A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder

A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user-friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R. Such an approach can easily require time-consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious, and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this approach permits simple retrieval of standard errors at the same time. We illustrate the performance of our routines using different data sets: first, two smaller samples from a mobile application for tinnitus patients and a well-known data set of fetal lamb movements with 87 and 240 data points, respectively. Second, we rely on larger data sets of simulated data of sizes 2000 and 5000 for further analysis. This tutorial is accompanied by a collection of scripts, which are all available in the Supporting Information. These scripts allow any user with moderate programming experience to benefit quickly from the computational advantages of TMB.publishedVersio

Predicting density-dependent somatic growth in Norwegian spring-spawning herring

Density-dependent growth, which might influence the effects of fisheries on a population, is often ignored when management strategies are evaluated, mainly due to a lack of appropriate models readily available to be implemented. To improve on this, we investigated if somatic growth in Norwegian spring-spawning herring (Clupea harengus) depends on cohort density using a formulation of the von Bertalanffy growth function on cohorts from 1921 to 2014 and found a significant negative correlation between estimated asymptotic length and density. This clearly indicates density-dependent effects on growth, and we propose a model that can be used to predict the size-at-age of Norwegian spring-spawning herring as a function of herring density (the abundance of two successive cohorts) in short-term predictions of catch advice, and in Management strategy evaluations, including estimation of their reference points such as FMSY.publishedVersio

Space-time recapture dynamics of PIT-tagged Northeast Atlantic mackerel (Scomber scombrus) reveal size-dependent migratory behaviour

Based on GIS-mapping and semi-parametric modelling of recaptures from PIT-tag experiments in the North Sea nursery area (September 2011), the Celtic Seas spawning area (May-June 2014-2021) and the Icelandic Waters feeding area (August 2015-2019), we argue that the distribution of Northeast Atlantic (NEA) mackerel is influenced by a size-dependent migratory behaviour. The time-space recapture dynamics revealed that larger mackerel tended to migrate a longer distance between spawning and feeding areas, either through a western route from the Celtic Seas into the Icelandic Waters and the Greenland Sea or by following the main route northwards through the Faroe-Shetland Channel into the Norwegian Sea. This long-distance travel resulted in turn in delayed arrival in the North Sea wintering area. During the return spawning migration into the Celtic Seas, larger individuals remained in the front, likely heading to spawning grounds farther south than smaller conspecifics. Migration patterns also evolved with time at liberty as the mackerel grew older and larger, while possibly covering a progressively wider area over its annual migration cycle as suggested from the tagging data. However, the study also showed large inter-annual variability in the recapture patterns which likely reflect changes in environmental condition (prey availability and ocean current), NEA mackerel population demographics, and the spatial fishery dynamics.publishedVersio

Barents Sea Capelin - Report of the Joint Russian-Norwegian Working Group on Arctic Fisheries (JRN-AFWG) 2022

publishedVersio

A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder

A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user-friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R. Such an approach can easily require time-consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious, and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this approach permits simple retrieval of standard errors at the same time. We illustrate the performance of our routines using different data sets: first, two smaller samples from a mobile application for tinnitus patients and a well-known data set of fetal lamb movements with 87 and 240 data points, respectively. Second, we rely on larger data sets of simulated data of sizes 2000 and 5000 for further analysis. This tutorial is accompanied by a collection of scripts, which are all available in the Supporting Information. These scripts allow any user with moderate programming experience to benefit quickly from the computational advantages of TMB

A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder

A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user-friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R. Such an approach can easily require time-consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious, and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this approach permits simple retrieval of standard errors at the same time. We illustrate the performance of our routines using different data sets: first, two smaller samples from a mobile application for tinnitus patients and a well-known data set of fetal lamb movements with 87 and 240 data points, respectively. Second, we rely on larger data sets of simulated data of sizes 2000 and 5000 for further analysis. This tutorial is accompanied by a collection of scripts, which are all available in the Supporting Information. These scripts allow any user with moderate programming experience to benefit quickly from the computational advantages of TMB

Predicting density-dependent somatic growth in Norwegian spring-spawning herring

Density-dependent growth, which might influence the effects of fisheries on a population, is often ignored when management strategies are evaluated, mainly due to a lack of appropriate models readily available to be implemented. To improve on this, we investigated if somatic growth in Norwegian spring-spawning herring (Clupea harengus) depends on cohort density using a formulation of the von Bertalanffy growth function on cohorts from 1921 to 2014 and found a significant negative correlation between estimated asymptotic length and density. This clearly indicates density-dependent effects on growth, and we propose a model that can be used to predict the size-at-age of Norwegian spring-spawning herring as a function of herring density (the abundance of two successive cohorts) in short-term predictions of catch advice, and in Management strategy evaluations, including estimation of their reference points such as FMSY

Predicting density-dependent somatic growth in Norwegian spring-spawning herring

Density-dependent growth, which might influence the effects of fisheries on a population, is often ignored when management strategies are evaluated, mainly due to a lack of appropriate models readily available to be implemented. To improve on this, we investigated if somatic growth in Norwegian spring-spawning herring (Clupea harengus) depends on cohort density using a formulation of the von Bertalanffy growth function on cohorts from 1921 to 2014 and found a significant negative correlation between estimated asymptotic length and density. This clearly indicates density-dependent effects on growth, and we propose a model that can be used to predict the size-at-age of Norwegian spring-spawning herring as a function of herring density (the abundance of two successive cohorts) in short-term predictions of catch advice, and in Management strategy evaluations, including estimation of their reference points such as FMSY

Poleward spawning of Atlantic mackerel (Scomber scombrus) is facilitated by ocean warming but triggered by energetic constraints

The Northeast Atlantic mackerel is an income breeder with indeterminate fecundity, spawning in multiple batches at optimal temperatures around 11°C in the upper water column during February–July along the continental shelf from 36–62°N. Based on macroscopic staging of gonads (N ∼62000) collected in 2004–2021, we detected an on-going extension of spawning activities into the Norwegian Sea feeding area (62–75°N), reaching stable levels around 2012 onwards. This poleward expansion increased as more fish entered the area, whilst the maximum proportions of spawners concurrently dropped from about 75 to 15% from May to July. Detailed histological examinations in 2018 confirmed the macroscopic results but clarified that 38% of the spawning-capable females in July terminated their spawning by atresia. We suggest that increased access to suitable spawning areas (≥10°C), following ocean warming from 2002 onwards, functions as a proximate cause behind the noticed expansion, whereas the ultimate trigger was the historic drop in body growth and condition about 10 years later. Driven by these energetic constraints, mackerel likely spawn in the direction of high prey concentrations to rebuild body resources and secure the future rather than current reproduction success. The ambient temperature that far north is considered suboptimal for egg and larval survival.publishedVersio

Advice on fishing opportunities for Northeast Arctic cod in 2025 in ICES subareas 1 and 2

publishedVersio