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 6 publications

(8 citation statements)

References 66 publications

(78 reference statements)

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.

Self Cite

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

Spatial Planning of Marine Protected Areas (MPAs) in the Southern Caspian Sea: Comparison of Multi-Criteria Evaluation (MCE) and Simulated Annealing Algorithm

Ashtab,

Gholamalifard,

Jokar

et al. 2024

JMSE

View full text Add to dashboard Cite

Protected areas are referred to around the world as the basis of conservation strategies. Designation of marine protected areas (MPAs) is to preserve marine biodiversity and protect species, habitats in the seas, and oceans. The simulated annealing algorithm (SAA) with other algorithms (swap iterative improvement, normal followed by two step, two step iterative improvement, and normal iterative improvement) in MARXAN conservation solutions software and the multi-criteria evaluation (MCE) method were used to locate MPAs in the Southern Caspian Sea. Then, four methods were examined for site selection that include: (1) Simulated annealing algorithm, (2) MCE with zonal land suitability (ZLS), (3) MCE with compactness and contiguity, and (4) combined method of multi-criteria evaluation with spatial constraints and a simulated annealing algorithm (improved MCE). In the MCE method, we applied different weighted scenarios to locate MPAs. The criteria for determining the desired regions of MPAs included 12 factors gathered in three groups, including: (1) Ecological criteria (distribution of fish Huso huso, Acipenser persicus, Acipenser stellatus, Rutilus frisii kutum, and Alosa braschnikowi; location of coastal protected areas, distance from coastal rivers (Coastline), distance from estuaries and deltas); (2) Physical criteria (distance from the coast, shore sensitive areas); and (3) Socio-economic criteria (distance from densely populated coastal cities, distance from industries near the coast). The results of comparing the algorithms in MARXAN 4.0.6 software showed that the simulated annealing algorithm has a better ratio of border-length/area than other algorithms. Also, the combined method of MCE (improved MCE) selects the best protection patches in terms of location, taking into account the seascape ecology metrics (e.g., patch compactness, edge density, normalized entropy, area metric for patches). Moreover, the results of the comparison of four methods for proposing MPAs based on seascape metrics showed that the combined method of MCE considers a protection network with more contiguity and compactness than the simulated annealing algorithm. The use of seascape ecology can help to preserve and create larger and denser patches in the arrangement of protective areas, because such a selection of protective areas is nature-inspired and can be more bold and appropriate in the course of conservation planning.

show abstract

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

Abstract: To

Cited by 6 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

Spatial Planning of Marine Protected Areas (MPAs) in the Southern Caspian Sea: Comparison of Multi-Criteria Evaluation (MCE) and Simulated Annealing Algorithm

Contact Info

Product

Resources

About