Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation

Funding Information: We thank Aalto University for supporting and financing part of this research. The university's Probabilistic Machine Learning Group has been especially helpful. Additionally, we thank the Instituto de Fomento Pesquero (IFOP) for providing the data for this study. We also thank two anonymous reviewers for helpful feedback on an earlier version of the manuscript. Publisher Copyright: © 2021 Elsevier B.V. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.Sea urchin (Loxechinus albus) is one of the most important benthic resource in Chile. Due to their large-scale spatial metapopulation structure, sea urchin subpopulations are interconnected by larval dispersion, so the recovery of local abundance depends on the distance and hydrodynamic characteristics of their spatial domain. Currently, this resource is evaluated with classical stock assessment models, using standardized catch per unit effort (an index of relative abundance) as a key piece of information to determine catch quotas and achieve sustainability. However, these estimates assume hyperstability for the total population, ignoring spatial dependence among fishing sites, which is a fundamental concept for populations structured as metapopulation. We develop a Bayesian catch standardization model with explicit spatial dependence to better address the structure of this population. The proposed model performs statistically better compared to a model without spatial dependence, based on leave-one-out cross-validation, and predictive distributions also show that parameter estimation is consistent with the data. We argue that incorporating spatial structure improves the estimated relative abundance index in a population structured as a metapopulation. Our improved index of abundance will lead to better assessments and management advice, thus improving the sustainability of the stock.Peer reviewe

No-U-turn sampling for fast Bayesian inference in ADMB and TMB: Introducing the adnuts and tmbstan R packages

<div><p>Statistical inference is a widely-used, powerful tool for learning about natural processes in diverse fields. The statistical software platforms AD Model Builder (ADMB) and Template Model Builder (TMB) are particularly popular in the ecological literature, where they are typically used to perform frequentist inference of complex models. However, both lack capabilities for flexible and efficient Markov chain Monte Carlo (MCMC) integration. Recently, the no-U-turn sampler (NUTS) MCMC algorithm has gained popularity for Bayesian inference through the software Stan because it is efficient for high dimensional, complex hierarchical models. Here, we introduce the R packages adnuts and tmbstan, which provide NUTS sampling in parallel and interactive diagnostics with ShinyStan. The ADMB source code was modified to provide NUTS, while TMB models are linked directly into Stan. We describe the packages, provide case studies demonstrating their use, and contrast performance against Stan. For TMB models, we show how to test the accuracy of the Laplace approximation using NUTS. For complex models, the performance of ADMB and TMB was typically within +/- 50% the speed of Stan. In one TMB case study we found inaccuracies in the Laplace approximation, potentially leading to biased inference. adnuts provides a new method for estimating hierarchical ADMB models which previously were infeasible. TMB users can fit the same model in both frequentist and Bayesian paradigms, including using NUTS to test the validity of the Laplace approximation of the marginal likelihood for arbitrary subsets of parameters. These software developments extend the available statistical methods of the ADMB and TMB user base with no additional effort by the user.</p></div

Summary of key functions from R packages.

<p>Summary of key functions from R packages.</p

Testing the Laplace approximation of the random effects.

<p>Bayesian integration was performed on the <i>wildflower</i> TMB model with random effects integrated using two “versions”: (1) the Laplace approximation and (2) full MCMC integration via NUTS. Bayesian posterior samples of selected fixed effects (estimated with NUTS) are shown. Columns and rows corresponds to a fixed effect parameter, with the diagonal showing a QQ-plot of the two versions of the model for that parameter, including a 1:1 line in gray. Lower diagonal plots contain pairwise parameter posterior points, with color corresponding to integration version, and larger colored circles the pairwise medians. Posterior rows were randomized to prevent consistent overplotting of one version. Differences in versions suggest the Laplace approximation assumptions are not met. Other fixed effects showed no differences and are left off for clarity.</p

ss3sim: An R Package for Fisheries Stock Assessment Simulation with Stock Synthesis

Simulation testing is an important approach to evaluating fishery stock assessment methods. In the last decade, the fisheries stock assessment modeling framework Stock Synthesis (SS3) has become widely used around the world. However, there lacks a generalized and scriptable framework for SS3 simulation testing. Here, we introduce ss3sim, an R package that facilitates reproducible, flexible, and rapid end-to-end simulation testing with SS3. ss3sim requires an existing SS3 model configuration along with plain-text control files describing alternative population dynamics, fishery properties, sampling scenarios, and assessment approaches. ss3sim then generates an underlying ‘truth’ from a specified operating model, samples from that truth, modifies and runs an estimation model, and synthesizes the results. The simulations can be run in parallel, reducing runtime, and the source code is free to be modified under an open-source MIT license. ss3sim is designed to explore structural differences between the underlying truth and assumptions of an estimation model, or between multiple estimation model configurations. For example, ss3sim can be used to answer questions about model misspecification, retrospective patterns, and the relative importance of different types of fisheries data. We demonstrate the software with an example, discuss how ss3sim complements other simulation software, and outline specific research questions that ss3sim could address

Estimating historical eastern North Pacific blue whale catches using spatial calling patterns

Blue whales (Balaenoptera musculus) were exploited extensively around the world and remain endangered. In the North Pacific their population structure is unclear and current status unknown, with the exception of a well-studied eastern North Pacific (ENP) population. Despite existing abundance estimates for the ENP population, it is difficult to estimate pre-exploitation abundance levels and gauge their recovery because historical catches of the ENP population are difficult to separate from catches of other populations in the North Pacific. We collated previously unreported Soviet catches and combined these with known catches to form the most current estimates of North Pacific blue whale catches. We split these conflated catches using recorded acoustic calls from throughout the North Pacific, the knowledge that the ENP population produces a different call than blue whales in the western North Pacific (WNP). The catches were split by estimating spatiotemporal occurrence of blue whales with generalized additive models fitted to acoustic call patterns, which predict the probability a catch belonged to the ENP population based on the proportion of calls of each population recorded by latitude, longitude, and month. When applied to the conflated historical catches, which totaled 9,773, we estimate that ENP blue whale catches totaled 3,411 (95% range 2,593 to 4,114) from 1905–1971, and amounted to 35% (95% range 27% to 42%) of all catches in the North Pacific. Thus most catches in the North Pacific were for WNP blue whales, totaling 6,362 (95% range 5,659 to 7,180). The uncertainty in the acoustic data influence the results substantially more than uncertainty in catch locations and dates, but the results are fairly insensitive to the ecological assumptions made in the analysis. The results of this study provide information for future studies investigating the recovery of these populations and the impact of continuing and future sources of anthropogenic mortality

Evidence for rapid avoidance of rockfish habitat under reduced quota and comprehensive at-sea monitoring in the British Columbia Pacific Halibut fishery

The British Columbia longline fishery for Pacific halibut (Hippoglossus stenolepis) has experienced important recent management changes, including the introduction of comprehensive electronic catch monitoring on all vessels; an integrated transferable quota system; a reduction in Pacific halibut quotas; and, beginning in 2016, sharp decreases in quota for yelloweye rockfish (Sebastes ruberrimus, an incidentally caught species). We describe this fishery before integration, after integration, and after the yelloweye rockfish quota reduction using spatial clustering methods to define discrete fishing opportunities. We calculate the relative utilization of these fishing opportunities and their overlap with areas with high encounter rates of yelloweye rockfish during each of the three periods. The spatial footprint (area fished) increased before integration, then decreased after integration. Each period showed shifts in utilization among four large fishing areas. Immediately after the reductions in yelloweye rockfish quota, fishing opportunities with high encounter rates of yelloweye rockfish had significantly lower utilization than areas with low encounter rates, implying rapid avoidance behaviour.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Flow diagram of the main steps in an ss3sim simulation carried out using run_ss3sim.

<p>Functions that are called internally are shown in a monospaced font.</p

Main ss3sim functions and a description of their purpose.

<p>Simulations can be run through the run_ss3sim function, which then calls the change functions. Users can control what the change functions do through a series of plain-text case files. For example, the case ID d1 corresponds to the case files lcomp1, agecomp1, and index1, as described in the table. Users can also skip setting up case files and specify arguments to ss3sim_base directly, or use the change functions as part of their own simulation structure (see the vignette).</p

Illustration of input and output folder and file structure for an ss3sim simulation.

<p>Folders are shown in blue, input files in orange, and output files in grey. All input and output files are in plain text format. OM refers to operating model and EM to estimation model. Case files (orange files at bottom left) combine cases (e.g. M0 for a given natural mortality trajectory) with species or life-history OMs and EMs (e.g. cod-like or sardine-like). Alternatively, a user can skip setting up case files and specify the simulation cases directly in R code (see the vignette).</p