The impacts of climate change on fish growth: A summary of conducted studies and current knowledge

Huang, Minrui; Ding, Liuyong; Wang, Jun; Ding, Chengzhi; Tao, Juan

doi:10.1016/j.ecolind.2020.106976

Cited by 85 publications

(75 citation statements)

References 84 publications

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“…This is a very important result because it shows that it is possible to create an effective strategy without knowing the consumption of the other agents in the micro grid, thus avoiding privacy issues. (1)(2)(3)(4)(5)(6), the CAGR are real (i.e., would be higher if adjusted for inflation), and assume a case in which there is no growth in the price of electricity, a chart assuming a 2% linear increase in the price of electricity from Scenario 4 is in the Appendix A.…”

Section: Interaction Of Competing Sale Strategies Within the Micro-grid (Scenario 5 And Scenario 6)mentioning

confidence: 99%

“…Looking at the difference between Scenarios 1 and 2, it is visible how the relative difference in average CAGR between groups is lower in Scenario 2. This means that larger households benefit more from inter- (1)(2)(3)(4)(5)(6), the CAGR are real (i.e., would be higher if adjusted for inflation), and assume a case in which there is no growth in the price of electricity, a chart assuming a 2% linear increase in the price of electricity from Scenario 4 is in the Appendix A.…”

Section: Interaction Of Competing Sale Strategies Within the Micro-grid (Scenario 5 And Scenario 6)mentioning

confidence: 99%

“…Climate change is one of the main challenges that threaten the wellbeing or the very existence of human society. This threat cannot be ignored because it can impact a wide range of natural ecosystems and socio-technical systems (e.g., [1][2][3][4][5][6][7]). In the last few decades, numerous technologies have been discovered, or improved, that can dramatically reduce our greenhouse gas emissions: renewable or low carbon energy generation facilities, energy storage systems, energy efficiency, and carbon capture devices.…”

mentioning

confidence: 99%

“…Figure 8. CAGR (Compound Annual Growth Rate) % in the different scenarios(1)(2)(3)(4)(5)(6), the CAGR are real (i.e., would be higher if adjusted for inflation), and assume a case in which there is no growth in the price of electricity, a chart assuming a 2% linear increase in the price of electricity from Scenario 4 is in the Appendix A.…”

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confidence: 99%

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Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

et al. 2021

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Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

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Section: Interaction Of Competing Sale Strategies Within the Micro-grid (Scenario 5 And Scenario 6)mentioning

confidence: 99%

Section: Interaction Of Competing Sale Strategies Within the Micro-grid (Scenario 5 And Scenario 6)mentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

et al. 2021

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“…Of these, temperature has the most significant effect on fish activity [4]. Changes in temperature directly or indirectly affect fish migration behaviors, growth, and habitat distributions [5][6][7]. Eveson et al [8] demonstrated that water temperature is the environmental variable most commonly used to forecast tuna fishing grounds.…”

Section: Introductionmentioning

confidence: 99%

Unusual Fish Assemblages Associated with Environmental Changes in the East China Sea in February and March 2017

Ding

Zhang

et al. 2021

Remote Sensing

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Two large fish assemblages were recorded in the overwintering fishing grounds of the East China Sea in February and March 2017. In this study, available time series of satellite-derived sea surface temperature, wind, chlorophyll a, and reanalysis data were used to explore the relationships between the observed large fish aggregations and environmental factors. The bottom waters of the fishing grounds were abnormally warm in winter 2017, and then experienced significant cooling due to the eastward movement of the Yellow Sea Cold Current, which was driven by the increased northwesterly wind from January to mid-March 2017. Fishing areas in the affected region, including No. 1891, which was abnormally warm, and No. 1592, which had a strong thermal front and high chlorophyll a concentration, might have provided suitable environments for the warm-temperature fish, resulting in the observed large fish assemblages. The abnormal temperature changes between winter and early spring 2017 may have been associated with changes in local ocean circulation.

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Sometimes (often?) responses to multiple stressors can be predicted from single‐stressor effects: A case study using an agent‐based population model of croaker in the Gulf of Mexico

Rose

2023

Mar Coast Fish

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ObjectiveRapid changes in the world's oceans make assessment of fish population responses to multiple stressors, especially on scales relevant to management, increasingly important. I used an existing agent‐based, spatially explicit model of Atlantic Croaker Micropogonias undulatus in the northern Gulf of Mexico to examine how temperature, hypoxia, and ocean acidification, singly and in combinations, affect long‐term population dynamics.MethodsI performed a factorial simulation experiment with each stressor at three levels and analyzed various treatment combinations to assess the additivity and multiplicity of interactions. The response variables were long‐term equilibrium (final year) values of spawning stock biomass (SSB), recruitment, weight at age, and two measures of stock productivity (recruits per SSB and maximum recruitment) derived from the spawner–recruit relationship fitted to model output. I used the single‐stressor effects from the experiment to predict how the response variables would change when all three stressors were changed. Single‐stressor effects were combined as the sum of the fractional changes (additive scale) and the product of ratios of changes (multiplicative scale) and compared to the responses in simulations with all stressors imposed.ResultAnalyzing the factorial design for two‐way and three‐way interactions showed that there were many interactions on the additive scale but very few on the multiplicative scale. Thus, the responses to multiple stressors were well predicted from single stressor effects when combined as multiplicative effects.ConclusionI discuss how the lack of strong interactions could be due to model assumptions, the structure of the model, or oversimplified representation of stressor effects. Alternatively, the model and analysis may be sufficiently realistic and weak interactions on the multiplicative scale may be common. This would reduce a complicated multi‐factor situation to a series of more tractable single‐factor effects. A critical next step is to determine how we can a priori identify situations of low interactions (i.e., predictable from single‐stressor effects) without having to already know the multi‐stressor response.

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The impacts of climate change on fish growth: A summary of conducted studies and current knowledge

Cited by 85 publications

References 84 publications

Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

Unusual Fish Assemblages Associated with Environmental Changes in the East China Sea in February and March 2017

Sometimes (often?) responses to multiple stressors can be predicted from single‐stressor effects: A case study using an agent‐based population model of croaker in the Gulf of Mexico

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