Habitat associations and effects of survey expansion on abundance indices and population demographics for 2 deepwater fish species off the Atlantic coast of the southeastern United States

Habitat associations and effects of survey expansion on abundance indices and population demographics for 2 deepwater fish species off the Atlantic coast of the southeastern United States: Supplementary Table

Stakeholder-government collaboration in developing cost-effective fishery-independent surveys in rights-based and co-managed fisheries

Fisheries-independent surveys are commonly used to create indices of relative abundance. If properly designed and calibrated, these surveys may also be used to estimate absolute abundance. Here, we demonstrate the efficacy of this approach by estimating the absolute abundance of red lionfish ( Pterois volitans), gray triggerfish ( Balistes capriscus), and red snapper ( Lutjanus campechanus) across an extensive network of artificial reefs using camera counts, indices of relative abundance, calibration factors, and index-removal estimators. From 2012 to 2017, per reef estimates increased for red lionfish (20×), gray triggerfish (2.1×), and red snapper (2.2×). Network-wide absolute abundances were calculated by multiplying the average per reef estimate by the estimated number of reefs in the network. All increases were consistent with predictions of stock assessment (red snapper), management actions (gray triggerfish), or invasive species colonization (red lionfish). Our methodology demonstrates how estimates of absolute abundance can be derived from fishery-independent surveys and used to evaluate the outputs of stock assessments both in direction and magnitude and quantify critical ecosystem components.

Bayesian methods are useful in fisheries stock assessment because they provide a conceptually elegant and statistically rigorous approach to making decisions under uncertainty. The application of Bayesian stock assessment methods in the management of Namibian orange roughy Hoplosthethus atlanticus within the 200 mile EEZ of Namibia is reviewed. Time-series of relative abundance are short and their reliability in indicating abundance trends is uncertain. The development of informative prior probability density functions (pdfs) for the constants of proportionality ( q ) for hydro-acoustic, commercial trawl swept area, and research trawl swept area indices produced statistically consistent prior estimates of absolute abundance for each of the three grounds where more than one index of abundance was available. The posterior pdfs for stock assessment model parameters were used to account for uncertainty in evaluations of the potential consequences of alternative harvesting policies under a stock reduction model in which catch removals were assumed to account for any declines. It appears that all orange roughy stocks off Namibia have been depleted below the limit reference point (50% of long-term unfished biomass). However, the stock reduction model could not easily account for the large declines in indices on the four fishing grounds over the period from 1995 until 1999 when the informative priors for q were applied. In the 2000 stock assessment, the Bayesian procedure was updated to account formally for uncertainty in model structures that could explain the decline in abundance. The possibility of very low stock abundance could still not be discounted when these uncertainties were accounted for. Although this most recent methodology applies more statistical rigour, its complexity has hindered its acceptance in Namibia. However, if it is worth quantifying risks and uncertainties in future stock assessments for the provision of precautionary management advice, it is proposed that the assessment protocols adopted be probabilistic to account for uncertainty in model parameters, that careful attention be given to subjective judgements about their inputs and the representation of uncertainty within them, and that, where appropriate, alternative hypotheses about stock abundance and mechanisms for catchability and stock decline be taken into account. Keywords: Bayesian, orange roughy, stock assessment, structural uncertainty African Journal of Marine Science 2001, 23: 241–264

The management and monitoring of deep-sea fisheries and stocks is challenging due to the diversity of life histories in exploited stocks and sparseness of biological knowledge about certain species. Moreover, these issues are often combined with a relative shortage of suitable data. The FAO defines deep-sea fisheries as those exploiting species that can only sustain low exploitation rates (FAO 2009). In the European Union (EU) deep-sea fisheries are covered by council regulation No. 2347/2002 on “establishing specific access requirements and associated conditions applicable to fishing for deepsea stocks", which lists 24 species, including many deepwater sharks. Other definitions are used in other jurisdictions around the world. The papers presented in this special issue address a number of questions relevant to assess deepwater stocks and moving towards an ecosystem approach to the management of European deepwater fisheries. The underlying research was carried out in the EU-funded Deepfishman project, “Management and Monitoring of Deep-sea Fisheries and Stocks” (Grant agreement No. 227390), which was conducted from 2009 to 2012. These papers complement a number of recently published studies on deepwater stocks and fisheries, many of which were part of the same project. A recent review of deepwater stocks and their management worldwide concluded that, although deepwater fisheries in different regions might exploit the same species, case-specific monitoring and management strategies are often necessary (Large et al. 2013). Furthermore, Edwards et al. (2012) suggested that, although a range of approaches exist for deepwater stock assessment, from very simple to more sophisticated and informative, some methodological developments are still required. A recent example of such a development is the statistical catch-at-age model, which was used to assess the recruitment strength of the beaked redfish, Sebastes mentella in the Barents Sea (Planque et al. 2012). Another statistical catch-atage model was developed to provide estimates of total mortality and abundance of blue ling, Molva dypterygia, to the west of the British Isles (Trenkel et al. 2012). This model makes stock assessments possible using only fishery-dependent data

Abundance indices are essential data for the application of stock assessment models to obtain fish abundance estimates. Abundance indices have usually been derived from fishery-dependent data, yet the increase in fisheries-independent surveys is now offering new opportunities for these calculations. In this study, we explored the usefulness of ichthyoplankton indices derived from scientific surveys in estimating spawning biomass. In addition, we also investigated whether the strength of the year–class of the commercial cohort of Atlantic hake, as a determinant, could be defined at an early life stage. We used samples collected during the triennial mackerel and horse mackerel egg surveys (MEGS), which cover the hake spawning area in the Bay of Biscay. The biomass indices were determined as the abundance of eggs in the early development stage (stage 1) when transformed into egg production (EP) from 1995 to 2019 in the months of March and April—which is considered a period of high spawning activity for hake in this area. Additionally, we built a metric for larval abundance and converted larval length into age. This was in addition to constructing a pre-recruit year-class index (YCI) while using the EVHOE bottom trawl abundance database for hake for the period of 1997 to 2016. The results of regression analysis of egg production and spawning stock biomass indicate that both parameters are significantly correlated (r = 0.76). By connecting the abundance of eggs and larvae in the adjoining stages, we are able to identify two periods of high mortality associated with the transition from “yolk-sac-first” to “feeding larvae” and “late larvae-YCI10”, but we were unable to discover when the strength of the recruitment year–class is determined. As such, it appears that for the northern stock of hake, recruitment is established in the late juvenile stages.

Abundance indices derived from fisheries-dependent data (catch-per-unit-effort or CPUE) are known to have potential for bias, in part because of the usual non-random nature of fisheries spatial distributions. However, given the cost and lack of availability of fisheries-independent surveys, fisheries-dependent CPUE remains a common and informative input to fisheries stock assessments. Recent research efforts have focused on the development of spatiotemporal delta-generalized linear mixed models (GLMMs) which simultaneously standardize the CPUE and predict abundance in unfished areas when estimating the abundance index. These models can include local seasonal environmental covariates (e.g. sea surface temperature) and a spatially varying response to regional annual indices (e.g. the El Niño Southern Oscillation) to interpolate into unfished areas. Spatiotemporal delta-GLMMs have been demonstrated in simulation studies to perform better than conventional, non-spatial delta-generalized linear models (GLMs). However, spatiotemporal delta-GLMMs have rarely been evaluated in situations where fisheries spatial sampling patterns change over time (e.g. fisheries expansion or spatial closures). This study develops a simulation framework to evaluate 1) how the nature of fisheries-dependent spatial sampling patterns may bias estimated abundance indices, 2) how shifts in spatial sampling over time impact our ability to estimate temporal changes in catchability, and 3) how including seasonal environmental covariates and/or regional annual indices in spatiotemporal delta-GLMMs can improve the estimation of abundance indices given shifts in spatial sampling. Spatiotemporal delta-GLMMs are then applied to a case study example where the spatial sampling pattern changed dramatically over time (contraction of the Japanese pole-and-line fishery for skipjack tuna Katsuwonus pelamis in the western and central Pacific Ocean). Results from simulations indicate that spatial sampling in proportion to the underlying biomass can produce similar abundance indices to those produced under random sampling. Though estimated abundance indices were not perfect, spatiotemporal GLMMs were generally able to disentangle shifts in spatial sampling from temporal changes in catchability when shifts in spatial sampling were not too extreme. Lastly, the inclusion of seasonal environmental covariates and/or regional oceanographic indices in spatiotemporal GLMMs did not improve abundance index estimation and in some cases resulted in degraded model performance.

Indices of abundance are important for estimating population trends in stock assessment and ideally should be based on fishery-independent surveys to avoid problems associated with the hyperstability of the commercial catch per unit effort (cpue) data. However, recent studies indicate that the efficiency of the survey bottom trawl (BT) for some species can be density-dependent, which could affect the reliability of survey-derived indices of abundance. A function qe∼f(u), where qe is the BT efficiency and u the catch rate, was derived using experimentally derived acoustic dead-zone correction and BT efficiency parameters obtained from combining a subset of BT catch data with synchronously collected acoustic data from walleye pollock (Theragra chalcogramma) in the eastern Bering Sea (EBS). We found that qe decreased with increasing BT catches resulting in hyperstability of the index of abundance derived from BT survey. Density-dependent qe resulted in spatially and temporarily variable bias in survey cpue and biased population age structure derived from survey data. We used the relationship qe∼f(u) to correct the EBS trawl survey index of abundance for density-dependence. We also obtained a variance–covariance matrix for a new index that accounted for sampling variability and the uncertainty associated with the qe. We found that incorporating estimates of the new index of abundance changed outputs from the walleye pollock stock assessment model. Although changes were minor, we advocate incorporating estimates of density-dependent qe into the walleye pollock stock assessment as a precautionary measure that should be undertaken to avoid negative consequences of the density-dependent qe.

Accurate estimation of fish stock abundance and exploitability is critical for effective fishery management; however, fishery-dependent data are often affected by temporal and spatial heterogeneities due to the seasonal migration of fish, posing challenges for refined stock management. Previous studies have largely ignored these spatio-temporal dynamics, assuming static populations. This study develops a seasonal spatio-temporal model for Japanese sardine (Sardinops melanostictus) in the Northwest Pacific Ocean (NPO), using fishery-dependent data from 2014 to 2022. Seasonal standardized abundance indices (spring, summer, autumn, and an overall average) were generated and integrated with the abundance maximum sustainable yield (AMSY) method for stock assessment. The performance of the AMSY method using a spatio-temporal index was compared with the conventional model-based index, showing the superiority of the spatio-temporal approach. Results indicate a gradual increase in sardine abundance, with a significant shift in the center of gravity toward the northeast. The stock is in a sustainable state with a 94.8-99% probability of recovery. Although the stock is recovering, careful management is advised to prevent future declines. This framework offers a novel approach for assessing distant water and coastal fishery resources.

Since 1979, fishery-independent data and fishery-dependent data have been used to estimate trends in the abundance of the northeastern stock of spotted dolphins in the eastern tropical Pacific. Data collected aboard tuna vessels have been used to estimate trends in relative abundance, while data collected from fishery-independent research vessels have been used to estimate trends in actual abundance. One of the largest discrepancies between the two data sources is that tuna vessels tend to report dolphin schools that are 400%—500% larger than schools observed by research vessels. After comparing research vessel and tuna vessel observations overlapping in space and time, it appears that either measurement error or selective reporting of large schools is the most likely explanation for the disparity. Comparing single-species schools against portions of mixed- species schools introduced bias. Revised fishery-dependent abundance estimates were generated using only single-species dolphin schools, resulting in estimates that appear more similar to recent fishery-independent abundance estimates. correctable in fishery-dependent data, suggesting that data on mixed-species schools should not be considered if fishery-dependent data are to be combined with fishery-independent data for stock assessment purposes.

Lessons from long-term monitoring of tropical rock lobsters to support fisheries management

Investigating the influence of length–frequency data on the stock assessment of Indian Ocean bigeye tuna

Lobster (infraorder Astacidea) is perhaps the most intensively studied shellfish around the world due to its economic importance (Phillips 2006). Fishery management tools almost universally include catch reporting, but fishery-dependent data such as this often fails to adequately inform managers about the true state of the population (Erisman et al. 2011). Increasingly, fishery-independent surveys are being relied upon to provide the robust information fishery managers require, such as the California Cooperative Oceanic Fisheries Investigation (CalCOFI). The CalCOFI program attempts to understand and predict variations in the Pacific Sardine (Sardinops sagax) fishery, among others, in California through quarterly sampling of fish larvae and other biological and hydrographic data. This focus on larval stages stems from Hjort’s seminal work in 1914, which hypothesized the recruitment and transition of larval forms into postlarval and juvenile life stages is a critical period in population dynamics (Houde 2008). In modern times, fishery management agencies expend significant effort towards cataloging and understanding recruitment levels and patterns which provide the foundation for most fishery management tools (e.g. stock assessments). Recruitment monitoring is at the core of many of global lobster fishery management programs (Cruz et al. 1995; Acosta et al. 1997; Cruz and Adriano 2001; Phillips et al. 2005; Phillips and Melville-Smith 2005; Phillips et al. 2006; Arteaga-Rios et al. 2007; Phillips et al. 2010). Recruitment monitoring of numerous lobster taxa globally have been well correlated with future catch-rate predictions, typically with a 4 to 5 year time lag (e.g. Gardner et al. 2002; Caputi and Brown 2011; Linnane et al. 2014). Long-term recruitment monitoring programs, such as that in Australia (Linnane et al. 2010), are vital in assessing future stock levels and setting the total allowable commercial catches. Like many lobster species, the California Spiny Lobster (Panulirus interruptus) is an economically important fishery species, supporting one of California’s most valuable fisheries with annual ex-vessel values exceeding $9 million (Porzio et al. 2012). However, unlike many of the world’s lobster fisheries, no California Spiny Lobster recruitment monitoring program exists. Recent attempts to address this data gap have been made using plankton collection (Koslow et al. 2012) and power plant entrapment records (Miller 2014). Plankton collections were unable to reliably predict recent landings while entrapment records were more successful, but both articles noted the likely effect of unknown recreational harvest levels impacting the analyses and final conclusions. While informative, neither existing program fulfills the need for targeted information on California Spiny Lobster recruitment in southern California. Furthermore, as southern California power plants shift away from once-throughcooling, lobster entrapment data may soon be unavailable, in which case no regular abundance estimates of California Spiny Lobster postlarvae will be available. Noting the clear need for lobster recruitment monitoring in California, a pilot program was initiated in Orange County, California with the hopes of establishing a model that Bull. Southern California Acad. Sci. 113(3), 2014, pp. 180–186 E Southern California Academy of Sciences, 2014

Food availability often drives consumer population dynamics. However, food availability may also influence capture probability, which if not accounted for may create bias in estimating consumer abundance and confound the effects of food availability on consumer population dynamics. This study compared two commonly used abundance indices (minimum number alive (MNA) and number of animals captured per night per grid) with an abundance estimator based on robust design model as applied to the white-footed mouse ( Peromyscus leucopus (Rafinesque, 1818)) in food supplementation experiments. MNA consistently generated abundance estimates similar to the robust design model, regardless of food supplementation. The number of animals captured per night per grid, however, consistently generated lower abundance estimates compared with MNA and the robust design model. Nevertheless, the correlations between abundance estimates from MNA, number of animals captured, and robust design model were not influenced by food supplementation. This study demonstrated that food supplementation is not likely to create bias among these different measures of abundance. Therefore, there is a great potential for conducting meta-analysis of food supplementation effect on consumer population dynamics (particularly in small mammals) across studies using different abundance indices and estimators.

The abundance index used in a tuned virtual population analysis (VPA) is usually assumed to be proportional to actual abundance. However, the actual abundance and abundance index do not always have a linear relationship. Such nonlinearity can cause biases in abundance estimates as well as retrospective biases arising from systematic differences in abundance estimates when more data are successively added. Severe retrospective biases can damage the reliability of stock assessments. In this study, we use an approach to estimate an additional parameter that controls the nonlinearity in a tuned VPA. A performance test using simulated data revealed that the tuned VPA was able to accurately estimate the nonlinearity parameter and thus yielded less biased abundance estimates and smaller retrospective biases. We also found that estimating the nonlinearity parameters was effective even under other model misspecification scenarios, such as disregarding historical increases in catchability and time-varying natural mortality. Moreover, we applied this approach to some Japanese fish stocks and evaluated its validity. We found that estimating the nonlinearity parameters in the tuned VPA enhances the reliability of fisheries stock assessments.