Biomass Dynamic Model Research Articles

Controlled Transition in a Modified Model of Biomass Dynamics of Root Hemiparasitic Plants

Controlled Transition in a Modified Model of Biomass Dynamics of Root Hemiparasitic Plants

Estimation of the maximum sustainable yield of giant grenadier <i>Albatrossia pectoralis</i> (Macrouridae) in the Russian Far East using Bayesian surplus production model JABBA

Statistical estimates for the exploitable stock of giant grenadier are determined, by fishing zones in the Russian Far East, and interannual dynamics of the species biomass is considered using historical data on catches and abundance. The assessments relationship with reference points of the biomass dynamic models is examined for the first time. With using the Bayesian approach, a priori assumptions about virgin biomass, which were made earlier on results of scientific surveys extrapolated to unsurveyed areas, are accounted in JABBA model; some errors in the catch and abundance indices are revealed. The parameters of instantaneous rate of recruitment and initial depletion of population are determined. Overfishing is not detected yet in any fishing zone, but a negative trend of the biomass dynamics is noticed for the Western Bering Sea fishing zone since 2015 (if the stock estimations based on surveys are realistic). The decline is reasoned by implementation of too high target for exploitation rate of vulnerable biomass (φF = 0.10) that is higher than such target for other fishing zones (< 0.08 or even 0.01 for the stocks exploited with the total allowable catch limits). That’s why annual catches exceeded the surplus production in the Western Bering Sea fishing zone in 2012 and since 2015 to nowadays. The sum of median estimations for the maximum sustainable yields in all fishing zones is about 47.4 . 103 t that exceeds the values of total annual catch. The instantaneous rate of recruitment is almost equal in the neighboring fishing zones in the western Bering Sea and at East Kamchatka (0.086–0.085 by median, with 95 % credible intervals from 0.05 to 0.14) and is much higher at Kuril Islands with the maximum in the North-Kuril fishing zone. The latter case is possibly conditioned by partially external recruitment. Therefore, population structure of giant grenadier should be clarified for correct modeling of the recruitment; until then, the stocks assessments by fishing zones are available.

Integrating habitat features into spatio-temporal biomass dynamics models for a better understanding of stock productivity: a case study of sea scallop in the Bay of Fundy

Abstract Recent efforts in ocean mapping of seafloor habitat have made data increasingly available. For bottom-dwelling and/or sessile species, there is often a strong relationship between population productivity and habitat, and stock assessment models are likely to be improved by the inclusion of habitat. Here, we extend a recently developed spatio-temporal biomass dynamics model to allow habitat to inform probabilities of non-zero tows and catchability. Simulation experiments demonstrate the ability of this new approach to reliably capture population trends over time and space, with the applicability of the method further demonstrated using data from the Canadian Maritimes Inshore Sea Scallop Fishery in the Bay of Fundy. This habitat-informed spatio-temporal biomass dynamics model better captures underlying processes, reduces uncertainty, thereby improving our understanding of stock status from which fisheries management decisions can be based.

Stock Assessment and Overexploitation Risk of Small Pelagic Fish in Fisheries Management Area 715 of Indonesia

This study presents updated stock assessments and risk analyses of overexploitation for the aggregate small pelagic fish complex and the dominant genus of the complex, Decapterus, for improving the management of the small pelagic fishery in Fisheries Management Area (FMA) 715. The analyses herein used non-equilibrium biomass dynamics models with available data on annual catch and catch per unit effort for 2005 to 2016. Fishing effort was standardised into the number of 20-meter length overall purse-seine vessels. The analyses show that the maximum sustainable yield (MSY) of the aggregate fish was about 121,600 tonnes, caught by 876 purse-seiners, while the MSY of the mackerel scad, Decapterus macarellus (Cuvier, 1833), was about 67,900 tonnes, caught by 805 purse-seiners. Since the mackerel scad and the aggregate fish stocks have been overexploited, a rebuilding strategy would be necessary to restore the stocks to a level capable of producing MSY (BMSY). After achieving the BMSY, it is recommended that a target reference point be implemented for the catch level with the maximum overexploitation risk level of 50 % in 10 years. The catch level meeting this requirement for mackerel scad would equal 80 % of its MSY, which could be achieved by controlling fishing effort at 427 purse-seiners. At this effort level, the fishery would produce 95,800 tonnes of aggregate fish catch with 54,300 tonnes of mackerel scad. These reductions in fishing efforts will be needed to maintain the future sustainability of the fish stocks in FMA 715.

Development of an individual-based tag recapture model to benchmark biomass and harvest rates in an iconic lobster fishery

The West Coast Rock Lobster Managed Fishery (WCRLMF) moved from input to output controls in 2010. This change affected the relativity of fishery-based data sources (e.g., catch rates and landed size composition), making the assessment of the fishery problematic. A novel examination of the stock dynamics was required to ensure the robustness of the stock assessment and associated management arrangements. This study derived estimates of current biomass levels and harvest rates from the release of over 60,000 tagged western rock lobsters (Panulirus cygnus). A Brownie tag-recapture (BTR) model was initially implemented to provide an assessment on a fishery-wide basis. Estimates from this were compared to those derived from a novel purpose-built tag-recapture individual-based model (IBM) that accounted for sex, size, month, and location-specific changes in catchability. The two models produced similar estimates on a fishery-wide scale—harvest rate (HR 0.26 vs 0.30, respectively) and legal-sized biomass (about 24,500 vs 20,735 t, respectively)—while the IBM also provided estimates on a far finer spatial and temporal scale. Both models indicate that the WCRLMF is currently in a very sustainable condition and is being fished at a rate below maximum economic yield (HRmey is about 0.39). These findings were in concert with estimates derived for this fishery based on two separate catch-rate based population models, an integrated population model and a biomass-dynamics model. Such strong agreement among all models provides great certainty in the current assessment and management of this important marine resource.

Bayesian inference of biomass growth characteristics for sugi (C. japonica) and hinoki (C. obtusa) forests in self-thinned and managed stands

BackgroundForests are an important sink for atmospheric carbon and could release that carbon upon deforestation and degradation. Knowing stand biomass dynamic of evergreen forests has become necessary to improve current biomass production models. The different growth processes of managed forests compared to self-managed forests imply an adaptation of biomass prediction models.MethodsIn this paper we model through three models the biomass growth of two tree species (Japanese cedar, Japanese cypress) at stand level whether they are managed or not (self-thinning). One of them is named self-thinned model which uses a specific self-thinning parameter α and adapted to self-managed forests and an other model is named thinned model adapted to managed forests. The latter is compared to a Mitscherlich model. The self-thinned model takes into account the light competition between trees relying on easily observable parameters (e.g. stand density). A Bayesian inference was carried out to determine parameters values according to a large database collected.ResultsIn managed forest, Bayesian inference results showed obviously a lack of identifiability of Mitscherlich model parameters and a strong evidence for the thinned model in comparison to Mitscherlich model. In self-thinning forest, the results of Bayesian inference are in accordance with the self-thinning 3/2 rule (α=1.4). Structural dependence between stand density and stand yield in self-thinned model allows to qualifying the expression of biological time as a function of physical time and better qualify growth and mortality rate. Relative mortality rate is 2.5 times more important than relative growth rate after about 40 years old. Stand density and stand yield can be expressed as function of biological time, showing that yield is independent of initial density.ConclusionsThis paper addressed stand biomass dynamic models of evergreen forests in order to improve biomass growth dynamic assessment at regional scale relying on easily observable parameters. These models can be used to dynamically estimate forest biomass and more generally estimate the carbon balance and could contribute to a better understanding of climate change factors.

Incorporating intra-annual variability in fisheries abundance data to better capture population dynamics

To reduce the risk of overexploitation and the ensuing conservation and socio-economic consequences, fisheries management relies on receiving accurate scientific advice from stock assessments. Biomass dynamics models used in stock assessment tend to rely primarily on indices of abundance and commercial landings data. Standard practice for calculating the indices used in these models typically involves taking averages of survey tow data over large, diverse spatial domains. There is a lot of variability in the choice of methodologies used to propagate index uncertainty into the assessment model, many of which require specifying it through expert knowledge or prior distributions. Here we propose an alternative approach that treats each individual survey tow as an independent estimate of the true underlying biomass in the stock assessment model itself. This reduces information loss and propagates uncertainties into the model directly. A simulation study demonstrates that this approach accurately captures underlying population dynamics and reliably estimates variance parameters. We further demonstrate its utility with data from the Inshore Scallop Fishery of south-west Nova Scotia. Results show significant improvements in parameter estimation over previous models while providing similar predictions of biomass with less uncertainty. This reduced uncertainty can improve the resulting scientific advice and lead to improved decision-making by fisheries managers.

Explicit incorporation of spatial variability in a biomass dynamics assessment model

Abstract The sustainable management of fisheries has largely relied on stock assessment models that assume stocks are homogeneous throughout their domain. However, ignoring complex underlying spatial patterns can lead to increased risk of failures in management. Utilizing geostatistical approaches in conjunction with a traditional fishery biomass dynamics model, we develop a spatially-explicit modelling framework that treats the underlying population dynamics as spatial processes. Simulation experiments demonstrate that this approach reliably estimates variance parameters and accurately captures true patterns of population change. We further demonstrate the utility of our modelling framework in a real setting using data from the Canadian Maritimes Inshore Scallop Fishery. The model captures time-varying spatial patterns in both population characteristics and fishing pressure without explicit knowledge of the underlying mechanisms and retains the ability to scale up to the whole spatial domain with less associated uncertainty than for temporal models. These results lead to improved scientific advice for management, future-proofing of the assessment to shifts in stock productivity and fishing effort, and provide information that can be used to develop more effective management approaches.

Bioeconomic impacts of two simple modifications to trawl nets in the NW Mediterranean

A bioeconomic model was built to assess the contribution of more selective trawl nets to the objectives of the European Multi-Annual Plan for demersal fisheries in the Western Mediterranean (WM MAP). The biological submodel was parameterized with age-structured population parameters for the five target stocks in the WM MAP (European hake, red mullet, deep-water rose shrimp, Nephrops and red shrimp) with a sixth stock combining the remaining commercial by catch and following a biomass dynamics model. The trawl fleet was composed of three fleet segments, according to Vessel Length class (VL1218, VL1824, VL2440), practicing two métiers: coastal mixed demersal fishery and deep-water crustacean fishery. The technological solutions analyzed are two simple technical modifications to the otter bottom trawl and based on i) using a panel of meshes turned 90° (T90) in the extension of the trawl net, and ii) inserting a selective grid built from 40 mm square mesh (SM40) netting into the extension of the trawl net. The Results show that, in terms of policy objectives, the reduction in the values of fishing mortality achieved with these selectivity modifications would be insufficient to reach the target fishing mortality at maximum sustainable yield (Fmsy) prescribed in the WM MAP by 2025. However, model results project a substantial recovery of hake and red mullet stock biomass. The recovery of these two important stocks would help improve the evolution of the economic indicators, resulting generally in higher income, profits and salaries in the short (2025) and mid term (2030).

Biomass dynamic model for multiple data series: An improved approach for the management of the red grouper (Epinephelus morio) fishery of the Campeche Bank, Mexico

This study analyzed the current status of the population and stock assessment of the red grouper Epinephelus morio from the Campeche Bank, Yucatán, based on the Schaefer biomass dynamic model and considering the three fleets that operate in the study area. Thus, five indices of relative abundance were used, expressed as catch per unit effort (CPUE). The carrying capacity (K) of the red grouper population was 293,851 t, with an intrinsic growth rate (r) of 0.232. The study indicated that the greatest contribution to the adjustment of the model was due to the CPUE of the industrial Mexican fleet. The catchability values showed differences between the fractions of red grouper caught using trips and hooks. Estimates of maximum sustainable yield (MSY= 17,031 t) and biomass at MSY (BMSY= 146,929 t) indicated that the current catch and the biomass are below their reference points, denoting a marine resource that has been overharvested. Historical analysis of the biomass and harvest ratio for the red grouper estimated from the Kobe diagram showed that the red grouper fishery has transitioned from overfished-overharvested from 1984 to 2014 to overfished-underharvested in 2015. Because the E. morio stock is particularly low in abundance, catches must be drastically reduced to allow the stock to recover more quickly. Only low quotas (3,200–4,000 t) or an annual harvest rate of 6% will reach the BMSY in the long term, recognizing that, for both management strategies, the MSY will not be reached for at least 15 years.

Controlled Transition in a Model of Biomass Dynamics of Root Hemiparasitic Plants

Controlled Transition in a Model of Biomass Dynamics of Root Hemiparasitic Plants

Factors influencing the dynamics of a collapsed blue swimmer crab (Portunus armatus) population and its lack of recovery

Identification of key drivers of population dynamics and prediction of rates of population recovery following stock decline are crucial aspects of fisheries management. The abundance of a blue swimmer crab population (Portunus armatus) in Cockburn Sound, Western Australia, which once supported the largest commercial fishery for this short-lived species in the State, declined markedly and has remained low despite two commercial fishery closures. This study employed state-space biomass dynamics models to explore evidence for potential factors contributing to the lack of stock recovery, including fishing pressure, changes in primary production (using chlorophyll-a as a proxy) and depensatory stock-recruitment dynamics. Likelihood ratio test results indicated better statistical fits for models with production functions modified to account for chlorophyll-a or both depensation and chlorophyll-a. Models incorporating both depensation and chlorophyll-a provided the most biologically-feasible results (e.g. estimated intrinsic increase, r, not near zero) and the estimated biomass trajectories were less uncertain. For all models, estimated annual harvest rates peaked in the late 1990s, prior to the first major stock decline, and again in 2009−12, when the fishery was briefly re-opened with tight management restrictions. Results for models including both depensation and chlorophyll-a indicated that stock productivity is positively-related to chlorophyll-a. Thus, below-average chlorophyll-a concentrations in Cockburn Sound in recent years, in combination with some form(s) of depensation (e.g. associated with predation pressure), may be preventing stock recovery. Despite a sustained period of very limited recreational fishing and no commercial fishing, stock recovery appears highly uncertain under current environmental conditions. The results of this study highlight the value of incorporating environmental data and alternative stock-recruitment assumptions when fitting production models to explore key factors influencing population dynamics.

Validation of stock assessment methods: is it me or my model talking?

Abstract The adoption of the Precautionary Approach requires providing advice that is robust to uncertainty. Therefore, when conducting stock assessment alternative, model structures and data sets are commonly considered. The primary diagnostics used to compare models are to examine residuals patterns to check goodness-of-fit and to conduct retrospective analysis to check the stability of estimates. However, residual patterns can be removed by adding more parameters than justified by the data, and retrospective patterns removed by ignoring the data. Therefore, neither alone can be used for validation, which requires assessing whether it is plausible that a system identical to the model generated the data. Therefore, we use hindcasting to estimate prediction skill, a measure of the accuracy of a predicted value unknown by the model relative to its observed value, to explore model misspecification and data conflicts. We compare alternative model structures based on integrated statistical and Bayesian state-space biomass dynamic models using, as an example, Indian Ocean yellowfin tuna. Validation is not a binary process (i.e. pass or fail) but a continuum; therefore, we discuss the use of prediction skill to identify alternative hypotheses, weight ensemble models and agree on reference sets of operating models when conducting Management Strategy Evaluation.

Status of Indian marine fish stocks: modelling stock biomass dynamics in multigear fisheries

Abstract A biomass dynamics modelling study to derive biological reference points and management requirements of 223 commercially important fish stocks in different maritime states of India was conducted. Two decades (1997–2016) of fishery-related data on the harvest of resources by different types of fishing fleets formed the input. The multigear nature of the fishery situation was solved by introducing a gear standardization parameter into the biomass dynamics model. The relative positions of the fish stocks were depicted through Kobe plots generated for the ten maritime states/union territory, and the fish stocks were categorized, based on the status, into sustainable, overfished, recovering, and overfishing. The results indicate that 34.1% of the assessed fish stocks in the country are sustainable, 36.3% are overfished, 26.5% are recovering, and 3.1% are in the overfishing status. Regionally, the percentage of sustainable fish stocks were high along the southwest coast (51.6%), overfished stocks were high along the northwest coast (54.2%), and recovering fish stocks were high along the northeast coast (47.8%). The national mean B/BMSY was estimated as 0.86, which is a strong reason for strengthening fisheries management. Fishing fleets harvesting overfished stocks were examined for each maritime state, and recommendations regarding reduction in annual fishing hours are made.

Comparative Modeling of Cod-Capelin Dynamics in the Newfoundland-Labrador Shelves and Barents Sea Ecosystems

The Atlantic cod (Gadus morhua) stocks in the Newfoundland-Labrador Shelves (NL) and Barents Sea (BS) ecosystems have shown divergent trajectories over the last 40 years. Both stocks experienced either an important decline (BS) or a collapse (NL) in the mid-1980s and early 1990s, respectively. After these population reductions, the BS stock quickly rebounded and it is currently at record high levels, while the NL stock, despite showing some improvement since the mid-2000s, remains at low levels. Fishing and environmental conditions are known to be important drivers of cod dynamics in both ecosystems, especially the availability of high energy prey like capelin (Mallotus villosus), however, the question of how different or similar these two stocks truly are remains. Could, for example, the NL cod stock rebuild if presented to conditions like the ones experienced by BS cod? To explore such questions, we developed a simple biomass dynamic model for cod using a bioenergetic-allometric approach. This model includes fisheries catches and capelin availability as external drivers and was implemented for both ecosystems. Despite the contrasting trends, the model produced very good fits, and showed some remarkably similar estimated parameters in both systems. We explored these similarities by (a) performing the thought experiment of transferring cod stocks between ecosystems by switching estimated key parameters between models and comparing the output, and (b) implementing an integrated model architecture which allowed fitting common parameters for both stocks to evaluate the similarity of key vital rates. Our results indicate that cod trajectories in NL and BS can be reliably described using simple bioenergetic-allometric arguments, fishery catches, and capelin availability. Model parameters that encapsulate intrinsic vital rates were not significantly different between stocks. This indicates that NL and BS cod stocks are biologically similar, and that the differences in their trajectories are driven by the ecosystem context in which these stocks are embedded, and suggests that the NL stock would be expected to rebuild if enough capelin were available. This also indicates that capelin status and trend should be an important consideration for effective management of these cod stocks.

A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction

When designing models for predicting phytoplankton biomass or characterizing traits, it is useful to aggregate the myriad of species into a few biologically meaningful groups and focus on group-level attributes, the common practice being to combine phytoplankton species by functional types. However, biogeochemists and plankton ecologists debate the most applicable grouping for describing phytoplankton biomass patterns and predicting future community structure. Although trait-based approaches are increasingly being advocated, methods are missing for the generation of trait-based taxa as alternatives to functional types. Here we introduce such a method and demonstrate the usefulness of the resulting clustering with field data. We parameterize a Bayesian model of biomass dynamics and analyze long-term phytoplankton data collected at Station L4 in the Western English Channel between April 2003 and December 2009. We examine the tradeoffs encountered regarding trait characterization and biomass prediction when aggregating biomass by (1) functional types, (2) the trait-based clusters generated by our method, and (3) total biomass. The model conveniently extracted trait values under the trait-based clustering, but required well-constrained priors under the functional type categorization. It also more accurately predicted total biomass under the trait-based clustering and the total biomass aggregation with comparable root mean squared prediction errors, which were roughly five-fold lower than under the functional type grouping. Although the total biomass grouping ignores taxonomic differences in phytoplankton traits, it predicts total biomass change as well as the trait-based clustering. Our results corroborate the value of trait-based approaches in investigating the mechanisms underlying phytoplankton biomass dynamics and predicting the community response to environmental changes.

Parameterizations for Bayesian state-space surplus production models

Bayesian state-space surplus production models are commonly applied in fisheries stock assessment when the only information available is an index of relative abundance. However, even relatively simple models such as these can be computationally expensive to fit, and diagnosing poor fits can be difficult. The Stan software package provides an advanced Markov chain Monte Carlo sampler and diagnostics that are not available in other packages for fitting Bayesian models. Here the sampler diagnostics, efficiency, and posterior inferences are compared among multiple parameterizations of a state-space biomass dynamics model, using both Pella-Tomlinson and Schaefer dynamics. Two parameterizations that prevent predictions of negative biomass are introduced, one of which allows for errors in catch. None of the parameterizations used avoid diagnostic warnings using the default sampler parameter values. Choosing the appropriate parameterization of a model, and paying attention to these diagnostics can increase computational efficiency and make inferences more robust.

Multispecies biomass dynamics models reveal effects of ocean temperature on predation of juvenile pollock in the eastern Bering Sea

AbstractWalleye pollock (Gadus chalcogrammus) supports one of the largest commercial fisheries in the world. Juvenile pollock are important forage fish in the eastern Bering Sea (EBS) ecosystem, often representing the largest fraction in the diets of major Bering Sea piscivores. Large variability in the EBS pollock stock biomass in recent years has been attributed primarily to fluctuations in recruitment. It has been hypothesized that predation rates on forage fishes increase when the cold pool (a body of cold water < 2°C) is extensive and covers much of the middle continental shelf, which tends to concentrate larger predatory fishes in the outer shelf and slope regions. In contrast, young pollock appear to tolerate colder temperatures than older fish and can stay in the cold pool, thereby reducing predation. We used a multispecies modeling approach to examine the effects of the cold pool size on predation of juvenile pollock. We found that predation on age‐1 pollock by age‐3+ pollock decreased, and predation on age‐1 and age‐2 pollock by arrowtooth flounder increased with increasing bottom temperature, which was used as a proxy for the cold pool size. These results suggest that the cold pool creates spatial separation between juvenile pollock and arrowtooth flounder, but not between adult and juvenile pollock. The model developed in this study could be used to examine the effects of other covariates on interspecific interactions, help explain observed changes in fish communities, and understand implications of climate change on ecosystems and their productivity.

Time-variant productivity in biomass dynamic models on seasonal and long-term scales

Abstract The productivity of fish populations varies naturally over time, dependent on integrated effects of abundance, ecological factors, and environmental conditions. These changes can be expressed as gradual or abrupt shifts in productivity as well as fluctuations on any time scale from seasonal oscillations to long-term changes. This study considers three extensions to biomass dynamic models that accommodate time-variant productivity in fish populations. Simulation results reveal that neglecting seasonal changes in productivity can bias derived stock sustainability reference levels and, thus, fisheries management advice. Results highlight the importance of biannual biomass indices and their timing relative to the peaks of the seasonal processes (i.e. recruitment, growth, mortality) for the estimation of seasonally time-variant productivity. The application to real-world data of the eastern Baltic cod (Gadus morhua) stock shows that the model is able to disentangle differences in seasonal fishing mortality as well as seasonal and long-term changes in productivity. The combined model with long-term and seasonally varying productivity performs significantly better than models that neglect time-variant productivity. The model extensions proposed here allow to account for time-variant productivity of fish populations leading to increased reliability of derived reference levels.

Crop yield forecasting based on the satellite monitoring of carbon dynamics in terrestrial ecosystems

A low-parametric model of crop biomass dynamics using the data of satellite remote sensing of terrestrial vegetation index and routine meteorological observations is developed. Modeling is performed with a data filtered by yield-correlation image masking technique. The simulation is based on the Monteith equation for carbon dynamics in terrestrial ecosystems. Meteorological parameters that have an effect on the photosynthesis intensity but rarely measured directly are calculated on the basis of analytical parameterizations obtained from reanalysis data. A validity of the model is demonstrated by the example of satellite monitoring of the spring wheat yield in the regions of the Republic of Belarus.