Biology subjects

Berg, C. W.

Publications and source records attributed to Berg, C. W..

4 recordsLinked to original sources

bioRxiv2026

DATRASextra: An R package for streamlined workflows with ICES DATRAS bottom-trawl survey data

Scientific bottom-trawl surveys provide essential fisheries-independent data for fisheries and ecosystem research. In the Northeast Atlantic, the ICES Database of Trawl Surveys (DATRAS) compiles haul-level information, species- and length-specific catch data, and individual biological observations across multiple long-term surveys. However, reproducible workflows for processing and integrating these relational datasets remain challenging. We present DATRASextra, an open-source R package that provides modular end-to-end workflows for accessing, cleaning, harmonising, quality-controlling, and analysing DATRAS survey data. The package supports derivation of standardised haul-level survey variables, integration of multiple surveys, and generation of analysis-ready datasets for downstream applications including stock assessment, biodiversity analyses, and large-scale synthesis efforts such as Fish-Glob.

ecology↗

bioRxiv2021

Can we fish on stocks that need rebuilding? Illustrating the trade-offs between stock conservation and fisheries considerations

In the Northeast Atlantic, advice for many fish stocks follows the ICES MSY approach, where a zero catch will be recommended if the stock is below its limit reference point, Blim, and cannot rebuild in the short-term. How-ever, zero catch advice are rarely implemented by managers. This study used medium-term stochastic forecasts with harvest control rules (HCRs) to investigate the consequences of allowing reduced fishing below Blim. We applied the method to western Baltic herring and North Sea cod, two contrasting species currently estimated below Blim. We show that the minimum rebuilding probability of 95% required by the MSY approach could be impossible to reach in the short-to medium-term. When this is the case, a lower probability may need to be considered instead in the short-term. Recruitment is the largest source of uncertainty in stock response to management, and can exceed differences between HCRs. Reference points should be estimated in accordance with current recruitment levels if they are to be used for short-term advice or as realistic rebuilding targets. For both stocks, it is possible to keep fishing at reduced levels for similar cumulative catch, SSB and risk on the stock in the medium-term compared to no catch below Blim. Medium-term trade-offs between stock conservation and fisheries considerations may be needed when fishery closure cannot be implemented in practice.

ecology↗

bioRxiv2020

Implementing the precautionary approach into fisheries management: Making the case for probability-based harvest control rules

The precautionary approach to fisheries management advocates for risk-averse management strategies that include biological reference points as well as decision rules and account for scientific uncertainty. In this regard, two approaches have been recommended: (i) harvest control rules (HCRs) with threshold reference points to safeguard against low stock biomass, and (ii) the P* method, a probability-based HCR that reduces the catch limit as a function of scientific uncertainty (i.e. process, model, and observation uncertainty). This study compares the effectiveness of these precautionary approaches in recovering over-exploited fish stocks with various life-history traits and under a wide range of levels of scientific uncertainty. We use management strategy evaluation based on a stochastic, age-based operating model with quarterly time steps and a stochastic surplus production model. The results show that the most effective HCR includes both a biomass threshold as well as the P* method, and leads to high and stable long-term yield with a decreased risk of low stock biomass. For highly dynamics stocks, management strategies that aim for higher biomass targets than the traditionally used BMSY result in higher long-term yield. This study makes the case for probability-based HCRs by demonstrating their benefit over deterministic HCRs and provides a list of recommendations regarding their definition and implementation.

ecology↗

bioRxiv2017

Modeling Zero-Inflated Count Data With glmmTMB

Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero for hosts with effective immune defenses but vary according to a negative binomial distribution for non-resistant hosts.\n\nWe present a new R package, glmmTMB, that increases the range of models that can easily be fitted to count data using maximum likelihood estimation. The interface was developed to be familiar to users of the lme4 R package, a common tool for fitting GLMMs. To maximize speed and flexibility, estimation is done using Template Model Builder (TMB), utilizing automatic differentiation to estimate model gradients and the Laplace approximation for handling random effects. We demonstrate glmmTMB and compare it to other available methods using two ecological case studies.\n\nIn general, glmmTMB is more flexible than other packages available for estimating zero-inflated models via maximum likelihood estimation and is faster than packages that use Markov chain Monte Carlo sampling for estimation; it is also more flexible for zero-inflated modelling than INLA, but speed comparisons vary with model and data structure. Our package can be used to fit GLMs and GLMMs with or without zero-inflation as well as hurdle models. By allowing ecologists to quickly estimate a wide variety of models using a single package, glmmTMB makes it easier to find appropriate models and test hypotheses to describe ecological processes.

ecology↗