Understanding the hydraulic properties of soil is essential to solve many management problems in agriculture and the environment. Water quality affects soil hydraulic conductivity. Soil hydraulic properties play an important role in nature's water cycle and are used as basic information in designing irrigation and drainage systems, hydrological issues, and soil quality assessment. In the current study, soil sampling is performed from different areas and its hydraulic conductivity was measured using the drop load method and then predicted using support vector machine (SVM) and least‐squares support vector machine (LSSVM) models. The model inputs were: soil texture (percentage of sand, silt, and clay particles), salinity (electrical conductivity), pH, sodium adsorption ratio, soil porosity, and bulk density and the output was soil hydraulic conductivity. Correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the models and compare them. Based on evaluation criteria the best performance was obtained for random forest (RF) (R = 0.89, RMSE = 0.53, mean absolute error (MAE) = 0.54, and NSE = 0.72). Following RF, the SVM with (R = 0.69, RMSE = 1.32, MAE = 0.69, and NSE = 0.48) performed better than LSSVM model.
{"title":"Predicting soil hydraulic conductivity using random forest, SVM, and LSSVM models","authors":"Masumeh Farasati, Morteza Seyedian, Abolhasan Fathaabadi","doi":"10.1111/nrm.12407","DOIUrl":"https://doi.org/10.1111/nrm.12407","url":null,"abstract":"Understanding the hydraulic properties of soil is essential to solve many management problems in agriculture and the environment. Water quality affects soil hydraulic conductivity. Soil hydraulic properties play an important role in nature's water cycle and are used as basic information in designing irrigation and drainage systems, hydrological issues, and soil quality assessment. In the current study, soil sampling is performed from different areas and its hydraulic conductivity was measured using the drop load method and then predicted using support vector machine (SVM) and least‐squares support vector machine (LSSVM) models. The model inputs were: soil texture (percentage of sand, silt, and clay particles), salinity (electrical conductivity), pH, sodium adsorption ratio, soil porosity, and bulk density and the output was soil hydraulic conductivity. Correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the models and compare them. Based on evaluation criteria the best performance was obtained for random forest (RF) (<jats:italic>R</jats:italic> = 0.89, RMSE = 0.53, mean absolute error (MAE) = 0.54, and NSE = 0.72). Following RF, the SVM with (<jats:italic>R</jats:italic> = 0.69, RMSE = 1.32, MAE = 0.69, and NSE = 0.48) performed better than LSSVM model.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this study is to perform a practical inquiry into the influence of environmental tax laws on the execution of climate‐related financial policies. Our research will assess the effectiveness of environmental tax measures in 23 European countries from 2011 to 2020. The panel‐corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model are used in the empirical examination of the link between environmental tax laws and the implementation of climate‐related financial measures. This study is based on panel data with cross‐sectional dependence. The results of our estimation highlight the need to improve policy effectiveness by using all four ecological tax indicators. These include total environmental tax revenue, energy tax revenue, pollution and resource tax revenue, and transportation tax revenue. Furthermore, we provide actual evidence clarifying the process by which the implementation of environmental tax policies improves the efficacy of climate‐related financial policies in the short term as well as the long term. According to the findings, a third of environmental tax policy indicators have a long‐term effect on the implementation of climate‐related financial measures, with no short‐term effects seen. Our findings are critical for economists and policy‐makers who support the environmental tax as an effective tool to promote a country's climate policy implementations.
{"title":"The role of environmental tax in guiding global climate policies to mitigate climate changes in European region","authors":"Le Thanh Ha","doi":"10.1111/nrm.12412","DOIUrl":"https://doi.org/10.1111/nrm.12412","url":null,"abstract":"The purpose of this study is to perform a practical inquiry into the influence of environmental tax laws on the execution of climate‐related financial policies. Our research will assess the effectiveness of environmental tax measures in 23 European countries from 2011 to 2020. The panel‐corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model are used in the empirical examination of the link between environmental tax laws and the implementation of climate‐related financial measures. This study is based on panel data with cross‐sectional dependence. The results of our estimation highlight the need to improve policy effectiveness by using all four ecological tax indicators. These include total environmental tax revenue, energy tax revenue, pollution and resource tax revenue, and transportation tax revenue. Furthermore, we provide actual evidence clarifying the process by which the implementation of environmental tax policies improves the efficacy of climate‐related financial policies in the short term as well as the long term. According to the findings, a third of environmental tax policy indicators have a long‐term effect on the implementation of climate‐related financial measures, with no short‐term effects seen. Our findings are critical for economists and policy‐makers who support the environmental tax as an effective tool to promote a country's climate policy implementations.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leila Goli Mokhtari, Nadiya Baghaei Nejad, Ali Beheshti
Gully erosion is a significant natural hazard and a form of soil erosion. This research aims to predict gully formation in the Kalshour basin, Sabzevar, Iran. Employing the Information Gain Ratio (IGR) index, we identified 13 key factors out of 22 for modeling, with elevation emerging as the most influential factor in gully formation. The study evaluated the performance of individual machine learning algorithms and ensemble algorithms, including the Functional Tree (FT) as the main classifier, Bagging (Bagg), AdaBoost (Ada), Rotation Forest (RoF), and Random Subspace (RSS). Using a data set of 400 gully and non‐gully points obtained through field investigations (70% for training and 30% for testing), the RoF model achieved an area under the curev (AUC) value of 0.99, indicating its high predictive ability for gully‐susceptible areas. Other algorithms also performed well (Ada: 0.90, FT: 0.92, RSS: 0.94, Bagg: 0.95). However, the RoF algorithm with the functional tree as the main classifier (RoF_FT) demonstrated the highest ability in gully classification and susceptibility mapping, enhancing the functional tree's performance. In addition to AUC, the RoF_FT model achieved an F1 score of 0.89 and an MCC of 0.78 on the validation set, indicating a high balance between precision and recall, and a strong correlation between predicted and actual classes, respectively. Similarly, other models showed robust performance with high F1 scores and MCC values, but the RoF_FT model consistently outperformed them, underscoring its robustness and reliability. The resulting gully erosion‐susceptibility map can be valuable for decision‐makers and local managers in soil conservation and minimizing damages. Implementing proactive measures based on these findings can contribute to sustainable land management practices in the Kalshour basin.Recommendations <jats:list list-type="bullet"> <jats:list-item>Gully erosion threat: Gully erosion poses a significant threat to soil, with far‐reaching environmental consequences.</jats:list-item> <jats:list-item>Predictive modeling: This research focuses on predicting gully formation in the Kalshour basin, Sabzevar, Iran, using advanced machine learning algorithms.</jats:list-item> <jats:list-item>Key findings for decision‐makers: The study evaluates the performance of various machine learning algorithms and ensemble algorithms, with the Functional Tree serving as the main classifier. This not only enhances our ability to predict gully formation but also provides a valuable tool for decision‐makers and local managers in soil conservation.</jats:list-item> <jats:list-item>Impact on sustainable land management: By offering a gully erosion‐susceptibility map, the research empowers decision‐makers to implement proactive measures, minimizing damage and contributing to sustainable land management practices.</jats:list-item> <jats:list-item>Interdisciplinary approach: The study's combination of geospatial analysis, machine learning, and s
{"title":"Predicting gully formation: An approach for assessing susceptibility and future risk","authors":"Leila Goli Mokhtari, Nadiya Baghaei Nejad, Ali Beheshti","doi":"10.1111/nrm.12409","DOIUrl":"https://doi.org/10.1111/nrm.12409","url":null,"abstract":"Gully erosion is a significant natural hazard and a form of soil erosion. This research aims to predict gully formation in the Kalshour basin, Sabzevar, Iran. Employing the Information Gain Ratio (IGR) index, we identified 13 key factors out of 22 for modeling, with elevation emerging as the most influential factor in gully formation. The study evaluated the performance of individual machine learning algorithms and ensemble algorithms, including the Functional Tree (FT) as the main classifier, Bagging (Bagg), AdaBoost (Ada), Rotation Forest (RoF), and Random Subspace (RSS). Using a data set of 400 gully and non‐gully points obtained through field investigations (70% for training and 30% for testing), the RoF model achieved an area under the curev (AUC) value of 0.99, indicating its high predictive ability for gully‐susceptible areas. Other algorithms also performed well (Ada: 0.90, FT: 0.92, RSS: 0.94, Bagg: 0.95). However, the RoF algorithm with the functional tree as the main classifier (RoF_FT) demonstrated the highest ability in gully classification and susceptibility mapping, enhancing the functional tree's performance. In addition to AUC, the RoF_FT model achieved an F1 score of 0.89 and an MCC of 0.78 on the validation set, indicating a high balance between precision and recall, and a strong correlation between predicted and actual classes, respectively. Similarly, other models showed robust performance with high F1 scores and MCC values, but the RoF_FT model consistently outperformed them, underscoring its robustness and reliability. The resulting gully erosion‐susceptibility map can be valuable for decision‐makers and local managers in soil conservation and minimizing damages. Implementing proactive measures based on these findings can contribute to sustainable land management practices in the Kalshour basin.Recommendations <jats:list list-type=\"bullet\"> <jats:list-item>Gully erosion threat: Gully erosion poses a significant threat to soil, with far‐reaching environmental consequences.</jats:list-item> <jats:list-item>Predictive modeling: This research focuses on predicting gully formation in the Kalshour basin, Sabzevar, Iran, using advanced machine learning algorithms.</jats:list-item> <jats:list-item>Key findings for decision‐makers: The study evaluates the performance of various machine learning algorithms and ensemble algorithms, with the Functional Tree serving as the main classifier. This not only enhances our ability to predict gully formation but also provides a valuable tool for decision‐makers and local managers in soil conservation.</jats:list-item> <jats:list-item>Impact on sustainable land management: By offering a gully erosion‐susceptibility map, the research empowers decision‐makers to implement proactive measures, minimizing damage and contributing to sustainable land management practices.</jats:list-item> <jats:list-item>Interdisciplinary approach: The study's combination of geospatial analysis, machine learning, and s","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing urbanization and growing urban population present significant challenges for urban water resources. Inadequate management has led to a decline in water quality and a mismatch between the availability and usage of city water resources. In this period of significant growth, improving urban water efficiency and reinforcing water resource protection are especially important. Leadership is essential in developing and carrying out urban water resource policies. This research investigates how leadership influences the operational efficiency of water resources protection policies. The study seeks to fill the gap in understanding the relationship between leadership, member trust, and policy effectiveness in water resource management by constructing a theoretical model and conducting empirical analysis through questionnaire surveys. The findings of this study are expected to provide valuable insights into the significance of leadership in driving improvements in water conservation policies. By establishing a link between leadership, member trust, and operational efficiency, the research contributes to both theoretical knowledge and practical implications for enhancing water resource management strategies.
{"title":"Research on the impact of leadership on improving urban water efficiency and water conservation policies","authors":"Meng Meng","doi":"10.1111/nrm.12410","DOIUrl":"https://doi.org/10.1111/nrm.12410","url":null,"abstract":"The increasing urbanization and growing urban population present significant challenges for urban water resources. Inadequate management has led to a decline in water quality and a mismatch between the availability and usage of city water resources. In this period of significant growth, improving urban water efficiency and reinforcing water resource protection are especially important. Leadership is essential in developing and carrying out urban water resource policies. This research investigates how leadership influences the operational efficiency of water resources protection policies. The study seeks to fill the gap in understanding the relationship between leadership, member trust, and policy effectiveness in water resource management by constructing a theoretical model and conducting empirical analysis through questionnaire surveys. The findings of this study are expected to provide valuable insights into the significance of leadership in driving improvements in water conservation policies. By establishing a link between leadership, member trust, and operational efficiency, the research contributes to both theoretical knowledge and practical implications for enhancing water resource management strategies.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xianying Pang, Sana Fatima, Onur Yağiş, Mohammad Haseeb, Md. Emran Hossain
The existing literature consists of various studies that have addressed the interrelationship between banking expansion and carbon emissions but failed to consider supply‐side ecological issues. Keeping this in view, the research aims to assess the impact of green energy transition, banking sector expansion, and import price of crude oil on the “load capacity factor (LCF)” in the United States from 1990 to 2021. The “LCF” has emerged as a novel ecological proxy to date that includes both “biocapacity and ecological footprint.” Using the “bootstrap autoregressive distributed lag” model, the research found that the consumption of renewable energy can enhance the ecological quality of the United States. The results verified the acceptance of the “load capacity curve” hypothesis. Moreover, it demonstrates that banking development promotes environmental quality. Specifically, a 1% improvement in the banking industry leads to a 0.93% increase in the LCF in the short term, as well as a 1.28% increase in the long run. Furthermore, the increase in crude oil import prices has a positive impact on the LCF and eventually promotes environmental sustainability. To be precise, a 1% rise in the price of imported crude oil results in a 0.35% increase in the long‐term LCF level. These results were backed by the findings of several robustness tests. The study, lastly, recommends that the banking sector and government policymakers should use banking growth in promoting green energy to attain their target of zero carbon emissions by 2050.
{"title":"Assessing the load capacity curve hypothesis considering the green energy transition, banking sector expansion, and import price of crude oil in the United States","authors":"Xianying Pang, Sana Fatima, Onur Yağiş, Mohammad Haseeb, Md. Emran Hossain","doi":"10.1111/nrm.12413","DOIUrl":"https://doi.org/10.1111/nrm.12413","url":null,"abstract":"The existing literature consists of various studies that have addressed the interrelationship between banking expansion and carbon emissions but failed to consider supply‐side ecological issues. Keeping this in view, the research aims to assess the impact of green energy transition, banking sector expansion, and import price of crude oil on the “load capacity factor (LCF)” in the United States from 1990 to 2021. The “LCF” has emerged as a novel ecological proxy to date that includes both “biocapacity and ecological footprint.” Using the “bootstrap autoregressive distributed lag” model, the research found that the consumption of renewable energy can enhance the ecological quality of the United States. The results verified the acceptance of the “load capacity curve” hypothesis. Moreover, it demonstrates that banking development promotes environmental quality. Specifically, a 1% improvement in the banking industry leads to a 0.93% increase in the LCF in the short term, as well as a 1.28% increase in the long run. Furthermore, the increase in crude oil import prices has a positive impact on the LCF and eventually promotes environmental sustainability. To be precise, a 1% rise in the price of imported crude oil results in a 0.35% increase in the long‐term LCF level. These results were backed by the findings of several robustness tests. The study, lastly, recommends that the banking sector and government policymakers should use banking growth in promoting green energy to attain their target of zero carbon emissions by 2050.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Correction to “Object‐Based Image Analysis (OBIA)‐based gully erosion dynamics, sediment loading rate and sediment yield study in Lake Hawassa Subbasin, Ethiopia”","authors":"","doi":"10.1111/nrm.12405","DOIUrl":"https://doi.org/10.1111/nrm.12405","url":null,"abstract":"","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingjing Tao, Kwamena K. Quagrainie, Kenneth A. Foster, Nicole Olynk Widmar
The snow crab fishery faces increasing vulnerability to environmental factors, yet the literature on the relationship between climate change and snow crab harvest remains limited. This study estimates snow crab harvest functions using climate change indicators with unbalanced panel data of snow crab production from the eastern Bering Sea (Alaska), the southern Gulf of St. Lawrence (Canada), the Sea of Japan, and the Barents Sea (Norway‐Russia). The relationship between snow crab biomass, stock, and catch is analyzed and the endogeneity of stock in the harvest function is also addressed using climate change indicators as instrumental variables (IVs). The results show that the extent of Arctic sea ice is effective in addressing the endogeneity, and the random effects IV model with error components two stage least squares estimator performs the best to control heterogeneity. A 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock‐harvest elasticity and provide supporting evidence to prioritize stock enhancement in snow crab fishery policy designs to maintain stocks at sustainable levels and minimize government expenditures on subsidies.Recommendations This study explores how snow crab harvests are influenced by snow crab populations and fishing efforts in the context of global warming across various global regions, including the Bering Sea, the Gulf of St. Lawrence, the Sea of Japan, and the Barents Sea.A 1% increase in fishing effort is associated with a 0.42% increase in harvest, while a 1% increase in snow crab population leads to a 0.98% increase in harvest, showing a high dependency on snow crab biomass.Arctic sea ice extent is identified as a crucial climate factor affecting snow crab biomass and harvests, making it a valuable variable for understanding and managing snow crab populations.The study supports the prioritization of stock enhancement policies by fishery agencies and suggests standardizing how fishing effort is measured across different regions to improve snow crab fishery management and future research.
{"title":"A regional analysis of climate change effects on global snow crab fishery","authors":"Jingjing Tao, Kwamena K. Quagrainie, Kenneth A. Foster, Nicole Olynk Widmar","doi":"10.1111/nrm.12406","DOIUrl":"https://doi.org/10.1111/nrm.12406","url":null,"abstract":"The snow crab fishery faces increasing vulnerability to environmental factors, yet the literature on the relationship between climate change and snow crab harvest remains limited. This study estimates snow crab harvest functions using climate change indicators with unbalanced panel data of snow crab production from the eastern Bering Sea (Alaska), the southern Gulf of St. Lawrence (Canada), the Sea of Japan, and the Barents Sea (Norway‐Russia). The relationship between snow crab biomass, stock, and catch is analyzed and the endogeneity of stock in the harvest function is also addressed using climate change indicators as instrumental variables (IVs). The results show that the extent of Arctic sea ice is effective in addressing the endogeneity, and the random effects IV model with error components two stage least squares estimator performs the best to control heterogeneity. A 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock‐harvest elasticity and provide supporting evidence to prioritize stock enhancement in snow crab fishery policy designs to maintain stocks at sustainable levels and minimize government expenditures on subsidies.Recommendations <jats:list list-type=\"bullet\"> <jats:list-item>This study explores how snow crab harvests are influenced by snow crab populations and fishing efforts in the context of global warming across various global regions, including the Bering Sea, the Gulf of St. Lawrence, the Sea of Japan, and the Barents Sea.</jats:list-item> <jats:list-item>A 1% increase in fishing effort is associated with a 0.42% increase in harvest, while a 1% increase in snow crab population leads to a 0.98% increase in harvest, showing a high dependency on snow crab biomass.</jats:list-item> <jats:list-item>Arctic sea ice extent is identified as a crucial climate factor affecting snow crab biomass and harvests, making it a valuable variable for understanding and managing snow crab populations.</jats:list-item> <jats:list-item>The study supports the prioritization of stock enhancement policies by fishery agencies and suggests standardizing how fishing effort is measured across different regions to improve snow crab fishery management and future research.</jats:list-item></jats:list>","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Age/size selective fishery, which targets large individuals and retains immature individuals, is practiced worldwide. Yet, selective fishing can result in bycatch of undersized target species, posing a serious problem in stock management. In addition, there is a growing concern that intensive selective removal alters the composition of a population. Therefore, a comprehensive understanding of the effects of selective fishery on stock dynamics is essential for sustainable fishery management. In this study, an age‐structured model based on the Schaefer model assumptions was used to investigate the effects of selective fisheries and their bycatch on stock dynamics. Local stability analyses suggest that selective fishery can cause undesirable oscillatory stock dynamics in fishery management. Numerical simulations revealed that maximum sustainable yield‐based management with a large age at maturity is more likely to lead to stock oscillations. Notably, bycatch, which is detrimental to the sustainability of the fishery, was found to reduce the oscillatory dynamics. Overall, selective fisheries tend to cause oscillatory stock dynamics in various situations. Selective fishing also tends to reduce the critical fishing effort that causes stock extinction; therefore, a precautionary approach is necessary to mitigate such issues.Recommendations for Resource Managers Age/size selective fishing that retains immature individuals is practiced worldwide and often cause unwanted bycatch, thus requiring a solid understanding for successful management. Selective fishing can cause fluctuations in stock dynamics that are undesirable for sustainable fisheries. This situation may be more likely to occur when the minimum age for fishing is large. It may also occur in fisheries based on maximum sustainable yield. Precautionary management, such as the establishment of marine protected areas, is therefore required.
{"title":"Understanding the impact of selective fishery and bycatch on stock dynamics","authors":"N. Takashina","doi":"10.1111/nrm.12403","DOIUrl":"https://doi.org/10.1111/nrm.12403","url":null,"abstract":"Age/size selective fishery, which targets large individuals and retains immature individuals, is practiced worldwide. Yet, selective fishing can result in bycatch of undersized target species, posing a serious problem in stock management. In addition, there is a growing concern that intensive selective removal alters the composition of a population. Therefore, a comprehensive understanding of the effects of selective fishery on stock dynamics is essential for sustainable fishery management. In this study, an age‐structured model based on the Schaefer model assumptions was used to investigate the effects of selective fisheries and their bycatch on stock dynamics. Local stability analyses suggest that selective fishery can cause undesirable oscillatory stock dynamics in fishery management. Numerical simulations revealed that maximum sustainable yield‐based management with a large age at maturity is more likely to lead to stock oscillations. Notably, bycatch, which is detrimental to the sustainability of the fishery, was found to reduce the oscillatory dynamics. Overall, selective fisheries tend to cause oscillatory stock dynamics in various situations. Selective fishing also tends to reduce the critical fishing effort that causes stock extinction; therefore, a precautionary approach is necessary to mitigate such issues.Recommendations for Resource Managers\u0000\u0000Age/size selective fishing that retains immature individuals is practiced worldwide and often cause unwanted bycatch, thus requiring a solid understanding for successful management.\u0000Selective fishing can cause fluctuations in stock dynamics that are undesirable for sustainable fisheries. This situation may be more likely to occur when the minimum age for fishing is large. It may also occur in fisheries based on maximum sustainable yield.\u0000Precautionary management, such as the establishment of marine protected areas, is therefore required.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rohan Gowda Thanh Quang, Melina Kourantidou, Di Jin
The continuous growth of the aquaculture industry implies increased demand for efficient sources of aquafeed, such as fishmeal. Pelagic fish are a desirable source of fishmeal due to their high nutritional content. Nevertheless, several pelagic stocks that have been exploited extensively for fishmeal production face ecological limits due to commercial exploitation, and the aquaculture industry is now seeking novel, efficient, and sustainable sources of aquafeed. The mesopelagic zone, an ecosystem with many scientific uncertainties, is being considered as a potential source for fishmeal, largely owing to the abundance of mesopelagic fish and their robust nutritional profile. However, both the ecological and economic viability of commercial exploitation of mesopelagic fish are not yet well understood. To understand the conditions that would make such an endeavor economically viable in the context of global fishmeal production systems, we use a bioeconomic model that assesses the economic consequences of including mesopelagic fish as a fishmeal source. Through simulations, we assess the economic implications of this hypothetical mesopelagic fishery on major pelagic fishmeal production systems. The mesopelagic fishery can be economically profitable for harvesters, and its addition to global fishmeal production reduces fishmeal market price, thus making it more accessible to aquaculture farmers and less profitable for pelagic fishers. While this may reduce fishing pressure on pelagic forage‐fish stocks, the implications of commercial exploitation of mesopelagic on key ecosystem services remain a concern.
{"title":"Assessing the potential economic effects of mesopelagic fisheries as a novel source of fishmeal","authors":"Rohan Gowda Thanh Quang, Melina Kourantidou, Di Jin","doi":"10.1111/nrm.12398","DOIUrl":"https://doi.org/10.1111/nrm.12398","url":null,"abstract":"The continuous growth of the aquaculture industry implies increased demand for efficient sources of aquafeed, such as fishmeal. Pelagic fish are a desirable source of fishmeal due to their high nutritional content. Nevertheless, several pelagic stocks that have been exploited extensively for fishmeal production face ecological limits due to commercial exploitation, and the aquaculture industry is now seeking novel, efficient, and sustainable sources of aquafeed. The mesopelagic zone, an ecosystem with many scientific uncertainties, is being considered as a potential source for fishmeal, largely owing to the abundance of mesopelagic fish and their robust nutritional profile. However, both the ecological and economic viability of commercial exploitation of mesopelagic fish are not yet well understood. To understand the conditions that would make such an endeavor economically viable in the context of global fishmeal production systems, we use a bioeconomic model that assesses the economic consequences of including mesopelagic fish as a fishmeal source. Through simulations, we assess the economic implications of this hypothetical mesopelagic fishery on major pelagic fishmeal production systems. The mesopelagic fishery can be economically profitable for harvesters, and its addition to global fishmeal production reduces fishmeal market price, thus making it more accessible to aquaculture farmers and less profitable for pelagic fishers. While this may reduce fishing pressure on pelagic forage‐fish stocks, the implications of commercial exploitation of mesopelagic on key ecosystem services remain a concern.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mehdi Eghbali, Maryam Azarakhshi, Mohammad R. Khalaj
In this study, we employed the evidential belief function model (EBF) to evaluate the potential for land subsidence in the primary aquifer of Bardaskan. Through field visits, we recorded GPS coordinates for 174 land subsidence points. Factors considered in assessing land subsidence potential included well density, groundwater extraction rate, geological characteristics, proximity to faults, vegetation cover, distance from the river, slope, and land use. To develop and validate the model, 70% of the recorded points were randomly selected for training and implementation, while the remaining 30% were reserved for model validation. The number and percentage of land subsidence points in the different classes of the corresponding layers were determined by integrating the training points with influential variables maps such as distance from the river, distance from the fault, land use, and extraction volume. The EBF model rate was calculated for different layer classes. For modeling, all rates of the EBF model in each cell were summated, and the potential of land subsidence was calculated. Finally, the map of land subsidence potential based on the EBF model was determined with GIS. The results showed that most of the subsidence points were located in alluvial sediment of the Holocene period, in areas with high groundwater harvesting, a distance of at least 3000 m from a river, a distance of at least 6000 m from a fault, low‐density rangelands, slopes of at least 0%–2%, and farmlands and gardens. A receiver operating characteristic curve analysis of the EBF model showed that it could accurately predict land subsidence in 87.5% of cases using 30% of the validation data. This suggests that the model can be used for practical applications.
{"title":"Determining land subsidence potential using the evidential belief function model: A case study for the Bardaskan Aquifer, Iran","authors":"Mehdi Eghbali, Maryam Azarakhshi, Mohammad R. Khalaj","doi":"10.1111/nrm.12397","DOIUrl":"https://doi.org/10.1111/nrm.12397","url":null,"abstract":"In this study, we employed the evidential belief function model (EBF) to evaluate the potential for land subsidence in the primary aquifer of Bardaskan. Through field visits, we recorded GPS coordinates for 174 land subsidence points. Factors considered in assessing land subsidence potential included well density, groundwater extraction rate, geological characteristics, proximity to faults, vegetation cover, distance from the river, slope, and land use. To develop and validate the model, 70% of the recorded points were randomly selected for training and implementation, while the remaining 30% were reserved for model validation. The number and percentage of land subsidence points in the different classes of the corresponding layers were determined by integrating the training points with influential variables maps such as distance from the river, distance from the fault, land use, and extraction volume. The EBF model rate was calculated for different layer classes. For modeling, all rates of the EBF model in each cell were summated, and the potential of land subsidence was calculated. Finally, the map of land subsidence potential based on the EBF model was determined with GIS. The results showed that most of the subsidence points were located in alluvial sediment of the Holocene period, in areas with high groundwater harvesting, a distance of at least 3000 m from a river, a distance of at least 6000 m from a fault, low‐density rangelands, slopes of at least 0%–2%, and farmlands and gardens. A receiver operating characteristic curve analysis of the EBF model showed that it could accurately predict land subsidence in 87.5% of cases using 30% of the validation data. This suggests that the model can be used for practical applications.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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