Temporal habitat suitability modeling of Caspian shad (Alosa spp.) in the southern Caspian Sea

Vayghan, Ali Haghi; Fazli, Hasan; Ghorbani, Rasul; Lee, Ming-An; Saravi, Hassan Nasrollahzadeh

doi:10.4081/jlimnol.2015.1215

Cited by 4 publications

(6 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To clarify the relationship between environmental preferences and the stock abundance index (i.e., standardized CPUE) of the fishing grounds, a suitability index (SI) for albacore was calculated based on the central tendency of habitat factors [28] by fitting the standardized CPUE data and environmental variables in the specific month, longitude, and latitude by using smoothing spline regression [24][25][26]28,49]. In constructing the SI model, the stock abundance index was assumed as the dependent (or response) variable, and environmental variables were used as the independent (or explanatory) variable.…”

Section: Suitability Index Of Cpue and Environmental Variablesmentioning

confidence: 99%

“…In constructing the SI model, the stock abundance index was assumed as the dependent (or response) variable, and environmental variables were used as the independent (or explanatory) variable. Hence, standardized CPUE and environmental variables (SST, SSC, SSS, MLD, SSH, and EKE) were applied and then normalized (scaled from 0 to 1) as follows [24,26,49]:…”

Section: Suitability Index Of Cpue and Environmental Variablesmentioning

confidence: 99%

“…The SI values prepared from each environmental factor of the fishing grounds were inputted into a common empirical HSI model [7,25,26]. Two of the most common empirical HSI models-the arithmetic mean model (AMM) [28,53] and the geometric mean model (GMM) [28,54]-are generally employed to evaluate habitat accessibility [7,28].…”

Section: Development Of the Hsi Modelmentioning

confidence: 99%

“…Habitat modeling is crucial for EBFM in tuna regions [15]. Indices of abundance using habitat data are used to determine spatiotemporal stock assessment to inform the dynamic of the population [22,23] and has been applied for many fish species throughout the world [24][25][26]. This methodology has been broadly developed in species monitoring, environmental impact assessment, and ecological recovery studies [27].…”

Section: Introductionmentioning

confidence: 99%

“…Zainuddin et al [9] applied a mixed GAM/GLM with multisatellite environmental data and integrated catch data to produce a simple prediction map for possible fishing grounds during 1998-2000. Thus, habitat suitability modeling, with its pros and cons (just as other methods [26]), is widely used to help decision makers monitor vital commercial fish habitat shifts, fleet behavior, prey abundance, and even climate effects [32] and implement or modify EBFM.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Vayghan

Lee

Weng³

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Decision strategies in fisheries management are often directed by the geographic distribution and habitat preferences of target species. This study used remote sensing data to identify the optimal feeding habitat of albacore tuna in the Southern Atlantic Ocean (SAO) using an empirical habitat suitability model applying longline fisheries data during 2009–2015. An arithmetic mean model with sea surface temperature (SST) and sea surface chlorophyll-a concentration (SSC) was determined to be suitable for defining the albacore habitat in the SAO. The optimal ranges of SST and SSC for the habitat were approximately 16.5 °C–19.5 °C and 0.11–0.33 mg/m3, respectively. The study revealed a considerable positive trend between the suitable habitat area and standardized catch per unit effort (r = 0.97; p < 0.05); due to the west-to-east and northward development of the suitable habitat, albacore schools moved to the northeast of the SAO, thus increasing catch probability in April to August in that region. Overall, the frontal structure of SST and SSC plays an essential role in the formation of potential albacore habitats in the SAO. Our findings could contribute to the establishment of regional ecosystem-based fisheries management in the SAO.

show abstract

Section: Suitability Index Of Cpue and Environmental Variablesmentioning

confidence: 99%

Section: Suitability Index Of Cpue and Environmental Variablesmentioning

confidence: 99%

Section: Development Of the Hsi Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Vayghan

Lee

Weng³

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

Modeling of swordtip squid (Uroteuthis edulis) monthly habitat preference using remote sensing environmental data and climate indices

Haghi Vayghan,

Ray,

Mondal

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

Understanding the spatial arrangement of species in maritime settings necessitates the study of oceanography. Hence, doing a study on the correlation between oceanography and species dispersion is imperative, considering the impacts of global climate change. The study used a generalized additive modeling approach to analyze the influence of oceanographic conditions on the distribution of swordtip squid in northeastern Taiwan, integrating fishing data, climatic oscillation and oceanography. Among seven oceanographic characteristics, bottom sea temperature (SSTB), sea surface height (SSH), sea surface chlorophyll (SSC), and sea surface temperature (SST) showed significant influence in generalized additive model (GAM) analysis (combined deviance explained: 40.30%). The monthly catch rate of swordtip squid is influenced by six climatic oscillations, with the Pacific Decadal Oscillation having the most significant impact, accounting for 31% of the distribution, followed by the North Pacific Gyre Oscillation at 10.8% and the Western Pacific Oscillation at 6.05%. From 2015 to 2019, the main areas for squid fishing were situated in the northeastern waters of Taiwan, precisely within the geographical coordinates of 25°N to 28°N and 121.5°E to 125°E. This study provides crucial insights for managing swordtip squid fisheries in Taiwan's northwest waters, highlighting the importance of incorporating oceanographic conditions relating to climate change information into decision-making to protect global ocean fisheries and their dependent communities.

show abstract

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

Teng,

Mondal,

et al. 2024

JMSE

View full text Add to dashboard Cite

The depletion of the grey mullet population in the Taiwan Strait has generated interest in conservation initiatives and to enhance the effectiveness of restoration initiatives, it is essential to comprehend the specific habitat preferences of the species in question, particularly in relation to environmental changes. In this study, habitat suitability modelling was used to investigate the distribution patterns of grey mullet. Sea surface height (SSH) was the most significant predictor of the grey mullet standardized catch per unit effort (S.CPUE). Furthermore, sea surface temperature (SST) was the second most important oceanographic parameter, followed by mixed-layer depth (MLD). The grey mullet S.CPUE peaked in regions with the summed SST, Chla, salinity, SSH, MLD and current values being 20 °C, 0.9 mg/m3, 34.5 psu, 0.45 m, 18–38 m, and 0.25 m2/s2, respectively. From late October, HSI levels in the northern Taiwan ocean increased, with a consistent upward trend. The distribution expanded in November and December, focusing on the northern and mid-ocean regions west of Taiwan. HSI levels peak in January and February, especially in Taiwan’s coastal region. High HSI distribution shifts away from coastlines and diminishes in April. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.

show abstract

Temporal habitat suitability modeling of Caspian shad (Alosa spp.) in the southern Caspian Sea

Abstract: To

Cited by 4 publications

References 66 publications

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Modeling of swordtip squid (Uroteuthis edulis) monthly habitat preference using remote sensing environmental data and climate indices

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

Contact Info

Product

Resources

About