Journal of Fisheries & Livestock Production
Open Access

Global climate change; Fishery management; Mechanistic technique; Empirical approach

Introduction

To produce a socially, economically, and politically acceptable trade-off between competing objectives is the overarching goal of fisheries management [1]. These goals include producing abundant and consistent yields and profitability as well as preserving the ecosystems and fished resources needed to support future fisheries. To aid in doing this, numerous scientific instruments have been created. These include stock assessment techniques that typically calculate stock biomass and status in relation to target, threshold, and/or limit reference points, as well as harvest control rules that use the results of the stock assessments to offer the scientific management guidance that serves as the foundation for decision-making.

There is frequently little correlation between the techniques utilised to produce scientific management advice and the calibre of the data at hand. However, a number of countries have now created tier systems that relate the HCR to be applied to the calibre and volume of data supplied [2]. In order to accomplish predetermined management objectives, management strategies are characterised as combinations of data collection plans, stock assessment techniques, and HCRs. It is debatable whether or not many of the methods employed to deliver scientific management advice qualify as management strategies. Before being used for the first time, several of these systems underwent simulation evaluation (i.e., using the management strategy evaluation, MSE, approach; these systems are frequently referred to as “management procedures”). The great majority of management plans, especially their HCR components, were created through committee negotiation as opposed to simulation evaluation. Such management methods could seem logical on the surface, but they don’t have to be thoroughly defined, and it’s unclear how well they’ll actually work in terms of reaching management objectives.

Management strategies have been developed and tested using simulation for a wide range of Baleen whales, which are targets of commercial and indigenous whaling, tiny pelagic fish, ground fish, and tuna stocks, and invertebrate stocks are some examples of marine renewable resources. Additionally, simulation has been used to assess management plans for achieving ecosystem goals [3]. The majority of these management techniques were created for commercially or recreationally significant fisheries with abundant data. For small-scale and data-poor fisheries, however, when conventional data sources like fishery-independent survey data are either unavailable or unreliable, management solutions are being created.

Environmental Factors Affecting Climatic Condition

Environmental factors cause oscillations in biological populations, and there are credible ideas connecting environmental factors to changes in biological parameters [4]. According to several authors, management techniques “must” take the consequences of environmental variability into account. But nearly no one does. As an exception, the HCR utilised to offer management guidance for Pacific sardine Sardinops sagax employed temperature at Scripps Pier in La Jolla, California, as a proxy for FMSY. But when recent data on recruitment and spawner biomass were analysed, it was recently demonstrated that the association between log (recruits/spawner) and that measure of temperature is not significant (p > 0.1). As a result, the procedure for creating a proxy for FMSY in Pacific sardine needs to be changed in order to account for temperature.

Despite the fact that the HCR for Pacific sardines was at least chosen after MSE-type simulation testing, the Bay of Biscay anchovy,

Engraulis encrasicolus, offers an illustration of the risks associated with using environmental indices for management choices without first performing such simulations [5]. For the years 1967–1996, there was a substantial correlation between an upwelling index and the yearly recruitment of Bay of Biscay anchovies, which accounted for around 59% of the recruitment variability. The estimate of spawningstock biomass based on this forecast fell below an established cautious threshold as a result of using the corresponding relationship as a basis for estimating recruitment [6]. As a result, the total allowed catch (TAC) for 2000 was cut in half. The upwelling index, however, had extremely limited utility as a predictor of absolute recruitment, according to ICES’s analysis of later data, which showed that the recruitment prediction was a significant underestimate. The practise of adjusting the TAC levels based on the upwelling index was eventually discontinued. The association between environment and recruit that was previously believed to be strong was later demonstrated to be weak, which contributed to the issue. Following simulation testing of this environmental index HCR, it was found that utilising precautionary measures rather than relying on unreliable or weak environmental links would better ensure successful management.

A wide range of uncertainties relating to the biological properties of the fished stocks, such as fluctuation in recruitment about the stockrecruitment relationship, are frequently considered in the simulations used to assess the effectiveness of candidate management measures [7]. The assumptions on which stock assessments are typically based, such as assuming a stationary stock-recruitment relationship, timeinvariant somatic growth, and age- and time-independent natural mortality, have, however, tended to assume that reality is identical with or very closely related to stimulations. The MSE approach, however, can be used to investigate violations of these hypotheses, and as is demonstrated below, violations of these hypotheses are likely to have negative consequences and present difficulties for the development of management strategies with the potential to achieve management objectives in a variety of different situations.

The aforementioned examples demonstrate that both the simulations used to assess management methods and the strategies themselves can take into account the effects of the environment and climate on the population dynamics of marine species [8]. The advantages of adopting a management approach that explicitly accounts for environmental and climate concerns can be assessed using these models. This study first describes the simulation-based method for evaluating management plans and then gives a brief overview of the potential effects of climate change and environmental variation on fish and invertebrate stocks and the ecosystems they are a part of. The effectiveness of management solutions is then evaluated using two fundamental methods. These methods include climate change and environmental variation. The important findings from a variety of studies evaluating management approaches that explicitly permit and do not expressly allow for environmental change are reviewed, and future research options are recommended.

There is still a great deal of mystery surrounding the functional relationship between spawners and recruits, as well as how environmental variability affects this relationship and other biological processes, even after 100 years have passed [9]. Though not in the context of actual stock evaluations, it is now possible to construct complicated non-linear connections across time and place. It is possible to identify new management systems that are robust to these effects, (ii) it is possible to identify new management systems that are robust to these effects, or (iii) it is possible to identify the key areas of monitoring and process research needed to resolve the processes. Operating models within an MSE can, however, be used to explore factors that cannot be included in standard stock assessment methods.

Incorporating environmental and climatic drivers into operating models for management strategy evaluation. In light of changes in biological parameters brought on by the climate, relatively few articles have reviewed management strategies [10]. The hypothesised links between biological parameters-typically recruitment and environmental factors have, in contrast, been the subject of numerous investigations. This implies that the connected stocks could use the mechanistic approach. Under potential future climatic conditions, three Eastern Bering Sea flatfish stocks-the flathead sole, the northern rock sole (Lepidopsetta polyxystra), and the arrowtooth flounder (Atheresthes stomias may exist.

It is appealing in theory and appears to be more biologically realistic than the empirical approach to base projections on models that relate biological parameters to environmental variables. To estimate future reactions to climate variability and change, it is vital to comprehend the mechanisms causing variations in production, even though they can provide useful information in the past [11]. Besides, due to the following factors, care should be taken when interpreting the results of such forecasts: I the possibility that relationships may be false; (ii) the likelihood that once-significant relationships may break down over time as more monitoring data become available; (iii) the poor ability to predict environmental variables beyond a few years into the future; and (iv) the likelihood that the relationship’s form will change over time. Additionally, the models that were employed to forecast the environmental factors may have a significant impact on the forecast outcomes.

Because the drivers of the biological parameters of the operating model are not based on the actual estimated relationships, the results of the empirical approach cannot naturally be considered as predictions. This approach’s key benefit is therefore to investigate how resilient management solutions are expected to be to shifting biological conditions.

Main staying stock size above a Minimum Stock Size Threshold and limiting fishing mortality to below the maximum fishing mortality threshold are important metrics used to assess the effectiveness of fisheries management in the United States (MFMT). For single-species fisheries with time-invariant biological characteristics, the MSST and the MFMT are both clearly characterised. Allowing biological factors to change over time in an MSE presents a number of challenges, including defining the MSST and the MFMT when calculating the performance statistics used to assess the behaviour of management techniques [12]. To remedy this, several ideas have been explored. For instance, the IWC has attempted to “scale out” the effects of time-varying biological characteristics by expressing the results of projections as the population size relative to what would have occurred had there been no exploitation. In contrast, some research has computed figures for the current regime, like the MSST. All of these options are not perfect, though, and questions concerning how to interpret the outcomes of simulations in which the values of biological factors fluctuate over time still exist.

The existing regime’s perception may alter significantly if the STARS method is used to characterise it. For instance, altering the years used to determine the MSST may result in the definition of an earlier “healthy” stock as being overfished (or vice versa).

Sometimes, provisions for extraordinary conditions are included in management strategies, and if resource monitoring statistics deviate from the intended range, the management strategy will automatically be reviewed. Additional fishing limitations may also be implemented [13]. In theory, these clauses might make an overall management plan resilient even in the face of biological parameter time-trends. It has not yet been determined whether this strategy would function as expected and employing simulation may be challenging because it would necessitate the specification of the actions to be taken in the event of extraordinary conditions, which is not presently the case.

It goes without saying that any management system will perform poorly in some scenarios, such as those including the effects of climate change. Furthermore, it is probable that how much emphasis is put on the theories supporting simulation experiments with such time-trends would affect how well management techniques perform in comparison to one another [14]. As a result, it’s important to weigh the relative probability of various situations. At that time, a less formal technique was agreed upon and put into practise. It involved the Delphi method, in which the Committee ranked the plausibility of several operating models as high, medium, low, or not at all. Operating models with low plausibility ratings are disregarded, and the tolerable risk for operating models with medium plausibility ratings is larger than for operating models with high plausibility ratings.

When modelling the effects of climate change on biological factors empirically, the problem of plausibility is more difficult. Because mechanistic models are often driven by strong ecological principles and the strength of any correlations between environmental factors and biological parameters is based on actual data, even though mechanistic models may fail given further data [15]. The strength of an empirical association, on the other hand, is typically an informed assumption, which makes establishing plausibility considerably more challenging. If the management technique being used performed effectively despite this weakness, it would not matter. However, because feedback control is imperfect, it frequently fails to implement enough self-correction to continue achieving management goals in increasingly extreme situations. Additionally, even when it does function effectively, it may be difficult to accept the lower biomass goal reference point that this may indicate. Therefore, even using an empirical method, further study is still necessary to try to strengthen these informed predictions about how much the environment affects biological processes.

Result and Discussion

In previous research, the operating models that were taken into consideration were often built on models of single-species population dynamics. However, it is conceivable to base assessments of management techniques on more intricate operational models that clearly depict the relationships between climatic conditions and the biological responses of the targeted species [16]. The Atlantis model, for instance, is a completely spatially resolved ecosystem model that can be pushed using projections from climate models. This is undoubtedly a potential study area, even if Atlantis has not yet been utilised to assess management methods in light of climate concerns. This is especially true if changes in species distribution, and subsequently predatorprey interactions, are one of the long-term repercussions of climate change. The distribution, quantity, or growth of marine fish are affected by dynamic ocean elements such as variations in temperature, salinity, current intensity, hypoxic zones, the oxygen minimum zone, and the depth of the mixed layer. These might be used to anticipate distribution and stock movement changes or to now-cast utilising current environmental variables.

There have been many suggestions on how to improve the effectiveness of the current management techniques in the face of environmental and climate-induced forces. These consist of the dynamic B0 technique, using the STARS approach, and calculating reference points using a changing window of model outputs [17]. All of these methods, however, only use a portion of the biomass and recruitment estimations or estimate additional characteristics for determining reference points and, consequently, HCRs. Greater variation can arise from this, which explains, for instance, the finding of Szuwalski and Punt (in press), which shows that management practises that aim to account for changes in productivity over time can instead increase risk when such changes are absent.

The ability to achieve management goals over timescales important to short- and medium-term fisheries management decision-making was not significantly, if at all, improved by many of the studies covered in detail above or by more general studies that modified management strategies to include environmental covariates. They only did so in cases when highly detailed knowledge of the external elements driving the system was available. Although many climatic consequences and variations across ensemble forecasts only become significant several decades into the future, it has not yet been completely determined if this conclusion applies over the very long period.

Conclusion

The result reached by King and McFarlane is at odds with the fact that management techniques that take environmental variables into account typically perform worse than those that do (2006). They discovered that accounting for the understanding of regime transitions might result in better managerial performance (although they did not model assessment error and assumed instead that fishing mortality rates could be implemented directly). The findings from the research under evaluation are more in line with the assertion that harvesting a fixed percentage of the stock year in the face of climatic change results in long-term yields that are probably within 15% of the highest feasible yields. As a means of evaluating the robustness of management strategies, rather than as specific predictions per se, it seems more appropriate to consider the implications of plausible broad forecasts related to how biological parameters may change in the future until the skill of stock projection models improves.

Acknowledgement

None

Conflict of Interest

None

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