time but a constant M is used in VPA (MVPA). Under such circumstances, VPA exaggerates variability in recruitment, which reduces the probability of correctly detecting environmental correlates with recruitment. The magnitude of these effects increases with increases in (1) the absolute value of the true mean M, (2) the variation in M over time, (3) the relative error in MVPA, and (4) the magnitude of MVPA relative to FL (instantaneous terminal fishing mortality rate) and decreases with increases in magnitude of the true variation of recruitment or the true correlation with the environmental factor (all else being equal). This bias is not large under most conditions, but it is likely to be more important in short-lived, high-M species than the similar but counteracting bias caused by aging errors. Sensitivity analyses can demonstrate how various MVPA values affect conclusions about environmental correlates with recruitment.

Virtual Population Analysis (VPA) Equations for Nonhomogeneous Populations, and a Family of Approximations Including Improvements on Pope's Cohort Analysis

1986 ◽  
Vol 43 (12) ◽  
pp. 2406-2409 ◽  
Author(s):  
Alec D. MacCall
Keyword(s):  
Mortality Rate ◽  
Population Analysis ◽  
Seasonal Pattern ◽  
Cohort Analysis ◽  
Natural Mortality ◽  
Close Approximation ◽  
Virtual Population Analysis ◽  
Virtual Population ◽  
Special Case

A set of "backward" virtual population analysis (VPA) equations relates catch (Ct) from continuous fishing between times t and t + 1 to population n size (Nt, Nt+1) when a portion of the stock is unavailable to fishing. The usual VPA equations become a special case where the entire stock is available (i.e. the stock is homogeneous). A close approximation to the VPA equations is Nt = Nt+1 exp(M) + CtM/(1 − exp(−M)), which has properties similar to Pope's "cohort analysis" and is somewhat more accurate in the case of a continuous fishery, especially if the natural mortality rate (M) is large. Much closer simple approximations are possible if the seasonal pattern of catches is known.

Trends in Fishing Mortality Rate along with Errors in Natural Mortality Rate can cause Spurious Time Trends in Fish Stock Abundances Estimated by Virtual Population Analysis (VPA)

1989 ◽  
Vol 46 (12) ◽  
pp. 2129-2139 ◽  
Author(s):  
Michael F. Lapointe ◽  
Randall M. Peterman ◽  
Alec D. MacCall
Keyword(s):  
Mortality Rate ◽  
Time Trends ◽  
Biological Effects ◽  
Population Analysis ◽  
Fish Stock ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Management Actions ◽  
Virtual Population Analysis ◽  
Virtual Population

Many researchers have reported biases in estimates offish abundance reconstructed by virtual population analysis (VPA). We document that VPA can produce changing levels of bias through time, thereby creating spurious time trends in recruitment and stock biomass estimates. We generated catch data from empirically based simulations of nine fish populations, estimated abundances using VPA with a deliberately mis-specified natural mortality rate, M, and compared the estimates to the models' "true" abundances. A period of increasing fishing mortality rate, F, combined with an overestimate of M produced spurious decreasing time trends in estimated abundance and recruitment, even when the true time series of F was known. Analogously, an underestimate of M led to a spurious increasing time trend. Bias was increased by a higher true M, and (for a given total change in F) by a slower increase in F. Because field estimates of M are uncertain and trends in F are common, some apparent trends (or lack of them) in abundances reconstructed by VPA may be artifacts. Therefore, inferences about the results of past management actions and about physical or biological effects on variability in recruitment must be made cautiously when VPA estimates are used.

scholarly journals Temporally varying natural mortality: Sensitivity of a virtual population analysis and an exploration of alternatives

2017 ◽  
Vol 185 ◽  
pp. 185-197 ◽  
Author(s):  
Shanae D. Allen ◽  
William H. Satterthwaite ◽  
David G. Hankin ◽  
Diana J. Cole ◽  
Michael S. Mohr
Keyword(s):  
Population Analysis ◽  
Natural Mortality ◽  
Virtual Population Analysis ◽  
Virtual Population

Multispecies versus Single-Species Assessment of North Sea Fish Stocks

1987 ◽  
Vol 44 (S2) ◽  
pp. s360-s370 ◽  
Author(s):  
Niels Daan
Keyword(s):  
Population Analysis ◽  
Stomach Contents ◽  
Single Species ◽  
Fish Stock ◽  
Natural Mortality ◽  
Stock Management ◽  
Fish Stocks ◽  
Virtual Population Analysis ◽  
Virtual Population ◽  
Class Strength

Development of multispecies virtual population analysis (MSVPA), which assesses interspecific and intraspecific predation through an analysis of stomach contents, has verified the hypothesis that predation among exploited fish species contributes significantly to their natural mortality and that predation, and thus natural mortality, is inherently variable from year to year. In single-species virtual population analysis (SSVPA), natural mortality is assumed to be constant. MSVPA also suggests that natural mortality among young fish after recruitment is much higher than previously thought. Although catch quotas based on predictions of short-term catches from multispecies assessments would appear to differ little from those derived from single-species assessments, and certain problems remain to be resolved before multispecies assessments can be accepted for fish stock management, the method has considerable implications for management. For instance, it suggests that effects of mesh sizes and bycatch on fisheries need reevaluation and that year class strength may not be as fixed as previously assumed.

Comparison of virtual population analysis and statistical kill-at-age analysis for a recreational, kill-dominated fishery

2005 ◽  
Vol 62 (2) ◽  
pp. 436-452 ◽  
Author(s):  
Paul Radomski ◽  
James R Bence ◽  
Terrance J Quinn II
Keyword(s):  
Measurement Errors ◽  
Population Analysis ◽  
Careful Consideration ◽  
Estimation Errors ◽  
Virtual Population Analysis ◽  
Virtual Population ◽  
Substantial Error ◽  
Age Structured ◽  
Forward Calculation ◽  
Over Time

We used simulations to compare the distributions of estimation errors for virtual population analysis using forward calculation (FVPA) and three variants of statistical kill-at-age analysis (KAA). The KAA variants assumed constant, time-blocked, and nonadditive selectivity. Simulations were based on a recreational walleye (Sander vitreus) fishery in Lake Mille Lacs, Minnesota. The focus of our experiments was on how model mis-specification (incorrect assumptions about selectivity for KAA or that kill had no error for FVPA) interacted with the magnitude of measurement errors and fishing mortality. We found that KAA models outperformed FVPA when they assumed the correct selectivity pattern, even when kill was measured without error. Of particular concern was a strong tendency by FVPA to overestimate stock size when kill was measured with substantial error. When KAA was based on an incorrect assumption regarding fishery selectivity and kill was measured with little error, wide distributions of errors and substantial biases sometimes resulted. KAA models that allowed fishery selectivity to change over time performed about as well as a constant-selectivity KAA model when selectivity was constant, and they performed much better when selectivity changed over time. Careful consideration of alternative fishery selectivity models should be a fundamental part of any age-structured assessment.

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