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Increasingly, mixed-effect fishery stock assessment models are being developed where deviations about functional forms of different processes are modelled as random effects and the extent of variance is estimated internal to the model. Concurrently, sampling variance parameters associated with likelihoods for fitting composition data within fisheries assessments are more often being estimated internal to the model as well. We examine the performance of stock assessment models when multiple process variance and sampling variance terms are simultaneously estimated within assessment models. We specifically examine how assessment performance is affected by the choice of composition likelihood, the degree of overdispersion in composition data, overparameterization, and modelling variation on the wrong process. In doing so, we build a simulation containing overdispersion and correlations in composition data, directional variation in catchability and/or selectivity, and estimation models which include random effects and composition likelihoods with theoretically estimable variances. Results suggest that with standard data available in fisheries assessments, process variance parameters associated with some commonly employed methods and sampling variance parameters can be simultaneously estimated internal to an assessment, and performance greatly improves with increased composition data. Our results also suggest little downside to overparameterization of selectivity and catchability when the true process is not time-varying, which largely agrees with previous research. However, when a process is truly time-varying and the assessment models time-variation on a different process, namely when selectivity is time-varying and instead natural mortality is modelled as potentially time-varying, we find a risk of severe increases in bias and decreases in confidence interval coverage for assessed quantities. This bias and decrease in coverage could, however, be partially mitigated by also modelling time-variation on the correct process. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

State-space models have received increasing attention in fisheries stock assessments given their flexibility to incorporate multiple sources of process errors. Identifying which process errors to include is important because incorrectly including some process errors can induce bias in management quantities. Existing model selection tools commonly applied in traditional statistical catch-at-age models may not perform as well for state-space models. We evaluated the efficacy of common diagnostic tools for correctly identifying the presence, absence, and magnitude of three process errors (survival, selectivity, and natural mortality) in a simulation–estimation experiment. No model diagnostic tools could consistently identify the correct process error structure in all situations. Incorrectly attributing the process error from natural mortality to other processes, or vice versa, led to relatively large bias in management quantities. Furthermore, incorrectly including an additional source of process error in the assessment models exhibited similar performance to the correct model and generally showed unbiased estimates of management quantities; incorrectly excluding a source of process error, however, generated large biases. Thus, despite not having generally reliable model diagnostic tools for state-space assessments, practitioners should err on the side of using overly complex models, except for natural mortality unless there is external corroborating evidence of changing natural mortality. [ABSTRACT FROM AUTHOR]

To guarantee the final assembly quality of the large-scale components, the assembly interfaces of large components need to be finish-machined on site. Such assembly interfaces are often in low-stiffness structure and made of difficult-to-cut materials, which makes it hard to fulfill machining tolerance. To solve this issue, a data-driven adaptive machining error analysis and compensation method is proposed based on on-machine measurement. Within this context, an initial definite plane is fitted via an improved robust iterating least-squares plane-fitting method based on the spatial statistical analysis result of machining errors of the key measurement points. Then, the parameters of the definite plane are solved by a simulated annealing-particle swarm optimization (SA-PSO) algorithm to determine the optimal definite plane; it effectively decomposes the machining error into systematic error and process error. To reduce these errors, compensation methods, tool-path adjustment method, and an optimized group of cutting parameters are proposed. The proposed method is validated by a set of cutting tests of an assembly interface of a large-scale aircraft vertical tail. The results indicate that the machining errors are successfully separated, and each type of error has been reduced by the proposed method. A 0.017 mm machining accuracy of the wall-thickness of the assembly interface has been achieved, well fulfilling the requirement of 0.05 mm tolerance. [ABSTRACT FROM AUTHOR]

Long-term field-based monitoring is essential to develop a deep understanding of how ecosystems function and to identify species at risk of decline. However, conducting long-term field-based research poses some unique challenges due to the harsh environmental conditions or extreme weather events that may be encountered. Such conditions are especially likely to occur in arid environments. Fieldwork issues can arise from vehicle breakdowns, wildfires and heavy rainfall events, all of which can delay or even cancel data collection. In addition, long-term monitoring typically requires multiple observers, which may add observation bias to estimates of measured parameters. Thus there is an increasing need to develop new statistical techniques that take advantage of the power of long time-series datasets that also are incomplete. Here we discuss multivariate autoregressive state-space (MARSS) modelling; a relatively new statistical technique for modelling long-term time-series data. MARSS models allow users to investigate incomplete datasets caused by missing values. In contrast to traditional modelling techniques, such as generalised linear models that only estimate error from environmental stochasticity (process error), MARSS models estimate both process and observation errors. By estimating observation errors, researchers can incorporate bias from different observers and methods into population or other parameter estimates. To illustrate the MARSS technique we interrogate long-term animal and plant datasets from arid central Australia that contain missing values and were collected by multiple observers. We then discuss the findings from the MARSS models and their implications for management. Lastly, we suggest future applications that this technique could be used for, such as studies of animal movements and food webs. [ABSTRACT FROM AUTHOR]

Quantitative predictions are ubiquitous in ecology, yet there is limited discussion on the nature of prediction in this field. Herein I derive a general quantitative framework for analyzing and partitioning the sources of uncertainty that control predictability. The implications of this framework are assessed conceptually and linked to classic questions in ecology, such as the relative importance of endogenous (density-dependent) vs. exogenous factors, stability vs. drift, and the spatial scaling of processes. The framework is used to make a number of novel predictions and reframe approaches to experimental design, model selection, and hypothesis testing. Next, the quantitative application of the framework to partitioning uncertainties is illustrated using a short-term forecast of net ecosystem exchange. Finally, I advocate for a new comparative approach to studying predictability across different ecological systems and processes and lay out a number of hypotheses about what limits predictability and how these limits should scale in space and time. [ABSTRACT FROM AUTHOR]

Integrated stock assessments consist of fitting several sources of catch, abundance, and auxiliary biological information to estimate parameters of equations that describe the population dynamics of fish stocks. Stock assessments are subject to uncertainty, and it is a common practice to characterize uncertainty using alternative hypotheses and assumptions within an ensemble of models to develop scientific advice for fisheries management. In this context, there is the need to assign levels of plausibility to each of the combinations of factors that ultimately reflect the uncertainty on different biological and fishery processes. In this study, we describe and apply a model diagnostic to identify trends in process error in recruitment deviation estimates within ensembles of integrated assessment models of tropical tunas. We demonstrate that assessment model ensembles for tropical tunas contain distinct scenarios with significant trends in process error that are overlooked, with the associated implications for fisheries management. Using the Indian Ocean yellowfin as a case study, we found that trends in recruitment deviates are linked to extreme productivity scenarios which strongly diverged in scale from deterministic models fitted without recruitment deviates. This indicates that when recruitment deviates show an increasing trend, these can compensate for the loss of biomass in periods of high catch beyond the surplus production. In these cases, variation in recruitment is not a random process, but rather takes the function of a compensatory, systematic driver in productivity. Significant trends in recruitment were positively correlated with increased standard deviations and auto-correlation coefficient, non-random residual pattern in fits to abundance indices, and particularly poor performance of the Age-Structured Production Model (ASPM) diagnostic. We suggest that trends in recruitment deviates can be caused by misspecification of the biological parameters used as fixed values in integrated assessment models. The process error diagnostic described here can provide a statistical criterion in support for hypotheses and assumptions when using ensembles of models to develop fisheries management advice. • We apply a method to identify trends in process error in fisheries assessments. • The assessments of tropical tunas contain trends in process error. • Trends in process error are overlooked and have management implications. • Trends of recruitment deviates compensate loss of biomass in periods of high catch. • The method provides statistical support for assumptions within ensembles of models. • Trends in recruitment deviates are correlated with poor diagnostics. [ABSTRACT FROM AUTHOR]

Integral projection models ( IPMs) are a powerful and popular approach to modeling population dynamics. Generalized linear models form the statistical backbone of an IPM. These models are typically fit using a frequentist approach. We suggest that hierarchical Bayesian statistical approaches offer important advantages over frequentist methods for building and interpreting IPMs, especially given the hierarchical nature of most demographic studies. Using a stochastic IPM for a desert cactus based on a 10-year study as a worked example, we highlight the application of a Bayesian approach for translating uncertainty in the vital rates (e.g., growth, survival, fertility) to uncertainty in population-level quantities derived from them (e.g., population growth rate). The best fit demographic model, which would have been difficult to fit under a frequentist framework, allowed for spatial and temporal variation in vital rates and correlated responses to temporal variation across vital rates. The corresponding posterior probability distribution for the stochastic population growth rate (λS) indicated that, if current vital rates continue, the study population will decline with nearly 100% probability. Interestingly, less supported candidate models that did not include spatial variance and vital rate correlations gave similar estimates of λS. This occurred because the best fitting model did a much better job of fitting vital rates to which the population growth rate was weakly sensitive. The cactus case study highlights several advantages of Bayesian approaches to IPM modeling, including that they: (1) provide a natural fit to demographic data, which are often collected in a hierarchical fashion (e.g., with random variance corresponding to temporal and spatial heterogeneity); (2) seamlessly combine multiple data sets or experiments; (3) readily incorporate covariance between vital rates; and, (4) easily integrate prior information, which may be particularly important for species of conservation concern where data availability may be limited. However, constructing a Bayesian IPM will often require the custom development of a statistical model tailored to the peculiarities of the sampling design and species considered; there may be circumstances under which simpler methods are adequate. Overall, Bayesian approaches provide a statistically sound way to get more information out of hard-won data, the goal of most demographic research endeavors. [ABSTRACT FROM AUTHOR]

Ecosystem models, specifically multispecies dynamic models, have been increasingly used to project impacts of fishing activity on the trophodynamics of ecosystems to support ecosystem-based fisheries management. Uncertainty is unavoidable in modelling processes and needs to be recognized and properly quantified before models are utilized. Uncertainty was assessed in this study for a multispecies size-spectrum model that quantifies community structure and ecological characteristics. The uncertainty was assumed to result from errors in fish life-history and metabolic scale parameters, environmental variability, fishing variability, and sampling errors. Given the same level of imprecision, metabolic scale parameters had the dominant influence on the uncertainty of the size spectrum modelling results, followed by life-history parameters. Both types of errors led to "scenario uncertainty", suggesting the possible existence of alternative states of community structure. Environmental variability, fishing variability, and observation errors resulted in "statistical uncertainty", implying that such uncertainty can be described adequately in statistical terms. The results derived from such a simulation study can provide guidance for identifying research priorities to help narrow the gap in scientific knowledge and reduce the uncertainty in fisheries management. [ABSTRACT FROM AUTHOR]

Forecasting the risk of population decline is crucial in the realm of biological conservation and figures prominently in population viability analyses (PVA). A common form of available data for a PVA is population counts through time. Previous research has suggested that improving estimates of population trends and risk from count data depends on longer observation periods, but that is often impractical or undesirable. Making multiple observations within a single time step is an alternative way to gather more data without extending the observation period. In this paper, we examine the trade-off between the length of the time period over which observations of the population have been taken and the total number of observations or samples that have been recorded through an analysis of simulated data. We found that when the ratio of process error to measurement error variance is high, more precise estimates of quasi-extinction risks can be obtained if replicated observations are taken at each time step, but when the ratio is low, replicated observations add little benefit in improving precision. These results can be used to efficiently design effective monitoring schemes for species of conservation concern. [ABSTRACT FROM AUTHOR]

Smith, S. J., and Hubley, B. 2014. Impact of survey design changes on stock assessment advice: sea scallops. – ICES Journal of Marine Science, 71: 320–327.Annual surveys of marine resources are used to monitor changes in population composition and abundance. Improvements in the performance and coverage of these surveys can readily be evaluated for the surveys themselves but should also be considered in the context of the stock assessment models that use the estimates from these surveys. For those surveys based on a probability design, improvements in the probability design are usually evaluated with respect to the resultant increase in precision of the survey estimates. Survey precision estimates can be included in many stock assessment models as observation error, as long as the process error component of the model is also identified. Advice on catch levels for sea scallop populations (Placopecten magellanicus) around Nova Scotia is developed using a Bayesian state space assessment model in which both observation and process error terms have been defined. Information on survey estimates of precision are included in the observation error component of the assessment model and the impacts of changes in survey precision on the provision of advice can be evaluated in terms of reference points and management advice. The sensitivity of stock assessment advice to changes in the level of precision of survey estimates was evaluated for three scallop fisheries around Nova Scotia. The results indicated that the impact of the changes depended upon the degree of concurrence between the annual changes in biomass as observed from the survey and those predicted by the model. [ABSTRACT FROM PUBLISHER]

An efficient method is proposed to diagnose a type of abnormal data. We first start with analyzing an example, carry through with development of the theory once more, and finally list the method steps and its application fields. Experiments show that we need more important and better ways to diagnose abnormal data and eliminate them along with the development of information technology and control technology. The quality of measured data is improved by the use of this technique.

Abstract: Population indices quantify changes in relative population sizes, which underpin much of basic ecology and conservation science. However, temporal changes in population counts may vary among survey sites for both ecological and artificial reasons, confounding existing population indices estimated without accounting for such variations. We created a smoothed hierarchical model, and compared its performance against the conventional approaches (generalized linear models and generalized additive models) and a non-smoothed hierarchical model using simulation data with a known nonlinear trend. The smoothed hierarchical model always estimated population indices with the best accuracy and precision; the performance of other models deteriorated substantially with increasing variation in trends of population counts among sites, causing inaccurate estimation of population growth rates. The estimated variations in trends of population counts among sites for 233 out of 518 North American breeding bird species were larger than the value used in the simulation where there was a considerable difference in the performance between hierarchical models and the conventional approaches. These estimated variations in trends of population counts among sites were particularly large in gregarious waterbirds. These results suggest that the smoothed hierarchical model developed in this study should play an important role in accurately assessing population indices, particularly for gregarious waterbirds, using count data from large-scale, long-term surveys in the field. [Copyright &y& Elsevier]

We examined the effects of process and observation errors on the selection of the stock-recruitment relationship (SRR) curve using simulations. When the process and observation errors were added to both spawning stock biomass and recruitment, the results were as follows: (1) When the proportional model was set as the true SRR model, there was a high probability that the Ricker or Beverton and Holt model was selected in response to the errors; (2) When the Ricker or Beverton and Holt model was set as the true SRR model, the proportional model was seldom selected in response to the errors; (3) The proportional SRR model should be accepted as the optimum SRR model for the Pacific stock of Japanese sardine; (4) We should use an SRR model that is constructed from more than two independent variables (spawning stock biomass, environmental factors, etc.) when we discuss management of fisheries resources. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to review a system that validates and documents the process of ensuring the correct patient, correct site and side, and correct procedure (commonly referred to as the 3 C''s) within medical imaging. Methods: A 4-step patient identification and procedure matching process was developed using health care and aviation models. The process was established in medical imaging departments after a successful interventional radiology pilot program. The success of the project was evaluated using compliance audit data, incident reporting data before and after the implementation of the process, and a staff satisfaction survey. Results: There was 95% to 100% verification of site and side and 100% verification of correct patient, procedure, and consent. Correct patient data and side markers were present in 82% to 95% of cases. The number of incidents before and after the implementation of the 3 C''s was difficult to assess because of a change in reporting systems and incident underreporting. More incidents are being reported, particularly “near misses.” All near misses were related to incorrect patient identification stickers being placed on request forms. The majority of staff members surveyed found the process easy (55.8%), quick (47.7%), relevant (51.7%), and useful (60.9%). Conclusion: Although identification error is difficult to eliminate, practical initiatives can engender significant systems improvement in complex health care environments. [Copyright &y& Elsevier]

Abstract: The focus of this article is to investigate the biological reference points, such as the maximum sustainable yield (MSY), in a common Schaefer (logistic) surplus production model in the presence of a multiplicative environmental noise. This type of model is used in fisheries stock assessment as a first-hand tool for biomass modelling. Under the assumption that catches are proportional to the biomass, we derive new conditions on the environmental noise distribution such that stationarity exists and extinction is avoided. We then get new explicit results about the stationary behavior of the biomass distribution for a particular specification of the noise, namely the biomass distribution itself and a redefinition of the MSY and related quantities that now depend on the value of the variance of the noise. Consequently, we obtain a more precise vision of how less optimistic the stochastic version of the MSY can be than the traditionally used (deterministic) MSY. In addition, we give empirical conditions on the error variance to approximate our specific noise by a lognormal noise, the latter being more natural and leading to easier inference in this context. These conditions are mild enough to make the explicit results of this paper valid in a number of practical applications. The outcomes of two case-studies about northwest Atlantic haddock [Spencer, P.D., Collie, J.S., 1997. Effect of nonlinear predation rates on rebuilding the Georges Bank haddock (Melanogrammus aeglefinus) stock. Can. J. Fish. Aquat. Sci. 54, 2920–2929] and South Atlantic albacore tuna [Millar, R.B., Meyer, R., 2000. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis–Hastings within-Gibbs sampling. Appl. Stat. 49, 327–342] are used to illustrate the impact of our results in bioeconomic terms. [Copyright &y& Elsevier]

Introduced predators can have pronounced effects on naïve prey species; thus, predator control is often essential for conservation of threatened native species. Complete eradication of the predator, although desirable, may be elusive in budget-limited situations, whereas predator suppression is more feasible and may still achieve conservation goals. We used a stochastic predator–prey model based on a Lotka-Volterra system to investigate the cost-effectiveness of predator control to achieve prey conservation. We compared five control strategies: immediate eradication, removal of a constant number of predators (fixed-number control), removal of a constant proportion of predators (fixed-rate control), removal of predators that exceed a predetermined threshold (upper-trigger harvest), and removal of predators whenever their population falls below a lower predetermined threshold (lower-trigger harvest). We looked at the performance of these strategies when managers could always remove the full number of predators targeted by each strategy, subject to budget availability. Under this assumption immediate eradication reduced the threat to the prey population the most. We then examined the effect of reduced management success in meeting removal targets, assuming removal is more difficult at low predator densities. In this case there was a pronounced reduction in performance of the immediate eradication, fixed-number, and lower-trigger strategies. Although immediate eradication still yielded the highest expected minimum prey population size, upper-trigger harvest yielded the lowest probability of prey extinction and the greatest return on investment (as measured by improvement in expected minimum population size per amount spent). Upper-trigger harvest was relatively successful because it operated when predator density was highest, which is when predator removal targets can be more easily met and the effect of predators on the prey is most damaging. This suggests that controlling predators only when they are most abundant is the “best” strategy when financial resources are limited and eradication is unlikely. [ABSTRACT FROM AUTHOR]

Abstract: A biomass dynamic model making use of an observation error estimator has been used over recent years to assess the status of the squid resource, Loligo vulgaris reynaudii, off the coast of South Africa. This assessment has indicated the resource to be at a low level and the risk of collapse under the current level of fishing effort to be high. Accordingly, past scientific management advice has been that effort should be reduced. This assessment is refined here to take account of both observation and process error, and a number of sensitivity tests related to assumptions made in the original model are conducted. Bayesian analyses to take full account of estimation uncertainty are also undertaken. The results from the respective analyses are reported, and it is shown that changing the “hockey-stick” stock–recruitment function of the original model to a Beverton–Holt form opens the possibility that the resource is under less risk from the current level of effort than has been supposed. [Copyright &y& Elsevier]

Environmental stochasticity is one of the premier features of population models used to forecast population persistence; however, most population viability analyses ignore interactions with other species. By contrast, theory in community ecology draws from a tradition of determinism: focusing on the processes (competition, predation) that promote the coexistence of groups of species (i.e., coexistence), while generally ignoring environmental variation in these processes (process noise or environmental stochasticity). Here I considered the role of deterministic and stochastic sources of variation in a predatorprey interaction on the probability of prey population extinction. Using discrete-time stochastic versions of a classic predator-prey model I show that even low levels of predator process noise increase the probability of prey extinction. Surprisingly, predator process noise has stronger negative effects on prey persistence than stochasticity in the population growth rate of the prey species itself. This conclusion is robust across a wide range of deterministic model behaviors, suggesting that the effects of stochastic variation in predator abundance on prey persistence may be pervasive. I then applied these insights to the management of endangered prey populations threatened by a nonnative predator and examined the relative efficacy of three predator control techniques at reviving declining prey populations. These techniques included immediate eradication and reduction of predator populations through either "proportional" harvest or "trigger" harvest. Under a proportional harvest regime, a constant proportion of predators were removed each year, with little attention paid to the natural variation experienced by predator populations. By contrast, I implemented trigger harvest by reducing predator abundance to a fixed threshold abundance level only in years in which the predator exceeds this harvest threshold. Trigger harvest reduces both the relative abundance and variance of predators, whereas proportional harvest only reduces the abundance of these species. My analysis suggests that the reduction of predator variance through trigger harvest can revive declining prey populations and, in some cases, revive them more effectively than a proportional reduction of predator abundance: the default control strategy when eradication cannot be achieved. [ABSTRACT FROM AUTHOR]

The article presents the study that investigates the effectiveness of diffusion approximation in the analysis of population viability of endangered species in the U.S. The topics discussed include the challenges experienced in the gathering of census date of the said species, the efficacy of quantitative population viability analysis (PVA) in addressing the problems, and ways of correcting data corruption.

A new age-structured stock dynamics approach including stochastic survival and recruitment processes is developed and implemented. The model is able to analyse detailed sources of information used in standard age-based fish stock assessment such as catch-at-age and effort data from commercial fleets and research surveys. The stock numbers are treated as unobserved variables subject to process errors while the catches are observed variables subject to both sampling and process errors. Results obtained for North Sea plaice using Markov Chain Monte Carlo methods indicate that the process error by far accounts for most of the variation compared to sampling error. Comparison with results from a simpler separable model indicates that the new model provides more precise estimates with fewer parameters. [Copyright &y& Elsevier]

The relationship between spot volume and variation for all protein spots observed on large format 2D gels when utilising silver stain technology and a model system based on mammalian NSO cell extracts is reported. By running multiple gels we have shown that the reproducibility of data generated in this way is dependent on individual protein spot volumes, which in turn are directly correlated with the coefficient of variation. The coefficients of variation across all observed protein spots were highest for low abundant proteins which are the primary contributors to process error, and lowest for more abundant proteins. Using the relationship between spot volume and coefficient of variation we show it is necessary to calculate variation for individual protein spot volumes. The inherent limitations of silver staining therefore mean that errors in individual protein spot volumes must be considered when assessing significant changes in protein spot volume and not global error. [Copyright &y& Elsevier]

Fitting nonlinear models to time-series is a technique of increasing importance in population ecology. In this article, we apply it to assess the importance of predator dependence in the predation process by comparing two alternative models of equal complexity (one with and one without predator dependence) to predator–prey time-series. Stochasticities in such data come from both observation error and process error. We consider how these errors must be taken into account in the fitting process, and we develop eight different model selection criteria. Applying these criteria to laboratory data on simple protozoan and arthropod predator–prey systems shows that little predator dependence is present, with one interesting exception. Field data are more ambiguous (either selection depends on the particular criterion or no significant differences can be detected), and we show that both models fit reasonably well. We conclude that, within our modeling framework, predator dependence is in general insignificant in simple systems in homogeneous environments. Relatively complex systems show significant predator dependence more often than simple ones but the data are also often inconclusive. The analysis of such systems should rely on several models to detect predictions that are sensitive to predator dependence and to direct further research if necessary. [ABSTRACT FROM AUTHOR]